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

Abstract:

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