Effective bias reduction methods for passive source localization using TDOA and GROA  

作　　者：HAO BenJian LI Zan QI PeiHan GUAN Lei 

机构地区：[1]State Key Laboratory of Integrated Services Networks, Xidian University

出　　处：《Science China(Information Sciences)》2013年第7期93-104,共12页中国科学（信息科学）（英文版）

基　　金：supported by Major National Science & Technology Projects (Grant No. 2010ZX03006-002-04);National Natural Science Foundation of China (Grant No. 61072070);Doctorial Programs Foundation of the Ministry of Education (Grant No. 20110203110011);Fundamental Research Funds of the Ministry of Education(Grant No. 72124338);Key Programs for Natural Science Foundation of Shaanxi Province (Grant No. 2012JZ-8002);111 Project (Grant No. B08038)

摘　　要：For passive source localization based on both TDOA and GROA, this paper proposes two bias reduction methods for the well-known Weighted-Least-Squares （WLS） estimator. We first derive the passive source localization bias from the two-step Mgebraic closed-form solution. This bias is found to be considerably larger than the Maximum Likelihood Estimator （MLE） and limits the WLS estimator＇s practical applications. In this paper, We develop two methods to reduce the bias. The first one called Bias-Subtraction-Method （BSM） directly subtracts the expected bias from the solution of the WLS estimator, and the second one called Bias- Reduction-Method （BRM） imposes a constraint to the equation error formulation to improve the source location estimate. The noise covariance matrix must be known exactly in calculating the expected bias in BSM, and we only need to know the structure of it in BRM. For far-field sources localization when the noise is Gaussian and not too large, both of the two proposed methods can reduce the localization bias effectively and achieve the Cramer-Rao Lower Bound （CRLB） performance very well, and the BRM almost has the same performance as the MLE estimator. Simulations corroborate the performance of the two proposed methods.For passive source localization based on both TDOA and GROA, this paper proposes two bias reduction methods for the well-known Weighted-Least-Squares （WLS） estimator. We first derive the passive source localization bias from the two-step Mgebraic closed-form solution. This bias is found to be considerably larger than the Maximum Likelihood Estimator （MLE） and limits the WLS estimator＇s practical applications. In this paper, We develop two methods to reduce the bias. The first one called Bias-Subtraction-Method （BSM） directly subtracts the expected bias from the solution of the WLS estimator, and the second one called Bias- Reduction-Method （BRM） imposes a constraint to the equation error formulation to improve the source location estimate. The noise covariance matrix must be known exactly in calculating the expected bias in BSM, and we only need to know the structure of it in BRM. For far-field sources localization when the noise is Gaussian and not too large, both of the two proposed methods can reduce the localization bias effectively and achieve the Cramer-Rao Lower Bound （CRLB） performance very well, and the BRM almost has the same performance as the MLE estimator. Simulations corroborate the performance of the two proposed methods.

关 键 词：source localization bias reduction Time Differences of Arrival （TDOA） Gain Ratios of Arrival（GROA） Cram^r-Rao Lower Bound （CRLB） 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TN911.7[自动化与计算机技术—控制科学与工程]