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Compressive sampling for range-doppler weather radar detection
Purnamasari R.a, Suksmono A.B.a, Joseph Matheus Edward I.a, Zakia I.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology (ITB), Bandung, Indonesia
b School of Electrical Engineering, Telkom University, Bandung, 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]© 2018 IEEE.Weather radar generate a lot of data observation to get the high resolution. We proposed the Compressive Sampling (CS) method by randomly sampling sample-beat that contain the range information on single polarization. The convex optimization then used in reconstruction to get sample-beat estimation. Finally the estimation sample-beat in each phase joined to be processed in radar signal processing. Since the sample-beat are real number so the sparsity transform used Discrete Cosine Transform (DCT). This paper then compare the change between compressed and not compressed data for range profile and power Doppler detection of weather radar at once sweep. We used real radar data from IRCTR Drizzle Radar (IDRA) to simulate our algorithm. Simulation result showed that proposed method have less normalized error in range profile than power Doppler. However the velocity in power Doppler are not change since the compression only on the row of sample-beat.[/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]Compressed datum,Compressive sampling,Discrete Cosine Transform(DCT),Normalized errors,Range information,Real radar data,Single polarization,Sparsity[/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]Compressive sampling,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]ACKNOWLEDGMENT The author would like to thank to Telkom University, Indonesia Ministry of Higher Education and Indonesia Ministry of Finance (BUDI-DN & LPDP), and LPPM ITB for financial support in this research.[/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/ICOMIS.2018.8644792[/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]