Performance enhancement of wide-band radar signals, using a new adaptive CAMP algorithm in compressive sensing

Eslam Ashraf, S. Gasser, M. El-Mahallawy, M. A. ElAzeem
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引用次数: 4

Abstract

High resolution radar signals demands the use of high speed signal processor. Due to the sparse nature of radar signals, Compressive sensing (CS) enables the wide-band radar signals to be sampled at a low sampling rate, rather than a high sampling rate. This is attained through the use of a sparse sensing matrix. Complex approximate message passing (CAMP) algorithm is vastly used in reconstructing radar signals, because of its low complexity for real-time recovery and suitable for hardware integration. However, the CAMP algorithm achieves low detection performance at low Signal to Noise Ratios (SNRs). This paper proposes a new adaptive CAMP algorithm based on signal threshold, in order to solve the aforementioned shortcoming. Through simulation, the new adaptive CAMP is compared against the classical CAMP and the digital matched filter (DMF) algorithm using the Receiver Operating Characteristic (ROC) curves. The receivers operating characteristic curves (ROC) show that the new adaptive CAMP improves the probability of detection at lower SNRs.
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基于压缩感知自适应CAMP算法的宽带雷达信号性能增强
高分辨率的雷达信号要求使用高速信号处理器。由于雷达信号的稀疏性,压缩感知(CS)能够以低采样率而不是高采样率对宽带雷达信号进行采样。这是通过使用稀疏感知矩阵实现的。复杂近似报文传递(CAMP)算法由于其实时恢复复杂度低、适合硬件集成等优点,在雷达信号重构中得到了广泛的应用。然而,在低信噪比(SNRs)下,CAMP算法的检测性能较差。为了解决上述缺点,本文提出了一种基于信号阈值的自适应CAMP算法。通过仿真,利用接收机工作特性(ROC)曲线,将新的自适应CAMP与经典CAMP和数字匹配滤波器(DMF)算法进行了比较。接收机工作特性曲线(ROC)表明,新的自适应CAMP提高了低信噪比下的检测概率。
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