Fast Fourier Transform Sparsity for High Quality Weather Radar Reconstruction

Purnamasari, Rita; Suksmono, Andriyan Bayu; Edward, Ian Joseph Matheus; Zakia, Irma

doi:10.1109/igarss.2019.8899152

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…The reconstruction algorithms compared are OMP, 1magic, and CVX-programming (MOSEK). The results plotted are similar to our previous works [20] and [21]. Fig.…”

Section: A Performance Of Reconstructing the Sample Beat Signalsupporting

confidence: 91%

“…In this paper, we follow the CS-based approach to reduce the large amount of data measured in FMCW polarimetric weather radar while preserving important information of the targets. Unlike existing literatures which utilized DCT, DWT, and SVD in creating the sparse signal [15]- [19], our previous results give indication that the sparsity of beat signal is best represented using FFT [20], [21]. Specifically, the PSNR using FFT sparsity representation is almost three times higher than that of the DCT and DWT in low CR.…”

Section: Introductioncontrasting

confidence: 53%

“…Although DCT, DWT and SVD have been used to exploit the sparsity of radar signal [15]- [19], we have demonstrated that fast fourier transform (FFT) exploits the sparsity of the beat signal better as quantified by a higher peak signal-to-noise ratio (PSNR) [20]. We have also showed that by increasing the compression rate (CR), the PSNR is reduced [21].…”

Section: Introductionmentioning

confidence: 99%

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Compressive Sampling of Polarimetric Doppler Weather Radar Processing Via Inverse Fast Fourier Transform

Purnamasari

Suksmono

Zakia

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Polarimetric Doppler frequency modulated continuous wave (FMCW) weather radar produces a tremendous amount of data during the observation of atmospheric conditions. Using conventional signal processing method, the beat signals are sampled at Nyquist rate, so that the range signal can be reconstructed perfectly. We propose compressive sampling (CS) technique to sample and reduce the data simultaneously by exploring the sparsity of the beat signal in transform domain. Reducing the number of the beat signal and the transform coefficients are natural choices, because sparse signals will be obtained by inverse Fourier transforming the beat signals. The proposed techniques are evaluated by constructing the plan position indicator (PPI) of reflectivity and mean Doppler velocity measurements from real weather polarimetric data. Compared to the conventional method, the CS polarimetric Doppler processing significantly reduce the number of the data, while pertaining important weather information. We show that the proposed method works properly to quantify and classify the precipitation, mainly when the number of the samples is above 25% of its original.Index Terms-Compressive sampling (CS), frequency modulated continuous wave (FMCW), inverse fast Fourier transform (IFFT), polarimetric Doppler weather radar (PDWR), sparse representation, weather radar.

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“…The reconstruction algorithms compared are OMP, 1magic, and CVX-programming (MOSEK). The results plotted are similar to our previous works [20] and [21]. Fig.…”

Section: A Performance Of Reconstructing the Sample Beat Signalsupporting

confidence: 91%