Constrained Robust Unscented Kalman Filter for BDS Navigation in Dense Urban Areas

In dense urban environment, Multipath (MP) and Non-line-of-sight (NLOS) signals will degrade the performance of BeiDou Navigation Satellite System (BDS) Position, Velocity and Timing (PVT). In order to mitigate this negative impact, a Constrained Robust Unscented Kalman Filter (CRUKF) is implemented based on pseudorange/Doppler shift measurements. An equivalent weight function based on the innovation vector is constructed, which can overcome the problem of performance degrading of traditional robust methods caused by the inaccurate initial state vector. Then, the motion information, navigation direction and elevation, is included to further constrain the Robust UKF (RUKF) solution. The performance of CRUKF is analyzed using two real car tests in a dense building area, Tokyo. It is shown that there is a clear correlation between MP/NLOS errors and Position Dilution of Precision (PDOP), which seriously lows the positioning accuracy. Regarding the horizontal position, the Root Mean Square Error (RMSE) of CRUKF is 5.6 m, while those of Robust Iterative Least Square (RILS) and RUKF are 15.3 m and 6.4 m, respectively. Similar improvements are presented in vertical position and velocity and hence show the superior positioning performance of CRUKF. In addition, without sensor aiding or coupling, CRUKF can be suitable for real-time application of low-cost receiver.