A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

• Published in: Electronics (Basel) Vol. 9; no. 8; p. 1308
• Main Authors: Zhu, Mingzhe, Feng, Zhenpeng, Zhou, Xianda
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020
• Subjects: Algorithms; Ambiguity; Audio frequencies; Cognition; Cognition & reasoning; Domains; Emitters; Feature extraction; Feature recognition; Identification; Machine learning; Power spectral density; Pulse amplitude; Radio frequency; Salience; Signal processing; Target recognition; Texture recognition; Wavelet transforms
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics9081308

Machine learning becomes increasingly promising in specific emitter identification (SEI), particularly in feature extraction and target recognition. Traditional features, such as radio frequency (RF), pulse amplitude (PA), power spectral density (PSD), and etc., usually show limited recognition effects when only a slight difference exists in radar signals. Numerous two-dimensional features on transform domain, like various time-frequency representation and ambiguity function are used to augment information abundance, whereas the unacceptable computational burden usually emerges. To solve this problem, some artfully handcrafted features in transformed domain are proposed, like representative slice of ambiguity function (AF-RS) and compressed sensing mask (CS-MASK), to extract representative information that contributes to machine recognition task. However, most handcrafted features only utilizing neural network as a classifier, few of them focus on mining deep informative features from the perspective of machine cognition. Such feature extraction that is based on human cognition instead of machine cognition may probably miss some seemingly nominal texture information which actually contributes greatly to recognition, or collect too much redundant information. In this paper, a novel data-driven feature extraction is proposed based on machine cognition (MC-Feature) resort to saliency detection. Saliency detection exhibits positive contributions and suppresses irrelevant contributions in a transform domain with the help of a saliency map calculated from the accumulated gradients of each neuron to input data. Finally, positive and irrelevant contributions in the saliency map are merged into a new feature. Numerous experimental results demonstrate that the MC-feature can greatly strengthen the slight intra-class difference in SEI and provides a possibility of interpretation of CNN.