High-speed FPGA implementation of orthogonal matching pursuit for analog to information converter

2017 ◽  
Author(s):  
SuJuan Liu ◽  
Ning Lyu ◽  
ZiSheng Wang
Keyword(s):  
High Speed ◽  
Matching Pursuit ◽  
Fpga Implementation ◽  
Orthogonal Matching Pursuit

scholarly journals Low Complexity FFT Factorization for CS Reconstruction

2020 ◽  
Vol 9 (3) ◽  
pp. 438-442
Keyword(s):  
Real Time ◽  
High Speed ◽  
Performance Metrics ◽  
Matching Pursuit ◽  
Matrix Multiplication ◽  
Low Complexity ◽  
Orthogonal Matching Pursuit ◽  
Reconstruction Algorithms ◽  
Wide Range ◽  
Computationally Intensive

In this paper is presented a novel area efficient Fast Fourier transform (FFT) for real-time compressive sensing (CS) reconstruction. Among various methodologies used for CS reconstruction algorithms, Greedy-based orthogonal matching pursuit (OMP) approach provides better solution in terms of accurate implementation with complex computations overhead. Several computationally intensive arithmetic operations like complex matrix multiplication are required to formulate correlative vectors making this algorithm highly complex and power consuming hardware implementation. Computational complexity becomes very important especially in complex FFT models to meet different operational standards and system requirements. In general, for real time applications, FFT transforms are required for high speed computations as well as with least possible complexity overhead in order to support wide range of applications. This paper presents an hardware efficient FFT computation technique with twiddle factor normalization for correlation optimization in orthogonal matching pursuit (OMP). Experimental results are provided to validate the performance metrics of the proposed normalization techniques against complexity and energy related issues. The proposed method is verified by FPGA synthesizer, and validated with appropriate currently available comparative analyzes.

FPGA Implementation of Orthogonal Matching Pursuit for Compressive Sensing Reconstruction

2015 ◽  
Vol 23 (10) ◽  
pp. 2209-2220 ◽  
Author(s):  
Hassan Rabah ◽  
Abbes Amira ◽  
Basant Kumar Mohanty ◽  
Somaya Almaadeed ◽  
Pramod Kumar Meher
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Fpga Implementation ◽  
Orthogonal Matching Pursuit

scholarly journals Pipelined Image Reconstruction of SAR Radar Based on Orthogonal Matching Pursuit using FPGA Implementation

2020 ◽  
Vol 8 (5) ◽  
pp. 2558-2564
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Image Data ◽  
Processing Technique ◽  
Fpga Implementation ◽  
Orthogonal Matching Pursuit ◽  
Good Resolution ◽  
New Process ◽  
Speed Up ◽  
Normal Processing

Synthetic Aperture Radar (SAR) imaging methods is an interesting field in remote sensing. Nowadays, the Orthogonal Matching Pursuit Compressive Sensing (OMP) algorithm is applied on the reconstruction of data that are produced by SAR. The OMP is iterative algorithm that needs high time consumption and processing delay. This issue is considered one of the main problems that face the designers of the SAR systems that how to speed up the performance of the system to be more applicable. This paper provides an applicable pipelined processing technique for the OMP compressive sensing algorithm to speed up the compression and reconstruction of SAR Image Data. Based on the goal of this paper, it is possible to reduce the time processing of the OMP as every clock a new process will be started and it is not required to wait the certain process to be finished. The good-resolution images of the SAR are used for mapping, identification and other applications. This article defines the compressive sensing algorithms and it also discusses the design, analysis of the proposed pipelined processing method for one of the CS algorithms to reduce the consumed time using FPGA implementation. Moreover, the paper includes the implementation of the both normal processing and the proposed pipelined processing for the used algorithm. Finally, a comparison between the two algorithms is presented to evaluate the performance.

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