Modified polar mapping classifier for SAR automatic target recognition

Park Jong-Il; Kim Kyung-Tae

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Modified polar mapping classifier for SAR automatic target recognition

机译：改进的极坐标映射分类器用于SAR自动目标识别

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摘要

In this study, we propose a modified polar mapping classifier (M-PMC) with an efficient preprocessing scheme to classify synthetic aperture radar (SAR) targets from the public release database for moving and stationary target acquisition and recognition. M-PMC is evaluated under various SAR extended operating conditions (EOCs), which are designed to reflect real-world battlefield scenarios. Experimental outcomes from various EOCs show that M-PMC has good generalization capabilities and outperforms conventional classifiers.

机译：在这项研究中，我们提出了一种改进的极坐标映射分类器（M-PMC），该分类器具有有效的预处理方案，可以从公开发布的数据库中对合成孔径雷达（SAR）目标进行分类，以进行移动和固定目标的获取和识别。 M-PMC在各种SAR扩展操作条件（EOC）下进行评估，这些条件旨在反映现实世界的战场场景。各种EOC的实验结果表明，M-PMC具有良好的泛化能力，并且优于常规分类器。

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来源
《Aerospace and Electronic Systems, IEEE Transactions on》 |2014年第2期|1092-1107|共16页
作者
Park Jong-Il; Kim Kyung-Tae;
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作者单位

Automotive IT Platform Research Team, Electronics and Telecommunications Research Institute, Daegu 711-880, Korea;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Azimuth; Clutter; Principal component analysis; Radar imaging; Synthetic aperture radar; Target recognition;

机译：方位角杂波主成分分析;雷达成像合成孔径雷达;目标识别;

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专利

1. High Range Resolution Profiles as Motion-Invariant Features for Moving Ground Targets Identification in SAR-Based Automatic Target Recognition [J] . Wong S. K. Aerospace and Electronic Systems, IEEE Transactions on . 2009,第3期

机译：基于SAR的自动目标识别中的高分辨分辨率轮廓作为运动不变特征，用于运动地面目标识别
2. Sparsity constraint nearest subspace classifier for target recognition of SAR images [J] . Ding Baiyuan, Wen Gongjian Journal of visual communication & image representation . 2018,第APRa期

机译：稀疏约束最近子空间分类器用于SAR图像目标识别
3. Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition [J] . Chuang Lin, Binghui Wang, Xuefeng Zhao, Mathematical Problems in Engineering . 2013,第pta4期

机译：使用基于稀疏表示的分类器优化内核PCA以进行MSTAR SAR图像目标识别
4. Geometric Hashing Classifier Based on Modified D-S Theory in SAR Target Recognition [C] . Zhang Jing, Wang Guohong, Liang Famai, CIE(Chinese Institute of Electronics) International Conference on Radar; 20061016-19; Shanghai(CN) . 2006

机译：基于改进D-S理论的哈希算法在SAR目标识别中的应用
5. Mixture of Factor Analyzers (MoFA) Models for the Design and Analysis of SAR Automatic Target Recognition (ATR) Algorithms [D] . Abdel-Rahman, Tarek. 2017

机译：用于SAR自动目标识别（ATR）算法设计和分析的因子分析器（MoFA）模型的混合
6. Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization [O] . Yikui Zhai, Wenbo Deng, Ying Xu, 2019

机译：基于L2正则化转移MS-CNN的鲁棒SAR自动目标识别
7. Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition [O] . Chuang Lin, Binghui Wang, Xuefeng Zhao, 2013

机译：利用基于稀疏表示的基于稀疏表示的基于MSTAR SAR图像目标识别的优化内核PCA
8. Feasibility Studies of Nearest Neighbor Residual Vector Quantizer Classifiers fora Collection of Signal and Sensor Waveforms: Automatic Target Recognition in SAR Images [R] . Barnes, C. F., Keel, B. M. 1998

机译：最近邻残差矢量量化器分类器对信号和传感器波形采集的可行性研究：saR图像中的自动目标识别

Modified polar mapping classifier for SAR automatic target recognition

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