<title>New implementation of the SME filter approach to multiple-target tracking</title>

Abstract

The symmetric measurement equation (SME) approach to multiple target tracking is markedly different from multiple target tackers based on probabilistic data association. THe key idea in the SME approach is to transform the original measurements data in such a way that the pairing of measurements and tracks becomes embedded in a nonlinear state estimation problem. In previous articles, a single extended Kalman filter (EKF) was used to derive estimates of each target's position and velocity. SImulation trials have shown the EKF implementation sometimes produces large estimation errors in the neighborhood of crossing targets. One of the causes of this unsatisfactory performance is the numerical instability of the EKF. Due to the recent discovery of anew iterated filter (NIF) based on the Levenberg-Marquardt algorithm, a better implementation of the SME approach is now possible. Improvements resulting from this recent work are demonstrated through Monte Carlo simulations comparing the performance of the EKF implementation of the SME, the NIF implementation of the SME, the Joint Probabilistic Data Association filter, and the associated filter (a benchmark).