An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking

Wei Zhu; Wei Wang; Gannan Yuan

doi:10.3390/s16060805

An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking

Sensors (Basel). 2016 Jun 1;16(6):805. doi: 10.3390/s16060805.

Authors

Wei Zhu¹, Wei Wang², Gannan Yuan³

Affiliations

¹ College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. zhuwei_heu@163.com.
² College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. wangwei407@hrbeu.edu.cn.
³ College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. yuangannan@163.com.

Abstract

In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM).

Keywords: cubature Kalman filter; interacting multiple models; target tracking; unscented Kalman filter.