Abstract
Radar behavior prediction is an important task in the field of electronic reconnaissance. For the extensive applied multi-function radar (MFR), which can flexibly transition between various work modes and make certain statistical rule of these radar behaviors exist in the signal sequence. Most of existing radar emission prediction methods are inapplicable to the non-cooperative scenario, since the labeled sequence samples are hard to obtain. To solve this challenge, an unsupervised framework is proposed for learning the behavior rule from the pulse sequence and predicting the radar mode in this paper. The framework includes three modules of sequence segmentation for mode switch boundaries detection, segment clustering for behavior mode recognition, and mode prediction for behavior rule extraction. The application of this framework can predict state and numerical values of next mode at the same time. Experimental results demonstrate that the proposed framework has a considerable prediction performance and shows good robustness under the non-ideal conditions.
Published Version
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