Efficient Closed-Form Algorithms for AOA Based Self-Localization of Sensor Nodes Using Auxiliary Variables

被引:222
作者
Shao, Hua-Jie [1 ]
Zhang, Xiao-Ping [2 ]
Wang, Zhi [1 ]
机构
[1] Zhejiang Univ, Dept Control Sci & Engn, Hangzhou 310027, Peoples R China
[2] Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON M5B 2K3, Canada
基金
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词
Angle of arrival; auxiliary variables; bias compensated auxiliary variable based pseudo-linear estimator; closed-form pseudo-linear estimator; node self-localization; weighted instrumental variables; wireless sensor networks; MAXIMUM-LIKELIHOOD; BIAS;
D O I
10.1109/TSP.2014.2314064
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Node self-localization is a key research topic for wireless sensor networks (WSNs). There are two main algorithms, the triangulation method and the maximum likelihood (ML) estimator, for angle of arrival (AOA) based self-localization. The ML estimator requires a good initialization close to the true location to avoid divergence, while the triangulation method cannot obtain the closed-form solution with high efficiency. In this paper, we develop a set of efficient closed-form AOA based self-localization algorithms using auxiliary variables based methods. First, we formulate the self-localization problem as a linear least squares problem using auxiliary variables. Based on its closed-form solution, a new auxiliary variables based pseudo-linear estimator (AVPLE) is developed. By analyzing its estimation error, we present a bias compensated AVPLE (BCAVPLE) to reduce the estimation error. Then we develop a novel BCAVPLE based weighted instrumental variable (BCAVPLE-WIV) estimator to achieve asymptotically unbiased estimation of locations and orientations of unknown nodes based on prior knowledge of the AOA noise variance. In the case that the AOA noise variance is unknown, a new AVPLE based WIV (AVPLE-WIV) estimator is developed to localize the unknown nodes. Also, we develop an autonomous coordinate rotation (ACR) method to overcome the tangent instability of the proposed algorithms when the orientation of the unknown node is near pi/2. We also derive the Cramer-Rao lower bound (CRLB) of the ML estimator. Extensive simulations demonstrate that the new algorithms achieve much higher localization accuracy than the triangulation method and avoid local minima and divergence in iterative ML estimators.
引用
收藏
页码:2580 / 2594
页数:15
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