A broadband digital receiving system with large dynamic range for solar radio observation

Yan, Fabao; Liu, Yang; Xu, Ke; Shang, Ziqian; Su, Yanrui; Lu, Guang; Yao, Chen; Wu, Zhao

doi:10.1088/1674-4527/20/9/156

Cited by 12 publications

(11 citation statements)

References 36 publications

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“…With the development of computer performance and artificial intelligence technology, increasingly more deep learning models and algorithms are used to solve natural language processing, computer vision and speech recognition tasks ( Fu et al, 1995 ). In light of the related problems in the field of astronomy, many previous works have used image processing algorithms and neural network models for reference, as well as big data technology and artificial intelligence methods to carry out the research of burst classification based on SBRS data, which has gradually been widely recognized ( Yan et al, 2020 ). In the early stage of research, researches first used a Canny operator to detect and reduce the bad data of instrument or environmental interference, and then used Hough and Radon transforms to detect the approximate straight line in the spectrum, through line matching to analyze and identify type II and type III bursts.…”

Section: Introductionmentioning

confidence: 99%

A deep learning method for the recognition of solar radio burst spectrum

Guo

Yan

Wan

et al. 2022

PeerJ Computer Science

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Solar radiation is the excitation source that affects the weather in the atmosphere of the earth, and some solar activities such as flares and coronal mass ejections are often accompanied by radio bursts. The spectrum of solar radio bursts is helpful for astronomers to explore the mechanism of radio bursts. With the development and progress of solar radio spectrum observation methods, the observation of the Sun can be done at almost all times of day. How to quickly and automatically identify the small proportion of burst data from the huge corpus of observation data has become an important research direction. The innovation of this study is to enhance the original radio spectrum dataset with unbalanced sample distribution, and a neural network model for solar radio spectrum image classification is proposed on this basis. This hybrid structure of joint convolution and a memory unit overcomes the shortcoming of the traditional convolution or memory model, which can only extract one-sided features of an image. By extracting the frequency structure features and time-series features at the same time, the sensitivity to the small features of the spectrum image can be enhanced. Based on the data of the Solar Broadband Radio Spectrometer (SBRS) in China, the proposed network model can improve the average classification accuracy of the spectrum image to 98.73%, which will be helpful for related astronomical research.

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Section: Introductionmentioning

confidence: 99%

A deep learning method for the recognition of solar radio burst spectrum

Guo

Yan

Wan

et al. 2022

PeerJ Computer Science

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“…Although solar radio emission is excited by different emission mechanisms in different frequency ranges, it always contains information on the magnetic field and plasma in the source regions in the solar corona (Casini, White, & Judge 2017). In particular, the bursts that are generated by gyrosynchrotrons have widely been used to diagnose coronal magnetic fields (Yan et al 2020;Chennamangalam et al 2014;Huang 2006;Gary, Fleishman, & Nita 2013;Nita et al 2015;Wu et al 2016;Kuroda et al 2018;Wu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Design of a high-resolution antenna array solar observing system for radio frequencies of 25–110 MHz

Wang

Dong

et al. 2022

Publ. Astron. Soc. Aust.

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Spectral observations with high temporal and frequency resolution are of great significance for studying the fine structures of solar radio bursts. In addition, it is helpful to understand the physical processes of solar eruptions. In this paper, we present the design of a system to observe solar radio bursts with high temporal and frequency resolutions at frequencies of 25–110 MHz. To reduce the impact of analog devices and improve the system flexibility, we employ various digital signal processing methods to achieve the function of analog devices, such as polarisation synthesis and beamforming. The resourceful field programmable gate array is used to process radio signals. The system has a frequency resolution of $\sim$ 30 kHz and a temporal resolution of up to 0.2 ms. The left/right circular polarisation signals can be simultaneously observed. At present, the system has been installed at Chashan Solar Observatory operated by the Institute of Space Science, Shandong University. The system is running well, multiple bursts have been observed, and relevant data have been obtained.

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“…The intermediate frequency (IF) signal is then processed by a digital receiver (spectrometer), in which the analog signal is converted into digital signals and yields the realtime dynamic spectrum. The recent development of a fast analogto-digital converter (ADC) has provided the chance to directly acquire high-frequency signals (several GHz) and could eliminate the mixing process in the AFE circuit (Yan et al 2020). The application of these high-performance devices could not only improve the time and spectral resolutions of real-time solar radio observations but also reduce the volume, weight and cost of the observation system.…”

Section: Introductionmentioning

confidence: 99%

A New Multichannel Parallel Real-time FFT Algorithm for a Solar Radio Observation System Based on FPGA

Zhang¹,

Zhang²,

Shang³

et al. 2022

PASP

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The real-time fast Fourier transform (FFT) is the essential algorithm for signal processing in a solar radio receiver. However, field-programmable gate array (FPGA) computation resources have become the limitation of real-time processing of signals with increasing time and spectral resolutions. It is necessary to design a real-time parallel FFT algorithm with reduced resource occupation in the development of future receiving systems. In this paper, we developed a multichannel parallel FFT algorithm named the multichannel parallel real-time fast Fourier transform (MPR-FFT), which can greatly reduce FPGA resource occupation while increasing the real-time processing speed. In this algorithm, the 4L simultaneous N-point FFTs are first converted into L simultaneous 4N-point FFTs. Fusion processing is then performed to obtain the 4 ∗ L ∗ N-point spectrum. This method has been used in developing a solar radio spectrometer, which works in the frequency range of 0.5–15 GHz in the Chashan Observatory. In this spectrometer, 16 channel MPR-FFT with 8k-point data is realized in a Xilinx UltraScale KU115 FPGA. The MPR-FFT algorithm reduced the computational resources to a large extent compared to the Cooley-Tukey-based parallel FFT method; for instance, the Look-Up-Table, Look-Up-Table RAM, Flip-Flop, and Digital Signal Process slices were reduced by 37%, 50%, 17%, and 2.48%, respectively. Although the MPR-FFT consumes 14 block RAM resources more than the Cooley-Tukey-based parallel FFT, the MPR-FFT algorithm presents an overall reduction in resource usage.

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A broadband digital receiving system with large dynamic range for solar radio observation

Cited by 12 publications

References 36 publications

A deep learning method for the recognition of solar radio burst spectrum

A deep learning method for the recognition of solar radio burst spectrum

Design of a high-resolution antenna array solar observing system for radio frequencies of 25–110 MHz

A New Multichannel Parallel Real-time FFT Algorithm for a Solar Radio Observation System Based on FPGA

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