It is well known that noise-like waveforms have the inherent property of suppressing ambiguities due to their nonrepetitive nature, as opposed to the more classical linear frequency modulated (LFM) pulsed waveforms. This aperiodicity also improves the robustness against jammers, making these waveforms harder to detect and classify by an enemy. However, noise waveforms are Doppler sensitive, requiring the use of Doppler-dependent matched filters for proper range compression. Also, when processed with classical matched filters, noise waveforms have sidelobes that spread out across the whole range-Doppler plane, potentially masking weak targets in the presence of strong ones. A possible solution to reduce the sidelobe interference is to use sparse signal processing (SSP). In this article, we demonstrate with simulated and experimental data how noise waveforms can be used in combination with SSP for unambiguous detection of strong as well as weak targets, improving upon the performance of more conventional LFM pulsed waveforms.

中文翻译：

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使用稀疏信号处理降低噪声雷达中的旁瓣

众所周知，与更经典的线性调频 (LFM) 脉冲波形相比，类噪声波形由于其非重复性而具有抑制模糊性的固有特性。这种非周期性还提高了对抗干扰器的鲁棒性，使敌人更难检测和分类这些波形。然而，噪声波形对多普勒敏感，需要使用与多普勒相关的匹配滤波器来进行适当的距离压缩。此外，当使用经典匹配滤波器处理时，噪声波形的旁瓣分布在整个距离多普勒平面上，在存在强目标时可能会掩盖弱目标。减少旁瓣干扰的一种可能解决方案是使用稀疏信号处理 (SSP)。在本文中，