PROJECT TITLE :
Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval
It's known that the foremost problem of ocean muddle simulation is the controlled generation of a continuous correlated non-Gaussian random process. In particular, for sea litter at low grazing angles and over a very long time scale, the spiky and nonstationary nature makes the simulation a lot of difficult. This paper proposes a completely unique procedure for the simulation of continuous correlated low-grazing-angle ocean clutter with prescribed statistic and correlation characteristics. The Pareto distribution is used to describe the statistics of the sea muddle intensity. In addition, different correlation characteristics for ocean litter over each short and very long time scales are thought of in this paper. First, the memoryless nonlinear transform is adopted to simulate the intensities of the correlated sea litter. Second, the Doppler spectra of different vary bins are generated separately. In particular, for the sea litter over long time scales, successive time-varying Doppler spectra are modeled to characterize the nonstationarity of the ocean clutter series. Then, the phases of the sea litter are retrieved by constraining to the specified Doppler spectra and given magnitudes. Totally different methods are adopted for the part retrieval of ocean clutter on short and while scales. Finally, simulation results show that the proposed procedure will generate continuous low-grazing-angle sea muddle with prescribed statistic and correlation characteristics. In particular, for the ocean litter over a while scale, the simulated sea clutter series enjoys the time-varying Doppler spectrum whereas maintaining continuity. In addition, the proposed methodology is also appropriate for alternative models, such as the K-distribution, the Weibull model, and therefore on.
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