Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (21): 85-92.DOI: 10.3778/j.issn.1002-8331.1909-0012

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Real-Time Correction of IMM Target Tracking Algorithm Based on Probability Model

ZHOU Fei, LUO Xiaoyong, LIU Yunping   

  1. 1.School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    2.Key Laboratory of Optical Communication and Networks(Chongqing University of Posts and Telecommunications), Chongqing 400065, China
  • Online:2020-11-01 Published:2020-11-03



  1. 1.重庆邮电大学 通信与信息工程学院,重庆 400065
    2.光通信与网络重点实验室(重庆邮电大学),重庆 400065


This paper addresses the problem of maneuvering target tracking in wireless sensor networks. In most cases, the covariance matrix of the measurement noise and Markov parameters are assumed constant, so it results in slow model switching and decreases tracking accuracy. To overcome this problem, this paper proposes an IMM maneuver target tracking algorithm based on model probability real-time correction. The algorithm collects the radio-fingerprint of the received signal strength indicators(RSSIs) in the monitoring area, and then utilizes the support vector regression algorithm to train the observation model. The fuzzy neural network is introduced to adaptively adjust the measurement error covariance matrix during the multiple model interacting output stage. The Markov probability transition matrix is adjusted by the probability ratio between two continuous time points in the IMM sub-model. The simulation results show that the proposed method has good performance in real-time and tracking accuracy.

Key words: wireless sensor network, Interacting Multiple Mode(IMM) algorithm, maneuvering target tracking, fuzzy neural network, Markov transition probability



关键词: 无线传感网络, IMM算法, 机动目标跟踪, 模糊神经网络, Markov转移概率