基于压缩感知的两阶段多目标定位算法
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摘要
针对传感器网络中基于接收信号强度(Received Signal Strength,RSS)的多目标定位具有天然稀疏性的问题,提出了基于压缩感知的两阶段多目标定位算法,该算法将基于网格的多目标定位问题分解为粗定位和细定位两个阶段。粗定位阶段,根据序贯压缩感知原理确定最优观测次数,然后利用l_p最优化问题重构出目标所在的初始候选网格;细定位阶段,由四分法不断划分候选网格,根据最小残差原则估计目标在候选网格中的确切位置。仿真结果表明,相较于传统的基于l_1最优化的多目标定位算法,基于压缩感知的两阶段多目标定位算法在目标个数未知的场景下具有更优的定位性能,且明显减少了定位时间。
The RSS-based multi-target location has the natural property of the sparsity in wireless sensor networks.In this paper,a two-phase multi-target localization algorithm based on compressed sensing was proposed.This algorithm divides the grid-based target localization problem into two phases:coarse location phase and fine location phase.In the coarse location phase,the optimal number of measurements is determined according to the sequential compressed sen-sing,and then the locations of the initial candidate grids are reconstructed by l_p optimization.In the fine location phase,all candidate grids are continually divided by quadripartition method,and the accurate locations of targets in the corresponding candidate grids are estimated by using the minimum residual principle.Compared with the traditional multi-target localization algorithm using l_1 optimization,the simulation results show that the proposed localization algorithm has better localization performance when the number of targets is unknown.Meanwhile,the localization time is significantly reduced.
The RSS-based multi-target location has the natural property of the sparsity in wireless sensor networks.In this paper,a two-phase multi-target localization algorithm based on compressed sensing was proposed.This algorithm divides the grid-based target localization problem into two phases:coarse location phase and fine location phase.In the coarse location phase,the optimal number of measurements is determined according to the sequential compressed sen-sing,and then the locations of the initial candidate grids are reconstructed by l_p optimization.In the fine location phase,all candidate grids are continually divided by quadripartition method,and the accurate locations of targets in the corresponding candidate grids are estimated by using the minimum residual principle.Compared with the traditional multi-target localization algorithm using l_1 optimization,the simulation results show that the proposed localization algorithm has better localization performance when the number of targets is unknown.Meanwhile,the localization time is significantly reduced.
引文
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[2] PIVATO P,PALOPOLI L,PETRI D.Accuracy of RSS-Based centroid localization algorithms in an indoor environment[J].IEEE Transactions on Instrumentation & Measurement,2011,60(10):3451-3460.
[3] TOMIC S,BEKO M,RUI D.3-D Target localization in wireless sensor network using RSS and AoA measurements[J].IEEE Transactions on Vehicular Technology,2017,66(4):3197-3210.
[4] 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.(in Chinese)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(自然科学版),2004,43(1):72-76.
[5] WU X L,SHAN Z L,CAO S L,et al.Monte Carlo boxed localization algorithm for mobile nodes based on received signal strength indication ranging[J].Journal of Computer Applications,2015,35(4):916-920.(in Chinese)武晓琳,单志龙,曹树林,等.基于接收信号强度指示测距的蒙特卡罗盒移动节点定位算法[J].计算机应用,2015,35(4):916-920.
[6] CANDéE J,WAKIN M B.An introduction to compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):21-30.
[7] AKL A,VALAEE S.Accelerometer-based gesture recognition via dynamic-time warping,affinity propagation,&compressive sensing[C]//IEEE International Conference on Acoustics Speech and Signal Processing.IEEE,2010:2270-2273.
[8] GRIBONVAL R,NIELSEN M.Sparse representations in unions of bases[J].IEEE Transactions on Information Theory,2004,49(12):3320-3325.
[9] PELANT J,TLAMSA Z,BENES V,et al.BLE device indoor lo- calization based on RSS fingerprinting mapped by propagation modes[C]//European Signal Processing Conference Lausanne,Switzerland,2008:25-29.
[10] FENGC,VALAEE S,TAN Z.Multiple target localization using compressive sensing[C]//IEEE Conference on Global Telecommunications.IEEE Press,2009:4356-4361.
[11] GU B,YANG Z,HUH F.Adaptive wideband spectrum detection based on sequential compressed sensing[J].Chinese Journal of Scientific Instrument,2011,6(32):1272-1277.
[12] MALIOUTOV D M,SANGHAVI S,WILLSKY A S.Com- pressed sensing with sequentialobservations[C]//IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2008:3357-3360.
[13] CHEN W,YAN J,ZHU W P.Wireless sensor network location algorithm using compressive sensing and multilateral measurements[J].Journal of Signal Processing,2014,30(6):728-735.
[14] YAN J,YU K G,CHEN R Z,et al.An improved compressive sensing and received signal strength-based target localization algorithm with unknown target population for wireless local area networks[J].Sensors,2017,17(6):1246-1264.
[15] BARANIUK R,DAVENPORT M,DEVORE R,et al.A simple proof of the restricted isometry property for random matrices[J].Constructive Approximation,2008,28(3):253-263.
[16] TROPP J,GILBERT A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transcations on Information Theory,2007,53(12):4655-4666.
[17] DONOHO D L,TSAIG Y,DRORI I,et al.Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2012,58(2):1094-1121.
[18] CUI B,ZHAO C,FENG C,et al.An improved greedy matching pursuit algorithm formultipletarget localization[C]//International Conference on Instrumentation.IEEE,2013:926-930.
[19] BECKK A,ELDAR Y C.Sparsity Constrained Nonlinear Optimization:Optimality Conditions and Algorithms[J].Siaml Journal on Optimization,2013,23(3):1480-1509.
[20] CHEN S S,DONOHO D L,SAUNDERS M A.Atomic decomposition by basis pursuit[J].SIAM Review,2001,43(1):129-159.
[21] LAN K C,WEI M Z.A compressibility-based clustering algorithm for hierarchical compressive data gathering[J].IEEE Sensors Journal,2017,PP(99):1-1.
[22] PATWARI N,ASH J N,KYPEROUNTAS S,et al.Locating the nodes:cooperative localization in wireless sensor networks[J].IEEE Signal Processing Magazine,2005,22(4):54-69.
[23] RAO BD,KREUTZDK.An affine scaling methodology for best basis selection[J].IEEE Transactions on Signal Processing,1999,47(1):187-200.