2-Dimensional high-quality reconstruction of compressive measurements of phased array weather radar

Kawami, Ryosuke; Hirabayashi, Akira; Tanaka, Nobuyuki; Shibata, M.; Ijiri, Takashi; Shimamura, Shigeharu; Kikuchi, Hiroshi; Kim, Gwan; Ushio, Tomoo

doi:10.1109/apsipa.2016.7820687

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…The defined matrix Θ = ΦΨ −1 where Θ ∈ C M ×N , represents a dual dictionary, usually called the sensing matrix. Based on (18), there are many combinations to reconstruct vector X, calledX. However, the advantage of the dual dictionary is its high compressibility because it consists of only a few significant coefficients.…”

Section: A Direct Subsampling Of the Beat Signal (Cs-fd)mentioning

confidence: 99%

“…Shimamura et al [16] exploited the discrete wavelet transform (DWT) and DCT to represent a one-dimensional range in sparsity domain and then reduced the data using Gaussian random selection. Ryosuke et al [17], [18] proposed DCT for sparsity technique to reduce the two-dimensional and three-dimensional of phased array weather radar (PAWR). Different from the others, Mishra et al [19] represented the sparsity data by modeling the matrix of weather data into the low-rank matrix using singular value decomposition (SVD).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: A Direct Subsampling Of the Beat Signal (Cs-fd)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Makalah lainnya yaitu (Kawami, et al, 2016) dan juga telah mengusulkan metode CS untuk kompresi radar cuaca jenis Phased Array Weather Radar (PAWR). Pada penelitian pertama, penulis meneliti kompresi radar cuaca untuk 2 dimensi sedangkan pada makalah kedua, kompresi radar cuaca dilakukan untuk 3 dimensi.…”

Section: Pendahuluanunclassified

“…Pada makalah ini, mengusulkan metode CS dengan menggunakan DCT sebagai metode transformasi sparsitas dan rekonstruksi ℓ pada data rill sinyal beat radar IWarp. Penelitian CS untuk radar cuaca dengan metode transformasi sparsitas menggunakan DCT sudah pernah dilakukan oleh (Kawami, et al, 2016) namun teknik rekonstruksi yang mereka gunakan adalah dengan mencari nilai minimum Total Variance, sedangkan pada makalah ini mengusulkan teknik rekonstruksi dengan menggunakan minimasi ℓ . Teknik ℓ sebagai bagian dari teknik basis pursuit yang digunakan pada makalah ini karena memiliki tingkat kecepatan yang lebih tinggi berdasarkan hasil penelitian pada makalah sebelumnya (Purnamasari, Suksmono, Edward, & Zakia, 2018).…”

Section: Pendahuluanunclassified

Compressive Sampling untuk Sinyal Beat Radar Cuaca via Discrete Cosine Transform (DCT)

Purnamasari

Suksmono

2019

ELKOMIKA

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ABSTRAKCompressive sampling (CS) merupakan metode baru yang memungkinkan proses pengambilan sampel dan kompresi dilakukan secara bersamaan sehingga dapat mempercepat waktu komputasi sekaligus memperkecil bandwidth saat dilewatkan pada media transmisi. Salah satu cara agar CS dapat bekerja secara optimal adalah jika sinyal yang akan diolah memiliki tingkat sparsitas yang tinggi. Pada makalah ini, mengusulkan algoritma Discrete Cosine Transform (DCT) sebagai metode transformasi sparsitas untuk sinyal beat radar cuaca IWarp. Sinyal beat menjadi sparse setelah direpresentasikan pada domain frekuensi, sehingga dapat mengambil sampelnya secara acak dan akhirnya mendapatkan sekumpulan sinyal sampel yang telah berukuran lebih kecil daripada sinyal beat radar awal. Pada penelitian ini menggunakan ℓ􀬵 -magic untuk melakukan rekonstruksi dari sinyal yang telah terkompresi tersebut. Simulasi numerik menunjukkan bahwa algoritma yang diusulkan berada pada performansi yang baik dengan rata-rata Peak Signal Noise to Ratio (PSNR) sebesar 15,17 dB.Kata kunci: CS, radar cuaca, sparsitas, DCT, rekonstruksi ℓ􀬵 ABSTRACTCompressive sampling (CS) is a new method that allows sampling and compression to be carried out simultaneously so that it can increase the computing time and reduce bandwidth while passed on the transmission media. One way for CS to work optimally is if the signal to be processed has a high sparsity level. In this paper we propose the Discrete Cosine Transform (DCT) algorithm as a sparsity transformation method for IWarp weather radar beat signals. The beat signal becomes sparse after being represented in the frequency domain, so this can randomly take samples and finally get a set of sample signals that are smaller than the initial radar beat signal. In this reasearch, use ℓ􀬵-magic to reconstruct the compressed signal. Numerical simulations show that the proposed algorithm is in good performance with an average Peak Signal Noise to Ratio (PSNR) of 15.17 dBKeywords: CS, weather radar, sparsity, DCT, ℓ􀬵-reconstruction

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“…Mishra et al [14] proposed an unconventional weather radar paradigm that employs compressed sensing techniques to reduce the radar scan time without any significant loss of target information. Kawami et al [15,16] proposed an effective three-dimensional compressive sensing method for the phased array weather radar (PAWR), which achieves normalized errors of less than 10% for a 25% compression ratio that outperforms conventional two-dimensional methods. These approaches use the compression sensing technique to reduce the amount of data based on the sparsity of weather radar signals.…”

Section: Introductionmentioning

confidence: 99%

Weather Radar Data Compression Based on Spatial and Temporal Prediction

Zeng

Zhao

et al. 2018

Atmosphere

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The transmission and storage of weather radar products will be an important problem for future weather radar applications. The aim of this work is to provide a solution for real-time transmission of weather radar data and efficient data storage. By upgrading the capabilities of radar, the amount of data that can be processed continues to increase. Weather radar compression is necessary to reduce the amount of data for transmission and archiving. The characteristics of weather radar data are not considered in general-purpose compression programs. The sparsity and data redundancy of weather radar data are analyzed. A lossless compression of weather radar data based on prediction coding is presented, which is called spatial and temporal prediction compression (STPC). The spatial and temporal correlations in weather radar data are utilized to improve the compression ratio. A specific prediction scheme for weather radar data is given, while the residual data and motion vectors are used to replace the original values for entropy coding. After this, the Level-II product from CINRAD SA is used to evaluate STPC. Experimental results show that the STPC achieves a better performance than the general-purpose compression programs, with the STPC yield being approximately 26% better than the next best approach.

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2-Dimensional high-quality reconstruction of compressive measurements of phased array weather radar

Cited by 9 publications

References 10 publications

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

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

Compressive Sampling untuk Sinyal Beat Radar Cuaca via Discrete Cosine Transform (DCT)

Weather Radar Data Compression Based on Spatial and Temporal Prediction

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