Abstract
ABSTRACT In shipborne high-frequency surface wave radar (HFSWR), the change of platform speed or heading will cause variations in the extension of the Doppler spectrum for vessel targets located in different directions, subsequently resulting in alterations to the signal-to-noise ratio (SNR). These alterations in vessel target echo present challenges for manual parameter adjustments in traditional constant false alarm rate (CFAR) methods for shipborne HFSWR, thereby hindering the maintenance of stable target detection capabilities. In this paper, a self-attention-convolutional neural network (SA-CNN)-based CFAR detector is proposed, which transforms the detection problem into signal structure classification. First, the extension characteristics of vessel target echoes resulting from changes in speed or heading of the shipborne platform are quantitatively analysed, thereby guiding the selection of an optimal sliding window and constructing input vectors for the neural network. Subsequently, the SA-CNN is designed to efficiently extract the structural features of the signal and accurately predict the probability of target presence. Finally, the Monte Carlo method is used to control the false alarm rate effectively. Simulation and real dataset verification demonstrate that the proposed method exhibits superior detection performance compared to traditional methods in shipborne HFSWR, especially for detecting extended targets.
Published Version
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