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基于KT-SRAF-LVD的机载雷达高速目标检测信号相干积分方法

KT-SRAF-LVD-Based Signal Coherent Integration Method for High-Speed Target Detecting in Airborne Radar.

作者信息

Xu Wenwen, Wang Yuhang, Cao Jianyin, Wang Hao

机构信息

School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

出版信息

Sensors (Basel). 2025 Mar 27;25(7):2128. doi: 10.3390/s25072128.

Abstract

In the application of an airborne radar platform, the rapid relative motion between target and airborne radar induces range migration (RM) and Doppler frequency migration (DFM). The motion errors caused by airflow, air friction, and navigation inaccuracies will exacerbate the RM and DFM problems and render traditional coherent integration methods ineffective. Previously reported airborne coherent integration methods are hindered by high computational complexity, limiting their practical application. Therefore, developing motion error compensation and coherent integration methods with reduced computational complexity and a high detection performance is of critical importance. To address these challenges, a novel method based on the keystone transform, sequence-reversing autocorrelation function, and Lv's distribution (KT-SRAF-LVD) is proposed. Simulation results demonstrate that the proposed method achieves a good balance between computational complexity and detection performance, indicating great potential for practical engineering applications.

摘要

在机载雷达平台的应用中,目标与机载雷达之间的快速相对运动会引起距离徙动(RM)和多普勒频率徙动(DFM)。由气流、空气摩擦和导航不准确引起的运动误差会加剧RM和DFM问题,并使传统的相干积累方法失效。先前报道的机载相干积累方法受到高计算复杂度的限制,限制了它们的实际应用。因此,开发具有降低计算复杂度和高检测性能的运动误差补偿和相干积累方法至关重要。为应对这些挑战,提出了一种基于Keystone变换、序列反转自相关函数和吕分布(KT-SRAF-LVD)的新方法。仿真结果表明,该方法在计算复杂度和检测性能之间取得了良好的平衡,显示出在实际工程应用中的巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f766/11991123/e5bf5c722685/sensors-25-02128-g001.jpg

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