Robust Kalman Filter Enhanced by Projection Statistic Detector for Multisensor Navigation in Urban Canyon Environment

Multisensor navigation via redundancy and complementary has been widely applied to safety-critical services, such as self-driving vehicles. To improve the robustness and reliability of the multisensor navigation system in urban canyon environments, a robust Kalman filter (RKF) method enhanced by the projection statistic (PS) detector is proposed in this work. Based on a statistical consistency check, the availability of measurements from observation sensors is preevaluated by the PS criterion. The new iterative Huber’s M-estimation with the exclusion function is implemented on the linear regression model for robust state estimation. The effectiveness of the proposed algorithm was verified by a dynamic test in the representative urban canyon environment. Our approach shows significant superiority and robustness among the comparative experiments, where the 3-D root-mean-square (rms) error is limited to 3.33 m. The biased measurements can be effectively identified and removed by the PS detector with a given significance level. The new iterative Huber’s M-estimation assisted by the prior availability knowledge enables the integration solution more robust and reliable. The presented robust method is pretty suitable for multisensor navigation in urban canyon environments.