FICOSA demonstrated a telematic unit integrating a multi-service antenna module for positioning and satellite navigation supporting all the geographic positioning standards at the 2013 Mobile World Congress in Barcelona in February.
The major advantage of this technological innovation is that the proposed multi-channel GPS/Galileo/GLONASS/BeiDou/QZSS receiver allows tracking across all the different navigation and positioning satellite standards worldwide, so that automakers can the having to manage different variants of in-vehicle telematic units (iVTUs) depending on the geographical market. iVTUs are needed for emergency call function, fleet management, and other functions. It incorporates an OriginGPS antenna.
The new module is a complete system-in-package featuring miniature surface mount device technology footprint designed to commit unique integration features for high volume, low power and cost-sensitive applications.
In addition, the reduced size of the receiver module makes the most of a stacked-up in board integration through miniaturized integrated circuits and surface mount devices, allows an aggressive reduction of the iVTU packaging, which is advantageous for the OEM for car assembly, iVTU localization inside the vehicle, and weight reduction.
The innovation represents the result of the international collaboration between FICOSA and OriginGPS. “We view the telematics market as a growing market and it is our privilege to cooperate and partner with Ficosa and its excellent engineering team,” said Haim Goldberger, CEO and founder of OriginGPS.
“In FICOSA, innovation and technology are two main tools for our future and working with OriginGPS is a great issue,” said Jose María Forcadell, Advanced Communications Business Unit Director at FICOSA.
Blue Marble Geographics has announced the release of Global Energy Mapper 14.1, making available a variety of enhancements in the its GIS tool for energy professionals. This update to the company’s desktop GIS software offers new and improved features and functions, including a significant improvement in the ability to process massive amounts of LiDAR point cloud data, jumping from tens of millions of points to hundreds of millions. B
lue Marble’s geospatial data manipulation, visualization and conversion solutions are used worldwide by thousands of GIS analysts at software, oil and gas, mining, civil engineering, surveying, and technology companies, as well as governmental and university organizations.
Global Energy Mapper 14.1 provides a dramatic increase in LiDAR processing and display speed and the ability to view and process point files in the hundreds of millions range, Blue Marble said. This is beneficial for previewing the data before creating a gridded surface model and includes several options for filtering the data during import and for rendering the point cloud to reflect return type or intensity. Improved metadata access provides a detailed statistical breakdown of the point cloud and customizable point size improves on-screen display. Global Mapper Package (.GMP) files are now able to store LiDAR point clouds in a special compressed format, much smaller than uncompressed LAS data and on par with the best compression available today. This allows LiDAR data to be efficiently archived or shared with other Global Mapper users.
Global Energy Mapper 14.1 also provides a new tool for creating whisker lines emanating from a selected point or points, useful for seismic survey coverage. Whisker lines are often used to estimate coverage from selected points to see if a point in a seismic survey covers what is needed. There is also a new digitizer tool for easily subdividing an existing area into four separate areas that is useful for subdividing parcels or properties.
Version 14.1 includes an enhancement to the Site Pad Placement tool so users can create a site pad for a non-level surface. There are also speed improvements when accessing Spatial On Demand data from our partner Spatial Energy, along with new built in point types for oil and gas symbology, Blue Marble said. Additionally USB dongle licensing is now available for GEM with this release.
“We are excited to be offering this significant upgrade to our Global Energy Mapper customers,” stated Blue Marble President Patrick Cunningham. “We are confident our users in the oil and gas and other energy sectors will be impressed with the improvements in processing LiDAR point clouds along with the new energy specific tools.”
A Case History Using the New Galileo E6-B/C Signal
By Sergei Yudanov, JAVAD GNSS
A method of decoding an unknown pseudorandom noise code uses a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation satellites.
Decoding an unknown GNSS pseudorandom noise (PRN) code can be rather easily done using a high-gain steerable dish antenna as was used, for example, in determine the BeiDou-M1 broadcast codes before they were publicly announced. The signal-to-noise ratio within one chip of the code is sufficient to determine its sign. This article describes a method of getting this information using a conventional GNSS antenna and receiver with modified firmware. The method was verified using the signals from the Galileo In-Orbit Validation (IOV) satellites. In spite of the fact that only pilot signal decoding seems to be possible at first glance, it is shown that in practice data signals can also be decoded.
Concept
The idea is to do coherent accumulation of each chip of an unknown signal during a rather long time interval. The interval may be as long as a full satellite pass; for medium Earth orbits, this could be up to six hours. One of the receiver’s channels is configured in the same way as for signal tracking. The I and Q signal components are accumulated during one chip length in the digital signal processor, and these values are added to an array cell, referenced by chip number, by the processor. Only a limited amount of information need be known about the signal: its RF frequency; the expected chip rate; the expected total code length; and the modulation method.
The decoding of binary-phase-shift-keying (BPSK) signals (as most often used) is the subject of this article. It appears that the decoding of more complicated signals is possible too, but this should be proved. A limitation of this method (in common with that of the dish method) is the maximum total code length that can be handled: for lengths greater than one second and bitrates higher than 10,000 kilobits per second, the receiver’s resources may not be sufficient to deal with the signal.
Reconstructing the Signal’s Phase
This method requires coherency. During the full accumulation period, the phase difference between the real signal phase and the phase of the signal generated by the receiver’s channel should be much less than one cycle of the carrier frequency. Depending on the GNSS’s available signals, different approaches may be used. The simplest case is reconstruction of a third signal while two other signals on different frequencies are of known structure and can be tracked.
The main (and possibly the only significant) disturbing factor is the ionosphere. The ionospheric delay (or, more correctly, the variation of ionospheric delay) is calculated using the two known tracked signals, then the phase of the third signal, as affected by the ionosphere, is predicted.
The final formula (the calculations are trivial and are widely available in the literature) is:
where:
φ1 , f1 are the phase and frequency of the first signal in cycles and Hz, respectively
φ2 , f2 are the phase and frequency of the second signal in cycles and Hz, respectively
φ3 , f3 are the phase and frequency of the third signal in cycles and Hz, respectively.
It was confirmed that for all pass periods (elevation angles less than 10 degrees were not tested), the difference between the calculated phase and real phase was always less than one-tenth of a cycle. GPS Block IIF satellites PRN 1 and PRN 25 were used to prove this: the L1 C/A-code and L5 signals were used as the first and second signals, with the L2C signal as the third unknown.
If two known signals are not available, and the ionospheric delay cannot be precisely calculated, it is theoretically possible to obtain an estimate of the delay from one or more neighboring satellites with two signals available. Calculations and estimations should be carried out to investigate the expected precision.
The Experiment
The Galileo E6-B/C signal as currently transmitted by the IOV satellites was selected for the experiment, as its structure has not been published. The E6 signal has three components: E6-A, E6-B and E6-C. The E6-A component is part of the Galileo Public Regulated Service, while the two other components will serve the Galileo Commercial Service. The E6-B component carries a data signal, while the E6-C component is a pilot signal.
From open sources, it is known that the carrier frequency of the E6 signal is 1278.75 MHz and that the E6-B and E6-C components use BPSK modulation at 5,115 chips per millisecond with a primary code length of one millisecond. E6-B’s data rate is 1,000 bits per second and the total length of the pilot code is 100 milliseconds (a secondary code of 100 bits over 100 milliseconds is also present in the E6-C signal, which aids in signal acquisition).
A slightly modified commercial high-precision multi-GNSS receiver, with the E6 band and without the GLONASS L2 band, was used for this experiment. The receiver was connected to a conventional GNSS antenna, placed on a roof and was configured as described above. The E1 signal was used as the first signal and E5a as the second signal. The E6 code tracking (using 5,115 chip values of zero) was 100 percent guided from the E1 code tracking (the changing of the code delay in the ionosphere was ignored). The E6 phase was guided from E1 and E5a using the above equation. Two arrays for 511,500 I and Q samples were organized in firmware. The integration period was set to one chip (200 nanoseconds).
Galileo IOV satellite PRN 11 (also variously known as E11, ProtoFlight Model and GSAT0101) was used initially, and the experiment started when the satellite’s elevation angle was about 60 degrees and lasted for only about 30 minutes. Then the I and Q vectors were downloaded to a PC and analyzed.
Decoding of Pilot Signal (E6-C)
Decoding of the pilot signal is made under the assumption that any possible influence of the data signal is small because the number of ones and zeros of E6-B in each of 511,500 chips of the 100-millisecond integration interval is about the same. First, the secondary code was obtained. Figure 1 shows the correlation of the first 5,115 chips with 5,115 chips shifted by 0 to 511,500 chips. Because the initial phase of the E6 signal is unknown, two hypotheses for computing the amplitude or signal level were checked: [A] = [I] + [Q] and [A] = [I] – [Q], and the combination with the higher correlation value was selected for all further analysis.
Figure 1. Un-normalized autocorrelation of E6-C signal chips.
In Figure 1, the secondary code is highly visible: we see a sequence of 100 positive and negative correlation peaks (11100000001111 …; interpreting the negative peaks as zeros).This code is the exact complement (all bits reversed) of the published E5a pilot secondary code for this satellite. More will be said about the derived codes and their complements later. It appears that, for all of the IOV satellites, the E6-C secondary codes are the same as the E5a secondary codes.
After obtaining the secondary code, it is possible to coherently add all 100 milliseconds of the integration interval with the secondary code sign to increase the energy in each chip by 100 times. Proceeding, we now have 5,115 chips of the pilot signal — the E6-C primary code.
To understand the correctness of the procedure and to check its results, we need to confirm that there is enough signal energy in each chip. To this end, a histogram of the pilot signal chip amplitudes can be plotted (see Figure 2). We see that there is nothing in the middle of the plot. This means that all 5,115 chips are correct, and there is no chance that even one bit is wrong.
Figure 2. Histogram of pilot signal chip amplitude in arbitrary units.
But there is one effect that seems strange at first glance: instead of two peaks we have four (two near each other). We will shortly see that this phenomenon results from the influence of the E6-B data signal and it may be decoded also.
Decoding the Data Signal
The presence of four peaks in the histogram of Figure 2 was not understood initially, so a plot of all 511,500 signal code chips was made (see Figure 3).
Interestingly, each millisecond of the signal has its own distribution, and milliseconds can be found where the distribution is close to that when two signals with the same chip rate are present. In this case, there should be three peaks in the energy (signal strength) spectrum: –2E, 0, and +2E, where E is the energy of one signal (assuming the B and C signals have the same strength).
Figure 3. Plot of 511,500 signal code chip amplitudes in arbitrary units.
One such time interval (starting at millisecond 92 and ending at millisecond 97) is shown in Figure 4. The middle of the plot (milliseconds 93 to 96) shows the described behavior. Figure 5 is a histogram of signal code chip amplitude for the signal from milliseconds 93 to 96.
Figure 4. Plot of signal code chip amplitude in arbitrary units from milliseconds 93 to 96.
Then we collect all such samples (milliseconds) with the same data sign together to increase the signal level. Finally, 5,115 values are obtained. Their distribution is shown in Figure 6.
The central peak is divided into two peaks (because of the presence of the pilot signal), but a gap between the central and side peaks (unlike the case of Figure 5) is achieved. This allows us to get the correct sign of all data signal chips. Subtracting the already known pilot signal chips, we get the 5,115 chips of the data signal — the E6-B primary code. This method works when there are at least some samples (milliseconds) where the number of chips with the same data bit in the data signal is significantly more than half.
Figure 5. Histogram of signal code chip amplitude.Figure 6. Histogram of the signed sum of milliseconds chip amplitude with a noticeable presence of the data signal.
Proving the Codes
The experimentally determined E6-B and E6-C primary codes and the E6-C secondary codes for all four IOVsatellites (PRNs 11, 12, 19, and 20) were put in the receiver firmware. The receiver was then able to autonomously track the E6-B and E6-C signals of the satellites.
Initial decoding of E6-B navigation data has been performed. It appears that the data has the same preamble (the 16-bit synchronization word) as that given for the E6-B signal in the GIOVE Interface Control Document (ICD). Convolutional encoding for forward error correction is applied as described in the Galileo Open Service ICD, and 24-bit cyclic redundancy check error detection (CRC-24) is used. At the time of the analysis, all four IOV satellites transmitted the same constant navigation data message.
Plots of PRN 11 E6 signal tracking are shown in Figure 7 and in Figure 8. The determined codes may be found at env-gpsworld-integration.kinsta.cloud/galileo-E6-codes. Some of these codes may be the exact complement of the official codes since the code-determination technique has a one-half cycle carrier-phase ambiguity resulting in an initial chip value ambiguity. But from the point of view of receiver tracking, this is immaterial.
Figure 7. Signal-to-noise-density ratio of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.Figure 8. Pseudorange minus carrier phase (in units of meters) of E1 (red), E5a (magenta), E5b (blue), and E6 (green) code tracking of Galileo IOV satellite PRN 11 on December 21–22, 2012.
Acknowledgments
Special thanks to JAVAD GNSS’s DSP system developers. The system is flexible so it allows us to do tricks like setting the integration period to one chip, and powerful enough to be able to do required jobs within a 200-nanosecond cycle. This article was prepared for publication by Richard Langley.
Manufacturers
A JAVAD GNSS TRE-G3T-E OEM receiver, a modification of the TRE-G3T receiver, was used in the experiment, connected to a conventional JAVAD GNSS antenna. Plots of E6 code tracking of all four IOV satellites may be found on the company’s website.
Sergei Yudanov is a senior firmware developer at JAVAD GNSS, Moscow.
Off-the-Shelf Antennas for Controlled-Reception-Pattern Antenna Arrays
By Yu-Hsuan Chen, Sherman Lo, Dennis M. Akos, David S. De Lorenzo, and Per Enge
INNOVATION INSIGHTS by Richard Langley
THE ANTENNA IS A CRITICAL COMPONENT OF ANY GNSS RECEIVING EQUIPMENT. It must be carefully designed for the frequencies and structures of the signals to be acquired and tracked. Important antenna properties include polarization, frequency coverage, phase-center stability, multipath suppression, the antenna’s impact on receiver sensitivity, reception or gain pattern, and interference handling. While all of these affect an antenna’s performance, let’s just look at the last two here.
The gain pattern of an antenna is the spatial variation of the gain, or ratio of the power delivered by the antenna for a signal arriving from a particular direction compared to that delivered by a hypothetical isotropic reference antenna. Typically, for GNSS antennas, the reference antenna is also circularly polarized and the gain is then expressed in dBic units.
An antenna may have a gain pattern with a narrow central lobe or beam if it is used for communications between two fixed locations or if the antenna can be physically steered to point in the direction of a particular transmitter. GNSS signals, however, arrive from many directions simultaneously, and so most GNSS receiving antennas tend to be omni-directional in azimuth with a gain roll-off from the antenna boresight to the horizon.
While such an antenna is satisfactory for many applications, it is susceptible to accidental or deliberate interference from signals arriving from directions other than those of GNSS signals. Interference effects could be minimized if the gain pattern could be adjusted to null-out the interfering signals or to peak the gain in the directions of all legitimate signals. Such a controlled-reception-pattern antenna (CRPA) can be constructed using an array of antenna elements, each one being a patch antenna, say, with the signals from the elements combined before feeding them to the receiver. The gain pattern of the array can then be manipulated by electronically adjusting the phase relationship between the elements before the signals are combined. However, an alternative approach is to feed the signals from each element to separate banks of tracking channels in the receiver and form a beam-steering vector based on the double-difference carrier-phase measurements from pairs of elements that is subsequently used to weight the signals from the elements before they are processed to obtain a position solution. In this month’s column, we learn how commercial off-the-shelf antennas and a software-defined receiver can be used to design and test such CRPA arrays.
“Innovation” features discussions about advances in GPS technology, its applications, and the fundamentals of GPS positioning. The column is coordinated by Richard Langley, Department of Geodesy and Geomatics Engineering, University of New Brunswick. To contact him with topic ideas, email him at lang @ unb.ca.
Signals from global navigation satellite systems are relatively weak and thus vulnerable to deliberate or unintentional interference. An electronically steered antenna array system provides an effective approach to mitigate interference by controlling the reception pattern and steering the system’s beams or nulls. As a result, so-called controlled-reception-pattern-antenna (CRPA) arrays have been deployed by organizations such as the U.S. Department of Defense, which seeks high levels of interference rejection.
Our efforts have focused on developing a commercially viable CRPA system using commercial off-the-shelf (COTS) components to support the needs of Federal Aviation Administration (FAA) alternative position navigation and timing (APNT) efforts. In 2010, we implemented a seven-element, two-bit-resolution, single-beam and real-time CRPA software receiver. In 2011, the receiver was upgraded to support all-in-view, 16-bit-resolution with four elements.
Even though we can implement these CRPA software receivers in real time, the performance of anti-interference is highly dependent on the antenna array layout and characteristics of the antenna elements. Our beamforming approach allows us to use several COTS antennas as an array rather than a custom-designed and fully calibrated antenna. The use of COTS antennas is important, as the goal of our effort is to develop a CRPA for commercial endeavors — specifically for robust timing for the national airspace. Hence, it is important to study the geometry layout of the individual antennas of the array to assess the layouts and to determine how antenna performance affects the array’s use.
In our work, we have developed a procedure for calculating the electrical layouts of an antenna array by differential carrier-phase positioning. When compared to the physical layout, the results of electrical layouts can be used to determine the mutual coupling effect of each combination. Using the electrical layout, the resultant gain patterns can be calculated and used to see the beamwidth and the side-lobe issue. This is important as these factors have significant effects on anti-interference performance. This study focuses on understanding the performance effects of geometry and developing a method for describing the best geometry.
We adopted three models of COTS antenna and two possible layouts for a four-element array. Then, signal collection hardware consisting of four Universal Software Radio Peripheral (USRP) software-defined radios and one host personal computer was assembled to collect array data sets for each layout/antenna combination. Our developed CRPA software receiver was used to process all data sets and output carrier-phase measurements.
In this article, we will present the pattern analysis for the two selected layouts and describe how we collected the experimental data. We’ll then show the results of calculating the electrical spacing for the layouts are compare them to the physical layouts. Lastly, we’ll show the resulting patterns, discuss the antenna mutual coupling effects, and give our conclusions.
Antenna Array Pattern Analysis
Pattern is defined as the directional strength of a radio-frequency signal viewed from the antenna. The pattern of an antenna array is the product of the isotropic array factor and the isolated element pattern. We assume that the pattern of each element is identical and only consider the isotropic array factor. FIGURE 1 shows the coordination of an antenna array. The first element is set as a reference position. The x-axis is the east direction, the y-axis is the north direction, and the z-axis is the up direction. The baseline vector of the ith antenna is given by and is the unit vector to the satellite.
Figure 1. Antenna array geometry and direction of satellite. Array elements are identified as E#1, E#2, E#3, and E#4.
The isotropic array factor is given by
(1)
where λ is wavelength, and Ai is a complex constant. Currently, we only implement a four-element-array CRPA software receiver in real time. Hence, we analyze two kinds of layout of half-wavelength four-element arrays: a symmetrical Y array and a square array. Each antenna is separated from its nearest neighbor by a half wavelength. FIGURE 2 shows photos of the two layouts. FIGURE 3 shows the physical layouts.
Figure 2. Photos of antenna arrays (left: Y array; right: square array).Figure 3A. Physical layout of antenna arrays (Y array).Figure 3B. Physical layout of antenna arrays (square array).
The antenna patterns towards an elevation angle of 90 degrees, computed using equation 1 and the design layouts, are shown in FIGURE 4. One of the key characteristics of a pattern is the beamwidth, which is defined as the angle with 3-dB loss. FIGURE 5 shows the patterns in elevation angle where the beamwidth of the Y layout is 74 degrees and 86 degrees for the square layout. A narrow beamwidth will benefit anti-interference performance particularly if the interference is close to the direction of a target satellite.
Figure 4. Patterns of antenna arrays (left: Y array; right: square array).Figure 5. Pattern beamwidths of Y and square arrays (3 dB beamwidth shown).
Specifications of COTS Antennas
Typically, the COTS antenna selection is determined by high gain and great out-of-band rejection. TABLE 1 shows the specifications of the three antenna models used in this article. These antennas are all patch antennas. The antennas are equipped with surface-acoustic-wave filters for rejecting out-of-band signals. A three-stage low noise amplifier with over 30 dB gain is also embedded in each antenna.
Table 1. Specifications of COTS antennas used.
Signal Collection Hardware and Experimental Setup
The hardware used to collect the antenna array datasets is shown in FIGURE 6 with block-diagram representation in FIGURE 7. The hardware includes a four-element antenna array, four USRP2 software radio systems and one host computer. The signal received from the COTS antenna passes to a USRP2 board equipped with a 800–2300 MHz DBSRX2 programmable mixing and down-conversion daughterboard. The individual USRP2 boards are synchronized by a 10-MHz external common clock generator and a pulse-per-second (PPS) signal. The USRP2s are controlled by the host computer running the Ubuntu distribution of Linux. The open-source GNU Radio software-defined radio block is used to configure USRP2s and collect datasets. All USRP2s are configured to collect the L1 (1575.42 MHz) signal. The signals are converted to near zero intermediate frequency (IF) and digitized to 14-bit complex outputs (I and Q).
Figure 6. Photo of the signal collection hardware.Figure 7. Block diagram of the signal collection hardware.
The sampling rate is set as 4 MHz. The host computer uses two solid state drives for storing data sets. For our study, a 64-megabytes per second data transfer rate is needed. The fast solid state drives are especially useful when using high bandwidth signals such as L5, which will require an even higher data streaming rate (80 megabytes per second per channel).
To compare the physical and electrical layouts of the antenna arrays, we set up the signal collection hardware to record six data sets for the two layouts and the three antenna models as shown in TABLE 2. All of the data sets were five minutes long to obtain enough carrier-phase measurements for positioning.
Table 2. Experimental setups.
Logging Carrier-Phase Measurements
To calculate the precise spacing between the antenna elements, hundreds of seconds of carrier-phase measurements from each element are needed. The collected data sets were provided by our in-house-developed CRPA software receiver. The receiver was developed using Visual Studio under Windows. Most of source code is programmed using C++. Assembly language is used to program the functions with high computational complexity such as correlation operations. The software architecture of the receiver is depicted in FIGURE 8. This architecture exploits four sets of 12 tracking channels in parallel to process each IF signal from an antenna element. Each channel is dedicated to tracking the signal of a single satellite. The tracking channels output carrier-phase measurements to build the steering vectors for each satellite. The Minimum Variance Distortionless Response (MVDR) algorithm was adopted for adaptively calculating the weights for beamforming. Here, there are 12 weight sets, one for each satellite in a tracking channel, for the desired directions of satellites.
Figure 8. Block diagram of the software architecture.
Using the pre-correlation beamforming approach, the weights are multiplied with IF data and summed over all elements to form 12 composite signals. These signals are then processed by composite tracking channels. Finally, positioning is performed if pseudoranges and navigation messages are obtained from these channels. FIGURE 9 is the graphical user interface (GUI) of the CRPA software receiver. It consists of the channel status of all channels, carrier-phase differences, positioning results, an east-north (EN) plot, a sky plot, a carrier-to-noise-density (C/N0) plot and the gain patterns of the array for each tracked satellite. In the figure, the CRPA software receiver is tracking 10 satellites and its positioning history is shown in the EN plot. The beamforming channels have about 6 dB more gain in C/N0 than the channels of a single element. In each pattern, the direction with highest gain corresponds to the direction of the satellite. While the CRPA software receiver is running, the carrier-phase measurements of all elements and the azimuth and elevation angle of the satellites are logged every 100 milliseconds. Each data set in Table 2 was processed by the software receiver to log the data.
Figure 9. Screenshot of the controlled-reception-pattern-antenna software-receiver graphical user interface.
Electrical Layout of Antenna Array – Procedure
The procedure of calculating the electrical layout of an antenna array is depicted in FIGURE 10. The single-difference integrated carrier phase (ICP) between the signals of an element, i, and a reference element, j, is represented as:
(2)
where rkijis differential range toward the kth satellite between the ith and jth antenna elements (a function of the baseline vector between the ith and jth elements), δLij is the cable-length difference between the ith and jth antenna elements, Nkijis the integer associated with Φkij, εkijand is the phase error. The double-difference ICP between the kth satellite and reference satellite l is represented as: (3)
The cable-length difference term is subtracted in the double difference. Since the distances between the antenna elements are close to one wavelength, equation (3) can be written as: (4)
where is the unit vector to satellite k,pij is the baseline vector between the ith and jth elements. By combining all the double-difference measurements of the ijth pair of elements, the observations equation can be represented as: (5)
From the positioning results of composite channels, the azimuth and elevation angle of satellites are used to manipulate matrix G. To solve equation (5), the LAMBDA method was adopted to give the integer vector N. Then, pij is solved by substituting Ninto equation (5). Finally, the cable-length differences are obtained by substituting the solutions of N and pij into equation (2).
This approach averages the array pattern across all satellite measurements observed during the calibration period.
Figure 10. Procedure for calculating antenna-array electrical spacing.
Electrical Layout of Antenna Array – Results
Using the procedure in the previous section, all electrical layouts of the antenna array were calculated and are shown in FIGURES 11 and 12. We aligned the vectors from element #1 to element #2 for all layouts. TABLE 3 lists the total differences between the physical and electrical layouts. For the same model of antenna, the Y layout has less difference than the square layout. And, in terms of antenna model, antenna #1 has the least difference for both Y and square layouts. We could conclude that the mutual coupling effect of the Y layout is less than that of the square layout, and that antenna #1 has the smallest mutual coupling effect among all three models of antenna for these particular elements and observations utilized.
Figure 11. Results of electrical layout using three models of antenna compared to the physical layout for the Y array.Figure 12. Results of electrical layout using three models of antenna compared to physical layout for the square array.Table 3. Total differences between physical and electrical layouts.
To compare the patterns of all calculated electrical layouts, we selected two specific directions: an elevation angle of 90 degrees and a target satellite, WAAS GEO PRN138, which was available for all data sets. The results are shown in FIGURES 13 and 14, respectively. From Figure 13, the beamwidth of the Y layout is narrower than that of the square layout for all antenna models. When compared to Figure 5, this result confirms the validity of our analysis approach. But, in Figure 14, a strong sidelobe appears at azimuth -60º in the pattern of Y layout for antenna #2. If there is some interference located in this direction, the anti-interference performance of the array will be limited. This is due to a high mutual coupling effect of antenna #2 and only can be seen after calculating the electrical layout.
Figure 13. Patterns of three models of antenna and two layouts toward an elevation angle of 90 degrees.Figure 14. Patterns of three models of antenna and two layouts toward the WAAS GEO satellite PRN138.
Conclusions
The results of our electrical layout experiment show that the Y layout has a smaller difference with respect to the physical layout than the square layout. That implies that the elements of the Y layout have less mutual coupling. For the antenna selection, arrays based on antenna model #1 showed the least difference between electrical and physical layout. And its pattern does not have a high grating lobe in a direction other than to the target satellite.
The hardware and methods used in this article can serve as a testing tool for any antenna array. Specifically, our methodology, which can be used to collect data, compare physical and electrical layouts, and assess resultant antenna gain patterns, allows us to compare the performances of different options and select the best antenna and layout combination. Results can be used to model mutual coupling and the overall effect of layout and antenna type on array gain pattern and overall CRPA capabilities. This procedure is especially important when using COTS antennas to assemble an antenna array and as we increase the number of antenna elements and the geometry possibilities of the array.
Acknowledgments
The authors gratefully acknowledge the work of Dr. Jiwon Seo in building the signal collection hardware. The authors also gratefully acknowledge the Federal Aviation Administration Cooperative Research and Development Agreement 08-G-007 for supporting this research. This article is based on the paper “A Study of Geometry and Commercial Off-The-Shelf (COTS) Antennas for Controlled Reception Pattern Antenna (CRPA) Arrays” presented at ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, held in Nashville, Tennessee, September 17–21, 2012.
Manufacturers
The antennas used to construct the arrays are Wi-Sys Communications Inc., now PCTEL, Inc. models WS3978 and WS3997 and PCTEL, Inc. model 3978D-HR. The equipment used to collect data sets includes Ettus Research LLC model USRP2 software-defined radios and associated DBSRX2 daughterboards.
Yu-Hsuan Chen is a postdoctoral scholar in the GNSS Research Laboratory at Stanford University, Stanford, California.
Sherman Lo is a senior research engineer at the Stanford GNSS Research Laboratory.
Dennis M. Akos is an associate professor with the Aerospace Engineering Science Department in the University of Colorado at Boulder with visiting appointments at Luleå Technical University, Sweden, and Stanford University.
David S. De Lorenzo is a principal research engineer at Polaris Wireless, Mountain View, California, and a consulting research associate to the Stanford GNSS Research Laboratory.
Per Enge is a professor of aeronautics and astronautics at Stanford University, where he is the Kleiner-Perkins Professor in the School of Engineering. He directs the GNSS Research Laboratory.
FURTHER READING
• Authors’ Publications
“A Study of Geometry and Commercial Off-The-Shelf (COTS) Antennas for Controlled Reception Pattern Antenna (CRPA) Arrays” by Y.-H. Chen in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of The Institute of Navigation, Nashville, Tennessee, September 17–21, 2012, pp. 907–914 (ION Student Paper Award winner).
“A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors” by J. Seo, Y.-H. Chen, D.S. De Lorenzo, S. Lo, P. Enge, D. Akos, and J. Lee in Sensors, Vol. 11, No. 9, 2011, pp. 8966–8991, doi: 10.3390/s110908966.
“Real-Time Software Receiver for GPS Controlled Reception Pattern Array Processing” by Y.-H. Chen, D.S. De Lorenzo, J. Seo, S. Lo, J.-C. Juang, P. Enge, and D.M. Akos in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 1932–1941.
“A GNSS Software Receiver Approach for the Processing of Intermittent Data” by Y.-H. Chen and J.-C. Juang in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of The Institute of Navigation, Fort Worth, Texas, September 25–28, 2007, pp. 2772–2777.
• Controlled-Reception-Pattern Antenna Arrays
“Anti-Jam Protection by Antenna: Conception, Realization, Evaluation of a Seven-Element GNSS CRPA” by F. Leveau, S. Boucher, E. Goron, and H. Lattard in GPS World, Vol. 24, No. 2, February 2013, pp. 30–33.
“Development of Robust Safety-of-Life Navigation Receivers” by M.V.T. Heckler, M. Cuntz, A. Konovaltsev, L.A. Greda, A. Dreher, and M. Meurer in IEEE Transactions on Microwave Theory and Techniques, Vol. 59, No. 4, April 2011, pp. 998–1005, doi: 10.1109/TMTT.2010.2103090.
Phased Array Antennas, 2nd Edition, by R. C. Hansen, published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2009.
• Antenna Principles
“Selecting the Right GNSS Antenna” by G. Ryley in GPS World, Vol. 24, No. 2, February 2013, pp. 40–41 (in PDF of 2013 Antenna Survey.)
“A USRP2-based Reconfigurable Multi-constellation Multi-frequency GNSS Software Receiver Front End” by S. Peng and Y. Morton in GPS Solutions, Vol. 17, No. 1, January 2013, pp. 89-102.
Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory by I.G. Petrovski and T. Tsujii with foreword by R.B. Langley, published by Cambridge University Press, Cambridge, U.K., 2012.
“A Real-Time Software Receiver for the GPS and Galileo L1 Signals” by B.M. Ledvina, M.L. Psiaki, T.E. Humphreys, S.P. Powell, and P.M. Kintner, Jr. in Proceedings of ION GNSS 2006, the 19th International Technical Meeting of The Institute of Navigation, Fort Worth, Texas, September 26–29, 2006, pp. 2321–2333.
Taoglas USA, Inc., provider of antenna solutions to the M2M and connected device market, has launched the FXP.611 Cloud, a GPS/GLONASS flexible loop antenna that the company says outperforms most active patch antennas with an efficiency of 80 percent and a peak gain of 3 dBi across the GPS and GLONASS bands (1575 to 1610Mhz).
This antenna can resist external detuning effects due to dual resonance and has a small form factor of 38 x 37 x 0.1 millimeters. At less than half the cost of heavy active patch antennas, this peel and stick, flexible loop antenna is suitable for any GPS/GLONASS M2M device, Taoglas said.
“We’ve been blown away by the performance of this linear polarized A-GPS GLONASS antenna,” said Dermot O’Shea, director at Taoglas. “Before we developed the FXP.611 Cloud, we had only seen this kind of performance from active patch antennas. We ran a drive test in downtown San Diego and were surprised by the real-time performance and first time to fix from cold-start of this passive loop, particularly in urban canyons where you expect active patches to out perform.”
Original equipment manufacturers will find the FXP.611 suitable for assisted GPS/GLONASS applications for industrial handheld devices, tablets and smartphones. According to Taoglas, the patent-pending FXP.611 Cloud antenna
eliminates the need for a filter or low noise amplifier (LNA), and can connect directly to a module or to a connector on a board.
offers a “peel and stick” mounting with 3M tape that can be attached to plastic device housings freeing up board space.
costs half the price of active patch antennas.
incorporates a detuning design with dual resonance.
radiates power uniformly with an omnidirectional design, making it suitable for use in devices that have fixed positions.
The FXP.611 Cloud antenna is available for purchase immediately from Taoglas by contacting [email protected] and online later in the first quarter of 2013 with Taoglas distributors.
Hexagon AB has acquired the business of GTA Geoinformatik GmbH, a pioneer in georeferenced virtual 3D city models and building reconstructions.
Founded in 1995, GTA Geoinformatik is the developer of tridicon software, which enables the automatic generation of high-quality, colored 3D point clouds. The company also specialises in uniting point cloud data with aerial images including oblique and LiDAR images to create intelligent, navigable 3D city models, or smart cities.
“The idea of creating a smart city has been an important part of Hexagon’s geospatial vision to merge maps with information and real-time updates,” said Hexagon President and CEO Ola Rollén. “Solutions such as those GTA Geoinformatik delivers are becoming increasingly important, as they build the foundation for industry-specific applications in areas of city development such as security, traffic, infrastructure management, energy and emergency response.”
GTA Geoinformatik, based in Berlin and Neubrandenburg, Germany, is now fully consolidated. The acquisition will not have any visible impact on Hexagon’s earnings in the short-term, according to the company.
Raytheon UK has been awarded a significant contract by the UK Ministry of Defence for delivery of a new GPS anti-jam antenna land system. The contract is for an undisclosed number of advanced systems for deployment in operational theaters spanning multiple vehicle platforms. This UOR (Urgent Operational Requirement) contract is the first award for Raytheon’s GPS Anti-Jam (AJ) Land product family.
“Raytheon UK has a track record of on-time delivery for GPS AJ systems, having delivered over 7,000 units for air and naval capabilities in the UK and U.S., said Bob Delorge, chief executive, Raytheon UK. “Many of the military platforms used in operations are protected by the proven Raytheon GPS anti-jam technology, and the first order for our Land GPS AJ product family marks a significant success.”
The contract will see the deployment of the systems under a very short timescale, with final delivery of the capability expected to be completed six months from contract award.
Raytheon UK is a subsidiary of Raytheon Company. It is a prime contractor and major supplier to the UK Ministry of Defence. Raytheon UK also designs, develops and manufactures a range of high-technology electronic systems and software at facilities in Harlow, Glenrothes, Uxbridge, Waddington and Broughton.
u-blox, the Swiss positioning and wireless module and chip company, announces UC530M, a tiny parallel GPS/GLONASS module with built-in antenna. The antenna module can be embedded in space-restricted environments because of its tiny footprint of 9.6 x 14.0 x 1.95 millimeters. The highly integrated SMT design reduces the need for external components and minimizes manufacturing costs, u-blox said.
“Location-aware functionality in ever-smaller consumer and industrial devices is a clear market trend. This presents an increasing challenge to OEMs,” said Thomas Nigg, vice president of product marketing at u-blox. “Manufacturers are confronted with the difficult task of providing fast and accurate positioning in compact devices, while time-to-market and price pressure call for minimal R&D effort and low cost. The new UC530M is built to address these requirements: a complete low-power, high performance multi-GNSS receiver with integrated antenna. The module is easy to integrate in a wide variety of devices cost-effectively.”
With high sensitivity, -165 dBm in tracking, and very low power consumption, typically only 66 mW average power consumption, the UC530M can be directly connected to a lithium battery, eliminating costly voltage regulators. Advanced low-power modes are also supported along with three days self-assistance support. Additional functionality includes a logger function which stores location information in internal memory. With a typical log interval of 15 seconds, log capacity can be up to 16 hours.
The integrated antenna of the UC530M exhibits significantly better radiation efficiency than small patch antennas, and performs well against larger and heavier patch antennas. Its circular radiation pattern brings flexibility to hardware designs, u-blox said. The optional connectivity to an external antenna extends the applicability of the module to a wider range of devices from handheld computers to asset tracking systems. The module is drop-in compatible with the UC530 GPS antenna module announced in June 2012.
Engineering samples of the UC530M modules are available in December 2012.
In a repeat performance, USGIF (United States Geospatial Intelligence Foundation) put on a superb conference that was really informative and well executed. GEOINT has always been a serious conference with very little partying, but this year was more so with the leadership even eliminating the traditional closing night social. The social events never were excessive in the past but no one wanted even a hint of over indulgence. It wasn’t missed, because quite frankly there was too much to see and too much to do to spend time on frivolity.
As in past years, there were so many noteworthy presentations. With more than 248 exhibitors in the Expo, it was impossible to see and hear it all. So this is just one man’s limited view of a mega conference. Luckily, many of the key presentations are on the USGIF website as daily summaries. See ShowDaily 1-5 and videos clips (make sure select the 2012 clips and not previous years).
Director of National Intelligence and keynote speaker James Clapper
The opening keynote was delivered by the director of National Intelligence, The Honorable James Clapper, who directly addressed two elephants in the room — sequestration and his take on the Benghazi attack. First he discussed several issues: the INCITE program to have an enterprise data model in the “cloud” by 2018, which he said was moving along nicely. He tied in the need for multi-int data such as GEOINT, SIGINT, MASINT, etc. and also expressed his concern that improvements were needed to speed up the clearance process. He cited reciprocity, so clearances would carry over from one agency and contract to others as a big issue.
Then he got to elephant one — sequestration. He said that it would be devastating to the intel community because there is no way to prioritize programs. Important programs would see the same cuts as less critical programs which could prove very dangerous.
The second elephant was the recent attack at Benghazi and death of four diplomatic staff members including the Ambassador. Director Clapper took a jab at our politicians and quoted a recent article by Paul R. Pillar, a 28-year veteran of the Central Intelligence Agency (CIA) that he said was thoughtful and resonated well with him.
“Information about lethal incidents is not total and immediate. The normal pattern after such events is for explanations to evolve as more and better information becomes available. We would and should criticize any investigators who settled on a particular explanation early amidst sketchy information and refused to amend that explanation even when more and better information came in. A demand for an explanation that is quick, definite and unchanging reflects a naive expectation — or in the present case, irresponsible politicking.” You can view Director Clapper’s full keynote here.
NGA Director Letitia Long addresses the opening session crowdNGA Director Latisha Long
Director Clapper was followed by NGA (National Geospatial-Intelligence Agency) Director Letitia Long, who discussed current efforts at NGA. She cited continuous creation of ever more capable applications. One example permitted a single user to locate a hard-to-find feature in imagery that took 10 minutes, which previously would have taken several analysts days to complete. She stated that during the past year NGA had developed more than 150 apps that are currently in their “app store.” Her goal was to have the majority of future apps created by commercial developers. They are even considering an “Apple iPhone like” commercial model that would pay compensation to developers based on the number of downloads and users rather than cumbersome and limited contracts.
Additionally, she spoke of their work to build a Common Desktop Environment (CDE) for NGA and DIA, which will soon top 2,000 users and is expected to grow to 60,000 users by the end of 2013. She said that through streamlining and redundancy elimination about 40 percent of their geospatial content is available to her users with a goal of 100 percent by next year.
This conference was an eye opener in that it was surprising how fast topics that were just sidebar discussions last year are moving to the forefront. Topics like human geography, social media, and pattern of life mapping seemed to be part of many presentations and some exhibitors. A few presentations stretched my concept of geospatial technology and tradecraft.
One of them was by Jeff Jonas, an IBM Fellow and chief scientist for IBM Entity Analytics, who gave a lunchtime keynote explaining work he was doing at IBM to help decipher seemingly duplicate data to cut processing time. He used a puzzle metaphor to explain his work with “big data.”
“Some of the pieces are missing, some of the pieces have errors, some of the pieces have fabricated lies,” he said, but by merging many different datasets a filtering occurs. He then explained an ultimate filter by using an example of two theoretical twins with the same IDs, same DNA, same accounts, etc. He said that with current technology we can now track the movements of individuals through their smartphones, and that unless the twins are joined at the hip, “Space time movement data is the ultimate biometric” and is one way to differentiate one person from another. This capability is also going test our concepts of privacy.
The GEOINT Expo
In the Expo area, there were more than 248 vendors ranging from the big companies such as Lockheed, Boeing, SAIC, and others to small start-ups at the fringe of the exhibit hall. Several were showing human geography / social media tools and numerous data storage and management solutions. I didn’t see much new hardware of note other than Ball Aerospace, who was showing the latest and greatly improved version of its Flash aerial LiDAR that can create 3D models draped with imagery continuously and in real time. This was so impressive that I’m going to learn more and write a column about it in the near future.
Klee Dienes, president of Hadron Industries and former medical helicopter pilot, demonstrated Hadron’s work developing hand-gesture language to use Oblong computer control equipment to navigate maps. Oblong Industries has developed equipment that permits touch-free control of applications just through the use of hand gestures, very much like in the science-fiction movie Minority Report. Oblong not only has equipment that can follow hand gestures using a special glove, but the technology has progress to tracking hand gestures in free space without special gloves. They also developed a special hand-gesture language called g-Speak. This technology is hard to describe and is best understood viewing video clips at the Oblong site.
There were numerous presentations on the growing use of human geography and the growing need for not only geospatial technicians but of all things, social scientists. The only “wet blanket” attendee that voiced a concern during a question-and-answer session was an academic researcher who voiced a concern that social scientists were being used for intel work. He said that the American Anthropological Association (AAA) may have a problem with “weaponizing” social science. The speaker had a good answer in that he asked “How could the AAA have a problem with preventing war and reducing human misery?” My feeling, considering the stellar high-paying job market for social science majors, is why bite the hand that could feed you?
There was so much to cover in the human geography realm that in next month’s column, I will focus on the human geography aspect of GEOINT.
Topcon Positioning Systems announces the MG-A8 antenna for navigation and precise positioning in marine applications. According to Topcon, the new MG-A8 marine antenna provides exemplary GNSS signal tracking while not being susceptible to signal jamming from other sources, such as Inmarsat communications.
The MG-A8 antenna can be used in DGPS mode for meter-level navigation purposes but can also be used for RTK centimeter level positioning in areas where there is a network of reference stations available to support this level of precision. With its RTK capabilities, the MG-A8 is a “preferred solution for applications such as dredging in inland river channels and waterways,” said Tom Morris, TPS senior product development manager.
“This antenna is designed with challenging marine applications in mind. It is accurate, rugged, reliable and affordable.”
Styling for consumer usage has progressively miniaturized of the antenna package to tiny dimensions compared to a free-space wavelength, even as devices with these miniscule antennas are designed to work close to the absorbent tissues of the user’s body and in the electromagnetic maelstrom of city street levels. GNSS antennas have responded with significant advances.
The selection of the GNSS antenna, especially for small portable wireless devices, demands careful consideration of how it will interact with its expected environment. A physical appreciation can explain how many impairment factors can actually have a common cause: often the effect of human body-loading. This explanation starts with a counter-intuitive foundation: though the GNSS receiver does not transmit signals, for the sake of clarity we invoke the law of reciprocity and proceed with the conceptual thinking that the antenna is radiating outwards. This gives us a basis for understanding the causal physics of how the antenna shares energy with the immediate environment.
We can visualize the basic radiating action of the antenna by recognizing that it is a resonant component. We must consider what exactly is in resonance, because the antenna designer has two different design options. In the self-resonant configuration, the antenna can be considered to be resonating autonomously, forming the entire dipole of the antenna within the antenna body. Here, dimensions and topological structure act in conjunction with reflecting and absorbing features surrounding it to define where and how the antenna radiates.
In the second or probe antenna case, a larger radiating space can be configured by resonating the antenna with the housing together. The antenna typically forms a monopole counterpoised by currents and voltages in the housing. Here, the topology of the radiating system (antenna and housing) acts in conjunction with the near environment to define the radiation pattern.
The value of distinguishing these two configurations is clearly reflected in the contrast between their behaviors with regard to radiation efficiencies in different uses. We conducted an experiment with three example antennas. Each antenna was installed in as common a package format as was practically feasible to model the top portion of a slim-line demonstration platform, with dimensions typical of consumer devices and containing a conductive chassis 55 millimeters wide. Obviously, a probe antenna must be installed in a chassis in order to function, and this directed the experimental approach to be structured around a similar-housing methodology.
The probe antenna was a small metal and ceramic chip, and we compared its performance with a small microstrip patch antenna mounted horizontally in a broader but otherwise similar housing, and a hexafilar antenna mounted in an identically dimensioned housing. Strictly, the microstrip antenna is a single terminal element, but it can be considered as self-resonant as the resonance fields are very tightly constrained. Figure 1 plots the radiation efficiencies for benign free-space conditions (without body-loading) together, as frequency responses.
Figure 1. Frequency response of radiated efficiency in unloaded (free-space conditions) and mounted in similar housings (ground-plane width 55mm).
In benign open-field conditions the probe antenna has excellent efficiency performance and superior bandwidth compared to the two self-resonant configurations. Conversely, the self-resonant antennas (patch and hexafilar) have similarly narrow frequency-response bandwidths and lower efficiencies. We will show how it is important to repeat the test for realistic use scenarios that determine how close the antenna will be juxtaposed to the user’s biological tissues before concluding that the probe antenna is the best solution.
Antenna studies have shown that the bandwidth reduces very rapidly as the resonant volume of the antenna reduces. This accounts for the reduction in bandwidth shown in Figure 1 for the self-resonant antennas (microstrip patch and hexafilar) with respect to the probe antenna (chip). In the case of the probe, the resonant structure is the entire metal chassis of the device (in this case the circuit-board ground-plane) so that the resonant volume of the resonating system is much larger than those of the self-resonant structures.
To analyze the behavior of antennas in different use scenarios, it helps to consider the nature of resonance in antennas: open fields, with equal time average amounts of electric and magnetic field energy oscillating in space. These fields, induced by the time-varying voltage potentials and currents in the antenna, can launch a radiating wave into space because time-varying electromagnetic fields can carry or displace energy. We need to appreciate that this volume is where the so-called reactance fields exist, where field oscillations function as a sort of pump that propagates the electromagnetic wave. The antenna induces those fields in a configuration that manages the propagation of waves in useful directions and with desired polarization.
Any invasion of the reactance field region will disrupt this process and cause impairment. Whilst obstruction of the radiating fields far away from the antenna will just cause a masking effect, a similar obstruction in the reactance-field region can disrupt the basic process of generating radiation. The density of fields in the reactance field region is much higher than would be implied by the straightforward application of the inverse square law.
Use Near the Body
We evaluated the antenna types, installed in packages as thin as test antenna dimensions allow, to draw conclusions as to how they might operate in slim-line consumer devices held close to the user’s body. Figure 2 shows CAD diagrams of the three antennas installed in their respective test packages.
Figure 2. Antenna test housings for the chip antenna (left), patch antenna (middle) and hexafilar antenna (right). The housings were constructed to have a height of 26mm, a width of 60mm and a depth of 11 mm for the chip antenna and the hexafilar antenna and of 20.5mm for the patch antenna. In all cases the horizontal width extent of the printed circuit board (with continuous copper ground-plane on at least one side) was set at 55mm.
Consumer devices have drawn antenna technologies from traditional GNSS applications as well as from terrestrial mobile telephone origins. The overall evolution combines adaptation of the circularly polarized technologies (multi-filar and microstrip patch) into smaller body-loaded platforms with insufficient space for effective ground-planes, together with adaptation of the art of low-cost cellular-telephone embedded antenna technologies that were never developed for circular polarization. Taking our three solutions in their embedded test platforms, we can appraise their body-loaded efficiencies by testing them juxtaposed to a phantom head, providing a means of assessing impairment due to body-loading.
The phantom head in the loading experiment was filled with a tissue simulating liquid conforming to requirements for specific energy absorption measurements according to CENELEC and IEEE procedures. Comparing the antenna efficiencies for open-field conditions (Figure 1) and body-loaded conditions (Figure 3), reveals impairment to antenna efficiency in all three cases, with the most severe loss of approximately 80 percent by the chip antenna.
Figure 3. Combination of FFT-based acquisition with FDAF.
The self-resonant antennas suffered less impairment: approximately 30 percent reduction for the patch and 65 percent for the hexafilar antenna. The probe’s significant loss of efficiency is typical of this class of antennas, as the resonant fields are heavily loaded by the phantom head. The peak efficiency for this chip antenna has tuned downwards in frequency as the dielectric loading effect of the head-phantom introduced a regime of net higher relative dielectric constant (εr) into the resonance field region of the antenna system.
By contrast, the self-resonant antennas did not tune down in frequency as they were brought into proximity with the phantom head. This indicates that the resonance fields were not offered to the dielectric materials of the head phantom to an extent that materially changed the relative dielectric constant (εr).
Nevertheless, there is a significant difference between the impairment that develops between the patch and hexafilar cases as body-loading is applied, with the hexafilar solution losing more radiation efficiency than the patch antenna. There are two explanations for this difference.
The first is that the patch housing is simply larger, with a greater depth required to accommodate the patch antenna horizontally at the top of the device housing. On average this larger housing size spaces the resonant fields further from the phantom and from the lossy simulated head tissues.
The second explanation offers an insight into the symbiotic relationship between the hexafilar antenna and the demonstration platform’s vertically orientated housing. The horizontal ground-plane required for the patch antenna is inconvenient from the style and total integration cost point of view, but also ineffective as a ground-plane as it lacks sufficient width in a device styled to minimize depth. In this scenario the patch antenna is not getting much reflection uplift from the ground-plane; therefore there is little impairment when the device is body-loaded.
The hexafilar solution is designed to benefit from reflective uplift from the vertically disposed ground-plane of the device. This property is convenient for device packaging because it allows the hexafilar antenna to be integrated at a device corner. The installation of a large and effective vertically oriented ground-plane for the hexafilar case is, by contrast, highly convenient and potentially more cost-effective. When the device is not body-loaded, reflections from the vertically disposed ground-plane uplift the gain and efficiency of the hexafilar antenna. The important advantage over the chip antenna (which is also convenient for space-constrained designs) is that for the self-resonant hexafilar antenna, the frequency of resonance does not change for open-field and body-loaded cases.
Polarization, Pattern, Positioning
Sufficient data has now been presented to make an antenna selection on the basis of efficiency and styling. The probe antenna in the guise of a chip antenna provided the highest radiation efficiency in free-space, comparable radiation efficiency to the hexafilar antenna in a body-loaded use scenario, and the small physical size supports compact product designs. However, for GNSS applications we must consider wave polarization, especially if there is multipath scattering. GNSS systems employ right-hand circular polarization (RHCP) and ideally should use antennas with hemisphereically omni-directional antennas. The zenith gain of a circularly polarized antenna is expected to be 3dB higher than that of a linearly polarized antenna of the same efficiency.
If a GNSS terminal is equipped with an omni-directional but linearly polarized antenna, it can receive circularly polarized signals from all directions (albeit with a spatial average 3dB polarization loss). However, the positioning performance of such a terminal will be compromised because a linearly polarized antenna cannot discriminate between RHCP or LHCP, and reflections change the direction of spin of the circularly polarized wave.
More color to the subjects of polarization, pattern, and consequential GNSS accuracy can be gained by focussing on the operation of the dielectric-loaded hexafilar antenna, as an example of a small antenna. Figure 4 shows the measured RHCP and LHCP elevation patterns of an exemplary small hexafilar antenna. These are excellent examples of the signature cardiod pattern shapes of good circular polarization antennas, but they point in opposite boresight directions. A dipole rotating anti-clockwise (viewed from above) in a plane would simultaneously excite a RHCP cardiod elevation pattern in the upwards direction and an oppositely directed, but otherwise similar, LHCP cardiod pattern downwards. If the antenna has no ground-plane and the dipole rotation is planar, the power of the upward RHCP and downward LHCP responses are equal. However, the dielectrically-loaded hexafilar antenna is a synthesis of a small travelling-wave upwardly spiralling dipole, emulating the axial-mode of a helical antenna. As the electrical size of such an antenna is increased, the area of the upwardly directed RHCP pattern progressively increases, and the area of the downwardly directed LHCP pattern progressively reduces. The antenna’s dielectric core enables this right-to-left discrimination within dimensions that are very much smaller than a free-space wavelength of the GNSS signal.
Figure 4. RHCP and LHCP elevation for small dielectrically loaded hexafilar antenna (with no ground-plane).
We can describe the polarization sorting behavior of the small dielectrically loaded antenna in figure 4 as follows. GNSS signals direct from the space vehicles will arrive in the directions of the upper hemisphere of the patterns where the highest sensitivity of the antenna to RHCP is deployed. GNSS signals bounced from a reflective object may also arrive in these upper hemisphere directions, but with reversed polarization: LHCP. In these directions the antenna has a very much lower sensitivity to LHCP, and the GNSS receiving process will accord a low value on these signals that as a result of the low antenna gain will be assessed as relatively noisy.
Signals that arrive at the antenna from directions in the lower hemisphere will certainly have reflected from the ground surface (assuming that the antenna is held upright). These reflected left-hand polarized signals may have been attenuated by absorption losses of materials present on ground surfaces and also reduced in GNSS receiver process weighting by the antenna’s discrimination in favor of RHCP.
RHCP and LHCP Gain
Whilst appraisal of antenna patterns is certainly the most important method for assessing the performance of antennas in different use scenarios, it is nevertheless difficult to report accurately because the three-dimensional data-set is inevitably complex. To provide a meaningful physical basis for discriminating performance between the test antennas for open-field and body-loaded, we propose a single parameter: cross-pole rejection at zenith as one which is directly relevant to GNSS accuracy in a multi-path environment. Figure 5 plots the right hand and left hand comparative frequency responses for open-field and body-loaded use scenarios. Table 1 summarizes these responses.
(a)
(b)
(c)
(d)
Figure 5. RHCP and LHCP frequency responses at the zenith direction for conditions of free-space and body-loading. From top to bottom: a) open-field conditions and RHCP, b) open-field conditions and LHCP, c) body-loaded conditions and RHCP, and d) body-loaded conditions and LHCP.Table 1. RHCP to LHCP gain ratio at the zenith direction (θ=0, φ=0) at GPS L1 center frequency (1.575.42 GHz).
In open field, the chip antenna does not have a gain advantage for right-hand versus left-hand polarization and also suffers the highest impairment in gain when body-loading is applied. In this test there is an advantage in favor of RHCP gain for the body-loaded test scenario, but we presume this depends on the mounting position of this particular probe antenna on the test device. Perhaps a mounting position towards the left of the assembly might have incurred a disadvantage of similar magnitude?
The patch antenna has an excellent RHCP over LHCP advantage in open-field conditions, but this advantage diminishes when this solution is body-loaded. This is the least gain-impacted solution as presumably the horizontal ground-plane and much greater device width produce a relatively low body-loading impact.
The most interesting result concerns the hexafilar antenna, for which the RHCP to LHCP advantage actually improved in the body-loaded test scenario. As this device had the same depth, one might have expected it to sustain a body-loading impairment similar to that of the chip antenna, but due to the self-resonant character of the hexafilar element the loss in gain (in this zenith direction) was actually only slightly greater than that of the patch antenna.
The hexafilar element’s CP performance is distorted by the lack of circular symmetry of the vertical ground-plane; therefore in open field this direction has a relatively modest RHCP to LHCP gain advantage of about 5dB. However, when the device containing the hexafilar antenna solution is body-loaded, the re-radiation from reflections from the circuit-board are heavily damped by the phantom head. The radiating source is then predominantly the hexafilar self-resonant element that by design is not itself so significantly impacted by the body-loading scenario. This source is restored to a more autonomous circularly polarized form with an advantage of RHC versus LHCP gain in zenith direction, nearly 13.5dB.
Walk Tests
Free-space and body-loaded test data, together with arguments concerning polarization discrimination and multipath led to an hypothesis that the antennas with the best circular polarization performance should provide the highest GNSS positioning accuracy. We tested the three devices, worn against the lower torso where the body provides a relatively homogeneous dielectric medium, so that position data could be compared with a reference antenna mounted over a large overhead ground plane.
Many walk tests were conducted around different routes in London, which collectively demonstrate the value of circular-polarization discrimination as a key enabler for accurate street-level position determination. One segment (Figure 6) in the vicinity of an iconic tall London building commonly known as the Gherkin showed that, though the circularly polarized antennas closely followed the path of the reference antenna, the linearly polarized chip antenna produced an error of as much as 200 meters. It is possible that the dominant reflector in this case is the Gherkin itself.
Figure 6. Data, central London walk test.
Conclusions
The chip and hexafilar antennas could be integrated tightly into consumer device housings; both experienced gain uplift from the vertically disposed circuit-board ground-plane. The gain uplift from the chip antenna arose as the resonant volume of the device is enlarged as the device size is increased. The gain uplift from the hexafilar antenna arose as a result of constructive reflections from the circuit-board functioning as a vertical ground-plane.
The patch antenna was not the most convenient from the styling point of view because the depth was dictated by the size of the horizontally orientated patch. Consequently the housing was significantly thicker than for the chip and hexafilar solutions, and the patch antenna was not receiving significant uplift from reflections from the housing because the depth limitation constrained the ground-plane to ineffective dimensions.
In body-loaded tests, the chip and hexafilar antennas demonstrated roughly equal radiation efficiency, but the hexafilar provided a significant RHCP advantage. Higher right-hand circular gain was measured for the patch antenna; this was expected due to the greater depth of the housing to accommodate the patch antenna. Urban walk tests showed that the RHCP antennas provided the highest position accuracy.
Whilst the hexafilar antenna did experience some uplift due to reflections from the device circuit board, these were negated when the device was body-loaded. However, the distorting effects of the device ground-plane were also lost, so that the antenna’s advantage of RHCP over LHCP was improved in the body-loading condition.
The GNSS industry has advanced the miniaturization of polarization-controlled antennas for small body-loaded uses. This is gaining currency as enabling polarization diversity in 4G data-communication terminals.
Manufacturers
Sarantel SL1350 antenna was the hexafilar element under test.
Position data for all four devices was measured with Telit SE868 evaluation kits using CSR (now Samsung) SiRFstarIV chipset.
Oliver Leisten is chief technical officer and founder of Sarantel Limited, where Viktor Knobe worked as a student intern from Imperial College London.
Tallysman Wireless, Inc., has announced the latest addition of the TW4320/4322 to its line of antenna products. The TW4320/TW4322 antennas are small wide-band, high-performance antennas housed in a compact IP67 magnetic mount enclosure, with a three-meter cable and a wide range of connectors.
“Most small low-cost GPS and GLONASS antenna have narrow-band patch elements tuned mid-way, but which are 2-dB down in both signal bands,” said Gyles Panther, CEO of Tallysman Wireless. “The TW4320/22 antennas feature a patch element with a 40% wider bandwidth and a very low noise amplifier which together allows the full benefits of multi-constellational GNSS to be realized.”
The TW4320/TW4322 antenna covers the GPS L1, GLONASS L1, and SBAS (WAAS, EGNOS, and MSAS) frequency bands (1575 to 1606 MHz). It features a small patch element with 40 percent wider bandwidth than previously available in this format. It provides both GPS-L1 and GLONASS signals in the 1-dB received power bandwidth.
The TW4320/TW4322 has a two stage low-noise amplifier with a mid-section SAW (Surface Acoustic Wave). A tight pre-filter is available in the TW4322 to protect against saturation by high-level sub-harmonics and L-band signals.
Features:
• 40% wider bandwidth in the same format
• Axial ratio: 6 dB max
• Low noise LNA: 1 dB
• High rejection mid-section SAW filter
• Available pre-filter (TW4322)
• High gain: 28 dB typ.
• Wide voltage input range: 2.5 to 10 VDC
• IP67 weather-proof housing
Models:
• TW4320 – GPS/GLONASS antenna, three-meter cable, SMA Male 32-4320-xx-yyyy
• TW4322 – GPS/GLONASS antenna, with pre-filter, three-meter cable, SMA Male 32-4322-xx-yyyy
Tallysman Wireless, Inc., has announced the latest addition of the TW4320/4322 to its line of antenna products. The TW4320/TW4322 antennas are small wide-band, high-performance antennas housed in a compact IP67 magnetic mount enclosure, with a three-meter cable and a wide range of connectors.