Title :
An algorithm based on interacting multiple models for maneuvering target tracking
Author :
Xulong Chen ; Jian Gao ; Xing Han
Author_Institution :
Xi´an Electron. Eng. Res. Inst., Xi´an, China
Abstract :
Kanlman filtering algorithm is commonly used as radar target tracking algorithm. In allusion to the problems caused by the filter divergence and inapposite model parameters of Kaiman filtering, such as low target tracking precision, this paper proposes an adaptive tracking algorithm with Markov probability, namely Interacting Multiple Models (IMM) algorithm, to improve the radar target tracking precision. IMM algorithm can efficiently track one maneuvering target and then realize the adaptive tracking of the target. Simulation results show that IMM algorithm has perfect tracking stability and high tracking precision.
Keywords :
Kalman filters; Markov processes; target tracking; IMM algorithm; Kalman filtering algorithm; Markov probability; adaptive target tracking; adaptive tracking algorithm; filter divergence; interacting multiple models algorithm; low target tracking precision; maneuvering target tracking; radar target tracking algorithm; Algorithm design and analysis; Kalman filters; Radar tracking; Standards; Target tracking; Interacting Multiple Model; Kaiman filtering; adaptive tracking; target tracking;
Conference_Titel :
Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International
Print_ISBN :
978-1-4799-4420-0
DOI :
10.1109/ITAIC.2014.7065080