Abstract:

In order to solve incomplete prior information of low probability of intercept (LPI) radar in non-cooperative electronic countermeasure environment, a novel recognition algorithm based on revised semi-supervised Naive Bayes (RSNB) is proposed. The RSNB algorithm extracts bispectrum diagonal slices of four LPI radar signals as the recognition feature. To overcome disadvantages of traditional semi-supervised Naive Bayes which comes from repeated errors in updating sample sets, it uses revised semi-supervised Naive Bayes to construct the classifier, and then completes the recognition of tested sample sets. RSNB selects those samples with high degree of confidence which comes from generated confidence list in unlabeled samples sets so as to add them to labeled samples sets, then improves the classifier parameters by using predicted results. It can work out low recognition rate and unstable classification performance effectively by using the revised semi-supervised Naive Bayes. The simulated results indicate that, the RSNB has higher recognition rate and better classification performance when compared with traditional SNB algorithms and the principal component analysis-support vector machine algorithm in LPI radar recognition.