基于集成深度神经网络的室内无线定位
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摘要
针对传统无线定位模型对指纹数据库容错性低、抗噪能力弱等问题,提出一种基于数据融合的集成深度神经网络无线定位方法,从原始指纹数据库中按照一定比例随机取样生成各基学习器的训练数据,能够有效克服异常样本与有噪数据对无线定位系统带来的干扰;在指纹数据库构建过程中,提出Gauss-Occupied (G-O)数据扩充方法以解决无线指纹数据库样本容量小的局限,大幅度降低人工采集的成本,进一步提高样本空间的表征范围。试验结果表明:提出的模型不仅能够有效提高无线定位系统的平均定位精度与抗噪能力,而且能够明显降低定位过程中出现的单点最大误差。
Because of the lowfault tolerance and weak anti-noise ability of fingerprint database in traditional wireless positioning model,an ensemble deep neural network wireless positioning method based on data fusion was proposed. This method could effectively overcome the interference caused by abnormal samples and noisy data on the wireless positioning system by sampling from the original fingerprint database randomly to generate train data for each base learner. During the process of fingerprint database construction,the Gauss-Occupied( G-O) data expansion method was proposed to solve the limitation of the small sample size of the wireless fingerprint database and decrease the cost of manual acquisition sharply,which increased the scope of the sample's characterization. The results of the experiment showed that the proposed ensemble deep neural network wireless positioning model could not only improve the average positioning accuracy and the anti-noise ability of the wireless positioning system,but also reduce the maximum single point error in the positioning process.
Because of the lowfault tolerance and weak anti-noise ability of fingerprint database in traditional wireless positioning model,an ensemble deep neural network wireless positioning method based on data fusion was proposed. This method could effectively overcome the interference caused by abnormal samples and noisy data on the wireless positioning system by sampling from the original fingerprint database randomly to generate train data for each base learner. During the process of fingerprint database construction,the Gauss-Occupied( G-O) data expansion method was proposed to solve the limitation of the small sample size of the wireless fingerprint database and decrease the cost of manual acquisition sharply,which increased the scope of the sample's characterization. The results of the experiment showed that the proposed ensemble deep neural network wireless positioning model could not only improve the average positioning accuracy and the anti-noise ability of the wireless positioning system,but also reduce the maximum single point error in the positioning process.
引文
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[4]ZHANG W,HUA X,YU K,et al.Domain clustering based WiFi indoor positioning algorithm[C]//International Conference on Indoor Positioning and Indoor Navigation.Alcaláde Henares,Spain:IEEE,2016:1-5.
[5]XIE Y,WANG Y,NALLANATHAN A,et al.An improved K-Nearest-Neighbor indoor localization method based on spearman distance[J].IEEE Signal Processing Letters,2016,23(3):351-355.
[6]GE X,QU Z.Optimization WIFI indoor positioning KNNalgorithm location-based fingerprint[C]//IEEE International Conference on Softw are Engineering and Service Science.Beijing,China:IEEE,2017:135-137.
[7]ZHU Y J,DENG Z L,LIU W L,et al.Multi-classification algorithm for indoor positioning based on support vector machine[J].Computer Science,2012,39(4):32-35.
[8]LIU S,SI P,XU M,et al.Edge big data-enabled lowcost indoor localization based on Bayesian analysis of RSS[C]//Wireless Communications and Networking Conference(WCNC).San Francisco,USA:IEEE,2017:1-6.
[9]DING G,TAN Z,ZHANG J,et al.Fingerprinting localization based on affinity propagation clustering and artificial neural netw orks[C]//Wireless Communications and Netw orking Conference.Shanghai,China:IEEE,2013:2317-2322.
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[11]QIU X,ZHANG L,REN Y,et al.Ensemble deep learning for regression and time series forecasting[C]//Computational Intelligence in Ensemble Learning.Orlando,USA:IEEE,2015:1-6.
[12]ZHOU X,XIE L,ZHANG P,et al.An ensemble of deep neural netw orks for object tracking[C]//IEEE International Conference on Image Processing.Paris,France:IEEE,2015:843-847.
[13]TSENG P H,FENG K T,LIN Y C,et al.Wireless location tracking algorithms for environments w ith insufficient signal sources[J].IEEE Transactions on M obile Computing,2009,8(12):1676-1689.
[14]WANG G,SO M C,LI Y.Robust convex approximation methods for TDOA-based localization under NLOSconditions[J].IEEE Transactions on Signal Processing,2016,64(13):3281-3296.
[15]TOMIC S,BEKO M,RUI D.3-D target localization in w ireless sensor netw ork using RSS and AOA measurements[J].IEEE Transactions on Vehicular Technology,2017,66(4):1.
[16]马春龙,张启英.基于高斯函数及分批估计融合理论的无线网络定位算法[J].长春工业大学学报(自然科学版),2012,33(1):59-63.M A Chunlong,ZHANG Qiying.Wireless netw ork positioning algorithm based on Gauss function and batch estimation fusion[J].Journal of Changchun University of Technology(Natural Science Edition),2012,33(1):59-63.
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[19]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:a simple w ay to prevent neural netw orks from overfitting[J].Journal of M achine Learning Research,2014,15(1):1929-1958.
[20]REN Y,ZHANG L,SUGANNTHAN P N.Ensemble classification and regression-recent developments,applications and future directions[J].IEEE Computational Intelligence M agazine,2016,11(1):41-53.
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[22]王田苗,陶永,魏洪兴,等.基于智能空间的家庭服务机器人混合定位方法[J].北京航空航天大学学报,2009,35(2):231-235.WANG Tianmiao,TAO Yong,WEI Hongxing,et al.Hybrid location method for home service robot based on intelligent space[J].Journal of Beijing University of Aeronautics and Astronautics,2009,35(2):231-235.