AFAKF for manoeuvring target tracking based on current statistical model. Issue 6 (1st September 2016)

• Record Type: Journal Article
• Authors: Yang, Yongjian; Fan, Xiaoguang; Zhuo, Zhenfu; Wang, Shengda; Nan, Jianguo; Huang, Jinke
• Is Part Of: IET science, measurement & technology. Volume 10:Issue 6(2016)
• Page Start: 637
• Page End: 643
• Publication Date: 2016-09-01
• Subjects: target tracking -- statistical analysis -- Kalman filters -- adaptive filters -- probability; AFAKF -- manoeuvring target tracking -- fixed maximum acceleration -- MCSM -- modified current statistical model -- adaptive fading Kalman filter -- amendatory Kalman filter -- probability theory; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2016.0030
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : The fixed maximum acceleration of current statistical model (CSM) will lead to the deterioration of Kalman filter. To improve the performance of CSM in target tracking, a new modified CSM (MCSM) and a new Kalman filter (KF) are proposed. The new model, which employs innovation dominated subjection function to adaptively adjust maximum acceleration, has a better performance in target tracking, but it is very sensitive to innovation and will lead to a fluctuant phenomenon when target manoeuvres occur. The new adaptive fading Kalman filter which is formed by amendatory KF (AKF) and adaptive fading KF can weaken the fluctuant phenomenon caused by MCSM. The principle and deducing of AKF are specifically elaborated based on probability theory. Three simulations results indicate the high performance and robustness of MCSM and MCSM‐adaptive fading amendatory Kalman filter in target tracking.