The signals in reality are sparse signal where a few numbers of samples are non-zero. So, a compression technique must be applied to reduce the overhead of processing, storing, and transmission. Blocking compressive s...
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The signals in reality are sparse signal where a few numbers of samples are non-zero. So, a compression technique must be applied to reduce the overhead of processing, storing, and transmission. Blocking compressivesamplingmatchingpursuit (BCoSaMP) algorithm is a recursive algorithm which provides an accurate reconstruction of sparse signal from a small number of noisy samples. It doesn't assume that the noise is Gaussian or bounded but it uses information about the noise magnitude for stopping criterion. However, BCoSaMP is a computationally intensive algorithm. So, BCoSaMP algorithm has been implemented on both field-programmable gate array (FPGA) and graphic processing units (GPU) by exploiting parallel and pipelining approaches. A new software tool called radar signal processing tool (RSPT) is also presented. It allows the designer to auto-generate fully optimized VHDL representation of BCoSaMP by specifying many user input parameters through graphical user interface (GUI). Moreover, it provides the designer a feedback on various performance parameters. This offer the designer the ability to make any adjustments to the BCoSaMP component until gets the desired performance of the overall system-on-chip (SoC). Our simulation results indicate that the achieved speed-up of FPGA and GPU over the sequential one is improved by up to 14 and 10.7, respectively.
A compressivesamplingmatchingpursuit (CoSaMP) iterative algorithm is proposed in this paper to identify parameters and time-delays of a class of closed-loop systems where the forward channel is a CARMA model. Due t...
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ISBN:
(纸本)9781538626184
A compressivesamplingmatchingpursuit (CoSaMP) iterative algorithm is proposed in this paper to identify parameters and time-delays of a class of closed-loop systems where the forward channel is a CARMA model. Due to the unknown time-delays of both the feedback controller and the controlled plant, a high dimensional identification model with a sparse parameter vector is derived by using an overparameterized method. Then combining the CoSaMP algorithm with the iterative idea, the parameter vector is estimated and the unmeasurable noise items are updated in each iteration. Finally, the parameters of the feedback controller are extracted based on the model equivalence principle and time-delays are estimated according to the sparse characteristic of the parameter vector. The proposed method can simultaneously estimate the parameters and time-delays from a small number of sampled data. The simulation results illustrate that the proposed algorithm is effective.
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