Compressive Sensing (CS) is a novel scheme, in which a signal that is sparse in a known transform domain can be reconstructed using fewer samples. The signal reconstruction techniques are computationally intensive and have sluggish performance, which make them impractical for real-time processing applications . The paper presents novel architectures for Orthogonal Matching Pursuit algorithm, one of the popular CS reconstruction algorithms. We show the implementation results of proposed architectures on FPGA, ASIC and on a custom many-core platform. For FPGA and ASIC implementation, a novel thresholding method is used to reduce the processing time for the optimization problem by at least 25%. Whereas, for the custom many-core platform, efficient parallelization techniques are applied, to reconstruct signals with variant signal lengths of N and sparsity of m. The algorithm is divided into three kernels. Each kernel is parallelized to reduce execution time, whereas efficient reuse of the matrix operators allows us to reduce area. Matrix operations are efficiently paralellized by taking advantage of blocked algorithms. For demonstration purpose, all architectures reconstruct a 256-length signal with maximum sparsity of 8 using 64 measurements. Implementation on Xilinx Virtex-5 FPGA, requires 27.14 μs to reconstruct the signal using basic OMP. Whereas, with thresholding method it requires 18 μs. ASIC implementation reconstructs the signal in 13 μs. However, our custom many-core, operating at 1.18 GHz, takes 18.28 μs to complete. Our results show that compared to the previous published work of the same algorithm and matrix size, proposed architectures for FPGA and ASIC implementations perform 1.3x and 1.8x respectively faster. Also, the proposed many-core implementation performs 3000x faster than the CPU and 2000x faster than the GPU.
KEYWORDS: Target detection, Radar, Signal to noise ratio, Detection and tracking algorithms, Matrices, Monte Carlo methods, Reconstruction algorithms, Signal detection, Environmental sensing, Compressed sensing
In this paper, radar detection via compressive sensing is explored. Compressive sensing is a new theory of
sampling which allows the reconstruction of a sparse signal by sampling at a much lower rate than the Nyquist
rate. By using this technique in radar, the use of matched filter can be eliminated and high rate sampling can be
replaced with low rate sampling. In this paper, compressive sensing is analyzed by applying varying factors such
as noise and different measurement matrices. Different reconstruction algorithms are compared by generating
ROC curves to determine their detection performance. We conduct simulations for a 64-length signal with 3
targets to determine the effectiveness of each algorithm in varying SNR. We also propose a simplified version
of Orthogonal Matching Pursuit (OMP). Through numerous simulations, we find that a simplified version of
Orthogonal Matching Pursuit (OMP), can give better results than the original OMP in noisy environments
when sparsity is highly over estimated, but does not work as well for low noise environments.