Abstract:

Comprehensive and accurate labeling of electromagnetic data is the prerequisite and foundation for intelligent analysis of electromagnetic big data. Aiming at the problems of low labeling rate and error redundant labeling information in electromagnetic sensing data under the condition of strong confrontation in battlefield games, an annotation and completion scheme based on tensor completeness theory is proposed. Theoretically, the feature parameters (such as radar pulse parameters) extracted from the observation of the same target using different sensing platforms in the same scene are similar (low-rank), and the measurement results over a period of observation time are piece-wise continuous and smooth. Therefore, the annotation and completion of target data received across platforms can be modeled as a feature restoration model based on low rank tensor completeness, and total variation regularization is introduced to characterize the piece-wise continuous smooth attributes of feature parameters over a period of time. Because the model is non-convex, a non-convex approximation algorithm based on the maximum rank decomposition of the matrix is used for iterative solution. The performance of the model is tested through simulation data and radar pulse description word (PDW) real detection data. The experimental results show that the proposed method can well achieve annotation and completion in the case of severe lack of target feature annotation information, and correct annotation errors efficiently.

Key words: annotation and completion, electromagnetic big data, low-rank tensor completion