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Robust Cooperative Navigation for AUVs Using the Student's T Distribution

In this paper, we propose a robust Student's t filtering algorithm designed for Cooperative Navigation (CN) for Autonomous Underwater Vehicles (AUVs). In a CN system, an acoustic communication technique is usually used to exchange information and measure range between AUVs, and many cheap but low-accuracy Micro-Electro-Mechanical Systems (MEMS)-based Inertial Measurement Units (IMUs) are used as Dead-Reckoning (DR) sensors on AUVs. The use of unreliable sensors and an acoustic communication technique can induce outliers leading to the probability densities of process and measurement noise having a heavier-tailed behavior than a Gaussian distribution. To cope with such non-Gaussian distributions, the process and measurement noises are modeled as Student's t distributions, and the Student's t filtering algorithm for CN is presented. Simulation results show the efficiency and superiority of the proposed robust CN algorithm as compared with the standard extended Kalman filtering-based CN algorithm.