Yu Dongping, Guo Yan, Li Ning, Yang Xiaoqin
College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China.
Sensors (Basel). 2019 Apr 17;19(8):1828. doi: 10.3390/s19081828.
As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.
作为一种新兴且有前景的技术，无设备定位（DFL）通过分析目标的阴影效应来估计目标位置。大多数现有的基于压缩感知（CS）的DFL方法利用接收信号强度（RSS）的变化来近似阴影效应。然而，在变化的环境中，RSS读数容易受到环境动态变化的影响。运行时RSS变化与固定字典中的数据之间的偏差会显著降低DFL的性能。在本文中，我们介绍了ComDec，一种新颖的基于CS的DFL方法，它使用信道状态信息（CSI）来提高定位精度和鲁棒性。为了利用CSI测量的信道多样性，将DFL问题表述为一个联合稀疏恢复问题，该问题可以恢复具有共同支撑的多个稀疏向量。为了解决这个问题，我们在变分贝叶斯推理框架下开发了一种联合稀疏恢复算法。在该算法中，字典基于鞍面模型进行参数化。为了适应环境变化和不同的信道特性，将字典参数建模为可调参数。仿真结果验证了ComDec与其他基于CS的最新DFL方法相比具有优越的性能。
