Inside GNSS Media & Research

SEP-OCT 2018

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40 Inside GNSS S E P T E M B E R / O C T O B E R 2 0 1 8 tory where the sky visibility falls below 20%. is is therefore a relatively chal- lenging environment for GNSS-based positioning. Data was collected using a front- end and processed using a soware receiver to generate correlator outputs. For the results shown, the coherent integration time was 10 milliseconds and no non-coherent summation was performed. Such short coherent inte- gration times would support snapshot based positioning, wherein a short period of data is recorded and used to compute a solution. In turn, this can offer tremendous power savings over "fully-tracking" receivers. e correlator outputs and a 3DBM were then input into Matlab, where the algorithm described above was implemented. e computed positions were derived using the above algorithm only. In other words, only reflected sig- nals were used, no LOS pseudoranges. is was done to directly assess the feasibility of using reflected signals for position determina- tion. e computed positions were then compared against a GNSS/INS refer- ence solution to assess performance. To best analyze results, Figure 2 shows a box-and- whisker plot of the horizontal posi- tion errors binned according to sky visibility. e most striking result is that accuracy improves as sky visibility decreases. is happens because reduced sky vis- ibility implies more NLOS signals, and since the algorithm only uses NLOS signals, it follows that results should improve in this case. Equally surpris- ing is that for sky visibilities below 20%, the median error (denoted by red line) is only about 3 meters. Finally, the spread of the data in each bin indicates the results are repeatable between tests and at different locations along the trajectories. As an extension of the above results, as sky visibility increases, the positioning error increases because there are fewer NLOS signals the algo- rithm can use. is behavior would change if LOS pseudoranges were used. Discussion Although the results presented are promising, the algorithm does have its drawbacks. e most obvious drawback is the need to perform ray tracing, which is computationally intensive, especially for power- and/or resource-constrained platforms. Use of a graphics processing units (GPU) could improve processing throughput, as could offline processing, but each of these approaches pose challenges of their own. e other drawback is initializa- tion of the algorithm. Until now, we have assumed that a grid of candidate positions is available without consid- eration for how that may be obtained. Our research has demonstrated that the algorithm described above is robust to large uncertainty regions (with corresponding computational chal- lenges) suggesting that a "standard" GNSS position could be used with a sufficiently conservative uncertainty region. However, further investigation would be necessary to verify this under a wide range of operational conditions. Notwithstanding the above chal- lenges, using NLOS signals to improve positioning performance has been demonstrated and it will be exciting to see how this emerging area of research and development evolves. Additional Reading More details on the algorithm described above is available here: Kumar, R. and Petovello, M.G. (2017) "3D building model-assisted snapshot positioning algorithm". GPS Solutions, 21(4), 1923–1935. Kumar, R. (2017) "3D Building Model-Assisted Snapshot GNSS Positioning". PhD Thesis, Depart- ment of Geomatics Engineering, University of Calgary, Calgary. Authors RAKESH KUMAR received a PhD degree from the University of Calgary where he worked on improving GNSS-based navigation in urban canyons and integration of 3D city models with GNSS. Prior to this, he completed his bachelors and masters in electrical engineering from India and had more than 10 years of experi- ence in the aerospace industry. Currently he works in systems engineering for the active safety and automated driving division of General Motors. MARK PETOVELLO , co-author, is the editor of this column. FIGURE 1 Test trajectories (magenta) in downtown Calgary, Canada. Buildings are shaded by height (relative to ground); maximum building height exceeds 200 m. Easting (m) Northing (m) –1000 –1200 –800 –600 –400 –200 Building Height (m) 200 100 0 –400 –600 –800 –1000 –1200 FIGURE 2 Horizontal position error as a function of sky visibility. Horizontal Error (m) 25 20 15 10 5 0 Sky Visibility (%) Sample C ount < 20 20-30 30-40 40-50 50-60 60-70 > 70 200 100 0 GNSS SOLUTIONS

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