New Optimization Method Based on Neural Networks for Designing Radar Waveforms With Good Correlation Properties

Xia, Meng; Chen, Shichuan; Yang, Xiaoniu

doi:10.1109/access.2021.3092006

Cited by 6 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there aren't yet AIbased methods being used for this specific topic, and following what was proposed in refs. [1,2], this methods may have a great impact in the near future.…”

Section: Nrt Waveform Designmentioning

confidence: 99%

“…Artificial Intelligence has been a topic with recurrent interest in a wide area of applications and radar technology is one of them. Designing waveforms with good properties using AIbased methods [1,2] is an alternative to the classical deterministic radar waveforms and can provide good results when tailored for a specific environment or with a certain objective. In NRT, waveform design is an important barrier to overcome and these methods may prove a helpful tool in order to design a waveform with the desired characteristics as demonstrated in Section 3 (3.1).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, ISL is a metric that expresses the goodness of the autocorrelation property of a sequence [25]. It evaluates the ratio between the energy of all sidelobes with respect to the energy of the main lobe [25], as in Equation (1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence applications in Noise Radar Technology

Sénica,

Marques,

Figueiredo

2024

IET Radar Sonar & Navi

View full text Add to dashboard Cite

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use.

show abstract

“…However, there aren't yet AIbased methods being used for this specific topic, and following what was proposed in refs. [1,2], this methods may have a great impact in the near future.…”

Section: Nrt Waveform Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence applications in Noise Radar Technology

Sénica,

Marques,

Figueiredo

2024

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…When the waveform received by the radar altimeter is disturbed, the received signal will be different from the standard model, causing the distance measured by the altimeter to deviate from the actual distance. At present, the radar industry has begun to introduce AI into radar waveform signal processing, and has verified the feasibility [133][134][135].…”

Section: Availability Of Datamentioning

confidence: 99%

Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry

Xie

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Satellite altimetry technology has unparalleled advantages in the monitoring of hydrological resources. After decades of development, satellite altimetry technology has achieved a perfect integration from the geometric research of geodesy to the natural resource monitoring research. Satellite altimetry technology has shown great potential, whether solid or liquid. In general, this paper systematically reviews the development of satellite altimetry technology, especially in terms of data availability and program practicability, and proposes a multi-source altimetry data fusion method based on deep learning. Secondly, in view of the development prospects of satellite altimetry technology, the challenges and opportunities in the monitoring application and expansion of surface water changes are sorted out. Among them, the limitations of the data and the redundancy of the program are emphasized. Finally, the fusion scheme of altimetry technology and deep learning proposed in this paper is presented. It is hoped that it can provide effective technical support for the monitoring and application research of hydrological resources.

show abstract

“…It applies the waveform optimisation criterion or constraint as a loss function for back propagation to solve the optimisation problem of long sequences. [33] designed a ResNet‐based one‐dimensional siamese network to gradient down the peak partials ratio of MIMO radar to give the radar better clutter rejection and better Doppler blur for potential targets. Ref.…”

Section: Introductionmentioning

confidence: 99%

Online unsupervised generative learning framework based radar jamming waveform design

Sun

Gong

Mao³

et al. 2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

The jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the radar signal power spectrum density (PSD), a novel jamming waveform design method with an online unsupervised generative learning framework is proposed. The proposed framework consists of two modules: the waveform online generation module and the waveform unsupervised optimisation module. For the waveform online generation module, the well‐designed neural network (NN) is used to generate the jamming waveform. While for the waveform unsupervised iterative optimisation module, two loss functions are developed as two learning tasks to guide back‐propagation online. Given transmit power, the jamming PSD is designed adaptively to focus on the radar band and fit the radar PSD well, which improves the receiver in‐band jamming‐to‐signal ratio (JSR). It is worth noting that NN is end‐to‐end trained online without extensive prior training data. The framework can generate jamming waveforms within reasonable physical parameter ranges, and numerical experiments have shown that the designed jamming waveform performed better jamming effects on radar target detection in comparison with the traditional one.

show abstract

New Optimization Method Based on Neural Networks for Designing Radar Waveforms With Good Correlation Properties

Cited by 6 publications

References 31 publications

Artificial Intelligence applications in Noise Radar Technology

Artificial Intelligence applications in Noise Radar Technology

Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry

Online unsupervised generative learning framework based radar jamming waveform design

Contact Info

Product

Resources

About