单站地基雷达对机动弱目标的检测是领域内长期存在的热点问题，通过提高积累时间可以提高运动目标检测性能。但随着积累时间的增加，机动目标的运动会引起跨距离单元（RCM）与跨多普勒单元（DFM）的现象，导致积累增益损失。针对这个问题，提出一种适用于单站雷达弱机动目标检测问题的长时积累方法。首先给出分段策略，接着2次使用二阶Keystone变换（SKT）的运动补偿算法校正RCM，然后使用吕变换（LVD）估计运动参数并进行相位补偿，最后利用滑窗非相参积累，进一步提高检测概率。仿真结果表明，在弱机动目标场景，所提方法检测性能优于传统积累检测算法。该方法对RCM与DFM带来的增益损失进行补偿，提高了信噪比，同时能够在计算复杂度与积累增益间取得平衡。

The detection of maneuvering weak targets by monostatic ground-based radar is a hot issue， and the detection performance of moving targets can be improved by increasing the accumulation time. However， with the increase of accumulation time， the movement of maneuvering target causes the phenomenon of range cell migration （RCM） and Doppler frequency migration （DFM）， which leads to the loss of accumulation gain. To solve this problem， a long-time accumulation method for weak maneuvering target detection in monostatic radar is proposed in this paper. The segmentation strategy is firstly given in the proposed method， then the motion compensation algorithm with the second-order keystone transform is used twice to correct the RCM， later LVD is used to estimate the motion parameters and the phase compensation is carried out. Finally， the detection probability and detection accuracy are further improved by sliding window non-coherent accumulation. The simulation results show that the detection performance of the proposed method is better than the traditional accumulation detection algorithm in the weak maneuvering target condition. The proposed method compensates for the gain loss caused by RCM and DFM and improves the signal-to-noise ratio of coherent and non-coherent accumulation gain. Furthermore， it can balance the computational complexity and accumulation gain at the same time.