Direction of arrival estimation based on smooth support vector regression

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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Least squares support vector machines for direction of arrival estimation

IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC/CNC/URSI North American Radio Sci. Meeting (Cat. No.03CH37450) ◽

10.1109/aps.2003.1217400 ◽

2004 ◽

Cited By ~ 5

Author(s):

J.A. Rohwer ◽

C.T. Abdallah ◽

C.G. Christodoulou

Keyword(s):

Support Vector Machines ◽

Least Squares ◽

Direction Of Arrival ◽

Support Vector ◽

Direction Of Arrival Estimation ◽

Vector Machines

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

Sensors ◽

10.3390/s21082692 ◽

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.

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