非直达目标定位跟踪的基础理论与算法研究
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摘要
自从美国联邦通信委员会强制要求各移动通信运营商为E-911服务提供准确的定位估计以来,基于蜂窝无线通信网络的无线定位技术受到了广泛关注。蜂窝移动通信系统中,收发间不一定存在直达波传播路径,非直达波传播效应是导致定位误差的主要因素,因此非直达波环境中定位算法的研究成为近年来研究的热点。
本文针对不同的无线信道环境、移动站的运动状态和先验信息,研究了蜂窝无线通信网络定位算法,特别是非直达波环境中的定位算法。主要创新概括如下:
1.针对直达波环境中的定位问题,首先推导了到达时间(TOA)、到达时间差(TDOA)、到达时间/信号强度(TOA/RSS)和到达时间差/信号强度(TDOA/RSS)定位技术的克拉美劳(CRLB)下限的闭式解;然后在此基础上提出了基于二步最小二乘方法的TOA/RSS和TDOA/RSS混合定位方法,这两种算法的定位性能分别优于TOA和TDOA定位算法。
2.针对直达波测量参量和非直达波测量参量并存的情况,对非直达波鉴别算法进行了研究。首先对基于TOA和TDOA定位技术的非直达波定位算法进行了性能分析,为采用非直达波鉴别算法提供了理论依据;随后针对TOA定位技术,提出了一种基于重叠面积的非直达波鉴别算法,算法具有计算简单、鉴别率高的特点,解决了现有算法或者运算量大、或者只适用于运动移动站的问题;最后针对TDOA定位技术,提出了一种基于x平方(Chi-Square)分布的鉴别方法,解决了现有算法不能判别非直达波基站个数的问题。
3.针对存在多径信号以及周围散射体较少的情况,提出了一种利用多径信息和二次散射模型的移动站定位算法,并推导了定位估计的CRLB下限。所提算法突破了现有的基于散射体信息的非直达波定位算法基于单次散射模型的限制。
4.针对不存在直达波基站或者多径数目不足的情况,提出了基于加权稳健代价函数的TOA、RSS和TOA/RSS定位算法。非直达波误差分布往往偏离高斯分布,基于加权均方代价函数最小化的常规定位方法失去了最优性。所提方法使用加权稳健代价函数代替加权均方代价函数抑制非直达波误差,不仅能给出闭式解,而且不需要知道非直达波误差的先验信息。
5.针对存在样本点先验信息的情况,对学习型定位算法进行了研究。首先考虑样本点不存在野值的情况,提出了一种基于克里金(Kriging)方法的学习型定位方法,仿真结果表明该算法性能在不同的测量噪声情况下接近定位估计的CRLB下限;现有的学习型定位算法都没考虑样本点存在野值的情况,为了抑制样本点中野值对定位效果的影响,提出了一种基于稳健代价函数的最小二乘支持向量机(LS-SVM)方法,仿真结果表明算法能有效抑制野值的影响。
6.针对非直达波传播环境中的跟踪问题,研究了非直达波跟踪算法。首先提出了一种基于增强象限信息的非直达波跟踪算法;随后提出了一种稳健LMS方法,使用该方法对前者进行滤波,进一步提高定位精度;最后提出了一种自适应非直达波跟踪算法,尽管由于需要搜索过程,运算量比前两种算法大,但是仿真结果表明该算法性能优于前两种算法。
The location estimation of a mobile station (MS) in wireless communication systems has gained considerable attention over the past decade, especially since the Federal Communication Commission (FCC) passed a mandate requiring cellular providers to generate accurate location estimates for Enhanced-911 (E-911) services. One of the main problems facing accurate location in wireless communication systems is non-line-of-sight (NLOS) propagation, since line-of-sight (LOS) propagation may not exist. This has boosted the research in the field of wireless location in NLOS environments.
According to difference channel environments, the statuses of MS and prior information, the location estimation methods in wireless communication systems, especially the methods for mitigating the influence of the NLOS propagation, are studies in this dissertation. The primary contributions are summarized as follows:
1. In order to locate MS in LOS environments, we derive the Cramer-Rao Lower Bounds (CRLB) of four localization techniques, including the time of arrival (TOA), time difference of arrival (TDOA), time of arrival/received signal strength (TOA/RSS) and time difference of arrival/received signal strength (TDOA/RSS) based methods. Then two hybrid location methods (TOA/RSS and TDOA/RSS) based on two steps least square method are proposed. Simulation results show that the proposed hybrid algorithms outperform TOA and TDOA based methods.
2. In order to locate MS in LOS/NLOS mix environments, we study NLOS identification algorithms. First, we analyze the performance of the TOA and TDOA based methods. The analytical results show that we should develop NLOS identification algorithms. Then a novel NLOS identification algorithm based on overlap Area is proposed to deal with TOA measurements. Comparison to other TOA NLOS identification algorithms, the proposed method can identify NLOS BS with more accuracy and less computation complexity, and fit for moveless MS. Finally, a novel NLOS identification algorithm based on Chi-square distribution is proposed to deal with TDOA measurements. Comparison to other TDOA NLOS identification algorithms, the proposed method can identify the number of NLOS BSs.
3.A novel algorithm based on quadratic reflection assumption and multi-path information is proposed to obtain precise mobile location in where there exist multi-path signals and little scatters. The CRLB is also derived. Comparison to other scatters information based location methods, the proposed algorithm breaks through a single reflection assumption.
4.A novel algorithm based on robust objective function is proposed to obtain precise mobile location in where the number of LOS BSs or multi-path signals is insufficient. Traditional location algorithms are based on mean-squares objective function and it loses optimality in NLOS environments. The proposed algorithm replace mean-squares objective function with robust objective function, it does not require the prior knowledge of the NLOS error distribution and can give a closed-form solution.
5.We study the learning location methods with prior information. First, a novel location algorithm based on Kriging method is proposed to mitigate NLOS errors when outliers not exist in the training data set. Simulation results show that the proposed algorithm comes close to meeting CRLB. Then we extend least squares support vector machine (LS-SVM) algorithm to robust objective functions, and use this algorithm to solve mobile location problem. Simulation results show that the proposed algorithms can effectively mitigate outliers.
6.We study the NLOS tracing algorithms. First, a NLOS tracing algorithm based on enhanced quadrant constraint is proposed. Then a robust LMS algorithm is proposed to filter measurements. Finally, an adaptive NLOS tracing algorithm is proposed. Although the proposed adaptive NLOS tracing algorithm will add the computation complexity, Simulation results show that it outperforms the other two.
本文针对不同的无线信道环境、移动站的运动状态和先验信息,研究了蜂窝无线通信网络定位算法,特别是非直达波环境中的定位算法。主要创新概括如下:
1.针对直达波环境中的定位问题,首先推导了到达时间(TOA)、到达时间差(TDOA)、到达时间/信号强度(TOA/RSS)和到达时间差/信号强度(TDOA/RSS)定位技术的克拉美劳(CRLB)下限的闭式解;然后在此基础上提出了基于二步最小二乘方法的TOA/RSS和TDOA/RSS混合定位方法,这两种算法的定位性能分别优于TOA和TDOA定位算法。
2.针对直达波测量参量和非直达波测量参量并存的情况,对非直达波鉴别算法进行了研究。首先对基于TOA和TDOA定位技术的非直达波定位算法进行了性能分析,为采用非直达波鉴别算法提供了理论依据;随后针对TOA定位技术,提出了一种基于重叠面积的非直达波鉴别算法,算法具有计算简单、鉴别率高的特点,解决了现有算法或者运算量大、或者只适用于运动移动站的问题;最后针对TDOA定位技术,提出了一种基于x平方(Chi-Square)分布的鉴别方法,解决了现有算法不能判别非直达波基站个数的问题。
3.针对存在多径信号以及周围散射体较少的情况,提出了一种利用多径信息和二次散射模型的移动站定位算法,并推导了定位估计的CRLB下限。所提算法突破了现有的基于散射体信息的非直达波定位算法基于单次散射模型的限制。
4.针对不存在直达波基站或者多径数目不足的情况,提出了基于加权稳健代价函数的TOA、RSS和TOA/RSS定位算法。非直达波误差分布往往偏离高斯分布,基于加权均方代价函数最小化的常规定位方法失去了最优性。所提方法使用加权稳健代价函数代替加权均方代价函数抑制非直达波误差,不仅能给出闭式解,而且不需要知道非直达波误差的先验信息。
5.针对存在样本点先验信息的情况,对学习型定位算法进行了研究。首先考虑样本点不存在野值的情况,提出了一种基于克里金(Kriging)方法的学习型定位方法,仿真结果表明该算法性能在不同的测量噪声情况下接近定位估计的CRLB下限;现有的学习型定位算法都没考虑样本点存在野值的情况,为了抑制样本点中野值对定位效果的影响,提出了一种基于稳健代价函数的最小二乘支持向量机(LS-SVM)方法,仿真结果表明算法能有效抑制野值的影响。
6.针对非直达波传播环境中的跟踪问题,研究了非直达波跟踪算法。首先提出了一种基于增强象限信息的非直达波跟踪算法;随后提出了一种稳健LMS方法,使用该方法对前者进行滤波,进一步提高定位精度;最后提出了一种自适应非直达波跟踪算法,尽管由于需要搜索过程,运算量比前两种算法大,但是仿真结果表明该算法性能优于前两种算法。
The location estimation of a mobile station (MS) in wireless communication systems has gained considerable attention over the past decade, especially since the Federal Communication Commission (FCC) passed a mandate requiring cellular providers to generate accurate location estimates for Enhanced-911 (E-911) services. One of the main problems facing accurate location in wireless communication systems is non-line-of-sight (NLOS) propagation, since line-of-sight (LOS) propagation may not exist. This has boosted the research in the field of wireless location in NLOS environments.
According to difference channel environments, the statuses of MS and prior information, the location estimation methods in wireless communication systems, especially the methods for mitigating the influence of the NLOS propagation, are studies in this dissertation. The primary contributions are summarized as follows:
1. In order to locate MS in LOS environments, we derive the Cramer-Rao Lower Bounds (CRLB) of four localization techniques, including the time of arrival (TOA), time difference of arrival (TDOA), time of arrival/received signal strength (TOA/RSS) and time difference of arrival/received signal strength (TDOA/RSS) based methods. Then two hybrid location methods (TOA/RSS and TDOA/RSS) based on two steps least square method are proposed. Simulation results show that the proposed hybrid algorithms outperform TOA and TDOA based methods.
2. In order to locate MS in LOS/NLOS mix environments, we study NLOS identification algorithms. First, we analyze the performance of the TOA and TDOA based methods. The analytical results show that we should develop NLOS identification algorithms. Then a novel NLOS identification algorithm based on overlap Area is proposed to deal with TOA measurements. Comparison to other TOA NLOS identification algorithms, the proposed method can identify NLOS BS with more accuracy and less computation complexity, and fit for moveless MS. Finally, a novel NLOS identification algorithm based on Chi-square distribution is proposed to deal with TDOA measurements. Comparison to other TDOA NLOS identification algorithms, the proposed method can identify the number of NLOS BSs.
3.A novel algorithm based on quadratic reflection assumption and multi-path information is proposed to obtain precise mobile location in where there exist multi-path signals and little scatters. The CRLB is also derived. Comparison to other scatters information based location methods, the proposed algorithm breaks through a single reflection assumption.
4.A novel algorithm based on robust objective function is proposed to obtain precise mobile location in where the number of LOS BSs or multi-path signals is insufficient. Traditional location algorithms are based on mean-squares objective function and it loses optimality in NLOS environments. The proposed algorithm replace mean-squares objective function with robust objective function, it does not require the prior knowledge of the NLOS error distribution and can give a closed-form solution.
5.We study the learning location methods with prior information. First, a novel location algorithm based on Kriging method is proposed to mitigate NLOS errors when outliers not exist in the training data set. Simulation results show that the proposed algorithm comes close to meeting CRLB. Then we extend least squares support vector machine (LS-SVM) algorithm to robust objective functions, and use this algorithm to solve mobile location problem. Simulation results show that the proposed algorithms can effectively mitigate outliers.
6.We study the NLOS tracing algorithms. First, a NLOS tracing algorithm based on enhanced quadrant constraint is proposed. Then a robust LMS algorithm is proposed to filter measurements. Finally, an adaptive NLOS tracing algorithm is proposed. Although the proposed adaptive NLOS tracing algorithm will add the computation complexity, Simulation results show that it outperforms the other two.
引文
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