Three-Dimensional Data Compression and Fast High-Quality Reconstruction for Phased Array Weather Radar

Kawami, Ryosuke; Kitahara, Daichi; Hirabayashi, Akira; Yoshikawa, Eiichi; Kikuchi, Hiroshi; Ushio, Tomoo

doi:10.1541/ieejeiss.140.40

“…This is the key technology in the PAWR because it gets rid of the mechanical vertical scan and hence the temporal resolution is significantly improved in the weather observation. For example, the PAWR developed at Osaka University performs a volume scan of 60 kilometers radius with 600 range, 300 azimuth, and 110 elevation points (i.e., with a resolution of about 100 [m]×1.2 [deg]× 0.82 [deg]) in 30 seconds by one rotation in the azimuth direction [5], [21], while the Doppler radar performs a similar scan with freely-selected 10 to 20 elevation points of about 1.2 [deg] beamwidth in 5 to 10 minutes by rotations equal to the number of the elevations [8] (see [22] for comparison of these radars).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

Kitahara

¹

,

Kuroda

²

,

Hirabayashi

³

et al. 2022

IEEE Trans. Geosci. Remote Sensing

7

0

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We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals.For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, 1) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and 2) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately but requires more time to solve. Both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.

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“…This is the key technology in the PAWR because it gets rid of the mechanical vertical scan and hence the temporal resolution is significantly improved in the weather observation. For example, the PAWR developed at Osaka University performs a volume scan of 60 kilometers radius with 600 range, 300 azimuth and 110 elevation points (i.e., with a resolution of about 100 [m]×1.2 [deg]× 0.82 [deg]) in 30 seconds by one rotation in the azimuth direction [5], [21], while the Doppler radar performs a similar scan with freely-selected 10 to 20 elevation points of about 1.2 [deg] beamwidth in 5 to 10 minutes by rotations equal to the number of the elevations [8] (see [22] for comparison of these radars).…”

mentioning

confidence: 99%

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

Kitahara¹,

Kuroda²,

Hirabayashi³

et al. 2022

Preprint

Self Cite

0

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<div>We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, (i) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and (ii) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately, but requires more time to solve. Both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.</div>

show abstract

“…This is the key technology in the PAWR because it gets rid of the mechanical vertical scan and hence the temporal resolution is significantly improved in the weather observation. For example, the PAWR developed at Osaka University performs a volume scan of 60 kilometers radius with 110 elevation and 300 azimuth points in 30 seconds [5], [21], while the Doppler radar requires 5 to 10 minutes for a similar scan with 10 to 20 elevation points [8].…”

mentioning

confidence: 99%

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

Kitahara¹,

Kuroda²,

Hirabayashi³

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

<div>We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, (i) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and (ii) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately, but requires more time to solve. Both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.</div>

show abstract

Three-Dimensional Data Compression and Fast High-Quality Reconstruction for Phased Array Weather Radar

Cited by 4 publications

References 8 publications

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

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