基于RSSI测距室内定位改进质心算法
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摘要
室内定位技术具有巨大的市场需求,但由于室内定位受到噪声、多径反射、温度、环境、阴影衰落等因素的影响,其定位精度显著降低。为了提高室内节点的定位精度,针对传统的质心定位算法精确度低的问题,提出了一种基于RSSI节点测距的改进质心定位算法。该算法对锚节点接收到的RSSI数据进行拟合,以此能够在BP神经网络基础上确定损耗模型参数值,采用改进的质心定位算法进行定位,并在原有的三点定位的基础上,通过节点之间的数学转换,将三点定位法改进为六点质心定位算法。为验证所提算法的有效性和可行性,基于Matlab仿真平台进行了仿真实验。仿真实验结果表明,相对于传统的质心定位算法,所提出的算法显著地提高了室内定位的精度。
Indoor positioning technology is of great demand.However,owing to the influence of noise,multi-path reflection,temperature,environment and shadow fading,the accuracy of indoor positioning decreases greatly.In order to improve the positioning accuracy of interior nodes,aimed at the problem of low accuracy of the traditional localization algorithm,an ameliorate triangle centroid localization algorithm based on RSSI is proposed,which is fitted to the values received by anchor node so that the parameter values of loss by BP neural network can be determined and then located with the ameliorate triangle centroid localization algorithm.Based on principle of three-point location the original three-point location algorithm has been modified to six-point centroid localization algorithm.In order to prove its effectiveness and feasibility,the experiments for verification have been conducted with Matlab simulation platform which show that compared with traditional centroid localization algorithm,it greatly increases indoor positioning accuracy.
Indoor positioning technology is of great demand.However,owing to the influence of noise,multi-path reflection,temperature,environment and shadow fading,the accuracy of indoor positioning decreases greatly.In order to improve the positioning accuracy of interior nodes,aimed at the problem of low accuracy of the traditional localization algorithm,an ameliorate triangle centroid localization algorithm based on RSSI is proposed,which is fitted to the values received by anchor node so that the parameter values of loss by BP neural network can be determined and then located with the ameliorate triangle centroid localization algorithm.Based on principle of three-point location the original three-point location algorithm has been modified to six-point centroid localization algorithm.In order to prove its effectiveness and feasibility,the experiments for verification have been conducted with Matlab simulation platform which show that compared with traditional centroid localization algorithm,it greatly increases indoor positioning accuracy.
引文
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[11]刘晓文,王振华,王淑涵,等.基于RSSI算法的矿井无线定位技术研究[J].煤矿机械,2009,30(3):59-60.
[12]张会清,石晓伟,邓贵华,等.基于BP神经网络和泰勒级数的室内定位算法的研究[J].电子学报,2012,40(9):1876-1879.
[13]刘雪兰,王宜怀,陆全华,等.无线传感器网络RSSI定位算法改进[J].计算机应用与软件,2013,30(11):87-89.
[14]张苍松.基于RSSI的室内定位算法优化技术[D].西安:西北大学,2014.
[15]徐凤燕,单杭冠,王宗欣.一种带参数估计的基于接收信号强度的室内定位算法[J].微波学报,2008,24(2):67-72.
[2]Gu Y Y,Lo A,Niemegeers I.A survey of indoor positioning systems for wireless personal networks[J].IEEE Communications Surveys&Tutorials,2009,11(1):13-32.
[3]Harter A,Hopper A,Steggles P,et al.The anatomy of a context-aware application[J].Wireless Networks,2002,8(2/3):187-197.
[4]Priyantha N B,Miu A K L,Balakrishnan H,et al.The cricket compass for context-aware mobile applications[C]//Proceedings of the 7th annual international conference on mobile computing and networking.[s.l.]:ACM,2001:1-14.
[5]Girod L,Estrin D.Robust range estimation using acoustic and multimodal sensing[C]//IEEE/RSJ international conference on intelligent robots and systems.[s.l.]:IEEE,2001:1312-1320.
[6]Karthick N,Prashanth K,Venkatraman K,et al.Location estimation using RSSI and application of extended Kalman filter in wireless sensor networks[C]//International conference on advanced computer control.[s.l.]:IEEE,2009:337-341.
[7]Lee S,Kim K.Determination of communication range for range-free multi-hop localization in wireless sensor networks[C]//Proceedings of 20th international conference on computer communications and networks.[s.l.]:IEEE,2011:1-4.
[8]Liu D,Xiong Y,Ma J.Exploit Kalman filter to improve fingerprint-based indoor localization[C]//International conference on computer science and network technology.[s.l.]:IEEE,2011:2290-2293.
[9]石欣,印爱民,陈曦.基于RSSI的多维标度室内定位算法[J].仪器仪表学报,2014,35(2):261-268.
[10]乔钢柱,曾建潮.一种适用于动态环境的改进RSSI定位方法[J].计算机研究与发展,2010,47(S):111-114.
[11]刘晓文,王振华,王淑涵,等.基于RSSI算法的矿井无线定位技术研究[J].煤矿机械,2009,30(3):59-60.
[12]张会清,石晓伟,邓贵华,等.基于BP神经网络和泰勒级数的室内定位算法的研究[J].电子学报,2012,40(9):1876-1879.
[13]刘雪兰,王宜怀,陆全华,等.无线传感器网络RSSI定位算法改进[J].计算机应用与软件,2013,30(11):87-89.
[14]张苍松.基于RSSI的室内定位算法优化技术[D].西安:西北大学,2014.
[15]徐凤燕,单杭冠,王宗欣.一种带参数估计的基于接收信号强度的室内定位算法[J].微波学报,2008,24(2):67-72.