An intelligent algorithm for bearings-only maneuvering target tracking

An intelligent target tracking algorithm is developed in this paper. Unlike the traditional MM method with fixed acceleration levels, the acceleration value of each sub-model of MM structure is adjusted adaptively by an economic on-line self-constructing neural fuzzy inference network (SONFIN) according to the changes of extracted feature information, a set of Unscented Kalman Filter (UKF) is then utilized to estimate target state. Numerical simulation results show that the performance of the proposed algorithm is nearly identical to that of the interactive multiple models (IMM), which is known as a best maneuvering target tracking algorithm. Moreover, the proposed algorithm is free of any prior information of target motion.