Bearing-Only Mobile Tracking with IMM Kalman and Particle Filtering

• Main Authors: Meng-wei Fan, 范夢葳
• Other Authors: Dah-chung Chang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/71614997725374664577

碩士 === 國立中央大學 === 通訊工程研究所 === 100 === Mobile location estimation is important to offer vehicular services in wireless communication applications. Some typical methods realize mobile tracking with the data fusion of the time of arrival (TOA) and received signal strength (RSS) measurements provided by base stations (BSs). Although the TOA/RSS method is not expensive under a concern of cost, it is very sensitive to multipath signal propagation effects. As the technology of the angle of arrival (AOA) antennas is showing rapid progress, we turn to consider AOA estimation. In this work, the nonlinear extended Kalman filter (EKF) and the particle filter (PF) along with a three models interacting multiple model (IMM) algorithm are utilized and compared for maneuvering mobile station (MS) tracking with bearingsonly measurements. A coordinated turn model is used to improve the tracking performance since the MS frequently turns in the streets. We propose a new particles resampling method to alleviate the degeneracy effect of particles propagation in the IMMPF algorithm. Besides, a BSs selection method is also proposed for the long-haul MS tracking case that needs to change BSs in a wireless BS sensor network. Numerical simulations show that the three-model IMMPF algorithm outperforms the IMMEKF algorithm and achieves a root mean square (RMS) position tracking error performance which is quite close to the posterior Cramer-Rao Lower bound (CRLB).