Abstract
Support system for safe driving heavily depends on global navigation satellite system. Pseudoranges between satellites and vehicles are measured to compute vehicles’ positions and their relative positions. In urban areas, however, multipath errors (MPEs) in pseudoranges, caused by obstruction and reflection of roadside buildings, greatly degrade the precision of relative positions. On the other hand, simply removing all reflected signals might lead to a shortage of satellites in fixing positions. In our previous work, we suggested solving this dilemma by cooperative relative positioning (CoRelPos) which exploits spatial correlation of MPEs. In this paper, we collected the trace data of pseudoranges by driving cars in urban areas, analyzed the properties of MPEs (specifically, their dependency on signal strength, elevation angles of satellites, and receivers’ speeds), and highlighted their spatial correlation. On this basis, the CoRelPos scheme is refined by considering the dynamics of MPEs. Evaluation results under practical vehicular scenarios confirm that properties of MPEs can be exploited to improve the precision of relative positions.
Highlights
The rapid and wide spread of motor vehicles after World War II has greatly changed human society
This fails to work when the line of sight (LOS) path between vehicles is obstructed. (ii) Vehicles cooperate to measure their distances, which relies on global navigation satellite system (GNSS) [1] and intervehicle communications (IVC) [2]
We investigate the spatial correlation of multipath errors (MPEs) at different interreceiver distances under practical vehicular scenarios
Summary
The rapid and wide spread of motor vehicles after World War II has greatly changed human society. In our previous work [20], we assumed local spatial correlation of MPEs by the following reasoning: usually higher precision of relative positions is required when intervehicle distances get shorter; on the other hand, nearby receivers have similar spatial propagation environments and their MPEs are correlated; for example, MPEs in the pseudoranges at car A and car B in Figure 1 are almost the same On this basis, we extended the DGPS concept and suggested a cooperative relative positioning (CoRelPos) scheme, using correlated information from common satellites, including reflected signals, to accurately compute relative positions. We installed GNSS receivers on different cars and collected the trace data of pseudoranges by driving cars in urban areas Based on these experimental data, we analyzed the properties of MPEs (their dependency on signal strength, elevation angles of satellites, and receivers’ moving speed) and, most importantly, highlighted their spatial correlation.
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