ISAR IMAGING BASED ON L1 L0 NORMS HOMOTOPY 2D BLOCK SPARSE SIGNAL RECOVERY ALGORITHM

In order to solve the problem of high-resolution ISAR imaging under the condition of finite pulses, an improved smoothed L0 norm (SL0) sparse signal reconstruction ISAR imaging algorithm is proposed. Firstly, the ISAR imaging is transformed into the optimization problem of minimum L0 norm. Secondly, a single-loop structure is used instead of two loop layers in SL0 algorithm which increases the searching density of variable parameter to ensure the recovery accuracy. Finally, the compared step is added to ensure the optimization solution along the steepest descent gradient direction. The experimental results show that the proposed algorithm has better imaging effect.

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Three dimensional ISAR imaging based on multi dimensional sparse signal recovery algorithm

2016 Progress in Electromagnetic Research Symposium (PIERS) ◽

10.1109/piers.2016.7735859 ◽

2016 ◽

Author(s):

Junjie Feng ◽

Gong Zhang

Keyword(s):

Three Dimensional ◽

Sparse Signal ◽

Sparse Signal Recovery ◽

Signal Recovery ◽

Recovery Algorithm ◽

Isar Imaging

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ISAR imaging based on 2D tensor sparse signal recovery algorithm

2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR) ◽

10.1109/apsar.2015.7306267 ◽

2015 ◽

Cited By ~ 1

Author(s):

Changzheng Ma ◽

Boon Poh Ng ◽

Kye Yak See ◽

Siew Kwok Lui ◽

Boon-Lum Lim

Keyword(s):

Sparse Signal ◽

Sparse Signal Recovery ◽

Signal Recovery ◽

Recovery Algorithm ◽

Isar Imaging

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ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

Mathematical Problems in Engineering ◽

10.1155/2020/1743593 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Junjie Feng

Keyword(s):

Vector Model ◽

Sparse Signal ◽

Sparse Signal Recovery ◽

Signal Recovery ◽

Negative Exponential Function ◽

Multiple Measurement ◽

Recovery Algorithm ◽

Multiple Measurement Vector ◽

Isar Imaging ◽

Measurement Vector

A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.

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Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽

10.3390/math7090834 ◽

2019 ◽

Vol 7 (9) ◽

pp. 834

Author(s):

Jin ◽

Yang ◽

Li ◽

Liu

Keyword(s):

Compressed Sensing ◽

Image Denoising ◽

Signal To Noise Ratio ◽

Sparse Recovery ◽

Coordinate Descent ◽

Sparse Signal ◽

Sparse Signal Recovery ◽

Signal Recovery ◽

Recovery Algorithm ◽

Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

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