JDMR-Net: Joint Detection and Modulation Recognition Networks for LPI Radar Signals

• Published in: IEEE transactions on aerospace and electronic systems Vol. 59; no. 6; pp. 1 - 15
• Main Authors: Zhang, Ziwei, Li, Yunjie, Zhu, Mengtao, Wang, Shafei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: automatic modulation recognition; cross-attention; deep learning; Feature extraction; Interception; LPI radar; Modulation; Modules; Neural networks; Radar; Radar detection; Recognition; Signal detection; Signal to noise ratio; Similarity; Support systems; Task analysis; Time-frequency analysis
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2023.3293074

Low probability of intercept (LPI) radars are widely used in modern electromagnetic environments due to their excellent anti-interception performance. However, this inevitably increases the difficulties in detecting and recognizing LPI radar signals for electronic support systems or radar warning receivers. To address this challenge, this paper proposes a multi-task neural network named JDMR-Net for joint detection and modulation recognition of LPI radar signals. The inherent multi-task learning capability obtains an improved performance through leveraging useful information across tasks. The JDMR-Net receives pulse sequence in I/Q format as input and is computational friendly compared to time-frequency image-based methods. The JDMR-Net consists of a local feature extraction module and a global similarity mining module. The local feature extraction module extracts modulation information within single pulse, while the global similarity mining module determines the similarity relationship among sequential pulses. The JDMR-Net can provide accurate time domain localization of detected pulses, and determine corresponding modulation type simultaneously. Through the multi-task framework, the processing steps of traditional processing chain are compressed efficiently and the two modules are highly parallelizable, making the proposed solution promising for on-line application with raw signal inputs. Extensive experiments on simulated and measured LPI signals demonstrate the effectiveness and robustness of the proposed method in terms of lower detectable signal to noise ratio (SNR) and low computational complexity.