The Implementation of the Improved OMP for AIC Reconstruction Based on Parallel Index Selection
Sparse signal recovery becomes extremely challenging for a variety of real-time applications. In this paper, we improve the orthogonal matching pursuit (OMP) algorithm based on parallel correlation indices selection mechanism in each iteration and Goldschmidt algorithm. Simulation results show that the improved OMP algorithm with a reduced number of iterations and low hardware complexity of matrix operations has higher success rate and recovery signal-to-noise-ratio (RSNR) for sparse signal recovery. This paper presents an efficient complex valued system hardware architecture of the recovery algorithm for analog-to-information structure based on compressive sensing. The proposed architecture is implemented and validated on the Xilinx Virtex6 field-programmable gate array (FPGA) for signal reconstruction with N = 1024, K = 36, and M = 256. The implementation results showed that the improved OMP algorithm achieved a higher RSNR of 31.04 dB compared with the original OMP algorithm. This synthesized design consumes a few percentages of the hardware resources of the FPGA chip with the clock frequency of 135.4 MHZ and reconstruction time of 170 µs, which is faster than the existing design.