Papers by Keyword: Compressed Sensing

Abstract: The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

Abstract: Traditional power quality signal samples are based on the Nyquist sampling theory. Because of the existence of disturbance signal for the presence of power, it requires two times higher than the sampling frequency of the original signal, resulting in many problems, such as a high cost of hardware. Compressed sensing algorithm abandoned the characteristics of Shannon theorem, using a lower sampling frequency and the less amount of the signal to reconstruct the signal, with the method of a loss compression, which can effectively solve this problem. A team in Beijing University of Chemical Technology has done a deep research in this direction and proposed the total variation gradient reduction algorithm, which has good effects on reduction. But the algorithm runs slower and needs higher sample volumes of signal. Therefore, this paper presents a modified algorithm based on Nesta algorithm to reduce the amount of data sampled of power quality signal, the complexity of the algorithm to improve the algorithm’s speed. The modified algorithm has a very important value in practical applications. This paper has carried out simulations in matlab, the results of the simulation show that this method is accurate and applied.

Abstract: A radar compressed sensing imaging method with 2-D separable sampling is proposed in this paper. Instead of converting the radar imaging problem into two 1-D compressed sensing problem, we use the 2-D Separable Projections to solve it directly. Unlike the 2-D separable sampling in visible imaging, the range and azimuth which are the two dimensions of the radar imaging couple with each other. This Coupling increases the storage and computation in radar compressed imaging, therefore some de-coupling processing using in Range Doppler algorithm are adopted in the proposed method to construct the 2-D separable sampling data. Accordingly the two dimensional scene has been reconstructed with the proposed 2-D compressed sensing algorithms. Compared with conventional compressed sensing imaging methods, the new method has reduced the memory usage and complexity with imaging performance improvement.

Abstract: To remove the noise of weak signals detected in the underwater environment, the existing de-noising algorithms cannot perform satisfactorily. This paper aims to developing a new method to solve the problem, where the compressed sensing theory, wavelet transformation based filtering techniques and sparse signal reconstruction algorithm are employed. The simulation results show that the proposed algorithm performs favorably and has good potential to be used in some engineering applications.

Abstract: Amount of data in collecting data of fabric image in the textile industry put forward a new challenge to sensor end. Compressed Sensing (CS) breaks limit of conventional Shannon’s sampling theorem, so we can reconstruct a signal in Sub-sampling rate. In addition, theoretical analysis tells us that collecting the fabric image data by CS method have a better advantage than collecting the general image data. Having reconstructed three fabric images and one general image by CS method, we can easily find that the former have a high quality of reconstruction.

Abstract: In this paper, we make study on the compressed matrices in the compressed sensing trilinear model-based angle estimation algorithm, whose complexity is lower than conventional trilinear decomposition-based method, due to the use of compressed matrices. And we take the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar as an example. Simulation results can provide reference for the choice of compressed matrices.

Abstract: A cooperative wideband signal detection approach in deep space communication is designed in this paper. In order to implement autonomous communication between explorers, detectors and satellites, we need to detect signal in advance. There are sparse active signals in the shadowing deep space environment, whose frequency support only occupies a small portion of a wide spectrum. Compressed sensing (CS) is a new technique which is able to utilize the sparsity of deep space communication signal. Wideband signal is sampled one time only with low sampling rate, and then signal spectrum is recovered by reconstruction algorithms. A discrete cosine transform (DCT) based compressed spectrum detection method has been studied which can significantly improve the probability of signal detection. Simulation result show the effectiveness of our new detect scheme in deep space communication.

Abstract: In order to improve the speed of compressed sensing image reconstruction algorithm, a two step rapid gradient projection for sparse reconstruction in medical image reconstruction is proposed. in traditional gradient projection for sparse reconstruction algorithm, the searching direction is alternate between the negative gradient direction when the direction is ill, the searching speed is slow. Now we search with two step gradient projection, the speed is increased when meets the ill-condition. Compared with the original GPSR algorithm, the TSGPSR algorithm not only accelerate the speed of operation, but also improves the accuracy of the reconstruction. and exhibits higher robustness under different noise intensities.

Abstract: Compressed Sensing is very efficient in reducing the relatively high sampling rate. But when it comes to the channel estimation of uncooperative communication, the common CS reconstruction algorithms seem impractical to implement since a pilot is required, which is difficult for uncooperative communication. In this paper, we combine the sparsity transform dictionary, which is formed by a sequence of delays of the template signal, together with the idea of alternative minimization to improve the traditional CoSaMP algorithm to reconstruct under-sampled UWB-2PPM signal transmitted by unkown complex channel without a knowledge of pilot. The theoretical analysis and simulations show that the proposed algorithm is capable of reconstructing the original transmitted signal without a pilot.

Abstract: Compressed Sensing is likely to provide an effective way for lowering the extremely high sampling speed of UWB signal while the design of CS measurement matrix is of great significance for reducing the number of observations and hardware costs as long as improving the reconstruction accuracy. In this paper, with the combination of the structural features of the Fourier matrix and the idea of entry permutation of determined matrices, we propose a new measurement matrix of which the Fourier transformed entries are randomly permuted. Simulation results show that the same algorithm has a better reconstruction performance with the proposed measurement matrix rather than Gaussian/ Bernoulli matrix.