解成博1,,,
尹川1,
郭兰图2,
王先义2
1.杭州电子科技大学电子信息学院 ??杭州 ??310018
2.中国电波传播研究所 ??青岛 ??266107
详细信息
作者简介:耿友林：男，1966年生，教授，研究方向为复杂介质目标电磁散射的解析和数值方法、复杂介质中的电波传播特性以及小天线设计和辐射特性等
解成博：男，1994年生，硕士生，研究方向为信号源定位
尹川：男，1987年生，讲师，研究方向为电磁成像
通讯作者:解成博 chengbo_x@163.com
中图分类号:TN911.7计量
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被引次数:0
出版历程
收稿日期:2018-03-21
修回日期:2018-08-13
网络出版日期:2018-08-31
刊出日期:2019-02-01
Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter
Youlin GENG1,Chengbo XIE1,,,
Chuan YIN1,
Lantu GUO2,
Xianyi WANG2
1. College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2. China Research Institute of Radioware Propagation, Qingdao 266107, China
摘要
摘要:针对频谱监测系统中被监测信号无法控制并且没有任何先验知识，只能通过对信号被动监测，即接收与处理信号来估计信号源位置的要求，该文提出一种基于接收信号强度指示差值(RSSID)的定位算法，并利用卡尔曼滤波提高其定位精度。该文将两监测站之间的RSSID转换成信号源到两监测站的距离之比，根据距离之比构造定位方程矩阵，进而利用最小二乘法求取信号源位置。仿真结果表明：所提算法比经典RSSI定位算法性能更优，降低了环境因素对定位精度的影响，并且能更好地满足参数较少的定位服务需求，可以有效地应用于频谱监测系统中。同时，卡尔曼滤波可以有效改善系统的定位精度，达到预期的定位效果。
关键词:定位算法/
接收信号强度指示/
卡尔曼滤波
Abstract:The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to construct the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.
Key words:Location algorithm/
Received Signal Strength Indication (RSSI)/
Kalman filter
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