针对合成孔径雷达（SAR）图象自动目标识别问题，在对SAR图象的特征提取问题进行分析的基础上，提出了一种特征点匹配算法，该算法根据Birkhoff-von Neumann定量，首先将广义置换矩阵约束松驰为广义双随机矩阵约束；然后利用拉格朗日乘子和障碍函数法，把约束加到目标函数中，从而将点集匹配问题转化为非线性最优化问题；最后利用确定性退火和软分配技术求解该问题，将得到的匹配代价用特征点数目的比值进行修正后，用于目标的识别。实验结果表明，该算法非常有效。

Automatic target recognition(ART) using Synthetic Aperture Radar(SAR) imagery is investigated in this paper. The feature extraction problem of SAR imagery is first analyzed, then a matching scheme that incorporates relative distance and magnitude between features is investigated. Following the Birkhoff-von Neumann theorem, we relax the match matrix constraints from permutation matrix constraints to doubly stochastic matrix constraints. Via Lagrange multipliers and a barrier function, the constraints are incorporated into the objective function, and the matching problem is posed as a nonlinear optimization problem. Using a combination of deterministic annealing and softassign, the objective function describing the matching problem is minimized. Recognition is performed by comparing the costs of the matches between the test image and deferent pattens. To account for the difference in the number of features, the computed costs are first caled by the ratio of the number of features between the two images. The test costs image belongs to the class of the pattern with the smallest acaled matching cost. Experimental results show the power of this approach in SAR target recognition.