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

Abstract

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.

A Knowledge Graph-Driven CNN for Radar Emitter Identification

“…Therefore, the emitter recognition algorithm began to integrate the work of feature extraction and classifiers into the deep learning network and yet only preprocessed the radar signal slightly. M. Zhu uses the deep learning network to extract new signal features for SEI [10]; P. Man uses the convolutional neural network with a relation computer block to identify radar emitters [28]. Kevin.…”

“…However, constructing the expert database requires a significant amount of labor from domain experts. In the third stage of radar emitter identification, the "feature extraction and classifier" model predominates [10][11][12][13][14]. Various methods of feature extraction and classification can be combined to create various emitter identification methods.…”

Specific Emitter Identification Based on Multichannel Depth Feature Fusion

“…There are many types of research for obtaining specific emitter identification features [7]. The specific transmitter identification (SEI) technology is based on transient or steady-state signals according to the type of signal.…”

A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing

“…In recent years, the advantages of machine learning algorithms in recognition have prompted an increasing number of researchers to apply cutting-edge machine learning techniques to the research of radar emitter recognition [7,8]. Currently, neural networks [9,10], Relevance Vector Machine (RVM) [11][12][13], extreme learning machine [14][15][16], weighted-xgboost [17], k-Nearest Neighbor (KNN) [18], and deep learning algorithm [19,20] are widely used in radar emitter recognition. Each machine learning algorithm has distinct advantages and disadvantages.…”