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- W2894730621 abstract "In this paper, a robust synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed by combining the global and local filters, especially aiming to improve the recognition performance under various extended operating conditions (EOCs). Due to the cleanness of the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, which is the prevalent benchmark for SAR ATR, extremely high performance has been reported in many literatures. However, in the real-world scenarios, there exist many types of EOCs such as noise corruption, partial occlusion, sensor variation, etc. Therefore, this study focuses on the SAR ATR problem under several typical EOCs. The intensities of the test image to be classified is taken as the global filter. The spatial correlation between the test image and its corresponding template is calculated as the global response, which describes the global consistency. To depict the local properties of the target, the attributed scattering centers (ASCs) are employed as local filters. Each ASC of the test image is matched with its counterpart from the template ASC set. Then, the local response is evaluated based on the attributes’ differences. The global response and local responses are linearly combined using a random weight matrix. Afterwards, a judgment variable is designed as the measure for target recognition. Experiments are conducted on the MSTAR dataset under the standard operating condition (SOC) and various EOCs including configuration variance, depression angle variance, noise corruption, resolution variance, and partial occlusion. Compared with several state-of-the-art SAR ATR methods, the validity and robustness of the proposed method is fully demonstrated." @default.
- W2894730621 created "2018-10-12" @default.
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- W2894730621 date "2019-02-01" @default.
- W2894730621 modified "2023-10-17" @default.
- W2894730621 title "Combination of global and local filters for robust SAR target recognition under various extended operating conditions" @default.
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- W2894730621 doi "https://doi.org/10.1016/j.ins.2018.10.001" @default.
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