KEYWORDS: Error analysis, Process modeling, Electronic filtering, Filtering (signal processing), Monte Carlo methods, Solids, Computer simulations, Digital filtering, Switching, Data modeling
Multiple Target Tracking (MTT) requires the proper association of measurement data with individual targets before the data is filtered and target positions estimated. Because data association is computationally intensive, there has been interest in the Symmetrical Measurement Equation (SME) approach to MTT. An SME tracker subsumes the data association step by defining a measurement equation where the ordering of measurements is no longer required. However, the acceptance of the SME approach has been curtailed by a lack of stability in the Extended Kalman Filter (EKF) and the ability of the SME track to properly maintain tracks during track crossings. Tweaking the EKF parameter can improve stability and track maintenance, but we show that an extension to the EKF filter, the Proportional-integral EKF, produces superior results. Integral action improved track maintenance at crossings, reducing the incidence of track switching by more than an order of magnitude. The SME tracker now shows promise of providing superior stability and reduced estimation error while tracking multiple, crossing, and maneuvering targets.
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