Direct Localization of Multiple Sources in Sensor Array Networks: A Joint Sparse Representation of Array Covariance Matrices Approach

2013 ◽  
Author(s):  
Ji-An Luo ◽  
Zhi Wang ◽  
Yu-Hen Hu
Keyword(s):  
Sparse Representation ◽  
Sensor Array ◽  
Covariance Matrices ◽  
Multiple Sources ◽  
Joint Sparse Representation ◽  
Direct Localization

scholarly journals Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 686 ◽  
Author(s):  
Kai Yu ◽  
Ming Yin ◽  
Ji-An Luo ◽  
Yingguan Wang ◽  
Ming Bao ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Sensor Array ◽  
Doa Estimation ◽  
Wireless Sensor ◽  
Array Data ◽  
Joint Sparse Representation

scholarly journals Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

2019 ◽  
Vol 11 (9) ◽  
pp. 1114
Author(s):  
Sixiu Hu ◽  
Jiangtao Peng ◽  
Yingxiong Fu ◽  
Luoqing Li
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Feature Space ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Neighborhood Structure ◽  
Joint Sparse Representation ◽  
Negative Effect ◽  
Sparse Coefficient

By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models.

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