Underwater acoustic positioning based on the robust zero-difference Kalman filter

The accuracy of underwater acoustic positioning is greatly influenced by both systematic error and gross error. Aiming at these problems, the paper proposes a robust zero-difference Kalman filter based on the random walk model and the equivalent gain matrix. The proposed algorithm takes systematic error as a random walk process, and estimates it together with the position parameters by using zero-difference Kalman filter. In addition, the equivalent gain matrix based on the robust estimation of Huber function is constructed to resist the influence of gross error. The proposed algorithm is verified by the simulation experiment and a real one for underwater acoustic positioning. The results demonstrate that the robust zero-difference Kalman filter can control both the effects of systematic error and gross error without amplifying the influence of the observation random noise, which is obviously superior to the zero-difference least squares (LS), the single-difference LS and zero-difference Kalman filter in underwater acoustic positioning.