基于变分贝叶斯推理的多目标无源定位算法
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摘要
为提高多目标无源定位的精度和鲁棒性,文中提出了一种基于变分贝叶斯推理的多目标无源定位算法。该算法首先建立分层的混合高斯先验模型以诱导目标位置向量的稀疏性;然后,利用变分贝叶斯推理的方法估计该先验模型中隐藏变量的后验分布;最后,根据目标位置向量的后验分布估计目标的位置。仿真结果表明,该算法较传统的基于压缩感知的多目标无源定位算法具有更高的定位精度以及更好的鲁棒性。
To improve the accuracy and robust of the multi-target device-free localization,a variational Bayesian inference based multi-target device-free localization algorithm is proposed. Firstly,by establishing a hierarchical prior model,the spatial sparsity of target location vector is induced. Then,the posteriors of the hidden variables in the hierarchical prior model are estimated by using the variational Bayesian inference method. Finally,the positions of multi-targets are estimated according to the posteriors of the estimated target location vector. Simulation results show that the proposed algorithm has better performance on localization accuracy and more robust than the conventional compressive sensing based multi-target device-free localization algorithms.
To improve the accuracy and robust of the multi-target device-free localization,a variational Bayesian inference based multi-target device-free localization algorithm is proposed. Firstly,by establishing a hierarchical prior model,the spatial sparsity of target location vector is induced. Then,the posteriors of the hidden variables in the hierarchical prior model are estimated by using the variational Bayesian inference method. Finally,the positions of multi-targets are estimated according to the posteriors of the estimated target location vector. Simulation results show that the proposed algorithm has better performance on localization accuracy and more robust than the conventional compressive sensing based multi-target device-free localization algorithms.
引文
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[7]WANG J,FANG D,YANG Z,et al.E-HIPA:an energy-efficient framework for high-precision multi-target-adaptive device-free localization[J].IEEE Transactions on Mobile Computing,2017,16(3):716-729.
[8]JI S,XUE Y,CARIN L.Bayesian compressive sensing[J].IEEE Transactions on Signal Processing,2008,56(6):2346-2356.
[9]SEEGER M,WIPF D.Variational Bayesian inference techniques[J].IEEE Signal Processing Magazine,2010,27(6):81-91.
[10]HASHEMI H.The indoor radio propagation channel[J].Proceedings of the IEEE,1993,81(7):943-968.
[11]JACKSON J,LEVITT L.Classical electrodynamics[J].American Institute of Physics,2009,15(11):62.
[12]WANG J,GAO Q,PAN M,et al.Towards accurate devicefree wireless localization with a saddle surface model[J].IEEE Transactions on Vehicular Technology,2016,65(8):6665-6677.
[13]TZIKAS D,LIKAS A,GALATSANOS N.The variational approximation for Bayesian inference[J].IEEE Signal Processing Magazine,2008,25(6):131-146.
[14]WIPF D,RAO B.Sparse Bayesian learning for basis selection[J].IEEE Transactions on Signal Processing,2004,52(8):2153-2164.
[2]YOUSSEF M,MAH M,AGRAWALA A.Challenges:device-free passive localization for wireless environments[C]∥ACM Conference on Mobile Computing and Networking.2007:222-229.
[3]CANDES E,WAKIN M.An introduction to compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):21-30.
[4]ZHANG D,MA J,CHEN Q,et al.An RF-based system for tracking transceiver-free objects[C]∥IEEE International Conference on Pervasive Computing and Communications.2007:135-144.
[5]WILSON J,PATWARI N.Radio tomographic imaging with wireless networks[J].IEEE Transactions on Mobile Computing,2010,9(5):621-632.
[6]ZHANG D,LIU Y,NI L.RASS:a real-time,accurate and scalable system for tracking transceiver-free objects[C]∥IEEE International Conference on Pervasive Computing and Communications.2011:21-25.
[7]WANG J,FANG D,YANG Z,et al.E-HIPA:an energy-efficient framework for high-precision multi-target-adaptive device-free localization[J].IEEE Transactions on Mobile Computing,2017,16(3):716-729.
[8]JI S,XUE Y,CARIN L.Bayesian compressive sensing[J].IEEE Transactions on Signal Processing,2008,56(6):2346-2356.
[9]SEEGER M,WIPF D.Variational Bayesian inference techniques[J].IEEE Signal Processing Magazine,2010,27(6):81-91.
[10]HASHEMI H.The indoor radio propagation channel[J].Proceedings of the IEEE,1993,81(7):943-968.
[11]JACKSON J,LEVITT L.Classical electrodynamics[J].American Institute of Physics,2009,15(11):62.
[12]WANG J,GAO Q,PAN M,et al.Towards accurate devicefree wireless localization with a saddle surface model[J].IEEE Transactions on Vehicular Technology,2016,65(8):6665-6677.
[13]TZIKAS D,LIKAS A,GALATSANOS N.The variational approximation for Bayesian inference[J].IEEE Signal Processing Magazine,2008,25(6):131-146.
[14]WIPF D,RAO B.Sparse Bayesian learning for basis selection[J].IEEE Transactions on Signal Processing,2004,52(8):2153-2164.