Automatic Waveform Recognition of Overlapping LPI Radar Signals Based on Multi-Instance Multi-Label Learning

• Published in: IEEE signal processing letters Vol. 27; pp. 1275 - 1279
• Main Authors: Pan, Zesi, Wang, Shafei, Zhu, Mengtao, Li, Yunjie
• Format: Journal Article
• Language: English
• Published: New York IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Computer simulation; deep convolutional neural network; Emitters; Feature extraction; Learning; LPI radar signals; Modulation; multi-instance multi-label learning; overlapping signal recognition; Radar; Robustness; Signal to noise ratio; Task analysis; Training; Waveforms
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2020.3009195

In an ever-increasingly complex electromagnetic environment, multiple low probability of intercept (LPI) radar emitters may transmit their own signals simultaneously on similar bands, resulting in overlapping receiving signals in both time and frequency domain. In this letter, a novel Multi-Instance Multi-Label learning framework based on Deep Convolutional Neural Network (MIML-DCNN) is proposed to automatically recognize the overlapping LPI radar signals,which is trained by single type of signals only. The framework handles signals in an end-to-end manner that is integrated with a well-designed instance generation module, a sophisticated MIML classifier, and an adaptive threshold calibration. Through comprehensive experiments on simulated overlapping signals with four different modulation types, we prove that the proposed framework identifies each individual signal type precisely in the presence of overlapping signals, and is also robust to variation of the signal-to-noise ratio (SNR) and power ratio conditions.