基于链路选择学习算法的无设备目标定位
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摘要
针对压缩感知框架下无设备目标定位(device-free localization,DFL)的字典失配问题,提出一种基于链路选择学习(link selection learning,LSL)算法的DFL方式.由于传统基于阴影模型的字典无法准确表达接收信号强度(received signal strength,RSS)变化与目标位置间的对应关系,本文算法首先在训练阶段通过字典学习的方式更新初始字典;同时该算法在更新字典的过程中,仅选取置信区域中的链路参与计算,这样既加速了字典学习过程,提高了算法实时性,又滤除了野值链路的影响.室内外实验结果表明,该方法可以有效地消除现有基于阴影模型字典所带来的模型误差,提高定位精度,同时具有运算速度快的优点.
In order to overcome the problem of dictionary mismatch in the compressive sensing based device-free localization(DFL),a link selection learning algorithm(LSL)is proposed to enhance the DFL performance.Because the traditional shadowing-based dictionary cannot correctly describe the relationship between the received signal strength(RSS)and the target position,our algorithm first utilizes the dictionary learning(DL)technique to update the dictionary in the training phase,and uses the updated dictionary as the weight in the subsequent positioning stage.In the process of updating the dictionary,the algorithm not only reduces the dictionary dimension to speed up the dictionary learning process and improve the realtime computing speed of the algorithm,but also filters out the outlier links by selecting those links through the confidence region.The indoor and outdoor experimental results show that the proposed method can effectively mitigate the model error and improve the positioning accuracy with the low computational complexity.
In order to overcome the problem of dictionary mismatch in the compressive sensing based device-free localization(DFL),a link selection learning algorithm(LSL)is proposed to enhance the DFL performance.Because the traditional shadowing-based dictionary cannot correctly describe the relationship between the received signal strength(RSS)and the target position,our algorithm first utilizes the dictionary learning(DL)technique to update the dictionary in the training phase,and uses the updated dictionary as the weight in the subsequent positioning stage.In the process of updating the dictionary,the algorithm not only reduces the dictionary dimension to speed up the dictionary learning process and improve the realtime computing speed of the algorithm,but also filters out the outlier links by selecting those links through the confidence region.The indoor and outdoor experimental results show that the proposed method can effectively mitigate the model error and improve the positioning accuracy with the low computational complexity.
引文
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[24] YANG Z,HUANG K,GUO X.A real-time devicefree localization system using correlated RSS measurements[J].Eurasip journal on wireless communications&networking,2013(1):1-12.
[2] YOUSSEF M,MAH M,AGRAWALA A.Challenges:Device-free passive localization for wireless environments[C]//Proceedings of 13th ACM MobiCom.Montreal,Canada,September 2007:222-229.
[3] MOUSSA M,YOUSSEF M.Smart devices for smart environments:device-free passive detection in real environments[C]//Proceedings of 7th IEEE PerCom.Galveston,USA,February 2009:1-6.
[4] WILSON J,PATWARI N.Radio tomographic imaging with wireless networks[J].IEEE transactions on mobile computing,2010,9(5):621-632.
[5] WILSON J,PATWARI N.See through walls:motion tracking using variance-based radio tomography networks[J].IEEE transactions on mobile computing,2011,10(5):612-621.
[6] WILSON J,PATWARI N.A fade level skew-laplace signal strength model for device-free localization with wireless networks[J].IEEE transactions on mobile computing,2011,11(6):947-958.
[7] PATWARI N,KASERA S K.Temporal link signature measurements for location distinction[J].IEEE transactions on mobile computing,2011,10(3):449-462.
[8] WANG J,GAO Q,WANG H.Device-free localization with multidimensional wireless link information[J].IEEE transactions on vehicular technology,2015,64(1):356-366.
[9] WANG Z H,LIU H,XU S X.A diffraction measurement model and particle filter tracking method for RSS based DFL[J].IEEE journal on selected areas in communications,2015,33(11):2391-2403.
[10] BENJAMIN R,MA X L,ROBERT J B.Propagation modeling for radio frequency tomography in wireless networks[J].IEEE journal of selected topics in signal processing,2014,8(1):55-65.
[11] WANG J,GAO Q,PAN M.Toward accurate devicefree wireless localization with a saddle surface model[J].IEEE transactions on vehicular technology,2016,65(8):6665-6677.
[12]常俪琼,房鼎益,陈晓江.一种有效消除环境噪声的被动式目标定位方法[J].计算机学报,2016,39(5):1052-1066.CHANG L Q,FANG D Y,CHEN X J.A devicefree passive localization method with the elimination of environmental noise[J].Chinese journal of computers,2016,39(5):1052-1066.(in Chinese)
[13]刘凯,方小俊,黄青华.基于RTI双重构的免携带设备目标无线定位[J].系统工程与电子技术,2016,38(1):31-36.LIU K,FANG X J,HUANG Q H.Device-free localization based on RTI bi-reconstruction algorithm with wireless sensor networks[J].Systems engineering and electronics,2016,38(1):31-36.(in Chinese)
[14] DONOHO D L.Compressed sensing[J].IEEE transactions on information theory,2006,52:1289-1306.
[15] CANDES E,ROMBERG J,TAO T.Robust uncertainty principles:exact signal reconstruction from highly incomplete Fourier information[J].IEEE transactions on information theory,2006,52:489-509.
[16] ZHANG B,CHENG X,ZHANG N.Sparse target counting and localization in sensor networks based on compressive sensing[C]//Proceedings of the 30th IEEE INFOCOM.Shanghai,April 2011:1-9.
[17] WANG J,GAO Q,ZHANG X.Device-free localisation with wireless networks based on compressive sensing[J].IET communications,2012,6(15):2395-2403.
[18] LI X,DING S,LI Z.Device-free localization via dictionary learning with difference of convex programming[J].IEEE sensors journal,2017,PP(99):1.
[19] KE W,WANG T,SHAO J.CS-based device-free localization in the presence of model errors[C]//IEEE International Conference on Acoustics,Speech and Signal Processing,2016:4443-4447.
[20] OLSHAUSEN B A,FIELD D J.Emergency of simple-cell receptive field properties by learning a sparse code for natural images[J]. Nature,1996,381(6583):607-609.
[21] LEE H,BATTLE A,RAINA R,Ng A Y.Efficient sparse coding algorithms[C]//Proceedings of the20th Annual Conference on Neural Information Processing Systems.Columbia,December,2006:801-808.
[22] ENGAN K,AASE S O,HUSOY J H.Method of optimal directions for frame design[C]//Proceedings of the IEEE International Conference on Acoustics,Speech,and Signal Processing. Phoenix, April,1999:2443-2446.
[23] AHARON M,ELAD M,BRUCKSTEIN A M.The KSVD:an algorithm for designing of overcomplete dictionaries for sparse representation[J].IEEE transactions on signal processing,2006,54(11):4311-4322.
[24] YANG Z,HUANG K,GUO X.A real-time devicefree localization system using correlated RSS measurements[J].Eurasip journal on wireless communications&networking,2013(1):1-12.