Deep belief networks based radar signal classification system

Jeong, Chang Min; Jung, Young Giu; Lee, Sang Jo

doi:10.1007/s12652-018-0774-7

Cited by 7 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once stacked with a DBN, an RBM's hidden layer is not visible to its visible layer, but to the visible layer of the next RBM. In radar, DBN has been used to radar emitter recognition and classification [185,186].…”

Section: A23 | Dee P Belief Network (Dbns)mentioning

confidence: 99%

Artificial intelligence meets radar resource management: A comprehensive background and literature review

Hashmi

Akbar

Adve

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

A multi‐function radar is designed to perform disparate functions, such as surveillance, tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) budget. A radar resource management (RRM) module within a radar system makes decisions on prioritisation, parameter selection, and scheduling of associated tasks. However, optimal RRM algorithms are generally computationally complex and operational radars resort to heuristics. On the other hand, algorithms based on artificial intelligence (AI) have been shown to yield near‐optimal radar resource allocation results at manageable computational complexity. This survey study aims at enabling researchers and practitioners better understand the application of AI in RRM‐related problems by providing a thorough literature review of AI‐based RRM techniques. We first provide background concepts in RRM followed by a brief review of Symbolic‐AI techniques for RRM. We mainly focus on the applications of state‐of‐the‐art machine learning techniques to RRM. We emphasise on the recent findings and their potential within practical RRM scenarios for real‐time resource allocation optimisation. The study concludes with a discussion of open research problems and future research directions in the light of the presented survey.

show abstract

Section: A23 | Dee P Belief Network (Dbns)mentioning

confidence: 99%

Artificial intelligence meets radar resource management: A comprehensive background and literature review

Hashmi

Akbar

Adve

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…5. In RSP domain, DBN has been used to radar emitter recognition and classification [234]- [236], HRRP-ATR [517], [518], SAR-ATR [312].…”

Section: ) Restricted Boltzmann Machines (Rbms) and Deep Belief Netwo...mentioning

confidence: 99%

“…A more efficient threat library was generated in [234] for radar signal classification based on DBN model, consisted of independent RBMs of frequency, pulse repetition interval, pulse width respectively, and a RBM fused the pervious results again. The experiments results showed more than 6% performance improvement over the existing system.…”

Section: Deep Learning In Rrscrmentioning

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

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

“…Traditional radar signal recognition methods rely on handcraft features extraction [5][6][7][8][9][10][11]. However, these methods lack flexibility and are computationally inefficient.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

Luo

Deng

2022

Journal of Sensors

View full text Add to dashboard Cite

The recognition technology of the radar signal modulation mode plays a critical role in electronic warfare, and the algorithm based on deep learning has significantly improved the recognition accuracy of radar signals. However, the convolutional neural networks became increasingly sophisticated with the progress of deep learning, making them unsuitable for platforms with limited computing resources. ResXNet, a novel multiscale lightweight attention model, is proposed in this paper. The proposed ResXNet model has a larger receptive field and a novel grouped residual structure to improve the feature representation capacity of the model. In addition, the convolution block attention module (CBAM) is utilized to effectively aggregate channel and spatial information, enabling the convolutional neural network model to extract features more effectively. The input time-frequency image size of the proposed model is increased to 600 × 600 , which effectively reduces the information loss of the input data. The average recognition accuracy of the proposed model achieves 91.1% at -8 dB. Furthermore, the proposed model performs better in terms of unsupervised object localization with the class activation map (CAM). The classification information and localization information of the radar signal can be fused for subsequent analysis.

show abstract

Deep belief networks based radar signal classification system

Cited by 7 publications

References 9 publications

Artificial intelligence meets radar resource management: A comprehensive background and literature review

Artificial intelligence meets radar resource management: A comprehensive background and literature review

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

Contact Info

Product

Resources

About