Multi-Epoch 3D Mapping Aided GNSS using a Grid Filter

3D mapping can significantly improve GNSS positioning accuracy in dense urban areas by predicting which signals are directly \nvisible and which are blocked by the buildings. Many different 3D-mapping-aided (3DMA) GNSS techniques have been \ndemonstrated in the last few years. Some use pseudo-range measurements, whereas others use signal-to-noise ratio. Some assume \nthe position is already known, while others can handle large initial position uncertainties. Some compute position epoch-byepoch, \nwhile others implement filtered solutions using measurements from multiple epochs. Finally, some approaches are \nefficient enough to run in real time on a mobile device, whereas others are too computationally intensive. \nPreviously, UCL has demonstrated that real-time single-epoch 3DMA GNSS using both pseudo-range and signal-to-noise \nmeasurements can reduce position errors in dense urban areas from tens of meters to a few meters. Here, we extend this to multiepoch \npositioning by using a grid filter. Filtering reduces the impact of noise-like errors on the position solution, bringing a \nfurther improvement in accuracy. This is the first time that a grid filter has been applied to 3DMA GNSS as other authors have \nused a particle filter. Both filters can represent nonlinear position likelihood distributions using a set of candidate position \nhypotheses. The key difference is that a particle filter’s hypotheses have equal likelihood and an irregular distribution whereas \nthe grid filter’s hypotheses are regularly distributed, forming a grid, but with unequal likelihoods. The grid-based approach better \nrepresents the physics of the problem. Position hypothesis spacing of less than a meter brings no significant performance benefit \nas the GNSS signal Fresnel zones are about this size in urban areas. Conversely, a spacing of more than 5 meters does not capture \nthe variation of the environment sufficiently and can lead to 3DMA GNSS failing. The grid-based approach is also compatible \nwith UCL’s use of pre-computed building boundary files to minimize the real-time processing load. These enable satellite \nvisibility across an array of candidate positions to be predicted quickly simply by comparing the satellite elevation with that of \neach building boundary at the corresponding azimuth. \nVersions of the grid filter with different state propagation algorithms have been implemented for static, pedestrian and vehicle \napplications. Using static, pedestrian and vehicle GNSS data collected in central London using a u-blox EVK 8MT receiver, \nthese new algorithms have been compared with single-epoch 3DMA GNSS and multi-epoch conventional GNSS using a basic \nextended Kalman filter with innovation-based outlier detection. In all cases, best positioning accuracy was obtained using the \nmulti-epoch 3DMA GNSS algorithm. This achieved an RMS horizontal position error of 2.2m in the static tests, 3.9m in the \npedestrian tests and 4.3m in the vehicle tests. Compared with single-epoch 3DMA, this represents a factor of 2.2 improvement for the static tests, a factor of 1.55 improvement for the pedestrian tests and a factor of 1.8 improvement for the vehicle tests. For \nall test scenarios, multi-epoch 3DMA GNSS was a factor of 4 more accurate than multi-epoch conventional GNSS positioning.