本文针对Ho提出的基于TDOA(Time Difference of Arrival)与GROA(Gain Ratio of Arrival)信号源定位的代数闭式解,提出两种偏差消减方法.首先对其闭式解偏差进行了推导,然后给出BiasRed法与BiasSub法两种偏差消减算法,BiasSub法从Ho给出...本文针对Ho提出的基于TDOA(Time Difference of Arrival)与GROA(Gain Ratio of Arrival)信号源定位的代数闭式解,提出两种偏差消减方法.首先对其闭式解偏差进行了推导,然后给出BiasRed法与BiasSub法两种偏差消减算法,BiasSub法从Ho给出的解中直接减去期望偏差,BiasRed法通过分析误差表达方程并引入二次约束来提升定位估计精度;分析表明两种方法均可针对远距离信号源,在较小高斯误差情况下有效消减定位偏差,BiasRed法可将偏差降低到最大似然估计算法的水平;计算机仿真分析验证了所提算法的性能.展开更多
节点定位是无线传感器网络中最为关键的一项技术。针对无源定位的问题,提出一种到达时间差(TDOA)和到达信号增益比(GROA)联合定位算法,并且采用飞行机制的萤火虫算法(GSO)来求得最终结果。结合TDOA和GROA定位模型,引入辅助变量将方程伪...节点定位是无线传感器网络中最为关键的一项技术。针对无源定位的问题,提出一种到达时间差(TDOA)和到达信号增益比(GROA)联合定位算法,并且采用飞行机制的萤火虫算法(GSO)来求得最终结果。结合TDOA和GROA定位模型,引入辅助变量将方程伪线性化;然后采用修正两步加权最小二乘算法(TSWLS)来进行求解。并且在不影响收敛速度和精度的前提下,采用带有飞行机制的GSO算法来寻求目标定位的最优解,克服粒子群算法易陷入局部最优的缺点。仿真结果表明,该算法相比较TDOA算法,定位精度提高了23 d B,并且具有相对较高和较稳定的定位精度。展开更多
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
With the emergence of location-based applications in various fields, the higher accuracy of positioning is demanded. By utilizing the time differences of arrival （TDOAs） and gain ratios of arrival （GROAs）, an effi...With the emergence of location-based applications in various fields, the higher accuracy of positioning is demanded. By utilizing the time differences of arrival （TDOAs） and gain ratios of arrival （GROAs）, an efficient algorithm for estimating the position is proposed, which exploits the Broyden-Fletcher-Goldfarb-Shanno （BFGS） quasi-Newton method to solve nonlinear equations at the source location under the additive measurement error. Although the accuracy of two-step weighted-least-square （WLS） method based on TDOAs and GROAs is very high, this method has a high computational complexity. While the proposed approach can achieve the same accuracy and bias with the lower computational complexity when the signal-to-noise ratio （SNR） is high, especially it can achieve better accuracy and smaller bias at a lower SNR. The proposed algorithm can be applied to the actual environment due to its real-time property and good robust performance. Simulation results show that with a good initial guess to begin with, the proposed estimator converges to the true solution and achieves the Cramer-Rao lower bound （CRLB） accuracy for both near-field and far-field sources.展开更多
文摘本文针对Ho提出的基于TDOA(Time Difference of Arrival)与GROA(Gain Ratio of Arrival)信号源定位的代数闭式解,提出两种偏差消减方法.首先对其闭式解偏差进行了推导,然后给出BiasRed法与BiasSub法两种偏差消减算法,BiasSub法从Ho给出的解中直接减去期望偏差,BiasRed法通过分析误差表达方程并引入二次约束来提升定位估计精度;分析表明两种方法均可针对远距离信号源,在较小高斯误差情况下有效消减定位偏差,BiasRed法可将偏差降低到最大似然估计算法的水平;计算机仿真分析验证了所提算法的性能.
文摘节点定位是无线传感器网络中最为关键的一项技术。针对无源定位的问题,提出一种到达时间差(TDOA)和到达信号增益比(GROA)联合定位算法,并且采用飞行机制的萤火虫算法(GSO)来求得最终结果。结合TDOA和GROA定位模型,引入辅助变量将方程伪线性化;然后采用修正两步加权最小二乘算法(TSWLS)来进行求解。并且在不影响收敛速度和精度的前提下,采用带有飞行机制的GSO算法来寻求目标定位的最优解,克服粒子群算法易陷入局部最优的缺点。仿真结果表明,该算法相比较TDOA算法,定位精度提高了23 d B,并且具有相对较高和较稳定的定位精度。
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.
基金supported by the Major National Science&Technology Projects(2010ZX03006-002-04)the National Natural Science Foundation of China(61072070)+4 种基金the Doctorial Programs Foundation of the Ministry of Education(20110203110011)the"111 Project"(B08038)the Fundamental Research Funds of the Ministry of Education(72124338)the Key Programs for Natural Science Foundation of Shanxi Province(2012JZ8002)the Foundation of State Key Laboratory of Integrated Services Networks(ISN1101002)
文摘With the emergence of location-based applications in various fields, the higher accuracy of positioning is demanded. By utilizing the time differences of arrival （TDOAs） and gain ratios of arrival （GROAs）, an efficient algorithm for estimating the position is proposed, which exploits the Broyden-Fletcher-Goldfarb-Shanno （BFGS） quasi-Newton method to solve nonlinear equations at the source location under the additive measurement error. Although the accuracy of two-step weighted-least-square （WLS） method based on TDOAs and GROAs is very high, this method has a high computational complexity. While the proposed approach can achieve the same accuracy and bias with the lower computational complexity when the signal-to-noise ratio （SNR） is high, especially it can achieve better accuracy and smaller bias at a lower SNR. The proposed algorithm can be applied to the actual environment due to its real-time property and good robust performance. Simulation results show that with a good initial guess to begin with, the proposed estimator converges to the true solution and achieves the Cramer-Rao lower bound （CRLB） accuracy for both near-field and far-field sources.
