Fault Detection Algorithm for Gaussian Mixture Noises:An Application in LiDAR/IMU Integrated Localization System

Fault detection is crucial to ensure the reliability of localization systems. However, conventional fault detection methods usually assume that noises in the system are Gaussian distributed, limiting their effectiveness in real-world applications. This study proposes a fault detection algorithm for the extended Kalman filter (EKF) based localization system by modeling non-Gaussian noises as a Gaussian mixture model (GMM). The relationship between GMM-distributed noises and measurement residual is rigorously established through error propagation, which is utilized for constructing the test statistic for a Chi-squared test. The proposed method is applied to an EKF-based 2D light detection and ranging (LiDAR) and inertial measurement units (IMU) integrated localization system. The experimental results in a simulated urban environment show that the proposed method exhibits a 30 % improvement in the detection rate and a 17–23 % reduction in detection delay, compared with the conventional method with Gaussian noise modeling.

Recommended citation: Yan, P., Li, Z., Huang, F., Wen, W., & Hsu, L. T. (2024). "Fault Detection Algorithm for Gaussian Mixture Noises: An Application in LiDAR/IMU Integrated Localization Systems". NAVIGATION: Journal of the Institute of Navigation, 72(1), https://doi.org/10.33012/navi.684
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