随着隐身技术的发展,飞机导弹等高速运动目标的RCS越来越小,需要采用长时间积累的方法实现目标检测。文中提出了一种基于K-邻相关和Radon-Fourier变换(RFT)的小目标长时间积累算法。首先,利用K-邻相关算法实现目标运动参数的粗略估计;然后,利用RFT实现信号的高效积累。仿真和实录数据验证表明:该方法能有效实现高速高机动小目标检测,信噪比损失0.5 d B以内。 With the development of stealth technology, the RCS of high-speed moving targets such as aircraft missiles is getting smaller and smaller, and the target detection needs to be implemented by a long time accumulation method. In this paper, a small target long-time accumulation algorithm based on K-neighbor correlation and Radon-Fourier transform (RFT) is proposed. First, the K-neighbor correlation algorithm is used to achieve a rough estimation of the target motion parameters. Then, the signal is efficiently accumulated by using the RFT. Simulation and real data verification show that this method can effectively detect small moving targets with high speed and high maneuver, and the SNR loss is within 0.5 dB.