A Parallel Neural Network-based Scheme for Radar Emitter Recognition

Nguyen, Ha Phan Khanh; Long, Van Cao; Dong, Quang Trung

doi:10.1109/imcom48794.2020.9001727

Cited by 4 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IQ 1D time sequences [138], [210], [218], [569]; STFT [133]- [135], [137], [212], [229], [230]; CWTFD [130], [215], [217]- [219], [227]; amplitude-phase shift [211]; CTFD [131], [221], [222]; bivariate image with FST [132]; bispectrum [237]; autocorrelation features [213]- [215]; ambiguity function images [140], [141]; fusion features [139], [220] CNNs [82], [210], [211], [217]- [222], [228]- [231], [233], [237], [569]; RNNs [142]- [144], [216]; DBNs [135], [136], [235], [236]; AEs [222]; SENet [212], [213]; ACSENet…”

Section: Features Models Accuracymentioning

confidence: 99%

“…As for features transformation, the one-dimension and twodimension features are usually the inputs of DNN models. The former are encoded IQ time sequences [138], [210], [218], [569], and the latter usually are time-frequency distribution (TFD) images, which are produced by short time fourier transformation (STFT) [212], Choi-Williams time-frequency distribution (CWTFD) [217], [218], and Cohen's time-frequency distribution (CTFD) image [221], [222]. In addition, there are some other two-dimension feature images, such as amplitudephase shift image [211], the spectrogram of the time domain waveform based on STFT [230], bispectrum of signals [237], ambiguity function images [140], [141], and autocorrelation function (ACF) features [213]- [215].…”

Section: A Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

View full text Add to dashboard Cite

Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

show abstract

Section: Features Models Accuracymentioning

confidence: 99%

Section: A Preprocessingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…With this calculated PRI and frequency information, we proceed to the final step, which is the radar pattern recognition process. Each radar emitter has a significant pattern, like a fingerprint, which allows us to recognize it among others [14]. Therefore, by recognizing the frequency pattern and PRI pattern, we can classify the radar emitter of the received signals.…”

Section: Introductionmentioning

confidence: 99%

A Novel Batch Streaming Pipeline for Radar Emitter Classification

Park,

Seo,

Baek

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In electronic warfare, radar emitter classification plays a crucial role in identifying threats in complex radar signal environments. Traditionally, this has been achieved using heuristic-based methods and handcrafted features. However, these methods struggle to adapt to the complexities of modern combat environments and varying radar signal characteristics. To address these challenges, this paper introduces a novel batch streaming pipeline for radar emitter classification. Our pipeline consists of two key components: radar deinterleaving and radar pattern recognition. We leveraged the DBSCAN algorithm and an RNN encoder, which are relatively light and simple models, considering the limited hardware resource environment of a military weapon system. Although we chose to utilize lightweight machine learning and deep learning models, we designed our pipeline to perform optimally through hyperparameter optimization of each component. We demonstrate the effectiveness of our proposed model and pipeline through experimental validation and analysis. Overall, this paper provides background knowledge on each model, introduces the proposed pipeline, and presents experimental results.

show abstract

“…In recent years, supervised learning based on machine learning has been applied to the field of radar behavior analysis, including radar emitter classification in [5][6][7]. This method uses a feature-based classification algorithm where the radar waveform library and the corresponding pattern label are known.…”

Section: Introductionmentioning

confidence: 99%

Multi-attribute overlapping radar working pattern recognition based on K-NN and SVM-BP

Liao

Chen

2021

J Supercomput

View full text Add to dashboard Cite

A recognition model named the SVM-NP is proposed in this paper to address the multi-attribute overlap in radar working recognition. The model is based on the K-NN boundary preselection algorithm and SVM-BP algorithm. Traditional classifiers tend to neglect the overlap of samples' attributes in classification, which leads to the low accuracy of classifiers. The K-NN boundary preselection can quickly select boundary samples from the total ones and reduce the whole samples' attribute overlap. The SVM-BP algorithm is improved based on the SVM-RFE algorithm, and the boundary samples with high attribute overlap are divided into many planes for training and testing. Compared with traditional methods, the overlap of sample attributes can be reduced twice in this model. Theoretical analysis and experimental results verify that the model proposed in this paper displays better performance in classification when appropriate parameters are provided.

show abstract

A Parallel Neural Network-based Scheme for Radar Emitter Recognition

Cited by 4 publications

References 16 publications

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

A Novel Batch Streaming Pipeline for Radar Emitter Classification

Multi-attribute overlapping radar working pattern recognition based on K-NN and SVM-BP

Contact Info

Product

Resources

About