Polarization-Aided Adaptive Processing for Ground Moving Target Indication in Heterogeneous Clutter

DU Wen-tao; LIAO Gui-sheng; YANG Zhi-wei; XIN Zhi-hui

doi:10.3969/j.iss.0372-2012-2014.03.016

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Polarization-Aided Adaptive Processing for Ground Moving Target Indication in Heterogeneous Clutter

更新时间：2025-07-16

- Polarization-Aided Adaptive Processing for Ground Moving Target Indication in Heterogeneous Clutter
- Acta Electronica Sinica Vol. 42, Issue 3, Pages: 523-528(2014)
- 作者机构：
  
  西安电子科技大学雷达信号处理国家重点实验室,陕西,西安,710071
- 作者简介：
- 基金信息：
  
  Program for Changjiang Scholars and Innovative Research Team in University (No.IRT0954);National Natural Science Foundation of China (No.60901066);Special Bonus Fund of Fundamental Research Fund for the Central Universities (No.K5051302007);Program of Research Project of Education Department of Shaanxi Province (No.2013JK1051)
- DOI：10.3969/j.iss.0372-2012-2014.03.016
  CLC： TN957
- Published：2014
- 稿件说明：
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DU Wen-tao, LIAO Gui-sheng, YANG Zhi-wei, et al. Polarization-Aided Adaptive Processing for Ground Moving Target Indication in Heterogeneous Clutter[J]. Acta Electronica Sinica, 2014, 42(3): 523-528.
DOI：

DU Wen-tao, LIAO Gui-sheng, YANG Zhi-wei, et al. Polarization-Aided Adaptive Processing for Ground Moving Target Indication in Heterogeneous Clutter[J]. Acta Electronica Sinica, 2014, 42(3): 523-528. DOI： 10.3969/j.iss.0372-2012-2014.03.016.

摘要

空时自适应处理是强杂波背景下动目标检测的有效途径，实际检测环境中训练样本与待检测单元背景统计特性的不一致将恶化处理器的输出信杂噪比，需要结合样本挑选技术来改善非均匀场景的处理器性能.在建立机载多通道信号模型基础上，提出一种利用极化信息辅助的空时自适应处理方法，该方法通过极化分类和功率分组指导训练样本选择，利用同类样本估计待检测单元的统计特性并计算自适应权矢量，具有杂波相关矩阵估计准确度高的特点，可显著改善动目标检测的性能.最后结合国外NASA JPL AIRSAR实测数据的多通道仿真实验验证了所提方法的有效性.

Abstract

Space-time adaptive processing is an effective method to detect ground moving targets embedded in clutter environment

and the estimation of clutter covariance matrix with training samples is required to perform the adaptive processing commonly.In realistic environment

the characteristics of clutter in the cell under test may be different with the training samples in other range cells

which would reduce the precision of the estimated clutter covariance matrix.To improve the performance of target detection

a training sample selecting method based on polarized classification and power grouping is proposed in this paper

which would improve the precision of the clutter covariance matrix.The simulation results demonstrate the effectiveness of the proposed method.

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