| 采用低秩稀疏建模的盲频谱检测算法 |
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| 引用本文： | 陈栩杉,张雄伟,杨吉斌,孙蒙,张新卫. 采用低秩稀疏建模的盲频谱检测算法[J]. 信号处理, 2015, 31(5): 521-527 |
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| 作者姓名： | 陈栩杉 张雄伟 杨吉斌 孙蒙 张新卫 |
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| 作者单位： | 解放军理工大学指挥信息系统学院 |
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| 基金项目： | 国家自然科学基金(61471394，61402519)；江苏省自然科学基金(BK20140071，BK20140074) |
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| 摘 要： | 频谱检测技术是认知无线电的一项关键技术，传统的频谱检测方法或依赖于相关的先验信息，或受限于低信噪比和运算复杂度的影响，在实际应用中均有一定的缺陷。针对此问题，本文基于协方差矩阵的检测算法，结合低秩稀疏建模理论，建立了频谱检测的低秩稀疏模型，提出了一种改进的频谱检测新方法。所提方法不需要事先获取主用户信号和噪声功率等先验信息，对信号样本的协方差矩阵进行低秩稀疏分解，以低秩矩阵之间的特征差异来判决当前是否存在主用户信号。仿真实验验证了所提方法具有较好的检测性能和鲁棒性。
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| 关 键 词： | 频谱感知 低秩 鲁棒主成分分析 Go Decomposition算法 |
| 收稿时间： | 2014-09-16 |
Blind Spectrum Sensing with Low Rank and Sparse Model |
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| Affiliation: | College of Command Information System, PLAUST |
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| Abstract: | Spectrum Sensing is a cornerstone in cognitive radio which can detect the spectrum holes in order to raise spectrum utilization ratio. Traditional spectrum sensing detectors depend on some prior information or restricted by low signal-noise ratio and computation complexity in practical application. A GoDec based spectrum sensing detector is proposed for combining covariance based methods with low rank and sparse model theory. The proposed detector divides the received signal into two segments of equal length, and then decomposes the covariance matrix respectively by low rank and sparse matrix decomposition. The primary user exists if the difference between the low rank matrices is lower than a predefined threshold. Simulation results show that the proposed detector has robustness and high detection probability. |
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