Gaussian Mixture initialization in passive tracking applications

This paper describes the approximation of a nonlinear posterior density by a Gaussian Mixture (GM). The GM is used to initialize a bank of Kalman filters. For each Gaussian term, a Kalman filter is started. The basic conditions and the quality of the approximation are discussed. Examples from different tracking applications, the multistatic tracking and passive emitter localization using TDOA measurements, are investigated. The results are discussed and compared with existing approaches. The RMS error of the estimate is used as an evaluation criterion. The performance of the Gaussian Mixture approach is analyzed in Monte Carlo simulations.