Robust Kalman Filter Enhanced by Projection Statistic Detector for Multisensor Navigation in Urban Canyon Environment
IEEE Sensors Journal(2023)
Abstract
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.
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Key words
Extended Kalman filter (EKF),huber’s M-estimation,loosely couple,multisensor integration,projection statistics (PSs),urban canyon
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