Abstract
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.
Highlights
Direction of arrival (DOA) estimation is a widely studied problem in various fields, including wireless communications [1], radar detection [2,3,4], target localization, and tracking [5, 6]
E interferometer estimates the DOA based on the phase difference of different direction-finding baselines. e accuracy of interferometer is sensitive to the phase difference of baselines
Machine learning have significant advantages over traditional methods based on array geometries and least square in solving direction-finding problems in complicated scenarios with multiple deviations, such as radial basis function (RBF) [23], least squares support vector classification (LSSVC) [24], support vector regression (SVR) [25,26,27,28,29], and deep neural networks (DNN) [30, 31]. ese methods are data driven and do not rely on preassumptions about array geometries and whether they are calibrated or not
Summary
Direction of arrival (DOA) estimation is a widely studied problem in various fields, including wireless communications [1], radar detection [2,3,4], target localization, and tracking [5, 6]. E interferometer estimates the DOA based on the phase difference of different direction-finding baselines. Due to the influence of multiple deviations, even if there are different gains of each antenna, the external field calibration method always uses the equal-weight least squares method. Machine learning have significant advantages over traditional methods based on array geometries and least square in solving direction-finding problems in complicated scenarios with multiple deviations, such as radial basis function (RBF) [23], least squares support vector classification (LSSVC) [24], support vector regression (SVR) [25,26,27,28,29], and deep neural networks (DNN) [30, 31]. Is paper proposes a direction-finding method for the interferometer based on the multioutput least squares support vector regression (MLSSVR) model.
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