An adaptive synthetic method for long sequence radar mode recognition

Abstract

Abstract Radar work mode recognition is crucial to analyse radar behaviour and intention. There are some challenges limiting the recognition of long sequences with multiple mode classes. First, the performance of recognition method relies on precise segregation of intercepted sequence, which is often unfeasible in reality. Second, the states at the boundaries of adjacent modes may create extraneous mode samples that intervenes the recognition. Third, current methods fail to deal with the scenarios where multiple modes share the same state sequence. To address these problems, a novel forward matching method (FMM) is proposed, comprising a shortest path method (SPM) for intra‐mode recognition, a matching strategy, and an adjustment mechanism. SPM is to provide potential recognition for short fragments of the given long sequence. The matching strategy is to assess the availability of current recognition. The adjustment mechanism tunes the segregation and improves the subsequent recognition. FMM offers several distinct advantages. First, the model can explicitly characterise the mode transition probability and is totally interpretable. Second, FMM can distinguish intentional ambiguities, alleviate mosaic ambiguity and probability deviation associated with inter‐mode recognition. Third, FMM is extendable to integrate with other intro‐mode recognition methods to cater to various scenarios.

Keywords

• artificial intelligence
• multifunction radar
• pattern classification
• radar emitter recognition