Nonlinear Regression-Based GNSS Multipath Dynamic Map Construction and Its Application in Deep Urban Areas

GNSS signals are easily blocked or degraded because of the dense presence high-rise buildings in urban areas, and positioning errors arising from reflected amount to as much hundreds meters. Various conventional techniques have been utilized resolve this problem, but applying them environments has difficult owing complexity their unpredictable nonlinear variation signal receiving environments. In study, multipath maps were generated for dynamic users at multiple positions on a road residual-based map selection algorithm was implemented solve problem user position uncertainty deep data collected over period 327 min used train corresponding 247 points near 2.5 km stretch Teheran-ro Seoul, South Korea. The proposed system performed efficiently—it verified be capable constructing with radius 25 m using only 4 data. Moreover, it improved accuracy by 45 % horizontally 80 vertically, enabling determination positional information an vehicle horizontal 18 during 99% duration one-hour-long test. Due nonreliance method prior implementation additional sensors, is expected widely map-based mitigation models part intelligent transportation infrastructure all cities future.