节点定位是无线传感网的关键技术之一.针对传统的基于RSSI的定位算法精度低的问题,提出一种基于最优信标组的扩展卡尔曼定位算法(BBG-EKF).该算法分析影响定位精度的两个因素:未知节点与信标节点的距离和信标节点间的共线性,提出一种最优信标组选择机制,进一步通过扩展卡尔曼滤波实现精确定位.新算法复杂度低,定位过程中节点间通信开销小.实验结果表明,BBG-EKF定位算法在不同通信半径及信标节点密度的网络中都具有较高的定位精度,可以满足大多数无线传感网定位系统的需求.%Node localization is one of the fundamental issues in wireless sensor networks. In order to solve the problem of low location accuracy in traditional RSSI-Based algorithm, a novel localization algorithm based on the best beacon group and extended Kalman filter(BBG-EKF) is proposed. Firstly, two factors which influence the location accuracy are analyzed. These two factors are the distance between beacons and unknown nodes,and,the colinearity of beacons. Then a new selection scheme for the best beacon group is proposed. Finally, the extended kalman filter method is applied to estimate the positions of unknown nodes. The proposed algorithm has the advantage of low complexity and low communication overhead. The simulation illustrates that the proposed scheme keeps high location accuracy under different communication radius and beacon density. Therefore, it meets the needs for the most positioning system in wireless sensor networks.
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