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Floating Point Implementation of the Improved QRD and OMP for Compressive Sensing Signal Reconstruction

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Abstract

In this paper, the Floating-Point Core Architecture based QR decomposition is proposed for solving least square problems in the Orthogonal Matching Pursuit algorithm (OMP-FPCA-QRD). To improve the computational performance of Orthogonal Matching Pursuit (OMP), it is necessary to modify the Orthogonal Matching Pursuit algorithm for analysing a wide range of signals in field programmable gate array (FPGA). As a result, it highly benefits from the available resources and acquires a scalable computational complexity. Since the solution of least square problem involves some iterative parts, like square root and division units, the processing time of the proposed QR Decomposition (QRD) approach is decreased by increasing parallelism using processing element driven systolic array implementation across all data-dependent operations. The hardware implementation on the ALTERA field programmable gate array shows optimal performance depends on hardware complexity and frequency of operation with the improved computational accuracy over existing QR Decomposition implementations. Moreover, the implementation of Orthogonal Matching Pursuit algorithm for signal reconstruction is also proposed to validate the performance metrics of floating point unit (FPU). The experimental results show that the optimization of floating point unit offers significant resource optimization in QR decomposition, and also better performance of high peak signal-to-noise ratio of 32.99 dB, which outperforms all other fixed point Orthogonal Matching Pursuit systems.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, JNT University, Kakinada, Andhra Pradesh, India

    Radhika Alahari

  2. Department of Electronics and Communication Engineering, UCEK, Kakinada, India

    Satya Prasad Kodati

  3. Department of Electronics and Communication Engineering, NITW, Warangal, India

    Kishan Rao Kalitkar

Authors
  1. Radhika Alahari
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  2. Satya Prasad Kodati
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  3. Kishan Rao Kalitkar
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Correspondence to Radhika Alahari.

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Alahari, R., Kodati, S.P. & Kalitkar, K.R. Floating Point Implementation of the Improved QRD and OMP for Compressive Sensing Signal Reconstruction. Sens Imaging 23, 20 (2022). https://doi.org/10.1007/s11220-022-00389-z

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  • DOI: https://doi.org/10.1007/s11220-022-00389-z

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