Compressed sensing-based radio frequency signal acquisition systems call for higher reconstruction speed and low dynamic power. In this study, a novel low power fast orthogonal matching pursuit (LPF-omp) algorithm is ...
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Compressed sensing-based radio frequency signal acquisition systems call for higher reconstruction speed and low dynamic power. In this study, a novel low power fast orthogonal matching pursuit (LPF-omp) algorithm is proposed for faster reconstruction of sparse signals from their compressively sensed samples and the reconstruction circuit consumes very low dynamic power. The searching time to find the best column is reduced by reducing the number of columns to be searched in successive iterations. A novel architecture of the proposed LPF-ompalgorithm is also presented here. The proposed architecture is implemented on field programmable gate array for demonstrating the performance enhancement. Computation of pseudoinverse in omp is avoided to save time and storage requirement to store the pseudoinverse matrix. The proposed design incorporates a novel strategy to stop the algorithm without consuming any extra circuitry. A case study is carried out to reconstruct the RADAR test pulses. The design is implemented for K = 256, N = 1024 using XILINX Virtex6 device and supports maximum of K/4 iterations. The proposed design is faster, hardware efficient and consumes very less dynamic power than the previous implementations of omp. In addition, the proposed implementation proves to be efficient in reconstructing low sparse signals.
A high-dimensional sparse signal usually should be realigned as a long 1D signal to be recovered by orthogonal matching pursuit (omp), an efficient algorithm for compressed sensing. Clearly, however, the realigned lon...
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A high-dimensional sparse signal usually should be realigned as a long 1D signal to be recovered by orthogonal matching pursuit (omp), an efficient algorithm for compressed sensing. Clearly, however, the realigned long signal will result in a large amount of computation in omp. If each atom in the dictionary can be expressed as the Kronecker product of two vectors, it can possible to decompose this dictionary into two sub-dictionaries. By exploiting this property, a fast omp algorithm for 2D sparse signals of this kind is presented, and applied to 2D angle estimation in MIMO radar. Simulation results verify its good reconstruction quality approximate to that of omp and greatly improved computational efficiency.
A fast super-resolution method for joint Doppler frequency, direction of departure and direction of arrival estimation in bistatic MIMO radar is presented. First, the 3D echo signal model of bistatic MIMO radar with u...
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A fast super-resolution method for joint Doppler frequency, direction of departure and direction of arrival estimation in bistatic MIMO radar is presented. First, the 3D echo signal model of bistatic MIMO radar with uniform linear transmit and uniform linear receive array is established. Then, the Kronecker compressive sensing (KCS) is introduced to improve the 3D parameters estimation performance. Moreover, a novel fast orthogonal matching pursuit (omp) algorithm utilising 3D fast Fourier transformation is also presented to reduce the computation burden of KCS. Finally, the effectiveness of the method is validated by the simulation.
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