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首页> 外文会议>2011 IEEE International Conference on Systems, Man, and Cybernetics >Compressive sensing indoor localization
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Compressive sensing indoor localization

机译：压缩感测室内定位

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摘要

Location based services in wireless sensor networks are quite demanding applications especially in indoors, such that accurate localization of objects and people in indoor environments has long been considered as one of important building blocks in wireless systems. In this paper, we investigate sensor location estimation problem where a target sensor measures inconsistent signals as received-signal-strength or time-of-arrival from anchor sensors with known locations, whereas target sensor location must be estimated. We know that even in large scale wireless sensor networks, information are relatively sparse compared with the number of sensors. In such networks, the localization problem can be recast as a sparse signal recovery problem in the discrete spatial domain from a small number of linear measurements by solving an under-determined linear system. By exploiting the compressive sensing theory, sparse signals can be recovered from far fewer samples than Nyquist sampling rate. Our approach uses a few number of inconsistent measurements to find the wireless device location over a non-symmetric spatial grid. In this method, an ℓ1-norm minimization program is used to recover the wireless user location. The performance of the proposed method is evaluated through simulations with synthetic and real measurements.
机译：无线传感器网络中基于位置的服务是非常苛刻的应用，尤其是在室内，因此，长期以来，室内环境中对象和人员的精确定位一直被认为是无线系统中的重要组成部分之一。在本文中，我们研究了传感器位置估计问题，其中目标传感器测量来自已知位置的锚定传感器的接收信号强度或到达时间的不一致信号，而必须估计目标传感器的位置。我们知道，即使在大规模无线传感器网络中，与传感器数量相比，信息也相对稀疏。在这样的网络中，可以通过解决线性不确定系统，将少量线性测量中的定位问题重现为离散空间域中的稀疏信号恢复问题。通过利用压缩感测理论，可以从比奈奎斯特采样率少得多的样本中恢复稀疏信号。我们的方法使用一些不一致的测量值来在非对称空间网格上找到无线设备的位置。在这种方法中，使用ℓ 1 -norm最小化程序来恢复无线用户位置。所提方法的性能是通过模拟综合和实际测量来评估的。

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