雷达高速高机动目标长时间相参积累检测方法

Long-time Coherent Integration-based Detection Method for High-speed and Highly Maneuvering Radar Target

  • 摘要: 复杂背景下的高速、微弱、动目标检测一直以来是空间目标、弹道目标的预警和探测以及外辐射源雷达信号处理领域的难题。该文利用机动目标的高阶运动信息,如加速度和急动度,提出两种机动目标长时间相参积累检测方法,即Radon-分数阶傅里叶变换(RFRFT)和Radon-分数阶模糊函数(RFRAF),能够同时补偿长时间积累过程中的距离和多普勒徙动,有效抑制背景杂波和噪声,提高积累增益。实测雷达数据验证结果表明,相参积累效果优于经典动目标检测(MTD),具有在强杂波中检测微弱机动目标的能力。

     

    Abstract: High-speed, low-observable, and maneuvering target detection is always the difficult problem for the early warning and detection of space target, ballistic target, and passive radar signal processing. The high-order motion information of maneuvering target is employed in this paper, such as acceleration and jerk, and two long-time coherent integration methods are proposed, i.e., Radon-fractional Fourier transform (RFRFT) and Radon fractional ambiguity function (RFRAF). The proposed methods can compensate the across range unit (ARU) and Doppler frequency migration (DFM) effects simultaneously during the long-time integration. The background clutter and noise can be well suppressed with higher integration gain. Experiment results using real radar data indicate that the coherent integration performance of the proposed methods is better than that of classical moving target detection (MTD), and ability of weak maneuvering target detection in strong clutter background.

     

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