Optimization algorithm for improving markedly the tracking of a maneuvering target

Existing algorithms using Interaction Multiple Model (IMM) and Extended Kalman Filter (EKF) for tracking maneuvering targets suffer, in our opinion, from two shortcomings; (1) traditional IMM algorithm requires a large amount of a priori knowledge, (2) error is unavoidable when a linear model is substituted for a non-linear model through EKF. Our efficient algorithm can suppress to a considerable extent these two shortcomings. In the full paper, we explain our algorithm in some detail; in this abstract, we just add some pertinent remarks to listing the three topics of explanation. The first topic is; Debiased Converted Measurements Kalman Filter (CMKF-D). The second topic is: simplified IMM algorithm. In these two topics, we discuss how our algorithm adaptively adjusts part of the CMKF-D parameters according to the relations between maneuvering discriminant function D(k) and threshold r, summarized in Eqs. (15) and (16) in the full paper. At the end of the paper, we simulate our algorithm using a photoelectrical theodolite to track a maneuvering target. The simulation results, given in 6 figures in the full paper, show preliminarily that our algorithm can significantly improve the tracking of a maneuvering target. The position-tracking error is less than 1.5m, the velocity-tracking error is less than 1.5 m/s and the acceleration-tracking error is less than 0.7 m/s².