Nonlinear IMM-SUKF Algorithm for Maneuvering Target Tracking with Bearings-Only Measurement

In this paper, we present an efficient filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, which we aim to contribute in solving the problem of model-based body motion estimation using bearings-only measurement, the Interacting Multiple Model (IMM) algorithm is specially designed to track accurately maneuvering targets whose state and/or measurement (assumed to be linear) models change during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In this paper we propose to avoid the Extended Kalman Filter (EKF) because of its limitations and substitute it with the Scaled Unscented Kalman Filter (SUKF) which seems to be more efficient especially according to the simulation results obtained with the Interacting Multiple Model Scaled Unscented Kalman Filter (IMM-SUKF).