Svm In The Field Of The Array Antenna Applications

This thesis includes three aspects of contents: Antenna array, SVM and their combination.SVM is a kind of universal learning algorithm, which developed from Statistical Learning Theory, that is, small sample learning theory proposed by Vapnik. It can represent complicated functions especially in high dimensions, which can avoid the trouble of the dimension tragedy that happened in general algorithm. It also will not affect the system performance by using original data of the array for the eigenvector directly. We apply SVM, the new machine-learning algorithm, to the domain of the antenna array, in order to realize the source location in this thesis mainly.In order to predict source location by using the SVM regression, we combine the SVM theory & the antenna array module, construct a training data structure; design a new system module which optimize the train machine by altering different parameter. It does not only exert the superiority of the SVM technology, but also extend its application domain. We use six linear equally spaced antenna arrays to simulate SVM algorithm for treating with the incident plane wave. The covariance of the training samples which are sampled from the antenna arrays is delivered into SVM training machine after transformed suitably, then we use SVM regression on the unknown samples according as the trained machine for getting the source location. We should adopt different parameter to get the optimal learning machine. This algorithm processes rapidly, and can track the movement of the source. It also has high precision where the SNR is not so bad, and can reduce the compute quantum efficiently. The SVM has high precision of the forecast & tracldng speed compared with the MUSIC algorithm.