| 王 骏,陈艳平,江立辉.结合稀疏表示和协同表示的 SAR 图像目标方位角估计[J].电子测量与仪器学报,2020,34(12):165-171 |
| 结合稀疏表示和协同表示的 SAR 图像目标方位角估计 |
| Target azimuth estimation of SAR images based on combination of sparse representation and collaborative representation |
| |
| DOI： |
| 中文关键词: 合成孔径雷达 目标方位角估计 稀疏表示 协同表示 加权融合 |
| 英文关键词:synthetic aperture radar target azimuth estimation sparse representation collaborative representation weighting fusion |
| 基金项目:安徽省自然科学基金(1908085MF184)、安徽高校省级自然科学研究重点项目(KJ2018A0555)资助 |
|
|
|
|
| 摘要点击次数: 248 |
| 全文下载次数: 328 |
| 中文摘要: |
| 提出结合稀疏表示和协同表示的合成孔径雷达( synthetic aperture radar,SAR)图像目标方位角估计方法。 稀疏表示和
协同表示分别在稀疏约束和最小误差的约束下对测试样本进行重构,具有良好的互补性。 分别在稀疏表示和协同表示下选取
与测试样本具有较强相关性的训练样本。 通过交集操作获得两者中最稳定的部分样本。 根据这些样本的方位角真值以及求解
的系数合理加权,获得测试样本的方位角估计值。 基于 MSTAR 数据集中 3 类目标的 SAR 图像进行方位角估计实验并与现有
方法进行对比。 实验结果表明方法的估计精度、稳定性以及噪声稳健性均优于现有的几类 SAR 目标方位角估计方法。 |
| 英文摘要: |
| This study proposed a target azimuth estimation method of synthetic aperture radar ( SAR) images based on combination of
sparse representation and collaborative representation. The sparse representation and collaborative representation reconstructed the test
sample under the sparsity constraint and the minimum error constraint, respectively, which have good complementarity. The highly
correlated training samples were selected by sparse representation and collaborative representation, respectively. And the two sets of
training samples were intersected to find the most stable part. According to the true azimuths and coefficients of these samples, the target
azimuth of the test sample can be estimated based on a proper weighting fusion algorithm. Experiments were investigated on SAR images
of three targets from the MSTAR dataset and comparison was made with some present methods. The results showed that the estimation
precision, stability and noise-robustness of the proposed method outperform some existing algorithms. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
