【作者】 宫纪刚；

【导师】 彭建华；

【作者基本信息】 解放军信息工程大学 ， 通信与信息系统， 2009， 硕士

【摘要】 蜂窝移动通信系统中现有的移动台定位技术主要是通过蜂窝网络实现的,而对第三方定位系统的研究非常少本文介绍了CDMA网络中一种基于第三方设备的TSOA/AOA定位技术,结合CDMA2000移动通信系统物理层协议介绍了TSOA/AOA定位的实现原理,并重点对TSOA/AOA定位的定位精度定位算法抗非视距(NLOS)误差技术及跟踪技术进行了研究具体工作如下:一结合CDMA2000移动通信系统空中接口协议介绍了TSOA/AOA定位的实现原理,给出了TSOAAOA定位设备与基站位置坐标等定位参数的获取方案二推导了高斯噪声环境下多台设备实现TSOA/AOA定位的Cramer-Rao下限(CRLB)和几何精度稀释因子(GDOP)的表达式分析了TSOAAOA测量误差及定位设备数量对CRLB的影响对单台设备TSOA/AOA定位的GDOP进行了理论研究,给出了GDOP的全局最小值,得出了一系列对定位设备合理布局有重要指导意义的结论三对Chan算法和Friedlander算法在TSOA定位中的应用进行了理论推导,并设计了两种新的算法:具有非迭代闭式解的TSOA/AOA定位算法及基于最小二乘和泰勒级数展开的TSOA/AOA定位算法通过仿真比较发现本文提出的两种算法充分利用了定位设备获取的TSOA和AOA测量信息,与Chan算法和Friedlander算法相比,有效提高了定位精度,在测量噪声服从高斯分布时定位精度接近CRLB四基于几何单反射模型建立了宏小区和微小区环境下TSOA/AOA定位的信道延时模型,通过理论分析和蒙特卡洛仿真得到了TSOAAOA测量值中NLOS误差的统计特性在此基础上设计了基于卡尔曼滤波器(KF)的AOA重构算法和基于改进有偏卡尔曼滤波器(BKF)的TSOA重构算法仿真结果表明,本文提出的算法充分利用了测量值的时间历史信息,有效消除了NLOS误差,成功实现了TSOAAOA测量值的重构五基于实际环境建立了非线性车辆运动状态模型,以粒子滤波算法为核心设计了基于单台定位设备的TSOA/AOA车辆跟踪系统,实现了对目标位置和速度的同时跟踪,并通过仿真对跟踪算法的性能及粒子数量对跟踪性能的影响进行了分析由仿真结果可以看出,与扩展卡尔曼滤波相比,采用粒子滤波可有效提高TSOA/AOA跟踪系统的性能更多还原

【Abstract】 In this thesis, a novel TSOA/AOA positioning system based on a third party location apparatus is proposed. This system uses the TSOA (Time Sum of Arrival) measurements and AOA (Angle of Arrival) measurements to locate a Mobile Station (MS) in CDMA cellular network, which is of great significance for ensuring the national security, fighting against criminals, and providing emergency relief services.First, the principle of the TSOA/AOA positioning system is presented associated with analysis of CDMA2000 physical layer protocols, and the scheme of obtaining the position parameters is introduced.Second, the CRLB(Cramer-Rao Lower Bound) and GDOP(Geometric Dilution of Precision) of the TSOA/AOA positioning system in Gaussian noise environment are derived and thoroughly analyzed. The minimum value of the GDOP when position using single apparatu is discussed and a series of conclusion essential for the layout of positioning apparatus are obtained.Third, Chan and Friedlander positioning algorithm are extended from TDOA into TSOA positioning system, and two novel TSOA/AOA positioning algorithm are proposed:―A Non-iterative TSOA/AOA Positioning Algorithm with Explicit Solution‖and―A TSOA/AOA Algorithm Based on Least Square and Taylor Series Expansion‖. Simulation results indicate these two methods perform better than Chan and Friedlander algorithm and their accuracy could attain the CRLB when the noise is Gaussian.Fourth, multi-path delay channel models for TSOA/AOA positioning both in micro-cell and macro-cell environment are designed based on Geometrical-Based Single Bounce Statistical Channel Model(GBSBM) model, and the statistics of the TSOA and AOA error in NLOS(Non Line of Sight) environment are attained. Then, an AOA measurements restructuring algorithm using Kalman filtering and a TSOA measurements restructuring algorithm using modified biased Kalman filtering are proposed. The simulation results show that the change of NLOS/LOS status can be identified accordingly and the NLOS errors can be mitigated effectively.Finally, a mobility target tracking system based on particle filtering is proposed. The dynamics of the system under consideration are described by a nonlinear state-space model. The technique allows for accurate estimation of both MS‘s position and speed. Simulation results indicate this method can achieve higher accuracy than extended Kalman filter tracking algorithm.更多还原