室内定位关键技术研究
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摘要
精确的室内定位对于公共安全、商业应用以及军事应用都具有非常重要的意义。然而室内环境非常复杂,信号传播会受到墙壁、隔板、天花板等障碍物的阻挡,引起信号发生反射、折射、衍射现象,发射信号经过多条路径、以不同的时间到达接收端,出现多径传播现象和非视距效应,使得室内定位极具挑战性。超宽带技术UWB (Ultra-wideband)拥有极宽电磁频谱,在穿透能力、精细分辨、精确测距、抗多径和抗干扰等方面具有独特的优势,其系统实现具有低复杂度、低功耗、低成本的潜力,成为室内定位最有前景的技术方案之一。但是,目前针对超宽带与室内定位的结合,还有众多问题亟待深入研究和完善。本文从超宽带和室内定位的基本特点出发,就超宽带在室内定位中遇到的关键问题展开了相关研究和探讨：
首先,论文研究了超宽带信号接收中的低位宽量化问题。低位宽量化是实现超宽带全数字接收的有效解决方案,然而传统的均匀量化将导致幅度信息的极大损失。本文从量化的基本原理出发,将量化参数与超宽带信号在噪声中的检测性能联系起来,研究了两类不同优化目标下的量化问题。第一类以噪声中信号检测为背景,依据纽曼-皮尔逊准则,以最大化信号检测概率为优化函数,给出了最优量化门限和最优量化电平应满足的条件；第二类以噪声中二元通信为背景,以最小化误码率为优化函数,推导了最优量化参数需满足的特定条件。另外,还将推导出的最优量化参数应用于零均值高斯噪声中的常值信号检测,并给出了量化参数在弱信噪比情况下的具体形式。据此研究了低位宽量化参数对超宽带信号检测与符号检测的性能影响,为建立低位宽量化的接收方案奠定了良好基础。
其次,论文对室内复杂环境下的稳健到达时间估计TOA (Time of Arrival)问题进行了研究。在室内环境下,信号传播会因为障碍物的影响而出现多径效应和非视距效应NLOS (Non-line-of-sight),而且这些效应会随着环境的变化而出现较大的起伏,这对准确的TOA估计提出了严峻的挑战。本文通过分析超宽带信号在室内环境传播所呈现出来的特征,将TOA估计问题转换为噪声中信号检测问题,提出了两种稳健的TOA估计算法：第一种方法以检测判决门限为噪声参数的函数为基础,提出了基于非参量检测的TOA估计算法,通过将基于条件检验的非参量检测与低位宽量化相结合,来降低判决门限对噪声参数估计误差的敏感性；第二种方法利用最优判决门限与信噪比紧密相关的先验信息,提出了基于自适应门限的TOA估计算法,通过实时估计接收信号的信噪比而动态改变门限,使得算法在相当宽的信噪比范围内都能保持较好的性能,提高了应对环境变化的适应性。
然后,论文研究了室内环境下的固定节点定位方法。室内环境的多径效应和非视距效应使得距离量测误差呈现出与传统视距情况下不同的特征,导致传统定位算法性能的严重下降。本文根据室内环境测距误差的特点,将其划分为两种类型：一类是负量测误差,是由虚警引起的噪声误判导致的；另一类是正量测误差,是由信号首径漏检导致的。针对第一类情况,提出了量测软判决技术,各基站保留多于一个备选距离量测形成量测组合,然后通过建立代价函数选择最佳量测组合来降低负量测误差的影响；对于第二类情况,根据正量测误差会导致量测值交汇出公共区域的特征,提出了公共区域优化技术,通过搜索公共区域中平均定位误差最小点,来降低正量测误差的影响。另外,通过联合上述两种技术措施,本文提出了联合定位方案以改善室内定位精度。
最后,论文对室内环境下的移动节点稳定跟踪进行了研究。信号非视距传播是影响室内移动节点跟踪的关键因素之一,它使得距离量测误差不再满足传统Kalman算法中量测误差的模型要求,导致跟踪算法的性能发生崩溃。为了降低NLOS环境对室内移动节点跟踪的影响,本文提出一种自适应跟踪滤波算法。该算法首先基于典型室内环境中非视距偏置误差的时间变化特性分析,建立了修正偏置扩展卡尔曼滤波去估计距离量测中的非视距误差,然后根据估计结果对LOS/NLOS环境进行鉴别,最后联合NLOS鉴别算法和修正偏置扩展卡尔曼滤波建立自适应跟踪滤波算法。数值仿真结果表明,这种自适应跟踪算法在室内环境中具有较好的跟踪精度,算法具有较强的适应性。
Accurate indoor geolocation is an important and novel emerging technology for commercial, public-safety, and military applications. However, indoor localization is very challenging due to the complex signal propagation that is caused by obstacles such as walls, clapboard, ceiling and so on. The electromagnetic wave may suffer reflection, refraction, diffraction and may result in the phenomenon of dense multipath arrivals and non line-of-sight propagation both of which will severely degrade the localization accuracy. Due to the large bandwidth, Impulse Radio Ultra-wide (IR-UWB) technology holds the advantages in anti-multipath, anti-interference, penetrability, high-precision ranging, low complexity implementation, low cost, low power consumption and becomes one of the most promising technologies. But at present, the indoor localization based on IR-UWB is still facing lots of problems and needs in-depth researching and improving. This dissertation has been launched to investigate the key technologies of indoor positioning, which is important and valuable for the practical application.
First of all, the optimum quantization for the UWB finite resolution digital receiver is studied. Low-resolution quantization is an effective scheme to deal with the large bandwidth in UWB digital receiver; however, traditional uniform quantization will lead to serious information loss. Based on the analysis of fundamental quantization theory and UWB signal detection, two different kinds of optimization problems about optimum quantization are investigated. The first problem comes from the signal detection in noise and connects the quantization parameters with the detection probability. The corresponding optimum quantization thresholds and levels are derived with maximizing the detection probability as the optimization function. The second problem comes from the binary communication and makes the minimization of the bit error rate as the optimization function. The influence of quantization parameters on UWB signal detection and sign detection is also explored, especially under the low signal-to-noise (SNR).
Secondly, the estimation of time of arrival (TOA) is studied. Dense multipath arrivals and NLOS propagation are common and changeable in indoor environment, which will decrease the TOA accuracy. Based on the analysis of the characteristic of UWB signal propagation in indoor environment, two robust algorithms for TOA estimation are proposed. The first algorithm combines the nonparametric detection based on conditional tests with low-resolution quantization and degrades the sensitivity of noise parameters on judgment threshold. The second algorithm exploits the information that the optimum threshold is closely related with the SNR and utilizes adaptive threshold with respect to timely estimated SNR.
Thirdly, indoor localization method for static nodes is studied. Dense multipath arrivals and NLOS propagation lead to distance measurement errors differing from the traditional model and then can reduce the positioning accuracy. Based upon the research about distance errors, two different types are obtained. The first type is negative error that is caused by false alarm detection of noise samples. The second type is positive error, which is caused by missing detection of the leading path. The negative error is reduced by soft-decision algorithm in which more than one measurement are reserved by each station and selected by minimizing the cost function. The positive error is decreased by public area optimization algorithm in which the point of minimal average distance error is deemed as the node's position. By combing those two algorithms, a comprehensive location method is proposed to improve the indoor positioning accuracy.
Finally, indoor tracking method of moving node is studied. NLOS propagation is one of the key factors that affect tracking accuracy in indoor environments. An adaptive tracking algorithm is proposed to mitigate the NLOS error for indoor mobile localization. The correlation between adjacent NLOS errors in time was analyzed and exploited. A modified extended Kalman filter (MEKF) is presented which includes the NLOS errors as part of the state variables. NLOS identification is achieved based on the state estimation of MEKF. MEKF and NLOS identification are combined to implement the adaptive tracking algorithm. Simulation results demonstrate that the proposed algorithm has better tracking accuracy and adaptability in indoor environments.
首先,论文研究了超宽带信号接收中的低位宽量化问题。低位宽量化是实现超宽带全数字接收的有效解决方案,然而传统的均匀量化将导致幅度信息的极大损失。本文从量化的基本原理出发,将量化参数与超宽带信号在噪声中的检测性能联系起来,研究了两类不同优化目标下的量化问题。第一类以噪声中信号检测为背景,依据纽曼-皮尔逊准则,以最大化信号检测概率为优化函数,给出了最优量化门限和最优量化电平应满足的条件；第二类以噪声中二元通信为背景,以最小化误码率为优化函数,推导了最优量化参数需满足的特定条件。另外,还将推导出的最优量化参数应用于零均值高斯噪声中的常值信号检测,并给出了量化参数在弱信噪比情况下的具体形式。据此研究了低位宽量化参数对超宽带信号检测与符号检测的性能影响,为建立低位宽量化的接收方案奠定了良好基础。
其次,论文对室内复杂环境下的稳健到达时间估计TOA (Time of Arrival)问题进行了研究。在室内环境下,信号传播会因为障碍物的影响而出现多径效应和非视距效应NLOS (Non-line-of-sight),而且这些效应会随着环境的变化而出现较大的起伏,这对准确的TOA估计提出了严峻的挑战。本文通过分析超宽带信号在室内环境传播所呈现出来的特征,将TOA估计问题转换为噪声中信号检测问题,提出了两种稳健的TOA估计算法：第一种方法以检测判决门限为噪声参数的函数为基础,提出了基于非参量检测的TOA估计算法,通过将基于条件检验的非参量检测与低位宽量化相结合,来降低判决门限对噪声参数估计误差的敏感性；第二种方法利用最优判决门限与信噪比紧密相关的先验信息,提出了基于自适应门限的TOA估计算法,通过实时估计接收信号的信噪比而动态改变门限,使得算法在相当宽的信噪比范围内都能保持较好的性能,提高了应对环境变化的适应性。
然后,论文研究了室内环境下的固定节点定位方法。室内环境的多径效应和非视距效应使得距离量测误差呈现出与传统视距情况下不同的特征,导致传统定位算法性能的严重下降。本文根据室内环境测距误差的特点,将其划分为两种类型：一类是负量测误差,是由虚警引起的噪声误判导致的；另一类是正量测误差,是由信号首径漏检导致的。针对第一类情况,提出了量测软判决技术,各基站保留多于一个备选距离量测形成量测组合,然后通过建立代价函数选择最佳量测组合来降低负量测误差的影响；对于第二类情况,根据正量测误差会导致量测值交汇出公共区域的特征,提出了公共区域优化技术,通过搜索公共区域中平均定位误差最小点,来降低正量测误差的影响。另外,通过联合上述两种技术措施,本文提出了联合定位方案以改善室内定位精度。
最后,论文对室内环境下的移动节点稳定跟踪进行了研究。信号非视距传播是影响室内移动节点跟踪的关键因素之一,它使得距离量测误差不再满足传统Kalman算法中量测误差的模型要求,导致跟踪算法的性能发生崩溃。为了降低NLOS环境对室内移动节点跟踪的影响,本文提出一种自适应跟踪滤波算法。该算法首先基于典型室内环境中非视距偏置误差的时间变化特性分析,建立了修正偏置扩展卡尔曼滤波去估计距离量测中的非视距误差,然后根据估计结果对LOS/NLOS环境进行鉴别,最后联合NLOS鉴别算法和修正偏置扩展卡尔曼滤波建立自适应跟踪滤波算法。数值仿真结果表明,这种自适应跟踪算法在室内环境中具有较好的跟踪精度,算法具有较强的适应性。
Accurate indoor geolocation is an important and novel emerging technology for commercial, public-safety, and military applications. However, indoor localization is very challenging due to the complex signal propagation that is caused by obstacles such as walls, clapboard, ceiling and so on. The electromagnetic wave may suffer reflection, refraction, diffraction and may result in the phenomenon of dense multipath arrivals and non line-of-sight propagation both of which will severely degrade the localization accuracy. Due to the large bandwidth, Impulse Radio Ultra-wide (IR-UWB) technology holds the advantages in anti-multipath, anti-interference, penetrability, high-precision ranging, low complexity implementation, low cost, low power consumption and becomes one of the most promising technologies. But at present, the indoor localization based on IR-UWB is still facing lots of problems and needs in-depth researching and improving. This dissertation has been launched to investigate the key technologies of indoor positioning, which is important and valuable for the practical application.
First of all, the optimum quantization for the UWB finite resolution digital receiver is studied. Low-resolution quantization is an effective scheme to deal with the large bandwidth in UWB digital receiver; however, traditional uniform quantization will lead to serious information loss. Based on the analysis of fundamental quantization theory and UWB signal detection, two different kinds of optimization problems about optimum quantization are investigated. The first problem comes from the signal detection in noise and connects the quantization parameters with the detection probability. The corresponding optimum quantization thresholds and levels are derived with maximizing the detection probability as the optimization function. The second problem comes from the binary communication and makes the minimization of the bit error rate as the optimization function. The influence of quantization parameters on UWB signal detection and sign detection is also explored, especially under the low signal-to-noise (SNR).
Secondly, the estimation of time of arrival (TOA) is studied. Dense multipath arrivals and NLOS propagation are common and changeable in indoor environment, which will decrease the TOA accuracy. Based on the analysis of the characteristic of UWB signal propagation in indoor environment, two robust algorithms for TOA estimation are proposed. The first algorithm combines the nonparametric detection based on conditional tests with low-resolution quantization and degrades the sensitivity of noise parameters on judgment threshold. The second algorithm exploits the information that the optimum threshold is closely related with the SNR and utilizes adaptive threshold with respect to timely estimated SNR.
Thirdly, indoor localization method for static nodes is studied. Dense multipath arrivals and NLOS propagation lead to distance measurement errors differing from the traditional model and then can reduce the positioning accuracy. Based upon the research about distance errors, two different types are obtained. The first type is negative error that is caused by false alarm detection of noise samples. The second type is positive error, which is caused by missing detection of the leading path. The negative error is reduced by soft-decision algorithm in which more than one measurement are reserved by each station and selected by minimizing the cost function. The positive error is decreased by public area optimization algorithm in which the point of minimal average distance error is deemed as the node's position. By combing those two algorithms, a comprehensive location method is proposed to improve the indoor positioning accuracy.
Finally, indoor tracking method of moving node is studied. NLOS propagation is one of the key factors that affect tracking accuracy in indoor environments. An adaptive tracking algorithm is proposed to mitigate the NLOS error for indoor mobile localization. The correlation between adjacent NLOS errors in time was analyzed and exploited. A modified extended Kalman filter (MEKF) is presented which includes the NLOS errors as part of the state variables. NLOS identification is achieved based on the state estimation of MEKF. MEKF and NLOS identification are combined to implement the adaptive tracking algorithm. Simulation results demonstrate that the proposed algorithm has better tracking accuracy and adaptability in indoor environments.
引文
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