Ground-based distributed radar is a potential technique for space target detection. However, in the case of a low signal-to-noise ratio (SNR), it is difficult to achieve long-term integration due to the limited ephemeris guidance accuracy and complex motion model. To solve this problem, a space target detection algorithm based on digital beamforming (DBF) and generalized radon-Fourier transform (GRFT) is proposed in this letter. To avoid the gain loss caused by ephemeris errors, small-scale beam-searching is conducted through the DBF technique, which also enables the measurement of target angle and even angular velocity. Besides, transforming the problem of energy accumulation into parameterized model matching, the GRFT process can achieve long-term integration effectively in the case of complex motion models. The effectiveness of the algorithm is verified via real data experiments based on a ground-based distributed radar. By showing an effective 30-s integration and a computational efficiency improvement of 40%, the validation of the proposed algorithm has been proved.

中文翻译：

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基于DBF和GRFT的地基分布式雷达空间目标检测

地基分布式雷达是一种潜在的空间目标检测技术。然而，在低信噪比（SNR）的情况下，由于星历制导精度有限和运动模型复杂，很难实现长期积分。针对这一问题，本文提出了一种基于数字波束形成（DBF）和广义氡-傅立叶变换（GRFT）的空间目标检测算法。为了避免星历误差带来的增益损失，通过DBF技术进行小范围波束搜索，同时可以测量目标角度甚至角速度。此外，GRFT过程将能量积累问题转化为参数化模型匹配问题，在复杂运动模型的情况下可以有效实现长期积分。通过基于地基分布式雷达的真实数据实验验证了算法的有效性。通过展示 30 秒的有效积分和 40% 的计算效率提升，证明了所提算法的有效性。