Enhanced PRIM recognition using PRI sound and deep learning techniques

Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters, and noise from radar antenna scans, often hinder...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2024-05, Vol.19 (5), p.e0298373-e0298373
Main Authors: Hasani Azhdari, Seyed Majid, Mahmoodzadeh, Azar, Khishe, Mohammad, Agahi, Hamed
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial neural networks
Automation
Biology and Life Sciences
Computer and Information Sciences
Datasets
Deep Learning
Ecology and Environmental Sciences
Electronic warfare
Engineering and Technology
Evaluation
Feature extraction
Health aspects
Intelligence gathering
Kalman filters
Machine learning
Management
Mechanization
Methods
Neural networks
Neural Networks, Computer
Optimization techniques
Pattern Recognition, Automated - methods
Pulse repetition interval
Radar
Radar antennas
Radar systems
Radiation
Real time
Recognition
Signal processing
Sound
Transfer learning
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters, and noise from radar antenna scans, often hinder the accurate recognition of PRIM. This research introduces a novel three-stage approach for PRIM recognition, emphasizing the innovative use of PRI sound. A transfer learning-aided deep convolutional neural network (DCNN) is initially used for feature extraction. This is followed by an extreme learning machine (ELM) for real-time PRIM classification. Finally, a gray wolf optimizer (GWO) refines the network's robustness. To evaluate the proposed method, we develop a real experimental dataset consisting of sound of six common PRI patterns. We utilized eight pre-trained DCNN architectures for evaluation, with VGG16 and ResNet50V2 notably achieving recognition accuracies of 97.53% and 96.92%. Integrating ELM and GWO further optimized the accuracy rates to 98.80% and 97.58. This research advances radar identification by offering an enhanced method for PRIM recognition, emphasizing the potential of PRI sound to address real-world distortions in ESM and ELINT systems.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0298373