Outlier-Robust Underwater Navigation Using a Dual-Robust-Kernel Kalman Filter
Jingqiang Bao, Xiaokai Mu, Xiang Yu, Zhongben Zhu, Hongde Qin · IEEE Signal Processing Letters · 2025
This paper addresses the challenge of robust navigation for unmanned underwater vehicles(UUVs) operating in harsh environments, where sensor data is often contaminated by outliers. To mitigate this issue, a dual-robust-kernel Kalman filter is proposed, which sequentially applies the Huber and Agarwal kernels during the measurement update phase. This method effectively combines the exceptional numerical stability of the Huber kernel with the superior robustness of the Agarwal kernel, leveraging their complementary strengths. The UUV experiments validate the proposed algorithm by benchmarking it against several representative methods.