Compressed Sensing for Range-Resolved Signal of Ballistic Target with Low Computational Complexity

2016 ◽  
Vol E99.A (6) ◽  
pp. 1238-1242
Author(s):  
Wentao LV ◽  
Jiliang LIU ◽  
Xiaomin BAO ◽  
Xiaocheng YANG ◽  
Long WU
Keyword(s):  
Computational Complexity ◽  
Compressed Sensing ◽  
Low Computational Complexity

scholarly journals Block Compressed Sensing of Images Using Adaptive Granular Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Ran Li ◽  
Hongbing Liu ◽  
Yu Zeng ◽  
Yanling Li
Keyword(s):  
Computational Complexity ◽  
Compressed Sensing ◽  
Structural Characteristic ◽  
Rate Distortion ◽  
Reconstructed Image ◽  
Hard Thresholding ◽  
Low Computational Complexity ◽  
Principle Components Analysis ◽  
Reconstruction Performance ◽  
Block Compressed Sensing

In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.

Array DOA Estimation by the Model of Four Elements in Quare Array Based on the Theory of Compressed Sensing

2014 ◽  
Vol 635-637 ◽  
pp. 971-977 ◽  
Author(s):  
Rui Yuan ◽  
Jun Yue ◽  
Hong Xiu Gao
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Doa Estimation ◽  
Low Computational Complexity ◽  
Matching Pursuit Algorithm ◽  
Square Array

The DOA estimation by the model of four elements in the square array has studied based on the theory of compressed sensing. Using matching pursuit algorithm and orthogonality matching pursuit algorithm, the computer simulation was presented. The results show the method of DOA estimation by compressed sensing theory is simple, practical and low computational complexity.

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