CAJ | 학술논문

为了准确地进行SAR图像目标识别，提出一种基于稀疏表示的SAR目标识别方法，在用主成分分析（PCA）进行降维的前提下，利用降维后的训练样本构建稀疏线性模型，通过?1范数最优化求解测试样本的稀疏系数解x，利用系数的稀疏性分布进行目标的分类识别。基于MSTAR数据进行了仿真验证，实验证明，基于稀疏表示的SAR目标识别方法在一定的特征维数下能够获得很好的识别性能，在目标方位角未知的情况下识别率仍可达到98%以上。
위료준학지진행SAR도상목표식별，제출일충기우희소표시적SAR목표식별방법，재용주성분분석（PCA）진행강유적전제하，이용강유후적훈련양본구건희소선성모형，통과?1범수최우화구해측시양본적희소계수해x，이용계수적희소성분포진행목표적분류식별。기우MSTAR수거진행료방진험증，실험증명，기우희소표시적SAR목표식별방법재일정적특정유수하능구획득흔호적식별성능，재목표방위각미지적정황하식별솔잉가체도98%이상。
In order to recognize SAR target accurately, an identification method based on sparse representation is proposed. The training samples after dimensionality reduction using principal component analysis are used to build a sparse linear model. The sparse coefficient solution x of the test sample is solved by ?1 -minimization. The identification task is solved by utilizing the sparse distribution of the sparse coefficient. Experimental results with MSTAR dataset verify that the identification method based on sparse representation in a certain characteristic dimension can obtain good recognition performance, and the recognition rate can reach more than 98%without knowing the target azimuth.