论文部分内容阅读
An algorithm for underwater target feature recognition is proposed using its highlights distribution.For an underwater target with large size and slender body,it is assumed that the heading course and the length of the target are both determined by the distribution of its highlights.By supposing that these highlights obey Gaussian mixture distribution,the feature recognition problem can be transformed into a clustering problem.Therefore,using the collinearly constrained expectation maximization algorithm,the clustering centers of these highlights can be calculated and then the estimation of the heading and length of the target can be obtained with high accuracy.The effectiveness of the proposed method is demonstrated using simulations.
An algorithm for underwater target feature recognition is proposed using its highlights distribution. For an underwater target with large size and slender body, it is assumes that the heading course and the length of the target are both determined by the distribution of its highlights. that these highlights obey Gaussian mixture distribution, the feature recognition problem can be transformed into a clustering problem.Therefore, using the collinearly constrained expectation maximization algorithm, the clustering centers of these highlights can be calculated and then the estimation of the heading and length of the target can be obtained with high accuracy. the effectiveness of the proposed method is demonstrated using simulations.