Deep Learning for Pulse Repetition Interval Classification

Nguyen, Ha Phan Khanh; Nguyen, Ha Q.; Ngo, Dat

doi:10.5220/0007253203130319

Cited by 11 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The available methodologies can be broadly classified into four distinct categories: statistical-based approaches, decision tree-based approaches, histogram-based approaches, and learning-based approaches [ 9 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Reference [ 9 ] introduces a unique technique based on DL. This technique utilizes a DCNN to classify seven distinct patterns of PRI changes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Decision Tree has been employed as the classifier in various investigations, including those referenced [35,40] One of the primary limitations of decision-tree-based methodologies is the requirement for manually determined thresholds. This process is not only time-consuming but also highly susceptible to variations in noise levels and changes in PRI parameters [9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another obstacle is developing algorithms that exhibit resilience due to alterations in PRIM variables, such as PRF fluctuations or signal to noise ratio (SNR) variations. Resolving these issues is essential in advancing precise and resilient PRIM recognition techniques for contemporary radar systems employing a diverse array of PRIMs [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced PRIM recognition using PRI sound and deep learning techniques

Hasani Azhdari,

Mahmoodzadeh,

Khishe

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Reference [ 9 ] introduces a unique technique based on DL. This technique utilizes a DCNN to classify seven distinct patterns of PRI changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced PRIM recognition using PRI sound and deep learning techniques

Hasani Azhdari,

Mahmoodzadeh,

Khishe

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Recently, modulation recognition methods using deep learning have also been proposed. In [36], a method of recognizing PRI modulation using a CNN without performing a preprocessing process on the input signal was proposed. After obtaining ACF results for the input signal, a method of defining an ACSE network using the features extracted from the ACF result as an input, was also proposed [37].…”

Section: B Pri Modulation Recognitionmentioning

confidence: 99%

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

Han

Park

2021

IEEE Access

View full text Add to dashboard Cite

In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments.

show abstract