Algorithms are proposed to address the radar target detection problem of compressed sensing (CS) under the conditions of a low signal-to-noise ratio (SNR) and a low signal-to-clutter ratio (SCR) echo signal. The algorithms include a two-stage classification for radar targets based on compressive detection (CD) without signal reconstruction and a support vector data description (SVDD) one-class classifier. First, we present the sparsity of the echo signal in the distance dimension to design a measurement matrix for CD of the echo signal. Constant false alarm rate (CFAR) detection is performed directly on the CD echo signal to complete the first-order target classification. In simulations, the detection performance is similar to that of the traditional matched filtering algorithm, but the data rate is lower, and the necessary data storage space is reduced. Then, the power spectrum features are extracted from the data after the first-order classification and converted to the feature domain. The SVDD one-class classifier is introduced to train and classify the characteristic signals to complete the separation of the targets and the false alarms. Finally, the performance of the algorithm is verified by simulation. The number of false alarms is reduced, and the detection probability of the targets is improved.

提出了在低信噪比（SNR）和低信杂比（SCR）回波信号条件下解决压缩感知（CS）雷达目标检测问题的算法。该算法包括基于压缩检测（CD）的雷达目标的两级分类，无需信号重建，以及支持向量数据描述（SVDD）一类分类器。首先，我们提出了回声信号在距离维度上的稀疏性，以设计回声信号CD的测量矩阵。对CD回声信号直接执行恒定的误报率（CFAR）检测，以完成一阶目标分类。在仿真中，检测性能类似于传统的匹配滤波算法，但是数据速率较低，并且减少了必要的数据存储空间。然后，从一阶分类后的数据中提取功率谱特征，并将其转换为特征域。引入SVDD一类分类器来对特征信号进行训练和分类，以完成目标和错误警报的分离。最后，通过仿真验证了算法的性能。减少了误报的次数，提高了目标的检测概率。仿真验证了算法的性能。减少了误报的次数，提高了目标的检测概率。仿真验证了算法的性能。减少了误报的次数，提高了目标的检测概率。