The article considers the synthesis of a binary radar recognition device in the absence of a priori analytical models of classification feature distributions. A methodology for sequential radar target recognition is proposed, based on the combined use of support vector machines and Wald’s sequential probability ratio test. At the training stage, a separating hyperplane is constructed, with respect to which the multidimensional feature space is transformed into a one-dimensional decision variable represented by the signed distance to the class-separation boundary. Based on the training sample, the conditional probabilities of correct and false recognition are estimated and used to form increments of the decision statistic. At the recognition stage, the statistic is recursively accumulated and compared with threshold levels until a decision is made. The methodology preserves a unified algorithmic structure when the feature set changes and allows adaptation through repeated classifier training.

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