Low Complexity FFT Factorization for CS Reconstruction
Alahari Radhika1, K. Satya Prasad2, K. Kishan Rao3

1Alahari Radhika*, Research Scholar, Department of ECE, JNT University, Kakinada, AP, India.
2K. Satya Prasad, Department of ECE, Rector, Vignan`s Deemed to be university, Guntur, AP, India.
3K. Kishan Rao, Department of ECE, Director-FD, Srinidhi institute of science and technology, Hyderabad, Telangana, India. 

Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 438-442 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4675029320/2020©BEIESP | DOI: 10.35940/ijeat.C4675.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Compressive sensing, Fast Fourier transform, Orthogonal matching pursuit, FPGA, etc.