基于RSSI高斯滤波的LSSVR无线传感网络定位算法

LSSVR wireless sensor network location algorithm based on Gaussian filter RSSI

为了降低基于接收信号强度指示(RSSI)测距误差对节点定位的影响,解决RSSI测距定位误差较大的问题,提出基于RSSI高斯滤波的最小二乘支持向量回归机LSSVR定位算法(LSSVR-GF-RSSI)。LSSVR-GF-RSSI算法先利用高斯函数滤除误差较大的RSSI值,筛选出较准确的RSSI值,再依据这些值计算未知节点离锚节点间的距离。将这些距离作为LSSVR的输入,建立基于RSSI测距的LSSVR定位算法模型,最终,估计未知节点的位置。仿真结果表明,提出的LSSVR-GF-RSSI算法能够有效地降低均方定位误差,比传统的基于RSSI的LSSVR定位算法减少了约12%~20%。

In order to minimize the influence of range-finding error of received signal strength index(RSSI)on node localization,and solve the problem of big location error existing in localization algorithm based on RSSI range-finding,a least-squares support vector regression location algorithm based on Gaussian filter RSSI(LSSVR-GF-RSSI)is proposed. The LSSVR-GF-RSSI algorithm uses the Gaussian function to filter the RSSI values with big error,and screen out the accurate RSSI values. According to the above values,the distance between the unknown node and anchor node is calculated. The distance is used as the input of LSSVR to establish the LSSVR location algorithm model based on RSSI range-finding to estimate the location of unknown node.The simulation results show that the LSSVR-GF-RSSI algorithm can reduce the mean square localization error effectively,which is 12%~20% lower than that of the traditional LSSVR localization algorithm based on RSSI.