Support vector regression based DOA estimation in heavy tailed noise environment

2016 ◽  
Author(s):  
Ashok C ◽  
N. Venkateswaran
Keyword(s):  
Support Vector Regression ◽  
Doa Estimation ◽  
Support Vector ◽  
Noise Environment ◽  
Heavy Tailed

Diagnosis of Array Failure in Impulsive Noise Environment Using Unsupervised Support Vector Regression Method

2013 ◽  
Vol 61 (11) ◽  
pp. 5508-5516 ◽  
Author(s):  
Huapeng Zhao ◽  
Ying Zhang ◽  
Er-Ping Li ◽  
Aniello Buonanno ◽  
Michele D'Urso
Keyword(s):  
Support Vector Regression ◽  
Impulsive Noise ◽  
Regression Method ◽  
Support Vector ◽  
Noise Environment

scholarly journals Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2692
Author(s):  
Faisal Alam ◽  
Mohammed Usman ◽  
Hend I. Alkhammash ◽  
Mohd Wajid
Keyword(s):  
Support Vector Regression ◽  
Cross Correlation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Regression Technique ◽  
Support Vector ◽  
Correlation Technique ◽  
Multilevel Regression ◽  
Acoustic Source ◽  
Regression Techniques

The direction-of-arrival (DoA) estimation of an acoustic source can be estimated with a uniform linear array using classical techniques such as generalized cross-correlation, beamforming, subspace techniques, etc. However, these methods require a search in the angular space and also have a higher angular error at the end-fire. In this paper, we propose the use of regression techniques to improve the results of DoA estimation at all angles including the end-fire. The proposed methodology employs curve-fitting on the received multi-channel microphone signals, which when applied in tandem with support vector regression (SVR) provides a better estimation of DoA as compared to the conventional techniques and other polynomial regression techniques. A multilevel regression technique is also proposed, which further improves the estimation accuracy at the end-fire. This multilevel regression technique employs the use of linear regression over the results obtained from SVR. The techniques employed here yielded an overall 63% improvement over the classical generalized cross-correlation technique.

Multiple Radar Subbands Fusion Algorithm Based on Support Vector Regression in Complex Noise Environment

2018 ◽  
Vol 66 (1) ◽  
pp. 381-392 ◽  
Author(s):  
Ying Zhang ◽  
Tingjing Wang ◽  
Huapeng Zhao ◽  
Yanxin Zhang ◽  
Hua Zhao
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Noise Environment

Support Vector Regression for Basis Selection in Laplacian Noise Environment

2007 ◽  
Vol 14 (11) ◽  
pp. 871-874 ◽  
Author(s):  
Ying Zhang ◽  
Qun Wan ◽  
Hua-Peng Zhao ◽  
Wan-Lin Yang
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Noise Environment ◽  
Laplacian Noise

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
Sign in / Sign up

Export Citation Format

Share Document