Authors:
Ha P. K. P. K. Nguyen
;
Ha Q. Q. Nguyen
and
Dat T. Ngo
Affiliation:
Viettel Research and Development Institute, Hoa Lac High-tech Park, Hanoi and Vietnam
Keyword(s):
Pulse Repetition Intervals, PRI Modulation Classification, Convolutional Neural Network, Deep Learning.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
Abstract:
Pulse Repetition Intervals (PRI)—the distances between consecutive times of arrival of radar pulses—is an important characteristic of the radar emitting source. The recognition of various PRI modulation types is therefore a key task of an Electronic Support Measure (ESM) system for accurate identification of threat emitters. This problem is challenging due to the missing and spurious pulses. In this paper, we introduce a deep-learning-based method for the classification of 7 popular PRI modulation types. In this approach, a convolutional neural network (CNN) is proposed as the classifier. Our method works well with raw input PRI sequences and, thus, gets rid of all preprocessing steps such as noise mitigation, feature extraction, and threshold setting, as required in previous approaches. Extensive simulations demonstrate that the proposed scheme outperforms existing methods by a significant margin over a variety of PRI parameters, especially in severely noisy conditions.