利用缺失数据幅度相位估计提取多角度SAR特征
doi: 10.3724/SP.J.1146.2012.00496
Multi-aspect SAR Feature Extraction via Gapped-data Amplitude and Phase Estimation
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摘要: 提取多角度SAR特征对雷达目标识别具有重要价值。该文利用缺失数据幅度相位估计提取多角度SAR特征，本质上是缺失数据情形下的参数估计问题。该算法无需利用目标参数化模型，是一种数据驱动的自适应估计方法；同时，它无需填充缺失数据，避免了因插值导致的误差。实验表明该算法不仅能够提高目标位置和幅度估计精度、实现超分辨成像，而且对噪声和模型失配具有鲁棒性，实验同时验证了多角度SAR重构目标轮廓的优势。Abstract: It is valuable to extract multi-aspect SAR feature for radar object recognition. The Gapped-data Amplitude and Phase EStimation (GAPES) are modified to extract multi-aspect SAR feature. Multi-aspect SAR Feature Extraction is a parameter estimation issue with gapped-data case. This algorithm does not need the parametric model of object. In addition, there is no need to fulfill the gapped data and it avoids the error induced by the interpolation. The new approach is not only able to improve the estimative precision of location and amplitude, but also to improve the resolution. Numerical experiments are provided to demonstrate the performance of the algorithm and to show the advantages of multi-aspect SAR data to reconstruct object.
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