Abstract
2D Angularly Dependent Array Error Calibration for 1D Array via Neural Network with Local Manifold Interpolation
Highlights
The array signal processing is a technique widely applied in civilian and military fields, such as radar, sonar, communication, and seismology [1]
For the one-dimensional (1D) linear array, which is usually applied in the automotive radar [7], its array error depends on the azimuth and on the elevation, even though it cannot estimate the elevation of a signal
Performing direction of arrival (DOA) estimation is equivalent to finding this function, which is not an easy problem for the conventional signal processing-based methods described in the last section
Summary
The array signal processing is a technique widely applied in civilian and military fields, such as radar, sonar, communication, and seismology [1]. In [16], a support vector regression (SVR)-based method is proposed for DOA estimation for a practical wide-beam highfrequency radar Both the methods in [17] and [16] have shown improved performance over the conventional signal processing-based methods. In [15], multiple machine learning-based methods for DOA estimation have been proposed, and they show good performance on testing data of different noise levels. We propose a new NN-based method for the 2D calibration of angularly dependent array error.
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