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...

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Bibliographic Details
Published in:IEEE sensors journal 2023-05, Vol.23 (9), p.9832-9847
Main Authors: Jiang, Yingying, Pan, Shuguo, Meng, Qian, Gao, Wang, Ma, Chun, Yu, Baoguo, Jia, Fengshuo
Format: Article
Language:English
Subjects:
Algorithms
Autonomous cars
Availability
Dynamic tests
Extended Kalman filter (EKF)
Global navigation satellite system
huber’s M-estimation
Iterative methods
Kalman filters
loosely couple
multisensor integration
Navigation systems
Particle measurements
Pollution measurement
projection statistics (PSs)
Redundancy
Regression models
Robustness
Robustness (mathematics)
Safety critical
Sensors
State estimation
Statistical analysis
Street canyons
urban canyon
Urban environments
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Summary: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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3230708