Mobile location using angle of arrival （AOA） measurements has received considerable attention. This paper presents an approximation of maximum likelihood estimator （MLE） for localizing a source based on AOA measur...Mobile location using angle of arrival （AOA） measurements has received considerable attention. This paper presents an approximation of maximum likelihood estimator （MLE） for localizing a source based on AOA measurements. By introducing an intermediate variable, the nonlinear equations relating AOA estimates can be transformed into a set of equations which are linear in the unknown parameters. It is an approximate realization of the MLE. Simulations show that the proposed algorithm outperforms the previous contribution.展开更多
The increasingly widespread use of sensor and actuator networks and in general of the Internet of Things (IoT) in several areas of precision, imposes upon localization systems that can often equip them with a robust a...The increasingly widespread use of sensor and actuator networks and in general of the Internet of Things (IoT) in several areas of precision, imposes upon localization systems that can often equip them with a robust and more precise localization. It is in this sense that UWB technology has proved to be one of the most powerful communication technologies for these localization systems;thanks, in particular to the bandwidth occupied instantaneously by the signal allowing a very fine temporal resolution. Constructors have set up localization kits based on various technologies. These kits facilitate in a way the work of localization of users. In this paper, we present results on the performance study of the Decawave PDoA Kit. This Kit uses the PDoA (Phase Difference of Arrival) to determine the Angle of Arrival (AoA) parameter with UWB technology. This study is in context of localization by AoA for an application to protect agricultural crops against grain-eating birds. The results of the study show overall AoA measurement errors around 10 degrees in an ideal environment.展开更多
Based on propagator method, a fast 2-D Angle-Of-Arrival (AOA) algorithm is proPosed in this paper. The proposed algorithm does not need the Eigen-Value Decomposition (EVD) or Singular Value Decomposition (SVD) of the ...Based on propagator method, a fast 2-D Angle-Of-Arrival (AOA) algorithm is proPosed in this paper. The proposed algorithm does not need the Eigen-Value Decomposition (EVD) or Singular Value Decomposition (SVD) of the Sample Covariance Matrix (SCM), thus the fast algorithm has lower computational complexity with insignificant performance degradation when comparing with conventional subspace approaches. Furthermore, the proposed algorithm has no performance degradation. Finally, computer simulations verify the effectiveness of the proposed algorithm.展开更多
As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to loc...As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates.展开更多
提出一种基于超宽带(ultra wideband,UWB)信号到达时间估计(time of arrival,TOA)/到达角度估计(angle of arrival,AOA)联合估计的无线传感器网络(wireless sensor networks,WSNs)定位方案,只需要一个参考节点就可以实现对其他传感器节...提出一种基于超宽带(ultra wideband,UWB)信号到达时间估计(time of arrival,TOA)/到达角度估计(angle of arrival,AOA)联合估计的无线传感器网络(wireless sensor networks,WSNs)定位方案,只需要一个参考节点就可以实现对其他传感器节点的2D相对定位,并且不需要时钟同步,适合于传感器网络节点的低成本设计需求.利用往返时间(round trip time,RTT)进行TOA估计,给出了基于多径检测的TOA估计算法;利用到达时间差估计(time difference of arrival,TDOA)进行AOA估计,因而无需借助复杂的天线波束赋形技术.同时,分析了定位误差模型对定位性能的影响,并通过IEEE802.15.4a信道下的仿真实验进行了验证,结果表明了所提方案的有效性.展开更多
为了提高室内三维空间的定位精度,提出了一种基于联合到达时间差与到达角度(time difference of arrival/angle of arrival,TDOA/AOA)信息的混合定位算法。由于构建的目标函数具有非凸性,采用传统定位算法在目标函数求解过程中会出现局...为了提高室内三维空间的定位精度,提出了一种基于联合到达时间差与到达角度(time difference of arrival/angle of arrival,TDOA/AOA)信息的混合定位算法。由于构建的目标函数具有非凸性,采用传统定位算法在目标函数求解过程中会出现局部最优解的问题。因此,针对该问题,将目标函数转成二次约束二次规划问题,通过引入半定松弛(semi-definite relaxation,SDR)方法将目标函数转换为二阶锥规划(second order cone programming,SOCP)问题,寻找全局最优解。其次,针对SOCP无法对凸包外的目标进行有效定位的问题,在该算法的基础上引入了惩罚项,使松弛后的约束条件进一步逼近原始约束条件,解决了定位过程中的凸包问题。数值仿真结果表明:在10m×10m×3m的三维定位空间内,选取40×40个测试点,平均定位误差为1.39cm,可实现室内三维空间高精度定位。与传统的混合定位算法相比,均能够获得较高的定位精度。展开更多
针对某些特殊的微小区移动通信环境,比如空旷的广场或者大型购物商厦等,移动台(Mobile station,MS)周围散射体可能很少,甚至于为零,在已有模型基础上建立了一种新的3-D空间模型——空心半球体空间模型。散射体均匀分布在移动台附近空间...针对某些特殊的微小区移动通信环境,比如空旷的广场或者大型购物商厦等,移动台(Mobile station,MS)周围散射体可能很少,甚至于为零,在已有模型基础上建立了一种新的3-D空间模型——空心半球体空间模型。散射体均匀分布在移动台附近空间内,而基站(Base station,BS)的位置在离地面有一定的高度。在水平平面和垂直平面内分别推导出来波信号的到达角度(Angle of arrival,AOA)的概率密度函数表达式,另外也研究了信号到达时间(Time of arrival,TOA)的概率密度函数。估计结果与某些2-D和3-D模型对比,表明本模型的信道参数估计符合以往的理论,对空间统计信道模型的研究和应用提供了有效的支持与拓展。展开更多
准确地估计信号的到达角(Angle Of Arrival,AOA)为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息(Channel Frequency Response,CFR)扩展...准确地估计信号的到达角(Angle Of Arrival,AOA)为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息(Channel Frequency Response,CFR)扩展阵列天线的亚米级室内定位系统.首先,采集CFR信息进行AOA和信号到达时间(Time Of Arrival,TOA)的联合估计;其次,提出了一种基于AOA和TOA二维聚类信息的直射路径识别算法;另外,还提出了可视环境(Line Of Sight,LOS)以及非可视环境(Non Line Of Sight,NLOS)的识别算法,可以准确的判断出当前接收机相对发射机是处于LOS还是NLOS环境;最后,利用现有的三天线Wi-Fi设备在室内进行了测角以及定位测试,实验结果表明本文提出的定位系统在室内LOS和NLOS环境下分别可以达到中值误差为0.8m,1.3m的定位精度,可用于室内高精度定位.展开更多
文摘Mobile location using angle of arrival （AOA） measurements has received considerable attention. This paper presents an approximation of maximum likelihood estimator （MLE） for localizing a source based on AOA measurements. By introducing an intermediate variable, the nonlinear equations relating AOA estimates can be transformed into a set of equations which are linear in the unknown parameters. It is an approximate realization of the MLE. Simulations show that the proposed algorithm outperforms the previous contribution.
文摘The increasingly widespread use of sensor and actuator networks and in general of the Internet of Things (IoT) in several areas of precision, imposes upon localization systems that can often equip them with a robust and more precise localization. It is in this sense that UWB technology has proved to be one of the most powerful communication technologies for these localization systems;thanks, in particular to the bandwidth occupied instantaneously by the signal allowing a very fine temporal resolution. Constructors have set up localization kits based on various technologies. These kits facilitate in a way the work of localization of users. In this paper, we present results on the performance study of the Decawave PDoA Kit. This Kit uses the PDoA (Phase Difference of Arrival) to determine the Angle of Arrival (AoA) parameter with UWB technology. This study is in context of localization by AoA for an application to protect agricultural crops against grain-eating birds. The results of the study show overall AoA measurement errors around 10 degrees in an ideal environment.
基金Supported by the Foundation of National Key Laboratory.
文摘Based on propagator method, a fast 2-D Angle-Of-Arrival (AOA) algorithm is proPosed in this paper. The proposed algorithm does not need the Eigen-Value Decomposition (EVD) or Singular Value Decomposition (SVD) of the Sample Covariance Matrix (SCM), thus the fast algorithm has lower computational complexity with insignificant performance degradation when comparing with conventional subspace approaches. Furthermore, the proposed algorithm has no performance degradation. Finally, computer simulations verify the effectiveness of the proposed algorithm.
文摘As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates.
文摘提出一种基于超宽带(ultra wideband,UWB)信号到达时间估计(time of arrival,TOA)/到达角度估计(angle of arrival,AOA)联合估计的无线传感器网络(wireless sensor networks,WSNs)定位方案,只需要一个参考节点就可以实现对其他传感器节点的2D相对定位,并且不需要时钟同步,适合于传感器网络节点的低成本设计需求.利用往返时间(round trip time,RTT)进行TOA估计,给出了基于多径检测的TOA估计算法;利用到达时间差估计(time difference of arrival,TDOA)进行AOA估计,因而无需借助复杂的天线波束赋形技术.同时,分析了定位误差模型对定位性能的影响,并通过IEEE802.15.4a信道下的仿真实验进行了验证,结果表明了所提方案的有效性.
文摘为了提高室内三维空间的定位精度,提出了一种基于联合到达时间差与到达角度(time difference of arrival/angle of arrival,TDOA/AOA)信息的混合定位算法。由于构建的目标函数具有非凸性,采用传统定位算法在目标函数求解过程中会出现局部最优解的问题。因此,针对该问题,将目标函数转成二次约束二次规划问题,通过引入半定松弛(semi-definite relaxation,SDR)方法将目标函数转换为二阶锥规划(second order cone programming,SOCP)问题,寻找全局最优解。其次,针对SOCP无法对凸包外的目标进行有效定位的问题,在该算法的基础上引入了惩罚项,使松弛后的约束条件进一步逼近原始约束条件,解决了定位过程中的凸包问题。数值仿真结果表明:在10m×10m×3m的三维定位空间内,选取40×40个测试点,平均定位误差为1.39cm,可实现室内三维空间高精度定位。与传统的混合定位算法相比,均能够获得较高的定位精度。
文摘针对某些特殊的微小区移动通信环境,比如空旷的广场或者大型购物商厦等,移动台(Mobile station,MS)周围散射体可能很少,甚至于为零,在已有模型基础上建立了一种新的3-D空间模型——空心半球体空间模型。散射体均匀分布在移动台附近空间内,而基站(Base station,BS)的位置在离地面有一定的高度。在水平平面和垂直平面内分别推导出来波信号的到达角度(Angle of arrival,AOA)的概率密度函数表达式,另外也研究了信号到达时间(Time of arrival,TOA)的概率密度函数。估计结果与某些2-D和3-D模型对比,表明本模型的信道参数估计符合以往的理论,对空间统计信道模型的研究和应用提供了有效的支持与拓展。
文摘准确地估计信号的到达角(Angle Of Arrival,AOA)为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息(Channel Frequency Response,CFR)扩展阵列天线的亚米级室内定位系统.首先,采集CFR信息进行AOA和信号到达时间(Time Of Arrival,TOA)的联合估计;其次,提出了一种基于AOA和TOA二维聚类信息的直射路径识别算法;另外,还提出了可视环境(Line Of Sight,LOS)以及非可视环境(Non Line Of Sight,NLOS)的识别算法,可以准确的判断出当前接收机相对发射机是处于LOS还是NLOS环境;最后,利用现有的三天线Wi-Fi设备在室内进行了测角以及定位测试,实验结果表明本文提出的定位系统在室内LOS和NLOS环境下分别可以达到中值误差为0.8m,1.3m的定位精度,可用于室内高精度定位.
