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Experimental Analysis of Block-Sparsity-Based Spectrum Sensing Techniques for Cognitive Radar

机译：基于块稀疏的谱检测技术的实验分析认知雷达

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摘要

Due to increasing demands for spectral resources in both communication and radar systems, the radio frequency electromagnetic spectrum is becoming more and more crowded with interfering nuisances. In order to tackle the scarcity of available spectral intervals, in recent years a multitude of sensing algorithms have been developed for improving spectrum sharing. Among these, two-dimensional (2-D) spectrum sensing can be used to obtain space-frequency electromagnetic spectrum awareness. Specifically, this approach makes it possible to optimize the spectrum usage of certain spectrum portions whose occupancy varies both temporally and spatially. In this article, we evaluate the effectiveness of certain space-frequency map recovery algorithms relying on the use of commercially available hardware. To this end, we employ an inexpensive four-channel coherent receiver, using software-defined radio components, for emitter localization. Hence, after proper calibration of the receiving system, the acquired samples are used to evaluate the performance of different signal processing strategies which exploits the inherent block-sparsity of the overall profile. At the analysis stage, results reveal the effectiveness of such algorithms.
机译：由于对通信和雷达系统的频谱资源的需求增加，射频电磁谱变得越来越拥挤，而干扰滋扰。为了解决可用光谱间隔的稀缺性，近年来已经开发了多种感测算法用于改善频谱共享。其中，二维（2-D）频谱感测可用于获得空间频率电磁谱意识。具体地，这种方法使得可以优化某些频谱部分的频谱使用，其占用率在时间和空间上都变化。在本文中，我们评估了某些空间频率映射恢复算法的有效性依赖于使用市售的硬件。为此，我们使用廉价的四通道相干接收器，使用软件定义的无线电分量，用于发射器本地化。因此，在适当地校准接收系统之后，所获取的样本用于评估不同信号处理策略的性能，该策略利用整体轮廓的固有块稀疏性。在分析阶段，结果揭示了这种算法的有效性。

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