Modeling Of Sea Clutter And Target Detection In Sea Clutter

Sea clutter impacts seriously on the performance of maritime radars. The researchon physical and statistical characteristic of sea clutter palys a key role in radar signalprocessing, target detection in sea surface and radar simulator design. Withdevelopment of nonlinear science, sea clutter is realized that it has much character inchaos and fractal. This makes people use neural network method to deal with sea cluttertime series and detect targets. We carry out research on electromagnetic scatteringmodel, statistical model and simulation of sea clutter, target detection in sea clutter. Thework mainly contains:The conventional two-scale model for electromagnetic scattering coefficentcomputation regards coarse sea surface as Gaussian sea surface, and does not considerslope and asymmetry of sea surface in upwind-downwind direction. In this paper theasymmetry of sea surface is depicted by introducing higher-order statistical bispectrum,and a modified two-scale model for electromagnetic scattering coefficent computationbased on a Non-fully Developed Full-range Sea Spectrum (NDFSS) fractal sea surfaceis presented.Temporal correlation characteristic and spatial correlation characteristic of seaclutter are considered simultaneously for multi-pulse radar echo data, a generationmethod of temporal-spatial correlated K-distributed sea clutter is proposed based onspherically invariant random processes (SIRP). The generated sea clutter satisfies thedemand of appointed temporal-spatial correlation, and has been applied in a cooperativeproject.K-distribution as amplitude statistical model for heterogeneous area is deficient.The expression of Probability Density Function (PDF) for K-distribution has themodified Bessel function, and the algorithm complexity is much higher.G0-distribution has modeling ability in clutter regional of widely homogeneous degreevariation and strong model compatibility. This distribution has easily engineeringrealizability, and it’s expression only contains index function. So we useG0-distribution as amplitude statistical model for the sea clutter data fitting. Comparedto K-distribution, we found the results ofG0-distribution and sea clutter real-life datahave better fitting effect. Kullback Leibler (KL) distance, Mean Squared Error (MSE) testand Kolmogorov-Smirnov (K-S) test are used for proving our conclusion.According to sea clutter with character in chaos and fractal, echo state network(ESN) is used in predicting sea clutter time series, and the great MSE differencesbetween prediction value for sea clutter time series with target and without target andreal-life data are available for detecting target. Improved ESN methods similarly predictsea clutter time setires and detect target, and these methods are decoupled echo state network (DESN), DESN with maximum available information (DESN+MaxInfo),DESN with reservoir prediction (DESN+RP), feed forward echo state network(FF-ESN), and tapped delay line with inputs (TDL-I).ESN method is used to predict sea spikes, and detect target that exists in one rangebin. This method provides a new route to further research and analyse the sea spikes.