Maneuvering Target Tracking with Hypothesis Testing

A new approach is proposed for maneuvering target tracking.Target motion is described by nonlinear models in a sphericalcoordinate system. States of these models are estimated byquantization, multiple hypothesis testing, and a suboptimumdecoding algorithm of information theory. This approach does notrequire linearization of nonlinear models. Hence it is superior toclassical estimation techniques, such as the extended Kalman filter.Simulation results, some of which are presented here, haveshown the superiority of the proposed approach over target trackingwith the extended Kalman filter.