Abstract
To improve the ability of radar to detect targets such as ships with a background of strong sea clutter, different sea-clutter suppression algorithms are developed based on the realistic Intelligent PIxel processing X-band (IPIX) radar datasets, and quantitative research is carried out. Four algorithms, namely root cycle cancellation, singular-value decomposition (SVD) suppression, wavelet weighted reconstruction, and empirical mode decomposition (EMD) weighted reconstruction and their corresponding suppression methods are introduced. Then, the differences between the four algorithms before and after sea-clutter suppression are compared and analyzed. The average clutter-suppression and target-suppression amplitudes are selected as measures to verify the suppression effect. Sea-clutter data collected in the high-sea state, low-sea state, near-sea area, and far-sea area are selected for statistical analysis after suppression. All four methods have certain suppression effects, among which EMD reconstruction is best, reaching an average clutter-suppression range of 15.507 dB and a signal-suppression range of about 1 dB, which can improve the ability of radar to detect targets such as ships with a background of strong sea clutter.
Highlights
The rough undulating sea surface interacts with the electromagnetic waves emitted by radar to produce backscattered echoes with strong energy [1,2,3]
For the sky-wave radar, due to the limitation of Doppler resolution, the first-order scattering spectrum of sea clutter in the Doppler domain appears as a clutter signal near zero frequency, which seriously affects the detection of ship targets
Huang et al proposed the empirical mode decomposition (EMD) algorithm [17], a multiresolution analysis method based on local features of the time domain, which has proven to be a good non-stationary, nonlinear signal method
Summary
The rough undulating sea surface interacts with the electromagnetic waves emitted by radar to produce backscattered echoes with strong energy [1,2,3] Huang et al proposed the empirical mode decomposition (EMD) algorithm [17], a multiresolution analysis method based on local features of the time domain, which has proven to be a good non-stationary, nonlinear signal method. It is widely used for the analysis of mechanical vibrations, waves, structural mechanics, and natural seismic signals [16,17,18,19,20,21].
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