Target identification performance improvement from enhanced HRR radar clutter suppression

Proceedings of the IEEE 2009 National Aerospace & Electronics Conference (NAECON) Pub Date : 2009-07-21 DOI:10.1109/NAECON.2009.5426625

B. Kahler, Erik Blasch

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引用次数: 4

Abstract

Airborne radar tracking in moving ground vehicle scenarios is impacted by sensor, target, and environmental dynamics. Moving targets can be assessed with 1-D High Range Resolution (HRR) Radar profiles with sufficient signal-to-noise (SNR) present which contain enough feature information to discern one target from another to help maintain track or to identify the vehicle. Typical radar clutter suppression algorithms developed for processing moving ground target data not only remove the surrounding clutter but also a portion of the target signature. Enhanced clutter suppression can be achieved using a multi-channel signal subspace (MSS) algorithm which preserves target features. In this paper, we exploit extra information from enhanced clutter suppression for automatic target recognition (ATR), present a gain comparison using displaced phase center antenna (DPCA) and MSS clutter suppressed HRR data, and generate confusion-matrix identification results. The results show that more channels for MSS increase ID over DCPA, result in a slightly noisier clutter suppressed image, and preserve more target features after clutter cancellation

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增强HRR雷达杂波抑制提高目标识别性能

在移动地面车辆场景下，机载雷达跟踪受到传感器、目标和环境动力学的影响。移动目标可以用1-D高距离分辨率(HRR)雷达剖面进行评估，该剖面具有足够的信噪比(SNR)，包含足够的特征信息来区分目标，以帮助保持跟踪或识别车辆。典型的雷达杂波抑制算法在处理运动地面目标数据时，不仅能去除周围的杂波，而且能去除目标的部分特征。采用保留目标特征的多通道信号子空间(MSS)算法可以增强杂波抑制。本文利用增强杂波抑制的额外信息进行自动目标识别(ATR)，比较了位移相位中心天线(DPCA)和MSS杂波抑制的HRR数据的增益，并生成了混淆矩阵识别结果。结果表明，与DCPA相比，MSS增加了更多的信道，使得杂波抑制后的图像噪声略高，并且在杂波消除后保留了更多的目标特征

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the IEEE 2009 National Aerospace & Electronics Conference (NAECON)

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