A free GPS World webinar on Nov. 21 tackles a new frontier, if not the final one, for GNSS. “Developments in Space GNSS Navigation,” sponsored by NovAtel, brings together experts from NASA, ESA, NovAtel and Spire (the CubeSat company) to discuss how they’re taking GNSS capabilities beyond Earth’s boundaries.
Navigating through space has long proven to be challenge for aerospace engineers and professionals because of the complex combination of technology and cost required for success.
However, with advancements in GNSS and receiver technology, organizations and nations around the world are increasingly interested in space exploration activities.
Today, the space industry is seeing GNSS technology used in low-Earth orbit (LEO) and highly elliptical orbit scenarios.
In this webinar, speakers from NASA, ESA (the European Space Agency), NovAtel and Spire will examine emerging trends regarding the usage of GNSS technology in the space industry, including an increasing need for situational awareness while navigating through space and the ability to service satellites while in orbit.
These experts will also provide a look into their own experiences with a variety of ambitious space projects and applications.
Speakers include:
Werner Enderle, Head of Navigation Support Office, ESA European Space Operations Center
Benjamin Ashman, Aerospace Engineer, NASA
Erin Kahr, Critical Safety Systems, NovAtel
Dallas Masters, GNSS Program Manager, Spire
Date: Thursday, November 21, 2019 Time: 1 p.m. EST / 10 a.m. PST / 7 p.m. (1900h) Central European Time
Taoglas, a provider of next-generation internet of things (IoT) solutions, has launched Edge Locate, a GNSS L1/L2/E5 module that combines antenna, RF electronics and receiver technology to deliver reliable centimeter-level positioning.
Taoglas, in partnership with u-blox, created a smart antenna that uses multi-band GNSS technology, providing between 1- to 3-centimeter-level accuracy.
With Edge Locate, manufacturers can quickly and effectively build devices with centimeter-level positioning technology, without having to invest in costly and lengthy RF design, integration and testing processes.
The device features multiband GNSS positioning that can be used in conjunction with cost-effective real-time kinematic (RTK) positioning capability.
Traditionally, most IoT devices use single-band GPS technology, delivering on average 10-meter accuracy with existing GPS modules and antennas, Taoglas said in a press release. This enables location-specific, mission-critical services such as emergency response, smart infrastructure, precision agriculture and microbility mobility applications where precise location provides critical value to the IoT application.
Taoglas can also consult and install the RTK network in any global location for any IoT use case.
“Centimeter-level positioning is absolutely key to the next-generation of IoT enabled applications,” said Ronan Quinlan, Co-CEO of Taoglas. “Take an example from the burgeoning micro-mobility industry. When granting licenses from a trial, the city authorities would like to monitor the riders of e-scooters, ensuring riders are staying off footpaths, or parking in designated areas. The problem is that today’s legacy GPS solutions don’t often know which side of the road a scooter is on. Whereas with our solution, fleet operators can pinpoint within just a few centimeters where a device is located. We do this by working with our customers to enable the whole solution and we make sure it works reliably in real life.”
Edge Locate can greatly accelerate the latest GNSS multiband product launch plans by offering a plug-and-play product that uses a common connector for integration into any electronics device. It also connects directly to the Taoglas Edge board for immediately connectivity options.
Taoglas is exhibiting at Mobile World Congress Americas, Booth 2602 in the South Hall of the Los Angeles Convention Center.
Neil Gerein, segment manager of defense and NAVWAR for NovAtel, explains NovAtel’s latest receiver technology at ION GNSS+ 2015 in Tampa, Fla. Learn about NovAtel’s SPAN technology and antenna technology, which was also showcased at the event.
SVN49 in space (artist’s rendering). The signal anomaly from SVN 49 alerted researchers to new possibilities in analysis and monitoring.
Chip Transition-Edge Based Signal Tracking for Ultra-Precise GNSS Monitoring Applications
By Sanjeev Gunawardena, John Raquet and Frank van Graas
Tracking GNSS signals using their underlying spreading sequence chip transition edges reveals positive versus negative chip asymmetries that are characteristic to each satellite. This asymmetry is due to various types of natural signal deformation that is known to occur within the satellite’s signal generation and transmission hardware. This novel concept of monitoring chip asymmetry can extend the state of the art in the areas of GNSS signal-quality monitoring and authentication. A technique to directly monitor chip asymmetry within a specially designed ChipShape GNSS receiver architecture employs separate code discriminators that align themselves to the chip rising-edge and falling-edge zero crossings.
The detailed study of naturally-present deformations in GNSS signals is a relatively new activity that was sparked by the GPS SVN49 anomaly and the associated research activities that followed. This research area has numerous applications that include:
Informing the design of sudden signal deformation detection and alerting algorithms for safety-of-life differential GNSS applications (such as aviation).
GNSS signal “fingerprinting” and authentication.
The detailed study of long-term degradation effects of GNSS satellite signal generation and transmission hardware.
Analysis of the impact to the first item in this list of swapping a satellite’s signal generation modules by its control segment.
Multipath detection, characterization, and mitigation are also closely tied to all research relating to GNSS signal deformation monitoring (SDM).
High-fidelity SDM can be performed using two methods:
observation of actual GNSS signals above the thermal noise floor using a high-gain dish antenna;
the combination of long coherent integration and multi-correlator processing.
Our previous research has revealed that these two methods are highly complementary for gaining full insight into the effects and causes of observed natural signal deformations.
Among the handful of multi-correlator processing techniques that can be applied for SDM, ChipShape processing allows the correlation function resolution to be finely adjustable while providing good numerical processing efficiency. This processing technique also allows chip-transition eye diagrams to be constructed in order to provide additional insight such as positive and negative chip width asymmetries.
One goal of our SDM research involves developing capabilities to observe GNSS signals with the highest levels of fidelity practically achievable in order to further the application areas described above. Key to this is developing techniques to track GNSS signals using a reference point that is both consistent and invariant (to the greatest extent possible) to nominal signal deformations and environmental effects such as multipath. Traditional multipath mitigating techniques such as narrow correlator and double-delta correlator are sub-optimal in this regard. This is because a significant portion of the signal around the chip transition point (that is, 10 percent and 20 percent for 0.1 chip correlator spacing, respectively) must be integrated to realize these discriminators and maintain robust tracking in moderate dynamics conditions. This integration tends to low-pass filter the desired observables.
Chip Transition Edge-Based Code Tracking
Figure 1 illustrates normalized C/A code chip rising edges for the GPS constellation of June 2014. These chip shapes were processed using a front-end with 24 MHz bandwidth. For visual comparison purposes, this and other related plots were obtained using 600 seconds of coherent integration.
Figure 1. Normalized ChipShape rising edges for the GPS SPS constellation of June 2014; each color represents a different GPS satellite.
The code tracking loop used to obtain this result employed an empirical normalized coherent rising-edge discriminator given by:
(1)
Where τ is relative code phase in chips, d is Early-Late correlator spacing,R’XYZ(i) is the differential correlation output for integer bin i obtained using ChipShape processing with masking sequence XYZ. bin(x) is a function that selects the closest ChipShape vector index that corresponds to relative code phase x. Each ChipShape processing bank is configured to span one chip early and one chip late with a resolution of N bins per chip, thus producing a ChipShape vector of 3N bins. α is a scale factor obtained through trial and error to yield robust tracking performance as observed by the code-minus-phase measurement. For the result shown in Figure 1, N=240 and d ≈ 0.017 chips.
The figure clearly shows that the rising-edge zero crossings vary by SV. This variation is due to nominal signal deformation present in each GPS-SPS signal.
Figure 2 illustrates the rising-edge zero crossings aligned to zero relative code phase. This alignment was performed by interpolating each R’NPN vector, precisely estimating code phase at the zero-crossing point, and shifting the curve appropriately.
Figure 2. Normalized ChipShape rising edges for the GPS SPS constellation of June 2014: Zero crossing compensated.
Figure 3 shows zero crossings for the falling edges after all rising edges were aligned to zero. The figure clearly illustrates subtle asymmetries between positive and negative chips which span a range of approximately ±1.5 meters. These asymmetries are not directly observable using typical GNSS receiver processing. However, they can lead to pseudorange biases through the resulting distortion that occurs to the traditional correlation function.
Figure 3. Normalized ChipShape falling edges for the GPS SPS Constellation of June 2014 when rising edges are aligned to zero.
In general, a family of code discriminators that precisely track chip rising-edge zero crossings can be defined by:
(2)
Where R’NPX is a linear combination of orthogonal ChipShape components that preserve the rising-edge transition, e.g.: R’NPX =R’NPN +R’NPP. R’FFX is a linear combination of orthogonal ChipShape components that preserve the non-transitioning (that is, flat) sections of chips, for example: R’FFX =R’PPP + R’PPN −R’NNP − R’NNN. a and b define an integration interval within the range −1 to +2 chips with respect to the chip transition edge. β is a bias compensation term. represents the real or imaginary component function for the coherent discriminator (depending on the modulation phase of the signal being tracked), or the magnitude function for a non-coherent discriminator implementation.
Similarly, a family of code discriminators that precisely track chip falling-edge zero crossings that occur one chip after the rising edges tracked by the discriminator of Equation 2 can be defined by:
(3)
Then, a two-step technique to precisely monitor chip asymmetry can be described as follows:
Setup two identical ChipShape processing channels to track a given PRN. Progressively tighten the code tracking loops to track the rising-edge zero crossings of the underlying signal using the discriminator of Equation 2.
After steady-state zero-crossing rising-edge tracking is achieved, switch the second channel’s code discriminator to that of Equation 3. This will cause the second channel to track the zero crossings of the falling edges that occur one chip later in the underlying signal’s spreading sequence. The discriminator’s linear range must be wide enough to pull-in the chip asymmetry shown in Figure 3.
When the second channel re-converges as a result of Step 2, the relative pseudorange displacement that occurs is equal to the chip asymmetry in meters. Hence, chip asymmetry can be monitored for the entire visible pass of a satellite. It is expected that positive and negative chip transitions are equally affected by channel distortions (that is, code and carrier multipath, ionosphere, troposphere, and the receiver antenna and front-end transfer function). Hence, the rising-edge-code-minus-falling-edge-code measure of chip asymmetry is expected to be invariant to most if not all channel distortions.
Estimating Compensation Parameters
As shown in Equations 2 and 3, due to natural signal deformation of many types, the rising and falling-edge zero-crossing discriminators are expected to be SV number, PRN code and elevation angle dependent. Hence, α and β must be estimated for a given correlator spacing d separately for all SV signals of the constellation. These values will also be specific to a given antenna and receiver front-end.
Figure 4 illustrates the procedure used to estimate the scale factor and bias terms starting with the empirical rising-edge tracking process described above.
Figure 4. Procedure for estimating scale factors and biases for rising-edge tracking early-late and double-delta code discriminators.
The following figures illustrate the edge tracking discriminator calibration process using R’NPN for a single SV.
Figure 5 illustrates the early-plus-late functions computed for various correlator spacings. As described previously, these functions typically do not cross through zero codephase due to natural signal deformation.
Figure 5. Uncorrected rising-edge early-late discriminator functions for various correlator spacings.
Figure 6 illustrates the rising-edge discriminator functions after bias compensation.
Figure 6. Rising-edge early-late discriminator functions for various correlator spacings after bias compensation.
Figure 7. Calibrated rising-edge early-late discriminator functions for various correlator spacings.
Figure 8 illustrates the multipath error envelopes for the rising edge-based coherent code discriminators. The performance of these discriminators is similar to the traditional Early-Late discriminators for the same correlator spacings. This result is consistent with the theoretical bounds for code multipath.
Figure 8. Multipath error envelopes for various rising edge-based coherent early-late code discriminator functions.
As shown in Figure 4, the edge-tracking discriminators described in Equations 2 and 3 that are based on Early-Late bin spacings can be combined to obtain edge-tracking double-delta discriminators. Double-delta discriminators provide significantly improved multipath performance.
In general, the edge-tracking double-delta discriminator for inner correlator spacing d is formed by the linear combination of two early-late edge-tracking discriminators, as follows:
(4)
Scale factor γ is estimated such that overall multipath error is minimized according to a given design criteria.
Figure 9 illustrates the double-delta rising-edge discriminator with inner spacing of 0.017 chips. This discriminator has a pull-in range of approximately ±0.01 C/A chips.
Figure 11 illustrates the multipath error envelope for the coherent rising-edge double-delta discriminator. Performance is consistent with a traditional second-derivative discriminator.
Figure 11. Multipath error envelope for coherent rising-edge double-delta code discriminator with inner spacing of ~0.017 C/A chips.
Figure 12 illustrates the performance of the various rising-edge tracking discriminators for a live-sky GPS-SPS signal (de-trended code-minus-carrier measurement). This figure clearly demonstrates robust code tracking and the multipath and noise mitigating benefit of ultra-narrow rising-edge discriminators.
Figure 12. Code tracking performance for live sky data of various rising edge-based coherent early-late code discriminator functions.
Conclusions
An empirical chip rising edge-based tracking technique was used to observe the underlying chip shapes of live sky GPS-SPS signals at high fidelity. These results reveal positive versus negative chip asymmetries that are characteristic to each satellite. The novel concept and technique of directly monitoring chip asymmetry has potential to extend the state of the art in the areas of GNSS signal quality monitoring and authentication.
Disclaimers. The views expressed in this paper are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government.
Acknowledgments. This research was supported by the Air Force Research Laboratory Sensors Directorate.
The authors thank Ohio University Avionics Engineering Center for making available a cluster of high-performance computers to process the 20 TB dataset for this research, and Kadi Merbouh of Ohio University for maintaining and overseeing operation of this equipment.
The ChipShape processing is an extension of the signal compression technique first published by Larry Weill and licensed by NovAtel for use in its Vision Correlator technology.
This article is based on a paper presented at ION Pacific PNT 2015 in Honolulu.
SANJEEV GUNAWARDENA is a research assistant professor with the Autonomy & Navigation Technology (ANT) Center at the Air Force Institute of Technology (AFIT). He earned a Ph.D. in electrical engineering from Ohio University.
JOHN RAQUET is a professor of electrical engineering and the Director of the ANT Center at AFIT. He has been involved in navigation-related research for more than 25 years.
FRANK VAN GRAAS is the Fritz J. and Dolores H. Russ professor of electrical engineering and principal investigator with the Avionics Engineering Center at Ohio University. He received the ION Johannes Kepler, Thurlow and Burka awards, and is a Fellow and past president of the ION.
This tri-band receiver technology, when combined with baseband search and track engines, allows true simultaneous tracking of all current L1 GNSS signals, including GPS, GLONASS, BeiDou, Galileo, Quasi-Zenith Satellite System (QZSS), and satellite-based augmentation systems (SBAS).
By Charles Norman and Andreas Warloe, Broadcom Corporation
Starting with the first commercial GPS receivers, adding support for incrementally more complex GNSS systems presents significant challenges for GNSS hardware and software developers. The latest systems, especially Galileo, were designed with the assumption that Moore’s law would provide nearly unlimited computing resources and memory over time. The expected improvements in ASIC technology have indeed occurred, but market demands have pushed the size, cost, and power consumption of GNSS chipsets down, rather than allowing capabilities to grow freely.
GNSS in cellular phones is now expected to be always-on and to add only a few dollars to the cost of a $600 smartphone. Even as customers and phone manufacturers demand GLONASS, BeiDou, and Galileo support, chipset cost is not allowed to increase significantly. Instead of, in essence, designing four separate GNSS receivers in the chip, cost and size pressures force designers to look for commonality among the signals in order to share hardware blocks and software or digital signal-processing algorithms.
GNSS L1 Signal Down-Conversion
Commercial L1 GNSS signals span a 50 MHz range. It is getting harder for a single antenna to cover the entire bandwidth, but it is possible. The radio input contains three frequency bands of interest, spanning a total of 15 MHz:
BeiDou, at 1561 MHz, is at the low end;
GPS, Galileo, satellite-based augmentation systems (SBAS), and Japan’s Quasi-Zenith Satellite System (QZSS), at 1575 MHz, are in the middle; and
GLONASS, at 1602 MHz, is at the top.
The radio process in the new tri-band receiver described here first amplifies the signal using a low-noise amplifier (LNA) to keep the system noise figure as low as possible. Then it downconverts to an intermediate frequency (IF) and filters the three bands into separate channels. The three bands are then digitized and sampled at the lowest possible sample rate. The sampled bands can be filtered digitally to remove blockers and downconverted to baseband. The baseband samples are buffered by constellations to allow parallel access for searching or tracking on each visible satellite.
All satellites in a code-division multiple access (CDMA) constellation can share baseband buffers, but the frequency-division multiple access (FDMA) constellation, GLONASS, uses a separate buffer for each satellite. This is because the memory and power required to store each satellite in use is less than storing the entire FDMA bandwidth.
Signal Similarities and Differences
All GNSS satellite signals use binary phase-shift keying (BPSK) modulation. The biphase modulation is generated from a high rate pseudorandom noise (PRN) code that is exclusive-ORedwith a low-rate data stream.
The PRN code for all constellations except Galileo is generated from linear feedback shift registers (LFSRs). Galileo’s PRN code is a memory code with a bit-offset carrier BOC(1,1)/BOC(6,1) modulation. All constellations except GLONASS are CDMA. Each satellite in a CDMA constellation is at the same frequency but has a unique PRN code. GLONASS is FDMA. Each visible GLONASS satellite has a unique frequency, but all use the same PRN code.
L1 GNSS constellations use four different code lengths: 511, 1023, 2046, and 4092. The code length has a large impact on the power required to detect a signal. Data modulation is different on each constellation. BeiDou data is exclusive-ORed with a secondary code. Galileo has a secondary code-only channel. The highest data or secondary code rate is 1 kHz on BeiDou, and the lowest is 50 Hz on GPS. Table 1 shows a detailed chart with the main signal parameters for all L1 GNSS signals.
Table 1. Parameters for all L1 GNSS signals.
Radio Overview
The radio processing starts with a LNA, which utilizes a 72-nanometer negative metal oxide semiconductor transistor in a cascade configuration, with deliberate capacitive feedback and inductive source degeneration to achieve an excellent noise figure (~1.5 dB system noise figure) while maintaining a good input match. Two external matching components are required to achieve an optimal input match.
Following the LNA is an in-phase/quadrature ring mixer switched-capacitor mixer. With this style of mixer, the LNA output is only connected to one mixer output at a time and, thus, the optimal noise figure is obtained. By switching the output of the LNA from the I+ output and then later to the I– output, a 2:1 voltage gain is achieved. This improves noise figure and eases the noise requirements of the IF amplifier following the mixer, thus reducing power consumption.
The local oscillator for the mixer is derived from a low-power, low phase-noise, phase-locked loop. It has many adjustments, so the circuit can be adapted to a wide variety of reference frequencies and system requirements. It employs a ΔΣ modulator in the feedback loop, allowing for very fine frequency-control resolution.
The complex IF output from the mixer is amplified by a transimpedance section followed by three parallel amplifier/filter/attenuator sections, one for GPS/Galileo/SBAS/QZSS, one for GLONASS, and one for BeiDou. The transimpedance section’s response is close to a simple pole but with a small amount of peaking. Each of the remaining sections is built with a single complex band-pass/band-notch section, followed by real poles and zeroes. Using real poles and zeroes considerably reduces the noise and bandwidth requirements of the amplifiers. The net effect is that the power consumption of the overall IF amplifier section is substantially reduced.
There are three parallel ΔΣ analog-digital converters (ADCs), one for each of the three IF sections. The ΔΣ ADC is a continuous-time, second-order, one-bit ΔΣ ADC, running at a sample rate of 395.75 Msps. The ΔΣ ADC comprises two operational amplifiers, two digital analog converters, and a quantizer. The ΔΣ ADCs are designed in such a way that the quantization noise is lowest not at zero frequency offset (DC), but at the offset frequency of the GNSS signal. The A/D samples are filtered with a third-order cascaded integrator-comb subsampled at 99.44 mega-samples per second. Additional finite impulse response (FIR) filters and subsampling to 33.1 MHz complete the sampling. The combined ΔΣ ADC and digital filtering provide more than 50 dB of dynamic range.
Digital processing at 33.1 MHz includes several filters that remove interference sources from the received radio signal and automatic gain control logic that adjusts the gain of the IF amplifiers to give an optimal signal level. A configurable 20-tap FIR filter is provided for each sample section and can be configured to remove wideband blockers. In addition, each section has eight narrowband, single-pole infinite impulse response filters for removing narrowband blockers.
Figure 1. Radio overview diagram.
Separate Search and Track Blocks
Separate search and track sections are employed to compute correlations between the three sample streams and multiple reference hypotheses. The three sample streams are buffered in memory to allow the search and track sections to process multiple correlations in parallel. Search employs a prime factor fast Fourier transform with a selectable size (1023, 2046, or 4092).
Search correlations are computed by first removing a hypothesis Doppler from a buffered set of samples and then combining a selectable number of code epochs. The filtered samples are translated to the frequency domain, multiplied by the frequency-domain representation of the desired PRN code, and finally translated back to the time domain. This process creates a coherent correlation vector for the entire code. The coherent correlation vector is non-coherently accumulated until the signal-to-noise ratio of the peak exceeds a detection threshold.
Track correlations are computed in the time domain by multiplying a multichip reference code by a set of buffered samples. Typically, the reference code is linearly delayed for N correlations to produce an N-sample coherent correlation vector. The correlation vectors are buffered to allow multiple filters to be processed in parallel. A coprocessor is used to run the filters. The outputs from the coprocessor provide estimates of code phase, Doppler, acceleration, data synchronization, data bits, signal power, and more.
All the buffering and multiple processing sections allow for multiple hypotheses to be tested in parallel. For example, on a tunnel entry, the attenuated signal can continue to be tracked while the search section tries to detect the full-power signal.
Secondary Code Resolution. Several constellations have secondary codes that limit the length of the coherent integration unless the code can be wiped. GLONASS has a 100-Hz Manchester code, BeiDou has a 1-kHz secondary code, and the Galileo Pilot has a 250-Hz secondary code. After the time accuracy drops below 1 millisecond, all of the secondary codes can be wiped in both search and track, so the coherent period can be optimized to maximize sensitivity and minimize measurement error. On a cold start, when time is unknown, it is best to first try to detect with coherent correlations less than the secondary code chip period.
When a signal is detected, the receiver either goes into track and computes correlations with longer coherent periods for multiple time hypotheses or continues in search with a longer coherence period and multiple time hypotheses. The search and track sections allow for either of these choices. For constellations like Galileo, the best choice is to remain in search. For others like BeiDou, it is best to move to track.
Benefits of Multi-GNSS Receivers
The ability to track all L1 constellations means that even in difficult environments, there are a sufficient number of satellites to produce a navigation solution. As can be seen from field-test results, not only are more satellites tracked, but more satellites with strong signals are tracked. The measurement errors of satellites received with strong signals will be smaller, leading to very low bit-error rates and allowing for a faster ephemeris collection. Field test results confirm that a receiver with BeiDou support achieves faster and more accurate fixes than a receiver without BeiDou support (see Figure 2).
Figure 2. A receiver with BeiDou support achieves faster and more accurate fixes than a receiver without BeiDou support.
In addition to speed and accuracy improvements, more constellations provide a higher reliability. Recently, an upload error in the GLONASS constellation caused otherwise healthy satellites to report orbit errors of several kilometers. GPS/GLONASS-only systems could not completely isolate the faulty satellites. In difficult environments, there are not enough good satellites to isolate the faulty ones. With the addition of BeiDou, the faulty satellites were correctly isolated (Figure 3).
Figure 3. (Top) Seoul, South Korea, third-party GPS/GLONASS-only receiver; (bottom) Broadcom GPS/GLONASS/BeiDou receiver enables isolation of faults.
Each constellation adds unique improvements. Narrowing the correlation triangle allows for improved multipath rejection and more accurate pseudorange measurements (Figure 4).
Figure 4. Narrower correlation triangle.
GLONASS, with the slowest code rate, has the broadest correlation triangle. BeiDou, with the highest code rate, has a correlation triangle that is narrower than GPS. The BOC code on Galileo gives the narrowest correlation triangle. Field test results confirm the improved measurements (Figure 5).
GLONASS, the only FDMA constellation, has the least cross-correlation. GPS uses Gold codes to keep the cross-correlations between any of its satellites at a minimum. BeiDou and Galileo have lengthened their codes and added a secondary code to reduce cross-correlations.
Conclusion
Taking advantage of similarities in the L1 GNSS constellations together with careful design choices to minimize size and current consumption has enabled the creation of commercial GNSS system-on-chips that support all current GNSS L1 systems and meet the cost, size, and power requirements of cellular phones. The addition of new constellations like BeiDou and Galileo has significantly improved speed, performance, and reliability.
Acknowledgments
Javier de Salas, Frank van Diggelen, and John Hutson, all of Broadcom.
Manufacturer
The BCM4774 single-chip GNSS location hub for smartphones with Galileo support was designed by Broadcom Corporation.
Charles Norman is a technical director in the GNSS group at Broadcom Corporation. Previously, he worked on GNSS systems at Magnavox, Interstate, SIRF, and RFMD. He holds 39 issued patents on GNSS systems and has an M.A. in mathematics from the University of California-Los Angeles.
Andreas Warloe is a senior technical director in the GNSS group at Broadcom Corporation. He previously worked on GNSS receivers at Magellan, Leica Geosystems, IBM, and RFMD. He holds an M.S. in electrical engineering from the University of Southern California.
On November 18, a Consultation Event will take place in Brussels on the subject of receiver technology. The event is being held to inform the stakeholders of the European GNSS receiver community about the format and timeline of funding opportunities for the period 2015-2020, and to gather input for the definition of R&D actions in the field of receiver technology.
The workshop will consist of one panel session, during which stakeholders from industry, SMEs, academia, and technology institutes will be asked to debate and recommend important lines of research in receiver technology.
Registration is now open on the Europa website. Interested participants are invited to fill in the registration form and to indicate which application area they are interested in and the fields of research that should be supported.
The workshop will be held at the Committee of the Regions, Jacques Delors building, rue Belliard 99-101, room JDE 53, Brussels.
