MicroSurvey, the maker of MicroSurvey CAD and the MapScenes System, announces the release of three new software versions optimized to make full use of datasets from the new Leica Nova MS50 MultiStation. MicroSurvey CAD Ultimate 2013, MicroSurvey CAD Studio 2013 and MapScenes PointCloud 2013 provide complete point cloud and Leica Nova MS 50 support in an intuitive interface that allows users to quickly and easily integrate 3D point cloud data into their workflows, the company said.
MicroSurvey CAD Ultimate 2013, a complete desktop survey and design software solution for surveyors, contractors and engineers, provides a field-to-finish CAD survey solution with the Leica Nova MS50 MultiStation. Users can import datasets from the Leica Nova MS50 complete with all TPS measurements, pictures, points and scan data. For users who need to enhance their data visualizations with fly-through movies and animations, MicroSurvey CAD Studio 2103 includes all the functionality of MicroSurvey CAD Ultimate plus a powerful animation module capable of creating movies.
MapScenes 2013, a powerful drafting, point cloud data visualization and animation tool for forensic investigators, accident scene reconstructionists and other public safety professionals, now includes the ability to take advantage of the rich 3D datasets captured by the Leica Nova MS50 MultiStation, MicroSurvey said. MapScenes 2013 lets the user quickly and easily draw in the point cloud view for extremely fast, accurate linework as well as use the scan data from the Leica Nova MS50 in animations for accurate and compelling reconstructions.
Navtech is offering a four-day version of Course 551, “Using Advanced GPS/GNSS Signals and Systems,” customized for those attending the ION GNSS+ 2013 conference.
This course will help attendees develop proficiency with advanced receiver processing of current, modernized, and new signals from GPS, GLONASS, Galileo, BeiDou, and QZSS. It teaches systems engineering skills, along with techniques for receiver processing and for assessing processing performance. Review problems, worked in class, help students understand and apply the key concepts.
Those who attend will become proficient with the essential aspects of using GPS and GNSS signals.
Course days:
Friday, Saturday, September 13-14
Monday, Tuesday, September 16-17
Nexteq Navigation has launched accelGRx, a platform for accelerating professional-grade GNSS receiver development. The platform provides open and production-ready hardware and software building blocks for GNSS receivers. accelGRx is designed for organizations looking to research and develop new techniques and algorithms requiring deep in-receiver integreation or quickly produce a small, high-performance receiver.
accelGRx supports GPS L1 and Beidou B1, and the hardware is GLONASS and Galileo ready. It pairs a compact form factor and industry standard pin layout with a code and phase precision of 4 cm and 0.4 mm respectively for both GPS L1 and Beidou B1. It incorporates an array of software development tools, including the ability to record and play back digitized signals.
An accelGRx licensee wil have tools to develop and test new deep in-receiver integration techniques and algorithms:
• Access to all source code, logic and tools
• Deep in-receiver access to real-time GNSS information
• PC-based software model of receiver platform
• Store and playback of digitized signals for development and testing
• Testing with production-ready receiver and real-world conditions
An accelGRx licensee will have the necessary assets and tools to begin commercialization immediately after development is complete:
• Hardware design (schematic, PCB layout, and BOM)
• FPGA logic design
• Full tracking and PVT source code
• Receiver operating system
• Design documentation and manuals
Nexteq also released two other products:
matrixRTK is a combination of the PPP and network RTK approaches to benefit network-RTK vendors. matrixRTK has the benefits of network RTK (fast initialization) with the benefit of PPP (no baseline restrictions).
L1-RTK-systems is a solution that allows our handheld users to use 2/L1 high sensitive GNSS handhelds working as base and rover to achieve 2-20 cm level accuracy. This is a reliable and cost-effective solution for field workers, Nexteq said.
Are you looking for an article you read in GPS World or one of its newsletters? Because of a server move in 2012, much of our older content disappeared from the websites of both GPS World and its sister publication Geospatial Solutions. We have been working hard to again make this content available to our readers.
As of today, we are happy to share that the following is again available:
Content of every issue of GPS World magazine from mid-2010 to the present (our archives have issues back to July 2009);
All columns from the Survey Scene newsletter, written by Eric Gakstatter;
Columns from our other newsletters are still being reposted; however, most of the columns from 2011 to the present are now available. These newsletters and authors include:
If you are looking for a particular feature and are unable to find it, we will try to track it down for you. Please email [email protected]with any past-article requests.
The Russian Federal Security Service is investigating the embezzlement of billions of rubles from the construction of the GLONASS center in Korolyov, a town outside Moscow, the Izvestia newspaper reports.
According to information shared by the Russian Legal Information Agency, the Investigative Committee’s department for the Moscow Region has launched a preliminary probe into the case.
Construction of the GLONASS satellite navigation system control and support center began in June 2010 on the site used by TsNIImash, the head research company of Russia’s federal space agency. The center was supposed to hold equipment for collecting and processing the data supplied by the GLONASS global network.
The construction was financed by a federal program, with 1.050 billion ($33.22 million) allocated for the project. By the end of 2010, it came to light that construction costs had been overstated, Izvestia reports. An expert appraisal revealed that the contractor had rigged the costs. The government did not allocate additional funds, so construction was suspended in December 2011 when the Federal GLONASS Program for 2002-2011 ended. The construction of the building has never been completed.
The Indian Space Research Organization (ISRO) Navigation Centre, an important element of the Indian Regional Navigation Satellite System (IRNSS), was inaugurated May 28. The INC has been established at the Indian Deep Space Network complex at Byalalu, about 40 kilometers from Bangalore, India.
IRNSS, an independent navigation satellite system being developed by India, will have a constellation of seven satellites that enables its users to determine their location and time accurately. These satellites will be positioned in geostationary and inclined geosynchronous orbits 36,000 kilometers above the Earth’s surface. IRNSS coverage will extend over India and the southeast Asia region. The satellites are equipped with high-precision atomic clocks and continuously transmit navigation signals to users.
As the focal point of many critical operations of IRNSS, the ISRO Navigation Centre (INC) is responsible for providing the time reference, generation of navigation messages, and monitoring and control of ground facilities including ranging stations of IRNSS. It hosts several key technical facilities for supporting various navigation functions.
Key to the navigation support is the time reference to which all ground systems and the satellite clocks are synchronized. This time reference is generated by the high-precision timing facility located at INC. This timing facility is equipped with high-stability, high-precision atomic clocks to provide stable and continuous time reference to the navigation system.
IRNSS will have a network of 21 ranging stations geographically distributed primarily across India. They provide data for the orbit determination of IRNSS satellites and monitoring of the navigation signals. The data from the ranging/monitoring stations is sent to the data processing facility at INC where it is processed to generate the navigation messages. The navigation messages are then transmitted from INC to IRNSS satellites through the spacecraft control facility at Hassan/Bhopal. The data processing and storage facilities at INC enable swift processing of data and support its systematic storage.
INC is connected to the ranging stations and to the satellite control facilities through two highly reliable dedicated communication networks consisting of satellite and terrestrial links. The hub for the satellite communication links is hosted at INC.
The INC was inaugurated by V. Narayanasamy, minister of state in the Indian prime minister’s office. Speaking on the occasion, Narayanasamy said he appreciated the commitment and dedication of Indian space scientists in realizing the objectives of the country’s space programme. The minister also gave away various awards instituted by Astronautical Society of India (ASI) and ISRO.
The Instant Alert Device enables drivers to immediately notify dispatch. Photo: GPSTrackIt
GPSTrackIt has developed an Instant Alert Device that can attach to a driver’s keyring, to enable mobile workforce team members to communicate with their dispatchers or fleet managers. If a driver is in trouble, help can be on the way with the touch of a button.
The compact communication device enables drivers to signal for help even if they’re not with the vehicle. Dispatchers are alerted that a driver is in trouble, and can provide vehicle location information to first responders for expedited assistance.
“The device works in a similar fashion to an electronic key,” explains Eddie Bermudez, GPSTrackIt product manager. “It’s a small plastic box with a single button on it. The driver can carry it on his or her keychain. So even if they’re not with the vehicle they can still call for assistance.”
When the button on the device is depressed, it sends a signal wirelessly to a receiver connected to the tracking device in the vehicle. The Instant Alert Device has a range of up to 500 feet.
Bermudez offered an example. “Let’s say you dispatch someone to a remote oil field and there is no cellular communication out there. The tracking device uses both GPS and satellite communications, a combination that provides optimum coverage. The worker can use the Instant Alert Device to notify their team members back at the office if something is wrong or to acknowledge the completion of a task. This gives real-time, up-to-the-minute notifications to the alert contacts via Fleet Manager.”
The feature can be used with any type of switch, button or Power Take Off (PTO) that connects to an input wire on the tracking device.
C-Nav, supplier of international GNSS Precise Point Positioning services, has launched its latest GNSS real-time tide measurement package, C-Tides.
The C-Tides suite combines the vertical accuracy of C-Nav’s GNSS Precise Point Positioning service with the latest advanced ocean and coastal tides models, the company said.
C-Tides Online features real-time filters and vessel dynamics, a choice of worldwide Mean Sea Surface or regional reference frame models, and tidal prediction for mission planning.
C-Tides Offline utilities include data smoothing and outlier rejection, harmonic analysis, Doodson X0 filter, and a LAT option.
“It’s been a privilege working with our academic partners to develop what is probably the worlds’ most advanced real-time GNSS tide solution,” said Russell Morton, C-Nav head of development.
C-Tides is a fully supported C-Nav utility. The results are suitable for combining with other suitably calibrated vertical components to achieve IHO SP44 Order 1 or better.
The storm chasing and weather community is honoring three storm chasers killed in an Oklahoma tornado on Friday. Tim Samaras, his son Paul Samaras, and Samaras’s chase partner Carl Young are being honored via the Spotter Network, where their initials are being spelled out.
The Spotter Network is a website used by storm chasers to follow weather movements. Users have been adding position locations to spell out the initials TS, PS, and CY, shown here in an image at sfgate.com.
The Samarases were well known to TV viewers, having been prominent subjects of the Discovery Channel series “Storm Chasers” and frequent contributors to The Weather Channel. They weren’t working for either channel last week, both networks said.
When a team of firefighters, first responders, or soldiers operates inside a building, in urban canyons, underground, in foliage, or under the forest canopy, the GPS-denied environment presents unique navigation challenges. An enhanced personal inertial navigation system (ePINS), based on a strapdown navigation solution using a mid-grade IMU and wavelet-based motion-classification algorithms, can track positions with errors of less than 2 percent of distance traveled in both indoor and outdoor environments.
By Yunqian Ma, Wayne Soehren, Wes Hawkinson, and Justin Syrstad
Numerous pedestrian navigation applications are currently available or proposed for development. Some of them include localization for coordinating firefighters, first responders, or soldiers. In these applications, the safety and efficiency of the entire team relies directly on the location and orientation of each team member. Operations in high signal interference areas such as cities, rugged terrain, forest, or indoor spaces deliver intermittent or no GPS signal. An alternative to GPS-based location is required.
In this article, we introduce an enhanced personal inertial navigation system (ePINS) solution specifically designed for environments where GPS is unavailable. ePINS combines an array of state-of-the-art sensors and fusion algorithms into a personal navigation system that provides accurate location information for pedestrian applications.
The ePINS concept.
The ePINS solution has the following benefits:
Accurate positioning in GPS-denied environments;
Small, lightweight unit can be easily carried by first responders, rescue workers, or soldiers;
Ruggedized packaging to withstand difficult first responder and military environments.
Features of the ePINS unit include:
State-of-the-art micro-electromechanical systems (MEMS) gyros and accelerometers, barometric altitude sensor, and advanced navigation software;
Advanced motion classification algorithms that accurately identify and measure user activity;
Immunity to magnetic disturbances.
Related Work
In the field of personal navigation, it is common to find systems that rely on sensors that need infrastructure (for example, Wi-Fi positioning) or sensors that actively emit electro-magnetic radiation (such as Doppler radar). These requirements are major drawbacks for communities such as dismounted soldiers in hostile environments.
Other approaches exploit the so-called Zero-velocity update (ZUPT) mechanism, which resets the inertial measurement unit (IMU) velocity errors during the stationary phase of motion. However, implementation of such schemes relies on sensors embedded in footwear, which is not readily accepted in many user communities.
To address these drawbacks, Honeywell has been developing advanced aiding techniques for personal navigation that do not rely on infrastructure and compute a self-contained, relative-navigation solution based only on passive sensors. One technique that Honeywell has developed uses displacement estimation from human-motion models. This technology has been implemented in the ePINS prototype and shows promising performance.
The human-motion model uses IMU measurements as inputs and was developed to infer distance traveled. It generates a displacement estimate that is used as a measurement in the navigation filtering process. The first version of this model was matured under the DARPA individual Precision Inertial Navigation System (iPINS) program. The iPINS system used an IMU, GPS, barometer, and motion classification to estimate a person’s position in both indoor and outdoor environments. In this system, IMU signal characteristics (e.g., peaks and valleys in the accelerations induced by walking) were exploited to differentiate between walking and running. Honeywell recently expanded the human-motion model to identify more specific motion types using a new wavelet motion classification method.
System Description
Figure 1 displays the hardware architecture of the ePINS, a small battery-powered, highly integrated electronic system. The ePINS processing platform is an ARM11-based, i.MX31 system-on-module, paired with support electronics. In addition to the processing platform, the ePINS assembly includes a MEMS IMU, a barometric pressure sensor, a digital magnetometer, and a GPS receiver.
Figure 1. ePINS hardware architecture.
The MEMS IMU provides inertial measurements for strapdown navigation. The IMU’s small package size, light weight, low power consumption, and impressive performance make it attractive for use in the ePINS system. The device is less than 5 cubic inches and weighs less than 0.35 pounds. It consumes about 3 watts of power with a typical current draw of 600mA at 5V.
The ePINS software system is shown in Figure 2. The navigation software runs within Honeywell’s Embedded Computing Toolbox and Operating System (ECTOS IIc), which provides a layered, customizable, and reusable software architecture for implementing navigation, guidance, and control software. A Honeywell-developed simulation tool for offline analysis and development of ECTOS-based software was also used in ePINS development and testing.
The ePINS demonstration device can achieve path performance of less 2 percent distance traveled for walking motion after 1 hour of operation, independent of the magnetic environment. Current performance, packaging characteristics, and interfaces are summarized in Table 1.
Table 1. ePINS performance objectives and physical specifications.
Algorithm Description
Figure 3 depicts the overall sensor integration and data processing scheme used in the ePINS device.
Figure 3. Sensor integration using the ECTOS extended Kalman filter.
Extended Kalman Filter (EKF). The EKF estimates the navigation and sensor errors and computes the resets applied to the strapdown navigation solution to increase its accuracy. Error models for the navigation sensors (IMU, barometric altimeter, magnetometer, GPS, and motion classification) are contained in the EKF. For the ePINS device, the virtual measurements from the step-length model and the strapdown navigation solution are fused by the EKF to assist in bounding the time dependent error growth of the strapdown navigator, which in turn helps maintain calibration of the inertial sensors. A key output of the EKF is the navigation confidence, which is an estimate of the accuracy of the navigation solution.
An important aspect of the EKF and step-length modeling is the residual test that the EKF supports. This test provides a reasonableness comparison between the step-length model estimate and the distance predicted by the strapdown navigation system. This capability significantly increases the robustness of the navigation solution, especially when the user is engaged in motions not recognized during motion classification.
Human-Motion Model. The human-motion model includes two components: wavelet motion classification and step-length model estimation. The wavelet motion classification identifies the type of motion the user is performing, and the step-length model acts as a virtual sensor that quantifies the motion as a distance-traveled estimate.
Wavelet Motion Classification. Human motions are very diverse and highly irregular. Determining what motion is being performed is a challenging problem of classification. Honeywell’s solution is based on wavelet transformation of IMU data. Predefined, or known, characteristics of a variety of motions (such as walking, running, crawling, etc.) are cataloged and stored to a device’s memory. Estimates of those same characteristics for a user are then computed in real time and compared to the catalog of stored information to find the best match.
Generating the catalog of stored information is an offline task that begins by “segmenting” recorded IMU time domain data into individual steps. An example of the output of the segmentation process is shown in Figure 4.
Figure 4. Segmentation of the IMU data using the y-axis accelerometer signal.
Figure 5 displays the segmentation results for two different walking styles (in red and blue) across approximately 15 example steps. As is evident from the graph, walking has characteristics that are common across users, for example, the sharp peaks in the z-axis acceleration caused by foot-ground impacts. Once the data has been segmented, a wavelet transformation on each data channel is performed. Wavelet transformation for many users over many different motion types takes place offline. Subsequently, a wavelet descriptor is built for each motion type based on the transformations into the wavelet domain. With this method, a wide variety of information (that is, descriptors) suitable for input to a classifier is captured about each motion. These descriptors are then cataloged and stored in memory on the ePINS device.
Figure 5. Sample steps for two subjects (red) and (blue).
Finally, for the online phase, the wavelet descriptor of the incoming IMU data is calculated by performing a wavelet transformation on each data channel. This descriptor is then compared to the pre-computed and stored descriptors to classify the motion. FIGURE 7 shows an example of the motion classifier output, where a running motion was used as an input. The classifier successfully determined the motion type (blue field), frequency and phase of the input motion, depicted by the tallest rectangle in the figure.
Figure 7. Classification results from a query of running at a certain frequency and phase (depicted by the dark sphere).
Step-Length Modeling. Once the current motion is identified, a step-length model specific to that motion is used to aid the navigation algorithms. The model for each motion type is obtained by first collecting data that measures step length and step frequency. From this data, the step-length models can be computed by performing a regression analysis of the step-length vs. step-frequency data. Since the step-length models act as a virtual sensor, the models must be as accurate as possible to achieve better system performance. To attain model accuracy, an accurate data collection method is needed.
For ePINS development, step-length models for multiple users have been identified from step-length and timing information using a precise GPS truth reference system. Step-length regression calculations then determine the step length as a function of step frequency (that is, inverse of the step time period). An example of GPS truth data and the corresponding regression model are shown in FIGURE 6 for walking motions.
Figure 6. Step length versus frequency for the walking of subject.
Although basic step-length models are created offline, online calibration of the step-length model can be performed by the EKF if GPS is available during operation. Online calibration tends to increase the overall position accuracy, as variations in the step-length models are likely due to slight variations in biometric differences across humans, terrain features, and even mission plans and duration.
Heading Determination. Heading initialization is one of the key concerns during system start up. In its current operational use, the ePINS device may perform a dynamic or a static initialization of heading. The static method requires the user to survey the system’s initial heading to an accuracy value that is usually specified by mission performance objectives; the absolute position accuracy is dependent upon the accuracy of the initial heading.
The dynamic method is a general method for heading initialization; it is performed without input from the user, but is possible only when GPS is available. This method of heading initialization does not use any a priori information about heading and requires an EKF implementation with a large-azimuth error model. This method requires an additional period of time in which the heading error uncertainty converges.
User Interface. During a mission, the user can interact with the navigation system and monitor its output on a display. The current ePINS prototype offers two-way communication via a serial connection. The serial communication is made wireless by the addition of a Bluetooth interface. Users can use this link to monitor the status of the navigation solution and to send commands to the device.
Honeywell has developed an application for the Android platform for this purpose. One of the key features of the interface design is that the navigation system outputs data in a standard NEMA format. Thus, publically available Android applications, not just proprietary applications, can also receive and display the navigation solution output by the ePINS device.
Honeywell’s personal navigation application displays the user’s traveled trajectory in real-time. The application can be adapted to include building floor plans as well as other navigation information.
Results
The ePINS prototype has been evaluated both in simulations and indoor/outdoor experiments. The navigation results presented here were obtained in February 2012 at a Honeywell facility (FIGURE 8). First, the user completed the heading calibration, and then online step parameter estimation in the presence of GPS was performed. Once calibration and training was completed, the GPS was disabled to simulate a GPS-denied environment outdoors. The user than transitioned to indoors (with GPS still disabled), and walked a course inside that included walking up and down stairs (FIGURE 9) and ended in a conference room (FIGURE 10).
Figure 8. Course for the Honeywell facility demonstration.Figure 9. The user walking up stairs.Figure 10. The user at the end of the demo.
Over these conditions, the ePINS system performed robustly and within performance specifications. Live demonstrations and testing showing similar levels of performance were performed at the 2012 Joint Navigation Conference (JNC) and at military test sites in California and Indiana.
Summary
The technical approach of the ePINS solution to the problem of personnel navigation in GPS-denied environments is based on a strapdown navigation solution maintained using a mid-grade IMU and advanced motion-classification algorithms. We integrated an array of sensors and software into a system that provides accurate position information and is suitable for use by first responders, soldiers, and other personnel where GPS is unavailable. ePINS works well for a variety of pedestrian motion types, including walking, running, crawling, walking upstairs, walking downstairs, sidestepping, and walking backwards. The motion classification and modeling method is extensible to other motion types.
We tested the ePINS system in indoor and outdoor environments. FIGURE 11 depicts the future ePINS concept, and TABLE 2 presents its future physical characteristics.
Figure 11. Future ePINS concept and mounting position.Table 2. Packaging characteristics of the future ePINS.
Acknowledgments
This article is based on a presentation made at ION GNSS 2012.
Manufacturers
The ePINS processing platform uses Honeywell Agile Navigation and Guidance Integrated Electronics support electronics. It includes a Honeywell HG1930 MEMS IMU, a Bosch Sensortec BMP085 barometric pressure sensor, a Honeywell HMC6343 digital magnetometer, and a NovAtel OEMStar GPS receiver.
Yunqian Ma is a principal scientist at Honeywell Aerospace. He received his Ph.D. degree in electrical engineering from the University of Minnesota, Twin Cities. He is currently the program manager of the GPS-denied navigation program and the next-generation personal navigation program.
Wayne Soehren is a senior technical manager at Honeywell Aerospace. He was the program manager for the development of Honeywell’s first MEMS-based GPS/INS, which developed the core capability now used in Honeywell’s IGS-2XX family of MEMS-based GPS/INS products. He holds an MSEE from the University of Minnesota.
Wes Hawkinson is an engineering fellow at Honeywell Aerospace. He holds a BSEE/CE from the University of Wisconsin–Madison. Justin Syrstad is a guidance and navigation scientist. He received a master’s degree in aerospace engineering from the University of Minnesota.
The first four Galileo satellites used for in-orbit validation were launched in October 2011 and October 2012.They are now transmitting their signals on an operational basis. Thanks to the simultaneous use of these four satellites, the European Space Agency was able to compute the first autonomous Galileo-only fix using broadcast ephemerides in March 2013.
Using data from the real-time service of the International GNSS Service (as supported by the Multi-GNSS Experiment), real-time protocols and new high-precision multiple signal messages and a new generation multi-constellation network of GNSS stations, the Centre National d’Etudes Spatiales (CNES) has been able for the first time to compute decimeter-accuracy Galileo orbits in real time.
The networks used in this work include the CNES/Institut Géographique National REGINA (REseau Gnss pour l’Igs et la NAvigation) network and the Deutsches Zentrum für Luft- und Raumfahrt (DLR) and associated organizations CONGO (COoperative Network for GNSS Observation) network (real-time access courtesy of Oliver Montenbruck). The filter used for the multi-constellation real-time orbit determination is a CNES proprietary tool based on a Kalman filter.
The CNES orbits have been compared to an accurate reference orbit computed by Technical University München (TUM) as part of the MGEX project. The following figure shows the 3D orbit differences for the two solutions (for the ProtoFlight Model (PFM) and Flight Model 2 (FM2) satellites), over the 10 days of the experiment. Excluding the first day during which the filter converges, the 3D root-mean-square orbit difference is about 15 centimeters. This demonstrates the feasibility of accurate real-time Galileo solutions using currently available networks and software tools.
