基于改进高斯滤波与加权环境参数自适应估计的定位方法

杨晔晨, 胡越黎, 徐杰, 承文龙, 郁怀波

doi:10.12066/j.issn.1007-2861.1990

上海大学学报(自然科学版) >

2019 , Vol. 25 >Issue 5: 701 - 711

DOI: https://doi.org/10.12066/j.issn.1007-2861.1990

研究论文

基于改进高斯滤波与加权环境参数自适应估计的定位方法

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^1. 上海大学微电子研究与开发中心, 上海 200444
^2. 上海市电站自动化技术重点实验室, 上海 200444
^3. 上海大学机电工程与自动化学院, 上海 200444

收稿日期: 2017-07-09

网络出版日期: 2019-10-31

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Location method based on improved Gaussian filter and adaptive estimation for weighted environment parameter

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^1. Microelectronics Research and Develop Center,Shanghai University, Shanghai 200444, China
^2. Shanghai Key Laboratory of Power Station Automation Technology,Shanghai 200444, China
^3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Received date: 2017-07-09

Online published: 2019-10-31

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摘要

基于接收信号强度指示(received signal strength indication, RSSI)测距的定位技术是一种成本比较低廉的定位技术. 为了能够有效降低 RSSI 值因环境因素的影响而产生的误差, 提出了一种改进的加权高斯滤波算法对 RSSI 值进行处理; 并建立了一种加权环境参数自适应估计算法对当前待定位的移动节点所处位置的环境参数进行估计; 然后根据估计所得的环境参数确定移动节点所在位置的路径损耗模型; 最后根据该模型估计移动节点的位置. 实验结果表明, 该方法能够有效提高系统的定位精度.

关键词： RSSI 测距; 改进的加权高斯滤波; 加权环境参数; 自适应估计

本文引用格式

杨晔晨, 胡越黎, 徐杰, 承文龙, 郁怀波 . 基于改进高斯滤波与加权环境参数自适应估计的定位方法[J]. 上海大学学报(自然科学版), 2019 , 25(5) : 701 -711 . DOI: 10.12066/j.issn.1007-2861.1990

Abstract

Location technology based on received signal strength indication (RSSI) ranging is a kind of low-cost location technology. In order to reduce the error of RSSI value due to environmental factors, this paper proposes an improved weighted Gaussian filtering algorithm which is used to deal with the RSSI value, and it also proposes an adaptive estimation algorithm for weighted environment parameter which is used to estimate the environmental parameters of the place where the mobile node to be located is. Then the path loss model is determined by the estimated environment parameters, and the location of the mobile node is estimated by the model. Experimental results show that the method can effectively improve the positioning accuracy of the system.

Key words： received signal strength indication (RSSI) ranging; improved weighted Gaussian filtering; weighted environment parameter; adaptive estimation

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