DOA estimation of near-field sources using support vector regression

2016 ◽  
Author(s):  
N. Venkateswaran ◽  
C. Ashok
Keyword(s):  
Support Vector Regression ◽  
Near Field ◽  
Doa Estimation ◽  
Support Vector

scholarly journals Support Vector Regression for the Modeling and Synthesis of Near-Field Focused Antenna Arrays

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1352 ◽  
Author(s):  
Rafael González Ayestarán
Keyword(s):  
Support Vector Regression ◽  
Antenna Arrays ◽  
Near Field ◽  
Computational Cost ◽  
Support Vector ◽  
Analysis Tool ◽  
Training Samples ◽  
Learning Capabilities ◽  
Novel Method ◽  
The Individual

The powerful support vector regression framework is proposed in a novel method for near-field focusing using antenna arrays. By using this machine-learning method, the set of weights required in the elements of an array can be calculated to achieve an assigned near-field distribution focused on one or more positions. The computational cost is concentrated in an initial training process so that the trained system is fast enough for applications where moving devices are involved. The increased learning capabilities of support vector machines allow using a reduced number of training samples. Thus, these training samples may be generated with a prototype or a convenient electromagnetic analysis tool, and hence realistic effects, such as coupling or the individual radiation patterns of the elements of the arrays, are accounted for. Illustrative examples are presented.

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

Research on Patch Near-Field Acoustic Holography

2011 ◽  
Vol 105-107 ◽  
pp. 196-199 ◽  
Author(s):  
Hai Chao Zhu ◽  
Zhi Min Chen ◽  
Xiang Hua Du ◽  
Rong Fu Mao
Keyword(s):  
Support Vector Regression ◽  
Sound Pressure ◽  
Near Field ◽  
Measured Data ◽  
Experimental Results ◽  
Support Vector ◽  
Reconstruction Accuracy ◽  
Acoustic Holography ◽  
Training Samples ◽  
Regression Functions

Support vector regression is used to establish a kind of patch near-field acoustic holography. The regression functions are constructed by treating the measured data on the patch holography as training samples, and then the data outside the measurement aperture are extrapolated. The experimental results show that the extrapolation of the sound pressure outside the smaller initial hologram aperture may be realized easily and effectively, and the reconstruction accuracy is satisfactory.

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.

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
Sign in / Sign up

Export Citation Format

Share Document