Sparse representation approaches to parameter estimation of completely polarized wideband signals

Huang, Yulin; Xu, Yougen; Shi, Shuli; Zhao, Kang; Liu, Zhiwen

doi:10.1016/j.sigpro.2020.107521

Cited by 2 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, sparse representation has received considerable interest from experts and researchers in many fields, which approximately reconstructs the query sample with a combination of training samples from all classes [41]- [44]. So far, SR has been extensively employed in many theoretical researches and industrial applications, especially in the fields of signal processing [45], [46] and pattern recognition [47], [48]. The mathematical expression of SR is compact.…”

Section: A Sparse Representationmentioning

confidence: 99%

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

2020

IEEE Access

View full text Add to dashboard Cite

It is always a serious challenge to deal with the influence of noise for fault detection of rotating electrical machines under varying operating environments. To extract the most discriminative features for machine fault classification, a novel dimensionality reduction algorithm called joint sparsity and collaboration preserving projections (JSCPP) is proposed in this paper, based on the platform of graph embedding framework. A joint combination of sparse representation (SR) and collaborative representation (CR) is used to construct the intrinsic and penalty graphs. SR emphasizes only on the dictionary atoms highly correlated to the query sample for local optimum while CR takes every atom into consideration for global optimum, thus applying a joint combination would make it benefit from the merits of both for graph construction. After JSCPP seeks the optimal projection directions by minimizing the intra-class compactness and maximizing the inter-class separability, a nearest neighbor classifier is then used. Through two sets of vibration data with white Gaussian noise interfered, the capability of JSCPP is demonstrated by effectively classifying different defect types and severity degrees of faulty bearings. The experimental results have also proved that the classification performance of the proposed method is much superior to the other compared algorithms in both accuracy rates and reliability.

show abstract

Section: A Sparse Representationmentioning

confidence: 99%

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, these methods can hardly realize spatial filtering when the SNR is not high enough or the number of the desired signal plus interferences is extremely high [12]. With the wide application of sparse representation in statistical signal processing and parameter estimation [19], [20], [21], [22], some sparse-constraint-based beamforming methods have emerged [23], [24]. Given the sparse distribution of interferences in the sidelobe region, the array response in the sidelobe region is sparsely constrained in these methods.…”

Section: Introductionmentioning

confidence: 99%

Robust Beamforming Based on Weighted Vector Norm Regularization

Ren

Wang

Zhang³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Steering vector mismatch rapidly degrades the performance of the minimum variance distortionless response beamformer. To solve this problem, a robust beamforming method based on weighted vector norm regularization is proposed. First, the factors affecting the robustness of the beamformer are analyzed. Second, by introducing the weighted vector norm, an optimization problem is constructed to increase the robustness of the beamformer. Furthermore, the regularization coefficient is provided to achieve a balance between the output power and the robustness of the beamformer. Meanwhile, a method of finding the appropriate regularization coefficient is provided. Then, simulations of an irregular arc array are carried out, showing that the proposed method is robust to the snapshot number. Finally, the results and data analysis indicate the effectiveness of the proposed method.

show abstract

Sparse representation approaches to parameter estimation of completely polarized wideband signals

Cited by 2 publications

References 30 publications

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

Robust Beamforming Based on Weighted Vector Norm Regularization

Contact Info

Product

Resources

About