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 dB以内。

With the development of stealth technology, the RCS of high-speed moving targets such as aircraft, the RCS of missile is getting smaller, which requires a long-time accumulation to improve detection capability. This paper proposed a small target long-time accumulation algorithm based on K-neighborhood correlation and Radon Fourier transform（RFT）. Firstly, a rough estimation of target motion parameters is processed by the K-neighborhood correlation. Subsequently, a high efficiency accumulation is executed by the RFT. Experimental results with simulated and measured data indicate that the proposed method provides an effective and efficient way for small target detection with the loss of SNR better than 0.5 dB.

TP971.1