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An Automatic Target Detection Method Based on Multidirection Dictionary Learning for HFSWR
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DOI：10.1109/LGRS.2021.3062610.png)
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Abstract
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Target detection in a high-frequency surface-wave radar (HFSWR) system is a challenging task because radar returns are strongly polluted by various sources of interference. To address the detection problem, this letter presents an automatic detection method based on multidirection dictionary learning for HFSWR. First, we perform clutter identification and statistical analysis to become aware of the time-varying clutter environment. The analysis of real data shows that the clutter in an HFSWR system has spatial correlations and geometric directions. Second, motivated by this information, we design a multidirection dictionary learning-based constant false alarm rate (MDDL-CFAR) detector in which the spatial and geometric direction information is well represented by multidirectional dictionaries. The MDDL-CFAR can simultaneously learn dictionaries and estimate clutter statistics to adaptively set the detection threshold. Experimental results on HFSWR data sets demonstrate the effectiveness of the proposed detection method.
Keywords:
Clutter
Detectors
Dictionaries
Weibull distribution
Shape
Machine learning
Radar
Clutter identification
constant false alarm rate (CFAR) detector
dictionary learning
high-frequency surface-wave radar (HFSWR)
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