Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWL...Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.展开更多
无线传感网中的多类应用均需要准确的定位算法。为了降低定位成本,减少能量消耗,常采用基于接收信号强度RSS（received signal strength）测距;再利用最大似然ML（maximum likelihood）估计法求解节点的位置。然而,ML估计为非线性、非凸...无线传感网中的多类应用均需要准确的定位算法。为了降低定位成本,减少能量消耗,常采用基于接收信号强度RSS（received signal strength）测距;再利用最大似然ML（maximum likelihood）估计法求解节点的位置。然而,ML估计为非线性、非凸性,难以获取全局最优解;为此,提出凸半定规划SDP（semidefinite programming）的合作式定位方案,利用凸半定规划策略将ML估计转换成凸优问题;同时,该方案考虑两类场景：源节点发射功率已知、未知。针对第一类场景,利用半凸松弛策略,并结合最小化最小二乘法,建立凸优表达式,最后利用CVX求解。针对第二类场景,先建立联合ML估计函数,再利用SDP估计,并结合起来简单的三步骤方案进行位置估计。仿真结果表明,提出的SDP算法的定位精度比SD/SOCP-1、SDPRSS平均提高了近15%~20%。此外,提出的SDP算法在所有场景的误差小于3 m的出现概率占0.8,而SD/SOCP-1、SDPRSS算法小于0.5。展开更多
基金supported by the National Natural Science Foundation of China(61201282)the Science and Technology on Communication Information Security Control Laboratory Foundation(9140C130304120C13064)
文摘Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.
