This paper focuses on the sensor subset optimization problem in time difference of arrival(TDOA) passive localization scenario. We seek for the best sensor combination by formulating a non-convex optimization problem,...This paper focuses on the sensor subset optimization problem in time difference of arrival(TDOA) passive localization scenario. We seek for the best sensor combination by formulating a non-convex optimization problem, which is to minimize the trace of covariance matrix of localization error under the condition that the number of selected sensors is given. The accuracy metric is described by the localization error covariance matrix of classical closed-form solution, which is introduced to convert the TDOA nonlinear equations into pseudo linear equations. The non-convex optimization problem is relaxed to a standard semi-definite program(SDP) and efficiently solved in a short time. In addition, we extend the sensor selection method to a mixed TDOA and angle of arrival(AOA) localization scenario with the presence of sensor position errors. Simulation results validate that the performance of the proposed sensor selection method is very close to the exhaustive search method.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
在利用传统Chan算法进行目标节点位置估算的基础上,提出了一种结合最陡下降算法SDA(Steepest Descent Algorithm)的TDOA(Time Difference of Arrival)/AOA(Angle of Arrival)融合算法,通过迭代消除由NLOS(Non Line of Sight)误差引起的...在利用传统Chan算法进行目标节点位置估算的基础上,提出了一种结合最陡下降算法SDA(Steepest Descent Algorithm)的TDOA(Time Difference of Arrival)/AOA(Angle of Arrival)融合算法,通过迭代消除由NLOS(Non Line of Sight)误差引起的误差因子,达到有效提高定位精度的目的.实验结果表明:本文提出的结合SDA的TDOA/AOA融合算法在复杂的室内环境下可以有效提高定位精度和定位稳定性,相对于传统的基于Chan算法的TDOA/AOA定位算法,定位精度提高28%.展开更多
基金supported by the National Natural Science Foundation of China under Grant (61631015, 61501354 61471395 and 61501356)the Key Scientific and Technological Innovation Team Plan (2016KCT-01)the Fundamental Research Funds of the Ministry of Education (7215433803 and XJS16063)
文摘This paper focuses on the sensor subset optimization problem in time difference of arrival(TDOA) passive localization scenario. We seek for the best sensor combination by formulating a non-convex optimization problem, which is to minimize the trace of covariance matrix of localization error under the condition that the number of selected sensors is given. The accuracy metric is described by the localization error covariance matrix of classical closed-form solution, which is introduced to convert the TDOA nonlinear equations into pseudo linear equations. The non-convex optimization problem is relaxed to a standard semi-definite program(SDP) and efficiently solved in a short time. In addition, we extend the sensor selection method to a mixed TDOA and angle of arrival(AOA) localization scenario with the presence of sensor position errors. Simulation results validate that the performance of the proposed sensor selection method is very close to the exhaustive search method.
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。
文摘在利用传统Chan算法进行目标节点位置估算的基础上,提出了一种结合最陡下降算法SDA(Steepest Descent Algorithm)的TDOA(Time Difference of Arrival)/AOA(Angle of Arrival)融合算法,通过迭代消除由NLOS(Non Line of Sight)误差引起的误差因子,达到有效提高定位精度的目的.实验结果表明:本文提出的结合SDA的TDOA/AOA融合算法在复杂的室内环境下可以有效提高定位精度和定位稳定性,相对于传统的基于Chan算法的TDOA/AOA定位算法,定位精度提高28%.
