基于RSSI极大似然估计定位算法的改进与实现

Improvement and implementation of maximum likelihood estimation positioning algorithm based on RSSI

针对基于RSSI(接收信号强度指示)的极大似然估计算法会出现消去二次项时坐标信息丢失,定位误差较大的问题,提出了一种改进的基于RSSI极大似然估计的定位算法.该算法先用卡尔曼滤波算法对采集到的RSSI进行数据处理,然后利用在极大似然估计算法中使用基于泰勒级数展开的最小二乘法对未知节点求解定位.仿真实验结果表明,该算法改善了原算法定位的稳定性,有效减小了定位误差,提高了定位精度.

In order to solve the problem that the maximum likelihood estimation algorithm based on RSSI（Receiving Signal Strength Indicator）will lose coordinate information when the quadratic term is eliminated,and have large positioning error,an improved location algorithm was proposed based on RSSI maximum likelihood estimation.Firstly,the collected RSSI values were processed by Kalman filtering algorithm,and then the unknown nodes were located by using the least square method based on Taylor series expansion in the maximum likelihood estimation algorithm.The simulation results show that the improved algorithm has improved the stability of the original algorithm,effectively reduced the location error,and improved the location accuracy.