摘要
RSSI定位方法已成为近年来研究热点,基于测距的RSSI定位算法本身具有一定缺陷,无线信号在传播过程中由于受到环境中各种高斯白噪声的干扰,致使获取的RSSI值会在某一中心值上下波动,极大影响了定位的实效性、准确性。为提高室内环境的定位精度,提出一种基于RSSI的高斯—卡尔曼滤波优化。先用极大似然估计得出RSSI测距模型的修正参数,然后使用最小二乘法(LSM)初步估计所求定点的坐标,最后利用高斯-卡尔曼滤波对计算出来的定位节点坐标和参数进行优化,利用Matlab实验仿真结果表明,算法具有定位误差小、精度高的明显特点。
The RSSI localization method has become a hot problem in recent years. The RSSI localization algorithm based on ran ging has its own shortcomings. The wireless signal is disturbed by various Gaussian white noise in the environment, The value will fluctuate at a certain center value, greatly affecting the positioning of the effectiveness and accuracy. In order to improve the positioning accuracy of the indoor environment, this paper proposes a Gaussian-Kalman filter optimization based on RSSI. Firstly, the corrective parameters of the RSSI range model are obtained by using the maximum likelihood estimation. The coordinates of the obtained point are estimated by the least squares method （LSM）. Finally, the coordinates and parameters of the calculated node areoptimized by indoor,The simulation results show that the algorithm has the characteristics of small positioning error and high precision.
作者
王俭
张轩雄
WANG Jian ZHANG Xuan-xiong(School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, Chin)
出处
《软件导刊》
2017年第10期64-67,共4页
Software Guide
