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Fast Fourier Transform Sparsity for High Quality Weather Radar Reconstruction
Purnamasari R.a,b, Suksmono A.B.a, Joseph Matheus Edward I.a, Zakia I.a
a Bandung Institute of Technology, School of Electrical Engineering and Informatics, Indonesia
b School of Electrical Engineering, Telkom University, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.We proposed the Fast Fourier Transform (FFT) to represent the sparsity of weather radar signals based on Compressive Sampling (CS). since the real weather radar signal that we used in this research was produced by Frequency Modulated Continuous Wave (FMCW) radar, the frequency beat signal is proportional to the delay of the targets. FFT then used to get the coefficient of frequency beat signal that could show the range information. Evidently, FFT produced the large number of sparse frequency beat signal, so we can perform CS to reduce the large volume of the weather radar data. We sampled the sparse of range information using random measurement and then reconstructed using Orthogonal Matching Pursuit (OMP) algorithm. We construct a Plan Position Indicator (PPI) from compressed beat signals and compare with the uncompressed ones. The reconstruction results at various compression rates show that the proposed method has works properly.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Compression rates,Compressive sampling,Frequency modulated continuous wave radars,Orthogonal matching pursuit,Plan position indicators,Random measurement,sparsity,Weather radar data[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]CS,FFT,reconstruction,sparsity,weather radar[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IGARSS.2019.8899152[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]