摘要
针对矿井结构复杂,井下未知节点定位存在信标节点布置冗余、定位精度低等问题,提出了一种基于粒子群优化算法的井下目标定位方法。根据矿井环境特点区块化布置信标节点,通过引入线性递减权重的粒子群算法对未知节点与信标节点的测量距离和估计距离的误差进行优化,降低定位误差。与四边测量法、加权最小二乘法和RSSI加权质心算法进行Matlab仿真对比实验。仿真结果显示:信标节点为5个,节点总数为15时,平均定位误差为0.877 m。高斯白噪声标准差取值范围从5递增到20,平均定位误差由1.21 m增长到4.65 m,增长幅度最小,抗噪性最好。信标节点密度由10%增加到40%,平均定位误差从2.82 m下降到0.76 m,定位精度明显好于其他三种算法,稳定性好于RSSI加权质心算法。定位精度更高,抗噪性更好,可靠稳定,在井下巷道环境中适应性更强。
Due to the problems of downhole complex construction,low positioning accuracy and redundant node layout,the downhole target location algorithm based on particle swarm optimization is proposed in this paper. First,the beacon nodes are arranged according to the regionalization of mine environment characteristics. Then the particle descent algorithm with linear descending weight is introduced to optimize the error of the measured and evaluated distance between the unknown and the beacon nodes to improve the positioning accuracy. The comparison among quadrilateral method,weighted least square algorithm,RSSI weighted centroid algorithm and the proposed method are conducted in Matlab. Simulation results show that with 5 beacon nodes and 15 nodes,the average positioning error is 0. 877 m. When the standard deviation of Gaussian noise increases from 5 to 20,the average positioning error rises from 1. 21 m to 4. 65 m,which is the least. When the beacon node density increases from 10% to 40%,the average positioning error decreases from 2. 82 m to 0. 76 m,which means the positioning accuracy of the proposed method is better than the other three algorithms,and the stability of the proposed method is better than RSSI weighted centroid algorithm. The proposed algorithm has the advantages of good noise immunity,reliability,stability and positioning accuracy,and suitable for downhole environment.
作者
张蕊
李晖
刘赛男
ZHANG Rui, LI Hui, LIU Sai-nan(School of Information Science and Engineering, Shenyang University of Technology, Shenyang Liaoning 110870, Chin)
出处
《大连民族大学学报》
2018年第3期234-238,共5页
Journal of Dalian Minzu University
关键词
无线传感器网络
距离估计
粒子群优化算法
信标节点
wireless sensor network
estimated distance
particle swarm optimization
beacon nodes
