A fault-detection method for relative navigation based on Kullback–Leibler divergence (KLD) is proposed. Different from the traditional χ 2-based approaches, the KLD for a filter is following a hybrid distribution that combines χ 2 distribution and F-distribution. Using extended Kalman filter (EKF) as the estimator, the distance between the priori and posteriori data of EKF is calculated to detect the abnormal measurements. After fault detection step, a fault exclusion method is applied to remove the error observations from the fusion procedure. The proposed method is suitable for the Kalman filter-based multisensor relative navigation system. Simulation and experimental results show that the proposed method can detect the abnormal measurement successfully, and its positioning accuracy after fault detection and exclusion outperforms the traditional χ 2-based method.

中文翻译：

---

基于Kullback-Leibler散度的容错相对导航

提出了一种基于Kullback-Leibler散度(KLD)的相对导航故障检测方法。与传统的基于 χ 2 的方法不同，滤波器的 KLD 遵循结合了 χ 2 分布和 F 分布的混合分布。使用扩展卡尔曼滤波器 (EKF) 作为估计器，计算 EKF 的先验数据和后验数据之间的距离以检测异常测量值。在故障检测步骤之后，应用故障排除方法从融合过程中去除错误观察。该方法适用于基于卡尔曼滤波器的多传感器相对导航系统。仿真和实验结果表明，该方法能够成功检测异常测量，