基于最大似然估计的加权质心定位算法

为解决无线传感器网络中节点自身定位问题,针对接收信号强度指示（received signal strength indication,RSSI）测距误差大和质心定位算法精度低的问题,提出一种基于最大似然估计的加权质心定位算法．首先通过计算将估计距离与实际距离之间的最大似然估计值作为权值,然后在权值模型中,引进一个参数k优化未知节点周围锚节点分布,最后计算出未知节点的位置并加以修正．仿真结果表明,基于最大似然估计的加权质心算法具有定位精度高和成本低的特点,优于基于距离倒数的质心加权和基于RSSI倒数的质心加权算法,适用于大面积的室内定位．

Weighted Centroid Localization Algorithm Based on Maximum Likelihood Estimation

In solving the problem of localizing nodes in a wireless sensor network, we propose a weighted centroid localization algorithm based on maximum likelihood estimation, with the specific goal of solving the problems of big received signal strength indication（RSSI) ranging error and low accuracy of the centroid localization algorithm. Firstly, the maximum likelihood estimation between the estimated distance and the actual distance is calculated as weights. Then, a parameter k is introduced to optimize the weights between the anchor nodes and the unknown nodes in the weight model. Finally, the locations of the unknown nodes are calculated and modified by using the proposed algorithm. The simulation results show that the weighted centroid algorithm based on the maximum likelihood estimation has the features of high localization accuracy and low cost, and has better performance compared with the inverse distance-based algorithm and the inverse RSSI-based algorithm. Hence, the proposed algorithm is more suitable for the indoor localization of large areas.