Short-distance Echo Recogniton Of Cobalt-rich Crusts In Deep Sea

Along with the shortage of land resources, as a kind of important ocean resources, cobalt-rich crusts(CRC) have been drawn much focus on by western advanced countries, such as America, Russia, German, Japan, etc. Based on the early related research achievements, Some countries have stepped into the trail stage of exploitation. By contrast, the related research work in our country is still in the initial stage. In order to protect our country's ocean right and obtain CRC resource for our country, it is necessary to do research on the exploring and exploiting techniques and equipments.CRC recognition is an important link of realize the exploiting work highly effectively. Under the support of the project of the National Natural Science Foundation of China, "Research on detection technology of deep-ocean cobalt-rich crusts micro-terrain and best cutting-depth controlling model", the author tried to do research on the related techniques of underwater materials classification and recognition using sonar exploring method.The author started with underwater sediments classification. After consulting a large quantity of related research papers and technique reports, the author determined to complete the classification and recognition of 23 kinds of underwater materials using the sonar exploring experiment system. At the further step, the author tried to do research through the following three approaches.1) The techniques of feature abstraction of echoesThe author tried to use the representative sediments echo feature abstraction methods in time-frequency domain:time sub-band energy feature in orthogonal wavelets domain, scale sub-band energy feature in orthogonal wavelets domain, singular value decomposition feature in stationary wavelets domain, wavelets modulus edge to abstract the features of above mentioned 23 kinds of underwater materials. Besides, a new kind of echo feature abstraction method named multi-resolution singular spectrum entropy was introduced. At the further step, in order to remove the influence of the heavily uneven surface of underwater materials, a novel kind of echo feature abstraction method named class energy feature in echo samples dictionary domain based on the theory of signal sparse decomposition is proposed. Among all above echo features, using the class energy feature the best classification and recognition results were obtained in the related experiment.2) The techniques of feature level fusion and its application to echo recognition of CRC depositThe author tried to do research on the techniques of feature level fusion in order to improve the effects of classification and recognition. Firstly, three representative feature level fusion methods:serial fusion, parallel fusion, matrix fusion method are introduced. Then the relationship among these three fusion methods was analyzed using Fisher criteria and one conclusion was drawn that the last two fusion methods are both a kind of special serial fusion methods with special restriction. Besides, the author indicated that the last two fusion methods are not of fisher stability except serial fusion method, which means using the last two methods are possible to lead to the degenerating results. At the further step, the sufficient conditions when the three fusion methods would be Bayes optimal for two-class classification are researched. And the research results show that the stricter conditions are needed for the last two fusion methods than serial fusion method. At last, a kind of fast Serial fusion technique based on discriminant space which can ensure none degenerateness was proposed.3) The techniques of nonlinear classification and recognition in kernel space and its application to echo recognition of CRC depositThe author tried to do research on the techniques of nonlinear classification and recognition in kernel space. At first, two Gaussian kernel parameters learning criteria were proposed. Then the improving techniques of nonlinear classification and recognition in kernel space were considered by using local information and multiple kernel fusion method. Based on the proposed discriminatively regularized least-squares classifier, a modified discriminatively regularized least-squares classifier mode which leads to a convex optimality problem was proposed. At the further step, the author designed the related optimality algorithm and obtained the related model in kernel space. Besides, the author tried to do research on multiple kernel learning methods in kernel sampling space. A kind of fusion parameters learning methods using FSM2 criteria and three multiple kernel learning algorithms in kernel sampling space were proposed. The experimental results of classification and recognition of the underwater materials such as CRC etc show that all above improving techniques can improve the classification and recognition results in certain extent.