Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas
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DOI：
10.1109/sahcn.2019.8824872
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
M. Joneidi;H. Yazdani;A. Vosoughi;Nazanin Rahnavard
中科院分区：
文献类型：
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作者：
M. Joneidi;H. Yazdani;A. Vosoughi;Nazanin Rahnavard
利用定向天线是实现高效频谱感知和准确源定位与跟踪的一种很有前途的解决方案。配备定向天线的频谱传感器应不断扫描空间，以跟踪发射源并发现感兴趣区域的新活动。在本文中，我们提出了一种在压缩感知（CS）框架中统一接收信号强度（RSS）和到达方向（DoA）的新公式。底层CS测量矩阵是传感器波束形成矢量的函数，称为传播矩阵。与全向天线相比，我们采用的传播矩阵提供了更多的非相干投影，这是压缩感知理论的一个重要因素。在此基础上，我们优化了天线波束，提高了频谱感知效率，准确跟踪活跃的主用户，并在感兴趣的领域监测频谱活动。在许多实际场景中，没有融合中心来整合来自频谱传感器的接收数据。对于这种情况，我们提出了我们算法的分布式版本。实验结果表明，与配置全向天线的传感器相比，该方法能显著提高源定位精度。说明了所提出的框架在动态无线电制图中的适用性。此外，将估计的动态射频图随时间的变化与地面真值进行比较，证明了我们提出的方法在准确估计和恢复信号方面的有效性。
Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional antennas should constantly scan the space in order to track emitting sources and discover new activities in the area of interest. In this paper, we propose a new formulation that unifies received-signal-strength (RSS) and direction of arrival (DoA) in a compressive sensing (CS) framework. The underlying CS measurement matrix is a function of beamforming vectors of sensors and is referred to as the propagation matrix. Comparing to the omni-directional antenna case, our employed propagation matrix provides more incoherent projections, an essential factor in the compressive sensing theory. Based on the new formulation, we optimize the antenna beams, enhance spectrum sensing efficiency, track active primary users accurately and monitor spectrum activities in an area of interest. In many practical scenarios there is no fusion center to integrate received data from spectrum sensors. We propose the distributed version of our algorithm for such cases. Experimental results show a significant improvement in source localization accuracy, compared with the scenario when sensors are equipped with omni-directional antennas. Applicability of the proposed framework for dynamic radio cartography is shown. Moreover, comparing the estimated dynamic RF map over time with the ground truth demonstrates the effectiveness of our proposed method for accurate signal estimation and recovery.