An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem

To benefit from the development of compressive sensing, we cast tracking as a sparse approximation problem in a particle filter framework based on multifeatures. In this framework, the target template is composed of multiple features extracted from visible and infrared frames; in addition, occlusion, interruption, and noises are addressed through a set of trivial templates. With this model, the sparsity is achieved via a compressive sensing approach without nonnegative constraints; then the residual between sparsity representation and the compressed sensing observation is used to measure the likelihood which weights particles. After that, the target template is adaptively updated according to the Bhattacharyya coefficients. Some experimental results demonstrate that the proposed tracker appears to have better robustness compared with four different algorithms.

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Fault Diagnosis for Satellite Attitude Control Systems With Four Flywheels

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4026515 ◽

2014 ◽

Vol 136 (4) ◽

Cited By ~ 11

Author(s):

Zhenhua Wang ◽

Yi Shen ◽

Xiaolei Zhang ◽

Danwei Wang

Keyword(s):

Fault Diagnosis ◽

Attitude Control ◽

Taylor Series Expansion ◽

Approximation Problem ◽

Sparse Approximation ◽

Fault Estimation ◽

Attitude Control System ◽

Multiple Faults ◽

Satellite Attitude ◽

Satellite Attitude Control

This paper proposes a novel fault diagnosis approach for the satellite attitude control system with flywheel faults. The key contributions include fault estimation by sparse approximation algorithm and diagnosis of multiple faults. In this paper, a Taylor series expansion is used to derive a fault estimation representation. Based on the sparse property of the faults, fault estimation is formulated as a sparse approximation problem and solved using the orthogonal matching pursuit (OMP) algorithm. Simulation results demonstrate the effectiveness of the proposed method.

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Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

10.36227/techrxiv.17704292 ◽

2022 ◽

Author(s):

Taner Ince ◽

Tugcan Dundar ◽

Seydi Kacmaz ◽

Hasari Karci

Keyword(s):

Spatial Structure ◽

Hyperspectral Image ◽

Real Data ◽

Approximation Problem ◽

Sparse Approximation ◽

Hyperspectral Data ◽

Low Rank ◽

Data Sets ◽

Spatial Weight ◽

Homogeneous Regions

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

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Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

10.36227/techrxiv.17704292.v1 ◽

2022 ◽

Author(s):

Taner Ince ◽

Tugcan Dundar ◽

Seydi Kacmaz ◽

Hasari Karci

Keyword(s):

Spatial Structure ◽

Hyperspectral Image ◽

Real Data ◽

Approximation Problem ◽

Sparse Approximation ◽

Hyperspectral Data ◽

Low Rank ◽

Data Sets ◽

Spatial Weight ◽

Homogeneous Regions

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

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