摘要
基于时差测量的TDOA(Time Difference OfArrival)无线定位技术,具有精度高,易于实现等特点,在无线定位导航、电子对抗中都取得了广泛的应用,特别是在蜂窝移动通信中的应用具有广阔的发展前景,存在很大的发展潜力。本文首先讨论了通信信号的循环平稳特性,在此基础上分析了TDOA 时延估计算法,并将加窗平均周期图法引入谱运算;然后分析了双曲线定位方程的求解算法,推导了泰勒级数展开结合最小二乘估计联合求解算法,并对算法性能进行了仿真;最后分析了两个定位精度增强算法,并进行了相应改进。
论文共分为六章:第一章概述了本课题的背景及意义,介绍了目前定位技术的研究现状,并对常用的定位技术进行了介绍,分析了各自的优缺点;第二章讨论了通信信号的二阶循环平稳特性,即循环相关函数和谱相关函数,并对BPSK 信号进行了讨论;第三章详细介绍了TDOA 定位技术的时延估计算法,并对谱相关比法(Spectrum Correlation)进行了仿真,同时将加窗平均周期图法引入到相关谱的运算中,提高了时延估计性能;第四章分析了TDOA 双曲线定位方程求解算法,提出了将泰勒级数展开法同最小二乘估计结合的联合算法,并进行了公式推导,仿真证明联合算法的性能接近于定位方程的最大似然解;第五章分析了非视线传播(No-Line-Of-Sight)误差消除问题和混合定位问题;第六章作为结束语,总结全文。
Wireless location technology based on Time Difference Of Arrivalmeasurements has characteristics including high precision and easy implementation.It has been widely used in wireless location navigation and electronic countermeasures, especially in mobile communication with a nice future and potential. Inthis dissertation, the characteristics of cyclostationary communication signal arediscussed first. Based on this theory, we analyze TDOA delay estimationalgorithms and introduce windowed average periodic diagrams into spectralcomputation. Then we analyze hyperbolic equations solving algorithms and deduceassociated algorithm of Taylor series expansion and Least-Lquares estimation,stimulate the algorithm. Finally, two location precision strengthen algorithms areintroduced and some corresponding improvements are made.
The dissertation is outlined as follows: the significance and background of thistopic is given in Chapter 1. Then we introduce the current research situation of thelocation technologies and discuss the common use of the technologies and analyzetheir merits and demerits. In Chapter 2, we focus on the seconded-ordercharacteristics of cyclostationary, that is cyclic correlation and spectral correlation,and discuss some features of the BPSK signal. TDOA delay estimation algorithmsare detailed in Chapter 3. Meanwhile, we stimulate the method of SPECCORR andintroduce the windowed average periodic diagrams into spectral computation toimprove the performance of this algorithm. In Chapter 4, TDOA hyperbolicequations solving algorithms are discussed and we deduce associated algorithm ofTaylor series expansion and Least-Lquares estimation, stimulate the algorithm. Wecan verify that the performance of the associated algorithm is close to theMaximum Likelihood solution of the location equations. We analyze the problemsof the elimination of the NLOS error and mix location. In Chapter 6, we take it as
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