提出一种适用于并行压缩成像系统的压缩域小目标检测算法，以便省去获得小目标位置信息时进行的图像重建环节，有效降低算法的复杂度。该方法通过并行压缩成像数学模型捕获背景以及待测图像压缩测量值，通过高斯混合模型进行压缩域背景建模，从而获得压缩域前景观测值。然后计算压缩域前景观测值与各压缩域目标位置模板的余弦相似度，根据局部阈值以及压缩域候选目标面积实现目标检测与定位。最后进行了仿真实验，分析了降采样率、测量次数、投影误差以及噪声等对目标检测效果的影响。结果表明: 增大降采样率及噪声均会降低检测效果; 测量次数对检测效果的贡献是有限的; 测量次数为2次或3次时，可以在保证检测效果的同时有效控制运行时间。此外，噪声对检测效果影响较大，因而需要严格控制系统噪声。该方法可以在不进行任何图像重建的情况下实现目标的实时检测。

A small target detection algorithm working at a compressed domain was proposed for parallel compressive imaging systems to reduce the computational complexity by eliminating the process of image reconstruction. A mathematical model of the parallel compressive imaging system was used to capture measuring values of background and current frames. Then, the background measurements were updated according to a compressive sensing-mixture of Gaussians model (CS-MoG) to obtain the measurement values of the foreground. The cosine similarities between the measurements of current frame and the compressed target-location templates were calculated. And the local threshold and target area in the compressed domain were adopted to screen candidate targets. Finally, the effects of down-sampling rate, number of measurements, projection error and noise on the detection results were studied by simulation experiments. Experimental results show that large down-sampling rate and noise would decrease the detection performance, but the number of measurements to detection results has limited contribution. When 2 or 3 measurements are set, the operation time could be controlled while ensuring the detection performance. It suggests that the noise in the system should be controlled strictly because the noise effects the detection ability greatly. Furthermore, the proposed algorithm can achieve real-time target detection without any image reconstruction.