Automatic Detection of Solar Radio Spectrum Based on Codebook Model

Space weather can affect human production and life, and solar radio burst will seriously affect space weather. Automatic detection of solar radio bursts in real time has a positive effect on space weather warning and prediction. Codebook model is used to simulate solar background radio to achieve automatic detection of solar radio bursts in this paper. Firstly, channel normalization was used to eliminate channel difference of original radio data. Then, a new automatic detection method for solar radio bursts based on codebook model was proposed to detect radio bursts. Finally, morphological processing was implemented to obtain burst parameters by detecting binary burst area. The experimental results show that the proposed method is effective.

[1]  Peijin Zhang,et al.  A type III radio burst automatic analysis system and statistic results for a half solar cycle with Nançay Decameter Array data , 2018, Astronomy & Astrophysics.

[2]  Guowu Yuan,et al.  Solar Radio Burst Automatic Detection Method for Decimetric and Metric Data of YNAO , 2019, ICPCSEE.

[3]  Iver H. Cairns,et al.  Automatic recognition of type III solar radio bursts: Automated Radio Burst Identification System method and first observations , 2009 .

[4]  Peter A. Robinson,et al.  AUTOMATIC RECOGNITION OF CORONAL TYPE II RADIO BURSTS: THE AUTOMATED RADIO BURST IDENTIFICATION SYSTEM METHOD AND FIRST OBSERVATIONS , 2010 .

[5]  Guowu Yuan,et al.  A Noise Reduction Method for Solar Radio Spectrum Based on Improved Guided Filter and Morphological Cascade , 2019, ICNC-FSKD.

[6]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

[7]  Zhou He-qin.  A two-layers background modeling method based on codebook and texture , 2012 .

[8]  Ming Xu,et al.  Parallelly generating halo orbit and its transfer trajectory in the full ephemeris model , 2019, Astrophysics and Space Science.

[9]  Long Xu,et al.  Multimodal deep learning for solar radio burst classification , 2017, Pattern Recognit..

[11]  Christian Monstein,et al.  Automated Detection of Solar Radio Bursts Using a Statistical Method , 2019, Solar Physics.

[12]  Long Xu,et al.  Convolutional neural network for classification of solar radio spectrum , 2017, 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).