在無線通訊應用中提供車輛的移動位置估計服務是個很重要的課題，一些典型的方法為基地台(Base Station, BS) 藉由融合到達時間(Time of Arrival, TOA) 以及接收訊號強度(Received Signal Strength, RSS) 等數據來對目標物作位置估計，雖然TOA/RSS 方法不用昂貴的成本，但是對多重路徑的傳播效應卻相當的敏感，而到達角度(Angle of Arrival, AOA) 的技術呈現快速的進步中，於是我們轉而考慮使用AOA 來對目標物作追蹤。在本篇論文中，我們利用交互性多模型(Interacting Multiple Model, IMM) 的延展型卡爾曼濾波器(Extended Kalman Filter, EKF) 與粒子濾波器(Particle Filter, PF)，僅使用角度來對擁有三種運動模型的目標物作追蹤，因為目標物行走在街道上時常伴隨著轉彎的行為，於是我們加入了轉彎模型來改善追蹤的性能，我們也提出了一個新的重新取樣的方法來改善IMMPF 的粒子退化問題。此外，我們提出了目標物在無線基地台感知網路中長途行走時，改變鄰近基地台來對目標物追蹤的方法。在系統模擬中，三個模型的IMMPF 的效能會優於三個模型的IMMEKF，並且IMMPF 的均方根(Root Mean Square, RMS)位置追蹤誤差會相當接近Cramer-Rao Lower bound (CRLB)。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 degeneracyeffect 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) positiontracking error performance which is quite close to the posterior Cramer-Rao Lower bound (CRLB).
