离网条件下稀疏延迟多普勒图像重建

Oguzhan Teke, A. Gürbüz, O. Arikan
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引用次数: 9

摘要

脉冲多普勒雷达已成功地应用于对运动目标和静止目标的监视和跟踪。为了有效地处理雷达回波,将延迟多普勒平面离散化,并利用FFT技术在离散网格上计算匹配滤波器输出。然而,对于延迟多普勒值与计算网格不一致的目标,检测性能会显著下降。特别是在探测强的和近距离的目标时,这会导致误报和误报。尽管基于压缩感知的技术在亚奈奎斯特采样率下提供了稀疏和高分辨率的结果,但这些技术的直接应用对离网问题明显更敏感。针对离网情况下的鲁棒延迟多普勒雷达处理,提出了一种新的基于参数摄动的稀疏重建方法,即PPOMP。在该技术中,对所选字典参数进行方向扰动以减小正交残差范数。为了更好地表征实际参数空间与重构参数空间之间的误差,提出了一种基于KLD的性能度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sparse delay-Doppler image reconstruction under off-grid problem
Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay-Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay-Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel and OMP based sparse reconstruction technique with parameter perturbation, named as PPOMP, is proposed for robust delay-Doppler radar processing even under the off-grid case. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. A new performance metric based on Kull-back-Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces.
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