Application of pseudolinear partitioned filter to passive vehicle tracking†
Keigo Watanabe · International Journal of Systems Science · 1984
The partitioned estimation method is applied to the bearing-only target tracking problem. A pseudolinear partitioned tracking filter is developed initially in the form of recursive processing. It is then shown that such a tracking filter consists mainly of two parts, a ‘ manoeuvre predictor ’, driven by the deterministic own-sensor manoeuvre input, and a ‘ psuedolinear partitioning fixed-point smoother ’, which gives the initial position and speed estimates. Furthermore, by taking into consideration the parallel processing mechanism, a pseudolinear partitioned tracking filter with data compression is proposed to average the bearing data contaminated by the measurement noise. The parametric relationship between r.m.s. estimation error, data compressing (or renovating) interval, measurement noise level, sensor manoeuvre structure and initial range estimate is presented through Monte Carlo simulations.