Abstract
Conventional direction-of-arrival (DOA) estimation methods are primarily used in point source scenarios and based on array signal processing. However, due to the local scattering caused by sea surface, signals observed from radar antenna cannot be regarded as a point source but rather as a spatially dispersed source. Besides, with the advantages of flexibility and comparably low cost, synthetic aperture radar (SAR) is the present and future trend of space-based systems. This paper proposes a novel DOA estimation approach for SAR systems using the simulated radar measurement of the sea surface at different operating frequencies and wind speeds. This article’s forward model is an advanced integral equation model (AIEM) to calculate the electromagnetic scattered from the sea surface. To solve the DOA estimation problem, we introduce a convolutional neural network (CNN) framework to estimate the transmitter’s incident angle and incident azimuth angle. Results demonstrate that the CNN can achieve a good performance in DOA estimation at a wide range of frequencies and sea wind speeds.
Highlights
We aim to find a proper convolutional neural network (CNN) structure trained to estimate from sea surface scattering datasets under different wind speeds and radar operating frequencies
We aimed to find a CNN structure with the best performance to estimate the direction of the incident source from simulated radar measurement data
We present a novel method of two-dimensional direction of arrival (DOA) estimation for synthetic aperture radar (SAR) systems based on sea surface scattering and convolutional neural networks
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Determining the direction of arrival (DOA) of the radar signal is a fundamental problem for sea surveillance. The task of DOA estimation is to identify the signal source directions in which the signal is transmitted. Conventional DOA estimation methods, including beamforming techniques [1,2,3] and subspace-based methods [4,5,6,7,8], are primarily used in point source scenarios. With the development of machine learning and artificial intelligence, neural network (NN) has been applied in the DOA estimation domain [9,10,11,12,13,14]
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