Abstract
In this paper, a sea clutter decomposition model is newlxy proposed. The decomposition structure is organized according to a comparison study between measured sea clutter and Lorenz chaotic signals. Based on the decomposition model, a sea clutter constituent synthesis approach is developed to reconstruct sea clutter series with neural networks. Simulation results demonstrate the effectiveness and stability of the proposed approach.
Highlights
Sea clutter modeling is a significant issue in radar signal processing
4.2 Performance comparison To further test the effectiveness of the sea clutter constituent synthesis (SCCS) approach, a longer series is reconstructed by SCCS
For most of the clutter datasets, we have investigated, the SCCS approach has shown a good overall fitting performance
Summary
A proper sea clutter model is important in radar signal processing for three reasons. It can describe the underlying dynamics of sea clutter. With a proper model, effective detectors can be developed for target tracking in sea clutter. It is useful for generating a representative clutter signal for radar system’s testing and receiver algorithm’s development [1]. A major drawback of statistical models is that they provide very little information about the underlying dynamics of sea clutter, which might be useful for improving signal processing performance [9,10,11]. It can be inappropriate to refer to this small amount of data with statistical models
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