| 基于稀疏域元素位置信息积累的微弱信号检测算法 |
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| 引用本文： | 马俊虎,廖红舒,谢进文,甘露.基于稀疏域元素位置信息积累的微弱信号检测算法[J].信号处理,2017,33(7):985-992. |
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| 作者姓名： | 马俊虎 廖红舒 谢进文 甘露 |
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| 作者单位： | 1.电子科技大学 |
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| 基金项目： | 国家自然科学基金委员会-中国工程物理研究院联合基金;中央高校基本科研业务费项目 |
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| 摘 要： | 该文提出一种利用信号稀疏特征实现基于压缩感知的微弱信号检测算法。稀疏信号在某一特定字典基上投影展开，其稀疏向量非0元素位置固定。而高斯白噪声在字典基上投影展开，其权向量中非0元素的位置呈现出是均匀分布的特点。该文所提方法实现了微弱信号在稀疏域中的积累。通过计算当只存在高斯白噪声时，接收信号的稀疏向量的非0元素位置向量与信号的稀疏向量非0元素位置向量的相关性，得到判决门限，完成微弱信号的检测。仿真结果表明本文所提方法在信噪比低至-15dB时仍能对信号正确检测。
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| 收稿时间： | 2016-11-29 |
A Weak Signal Detection Algorithm based on Accumulation of Sparse Domain Element Location Information |
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| Abstract: | In this paper, a new algorithm is proposed to detect weak signal based on Compressive Sensing(CS) and the sparse feature of the signal. When the sparse signal is projected in a special dictionary, we can obtain the sparse vector whose positions of non-zero elements are fixed. When the Gaussian white noise is projected in a dictionary, the weight vector whose positions of non-zero elements presents the characteristics of uniform distribution. In our study, the proposed method can accomplish the accumulation of weak signal in the sparse domain with the characteristics of the sparse representation mentioned above. The threshold is obtained by calculating the correlation of the positions non-zero elements of the Gauss white noise and the positions of non-zero elements of the signal to finish detecting the target signal. Finally, the simulations verify the proposed algorithm can achieve detecting the signal precisely with a low signal-to-noise ratio(SNR) of -15dB. |
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