A Noise Interaction Kalman Filter Based on Variational Bayesian for Handling Non-Gaussian Measurement and Process Noise

Underwater target tracking for unmanned systems has always played a crucial role. However, target measurements and process transitions are contaminated by non-Gaussian noise due to the underwater environment and the intrinsic system of the target, thereby degrading tracking performance. This paper investigates a filter capable of simultaneously mitigating measurement noise and unknown process noise. Specifically, underwater measurement noise is modeled as a Gaussian Mixture Model (GMM) with multiple noise components. Different component parameters form models to handle observational noise, and each model is assigned a different probability. Variational Bayesian (VB) inference is employed within each model to approximate the parameters of the unknown process noise distribution, and finally, fusion is performed based on noise probabilities. Simulations conducted with an Unmanned Underwater Vehicle (UUV) demonstrate that the proposed algorithm outperforms existing major algorithms when both measurement and process noises follow non-Gaussian distributions.