Abstract
High-frequency surface wave radar (HFSWR) is an important marine monitoring technology, and this new regime of radar plays an important role in large-scale, continuous early-warning monitoring at sea. In particular, shipborne HFSWR has wider applications in detecting interesting sea areas, with the advantages of flexible deployment and extended detection capability. Due to the large amount of sea clutter accompanying the echo signals of shipborne HFSWR and the spread of sea clutter due to platform motion, the detection of targets in clutter regions is extremely difficult. In this paper, a multi-frame time-frequency (TF) analysis–based target-detection method is proposed. First, the sea clutter spreading area in the HFSWR echo signal is modeled, and the effects of platform motion and currents on the sea clutter spread are analyzed to determine the sea clutter coverage area; this paper focuses on frequency modeling. Then the TF image (TFI) of each range cell is obtained by TF analysis of the cells within a certain range of the echo signal, and the range cells of possible target points are determined by binary classification of the TFI through a convolutional neural network. Finally, the location of the final target point is obtained by correlation of multi-frame TFIs. Shipborne HFSWR field experiments show that the proposed detection method performs well in detecting targets concealed by sea clutter.
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