基于证据聚类的水声目标识别算法研究
The Underwater Acoustic Target Recognition Algorithm Based on Evidence Clustering

针对水声目标信号复杂、样本获取难度大且富含不确定信息的问题，研究了一种证据聚类识别算法。首先在水声目标的各类训练样本中，根据特征距离大小选取待识别目标的K近邻，并采用证据近邻分类优化算法为各目标数据构造一组合理的初始基本置信指派。然后对算法的目标函数进行循环迭代优化，计算出目标数据最终的全局基本置信指派。最后根据融合结果和所设立的分类规则即可判断目标的类别属性。通过水声目标实测数据的实验，将新算法与其他几种常用的水声目标识别算法进行了对比分析，结果表明其能有效提高识别准确率。
In underwater acoustic target recognition, the target signal is usually complex and the samples which are difficult to obtain also have some uncertain information. In order to effectively solve these problems, the evidence clustering recognition algorithm (TECRA) is presented. In this new method, the k-nearest neighbor are first determined by using the feature distance between the object and its neighbors in each class of the training set, and a reasonable initial basic belief assignments (bba's) for each target data are constructed by the improved k-nearest neighbor classification algorithm. Then the final global bba's of the target is obtained by optimizing the objective function of the algorithm. Finally the object can be recognized by the fusion result and the classification rule presented in the paper. Several experiments based on real underwater acoustic data sets are made to test the effectiveness of TECRA in comparison with some other methods. The results indicate that TECRA can effectively improve the recognition accuracy