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A BP based VSIMM-2-D track-to-measurement association approach

A BP based Variable Structure IMM-2D track- to-measurement association approach is proposed to simultaneously resolve the two most important uncertainties i.e. measurement origin uncertainty and the target motion uncertainy, which exist in the data association problem. Afer handling the measurement to track correlation via global 2-0 assignment, the novel method apply a discrete wavelet transform to map the measurement sequences to meta-mode sequences, then identifies the target's probable model set through BP neural network based on the meta-modes, finally filters and estimates via a variable structure IMM estimator. Simulation results show that the proposed technique can solve track-to-measurement association especially maneuvering target association problem properly.