Variable-structure interacting multiple-model estimation for group targets tracking with random matrices

Abstract

In order to improve the estimation performance of interacting multiple model tracking algorithm for group targets, the expected-mode augmentation variable-structure interacting multiple model (EMA-VSIMM) and the best model augmentation variable-structure interacting multiple model (BMA-VSIMM) tracking algorithms are presented in this paper. First, by using the EMA method, a more proper expected-mode set has been chosen from the basic model set of group targets, which can make the selected tracking models better match up to the true mode. The BMA algorithm uses a fixed parameter model of different structures to constitute a candidate model set and selects a minimum difference model from target state as the present extended model from the set of candidates at real time. Second, in the filtering process of VSIMM, the fusion estimation of extension state is implemented by the scalar coefficients weighting method, where weight coefficient is calculated by the trace of the corresponding covariance matrix of extension state. The performances of the proposed EMA-VSIMM and BMA-VSIMM algorithms are evaluated via simulation of a generic group targets maneuvering tracking problem.

Multi-Target State and Extent Estimation for High Resolution Automotive Sensor Detections

“…In general, in the approach based on the random matrices, the extent is considered to be a random process and hence is normally assigned a corresponding prior (e.g., Wishart distribution [ 19 , 20 , 21 ]) and a transition kernel. In [ 24 ], in order to improve the estimation performance of interacting multiple model (IMM) tracking algorithm for group targets, two variable structure IMM algorithms are presented within the random matrices framework. A similar effort that uses the multiple model structure to improve the Gamma Gaussian inverse Wishart probability hypothesis density (GGIW-PHD) filter algorithm is also proposed in [ 25 ].…”