Interacting Multi-Model Strong Robust Adaptive Unscented Kalman Filter to Bearing only Tracking of Underwater Vehicle Approaching the Observer

We have designed an interacting multi-model strong robust adaptive unscented Kalman filter for bearing only tracking of an underwater vehicle approaching the observer. To solve the problem of tracking an approaching underwater vehicle to the observer based on only its bearing, an interactive multi-model robust adaptive unscented Kalman filter is proposed in this paper. First, a new model of the bearing sense motion towards the observer is proposed to construct a set of realistic target motion modes consisting of linear and curved motion modes. In addition, to account for the influence of outliers in the target bearing measurements, the distribution of measurement noise is assumed to have a Student’s t-distribution, and the probability distribution of the degree of function and the scaling matrix of this distribution is assumed to have a gamma distribution and an inverse Wishart distribution. Thus, the model interaction step is to factorize the mixed probability density function using variational Bayesian method and, based on this, a predictive update method is proposed. In the measurement update phase, the posterior probability density function is obtained in factorization form using variational Bayesian method, and based on this, a posteriori mode probability calculation method is proposed. Simulation results show that our proposed method greatly improves the convergence rate of target tracking error.