Low-Complexity Architecture of Orthogonal Matching Pursuit Based on QR Decomposition

Roy, Shirshendu; Acharya, Debiprasad Priyabrata; Sahoo, Ajit Kumar

doi:10.1109/tvlsi.2019.2909754

Cited by 16 publications

(16 citation statements)

References 19 publications

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“…The design metric recovery signal‐to‐noise ratio (RSNR) [25] is preferred here to estimate the design performance. RSNR is measured as:

R S N R = 20 \log_{10} ((), \frac{1}{R M S E})

…”

Section: Performance Of the Lpf‐omp Algorithm And Its Implementationmentioning

confidence: 99%

“…In addition, the hardware complexity must have less dependency on unknown signal sparsity. Previously in [25,26] we have presented hardware efficient architectures of OMP based on QR decomposition and incremental Gaussian elimination, respectively where same hardware resources are shared to perform different operations.…”

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confidence: 99%

“…20 of Algorithm 2 are for matrix inversion. The matrix B is evaluated in the same way as depicted in[25]. In each iteration, R_mem stores an evaluated column of R and B_mem stores the elements of matrix B.…”

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confidence: 99%

“…| PROPOSED ARCHITECTURE FOR LPF-OMP ALGORITHMCompared to our earlier study[25], a simpler and faster architecture is proposed here to implement LPF-OMP algorithm. The proposed architecture uses a single vector multiplication unit (VMU) to perform all the vector arithmetic operations involved in OMP algorithm.…”

mentioning

confidence: 99%

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Fast OMP algorithm and its FPGA implementation for compressed sensing‐based sparse signal acquisition systems

Roy

Acharya

Sahoo

2021

IET Circuits, Devices & Syst

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Compressed sensing-based radio frequency signal acquisition systems call for higher reconstruction speed and low dynamic power. In this study, a novel low power fast orthogonal matching pursuit (LPF-OMP) algorithm is proposed for faster reconstruction of sparse signals from their compressively sensed samples and the reconstruction circuit consumes very low dynamic power. The searching time to find the best column is reduced by reducing the number of columns to be searched in successive iterations. A novel architecture of the proposed LPF-OMP algorithm is also presented here. The proposed architecture is implemented on field programmable gate array for demonstrating the performance enhancement. Computation of pseudoinverse in OMP is avoided to save time and storage requirement to store the pseudoinverse matrix. The proposed design incorporates a novel strategy to stop the algorithm without consuming any extra circuitry. A case study is carried out to reconstruct the RADAR test pulses. The design is implemented for K = 256, N = 1024 using XILINX Virtex6 device and supports maximum of K/4 iterations. The proposed design is faster, hardware efficient and consumes very less dynamic power than the previous implementations of OMP. In addition, the proposed implementation proves to be efficient in reconstructing low sparse signals. 1 | INTRODUCTION High-frequency radio frequency (RF) signals, such as RADAR pulses, are sparse in nature in the transform domain. Exploiting this sparsity property, modern signal measurement systems use compressed sensing (CS) [1,2] in place of other existing sampling techniques [3] to acquire RF signals. CSbased acquisition systems can work with low speed analog-todigital converters due to sampling at sub-Nyquist rate [4]. In CS-based sampling paradigm, random measurements are taken from the signal and then the original signal is recovered from the measurement samples using signal recovery algorithms. Orthogonal matching pursuit (OMP) [5,6] is a well known recovery algorithm. Unlike the other greedy pursuit algorithms, OMP provides better performance with moderate computational complexity. OMP estimates a sparse signal by executing two steps in every iteration, viz., perform the atom searching (AS) and solve a least squares (LS) problem. In AS step, OMP identifies an atom or a column of the sampling matrix which gives maximum correlation with the current residual. Subsequently the signal is estimated by solving an LS problem. The timing complexity of the AS step is very high as it is a linear function of the signal sparsity and the number of samples. Many techniques are reported in literature to reduce the timing complexity of AS step. In [7] authors applied clustering algorithms to group the similar columns and reported a tree-based pursuit algorithm. But such algorithm has no reports of implementation. Researchers reported parallel selection of multiple columns to address the timing complexity problem in [8,9]. Multiple selection of columns reduces the timing complexity but with greater cha...

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“…The design metric recovery signal‐to‐noise ratio (RSNR) [25] is preferred here to estimate the design performance. RSNR is measured as:

R S N R = 20 \log_{10} ((), \frac{1}{R M S E})

…”

Section: Performance Of the Lpf‐omp Algorithm And Its Implementationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Fast OMP algorithm and its FPGA implementation for compressed sensing‐based sparse signal acquisition systems

Roy

Acharya

Sahoo

2021

IET Circuits, Devices & Syst

Self Cite

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“…Matrix Θ and Φ are defined as the sensing matrices. Different from [26] which uses Gabor dictionary, our method uses Fourier dictionary in CS-based radar signal reconstruction.…”

Section: B Subsampling the Range Direction Along Sweep In Time Domain (Cs-td)mentioning

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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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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An Optimization Methodology for Designing Hardware-Based Function Evaluation Modules with Reduced Complexity

González-Díaz_Conti

Longoria-Gándara

Carrasco-Alvarez

et al. 2021

Circuits Syst Signal Process

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Low-Complexity Architecture of Orthogonal Matching Pursuit Based on QR Decomposition

Cited by 16 publications

References 19 publications

Fast OMP algorithm and its FPGA implementation for compressed sensing‐based sparse signal acquisition systems

Fast OMP algorithm and its FPGA implementation for compressed sensing‐based sparse signal acquisition systems

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

An Optimization Methodology for Designing Hardware-Based Function Evaluation Modules with Reduced Complexity

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