多模式速度移动节点的动态距离估计方法
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摘要
针对传感器网络中目标节点移动速度的不确定性,给距离估计带来了现实挑战,论文提出了改进的基于滑动窗口匹配的动态距离估计方法。通信过程中,节点通过对信号强度(Received Signal Strength Indicator,RSSI)的测量,分析确定信号强度与时间(Received SignalStrengthIndicator-Time,RSSI-T)的映射关系。基于此,在移动过程中,对实时获得的RSSI数据流进行在线线性处理,通过滑动窗口模式进行匹配,实现在匀速,匀变速和变加速多模式速度下的高精度动态距离估计。经实验测试,该方法在克服RSSI数据不确定性的同时,能对多模式速度的移动目标节点进行误差小于2.6%的动态距离估计。
The uncertainty of the moving speed of the target nodes in the sensor network, brings a serious challenge for dynamic ranging. An improved dynamic RSSI-based distance estimation method is proposed,which utilizes pattern matching in term of the sliding window. By measuring the received signal strength indicator in the communication process of nodes, the mapping relation between the received signal strength indicator and time is analyzed and determined. The linear treatment for the real-time RSSI data streams obtained is produced in the moving process. The sliding window pattern matching is used to realize high precision dynamic distance estimation for multi-mode speed mobile nodes, which contains uniform, uniform variable and variable acceleration nodes. The experimental test shows that the method can overcome the uncertainty of RSSI data, and can realize the dynamic distance estimate error for multi-mode speed mobile nodes less than 2.6% at the same time.
The uncertainty of the moving speed of the target nodes in the sensor network, brings a serious challenge for dynamic ranging. An improved dynamic RSSI-based distance estimation method is proposed,which utilizes pattern matching in term of the sliding window. By measuring the received signal strength indicator in the communication process of nodes, the mapping relation between the received signal strength indicator and time is analyzed and determined. The linear treatment for the real-time RSSI data streams obtained is produced in the moving process. The sliding window pattern matching is used to realize high precision dynamic distance estimation for multi-mode speed mobile nodes, which contains uniform, uniform variable and variable acceleration nodes. The experimental test shows that the method can overcome the uncertainty of RSSI data, and can realize the dynamic distance estimate error for multi-mode speed mobile nodes less than 2.6% at the same time.
引文
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[2]彭宇,王丹.无线传感器网络定位技术综述[J].电子测量与仪器学报,2011,25(5):389-399.Peng Y,Wang D.A Review:Wireless Sensor Networks Localization[J].Journal of Electronic Measurement and Instrument,2011,25(5):389-399.
[3]方震,赵湛,郭鹏.基于RSSI测距分析[J].传感技术学报,2007,20(11):2526-2530.Fang Z,Zhao Z,Guo P.Analysis of Distance Measurement Based on RSSI[J].Journal of Electronic Measurement and Instrument,2007,20(11):2526-2530.
[4]林景栋,萧绪泉.基于无线网络节点相似度的室内定位算法[J].控制工程,2012,19(1):106-109.Lin J D,Xiao X Q.An Indoor Location Algorithm Based on The Wireless Network Node Similarity[J].Control Engineering of China,2012,19(1):106-109.
[5]倪巍,王宗欣.基于接收信号强度测量的室内定位算法[J].复旦学报,200,43(1):72-76.Ni W,Wang Z X.An Indoor Location Algorithm Based on the Measurement of the Received Signal Strength[J].Journal of Fudan University(Natural Science),2004,43(1):72-76.
[6]王建平,徐恒,李奇越.基于卡尔曼滤波的矿井移动节点定位算法研究[J].电子测量与仪器学报,2014,27(2):120-126.Wang J P,Xu H,Li Q Y.Research of algorithm for mobile node localization in mine based on Kalman filtering[J].Journal of Electronic Measurement and Instrument,2014,27(2):120-126.
[7]邱岩,赵冲冲,戴桂兰.无线传感器网络节点定位技术研究[J].计算机科学,2008,35(5):47-50.Qiu Y,Zhao C C,Dai Guilan.Research on Localization Technology for Wireless Sensor Networks[J].Computer Science,2008,35(5):47-50.
[8]彭宇,罗清华,王丹.一种基于区间数聚类的RSSI-D估计方法[J].仪器仪表学报,2012,33(3):491-498.Peng Y,Luo Q H,Wang D.WSNs Distance Estimation Method with RSSI-D Using Interval Data Clustering Algorithm[J].Chinese Journal of Scientific Instrument,2012,33(3):491-498.
[9]罗清华,焉晓贞,彭宇.基于滑动窗口模式匹配的动态距离估计方法[J].仪器仪表学报,2015,36(3):499-506.Luo Q H,Yan X Z,Peng Y.A Dynamic RSSI-based Ranging Method Using Pattern Matching of Slide Window[J].Chinese Journal of Scientific Instrument,2015,36(3):499-506.
[10]王群,李宗骍,李千目,等.基于实时运动状态的RSSI测距室内定位算法[J].南京理工大学学报(自然科学版),2015,39(2):229-235.Wang Q,Li Z X,Li Q M,et al.Indoor localization algorithm based on real time state of motion and RSSI ranging[J].Journal of Nanjing University of Science and Technology,2015,39(2):229-235.
[11]韩江洪,祝满拳,马学森.基于RSSI的极大似然与加权质心混合定位算法[J].电子测量与仪器学报,2013,27(10):937-943.Han J H,Zhu M Q,Ma X S.Hybrid Localization Algorithm of Maximum Likelihood and Weighted Centroid Based on RSSI[J].Journal of Electronic Measurement and Instrument,2013,27(10):937-943.
[12]朱明辉,张会清.基于RSSI的室内测距模型的研究[J].传感器与微系统,2010,29(8):19-22.Zhu M H,Zhang H Q.Research on model of indoor distance measurement based on RSSI[J].Transducer and Microsystem Technologies,2010,29(8):19-22.
[13]Luo Q,Yan X,Li J.DDEUDSC:A Dynamic Distance Estimation using Uncertain Data Stream Clustering in mobile wireless sensor networks[J].Measurement,2014,55(3):423-433.
[14]Franzke C.Nonlinear Trends,Long-Range Dependence,and Climate Noise Properties of Surface Temperature[J].Journal of Climate,2012,25(12):1487.
[15]王丹.基于RSSI的无线传感器网络定位方法研究[D].哈尔滨工业大学,2011.Wang D.Research on the localization method based on RSSI for wireless sensor network[D].Harbin Institute of Technology,2011.