Sea Clutter Chaotic And Fractal Characteristic Analysis, Modeling And Small Target Detection

"Sea clutter", refers to radar backscatter from local sea surface. In order to disclosure the intrinsic physical characteristics and laws of sea clutter, chaotic and fractal analysis and modeling is performed based on the real-life IPIX sea clutter datasets. Meanwhile, aiming at the present issue that small target detection within sea clutter is very difficult for traditional and coast-sited radar, investigation of small target detection is achieved based on the chaotic and fractal characteristics and laws of sea clutter obtained from this dissertation. This thesis concentrates on as follows:1. Based on the real sea clutter data, the sea clutter is regarded as chaotic by calculating Correlation dimension, Lyapunov exponent and Kolmogorov entropy. Considering radar electromagnetic wave scattering from a rough sea surface is basically a spatiotemporal phenomenon, this dissertation presents that the study of spatiotemporal chaos (STC) can further disclosure the true nature of sea clutter, STC qualitative, analysis is achieved by real sea clutter data, STC quantitative analysis is achieved by calculating Correlation length and the largest Lyapunov exponent via Coupled Map Lattice (CML) model.2. Fractal characteristic of sea clutter is deeply investigated. Wavelet analysis is applied to calculate Hurst exponents of sea clutter, it is found that the Hurst exponent of the range bin hosting a small target is different from hosting only sea clutter. Considering dense singularities and nonlinearity and nonstationary of sea clutter, Wavelet Transform Modulus Maxima (WTMM) and Multifractal Detrended Fluctuation Analysis (MFDFA) are derived for multifractal analysis of sea clutter, scale exponent r(q), generalized Hurst exponent h(q), singularity spectrum f(a) are calculated, the results indicate that sea clutter time series of a certain range bin is multifractal. For further study of the fractality of a sea clutter dataset with many range bins, a new method of Time-Range bin-Amplitude Plot (TRAP) is presented, experiment results imply that a sea clutter dataset with several range bins has fractality characteristic, especially when the dataset contain a small target, it performs multifractal characteristic, which further implies that sea clutter and small target has different fractality.3. As to sea clutter is a spatiotemporal phenomenon and nonlinear and nonstationary, a new algorithm of Least Squares Support Vector Machines-Coupled Map Lattice (LSSVM-CML) is presented for spatiotemporal prediction of sea clutter. Experiments results indicate that the prediction precision of LSSVM-CML algorithm is superior to Weighted One-order Local-region algorithm, Normalized RBF Neural Network, Volterra Predictor, LSSVM algorithm for prediction of sea clutter time series.4. In order to solve the problem that the traditional radar Constant False Alarm Rate (CFAR) method can not detect small target in sea clutter, based on the STC characteristic, Multifractal characteristic, time-frequency analysis, statistical regularities obtained in this dissertation, four new methods including LSSVM-CML algorithm, Generalized Fractal Dimension Difference (GFDD) algorithm, Time-Doppler analysis (TDA) and Local Amplitude Statistics (LAS) method are derived for small target detection within sea clutter. Experiments results indicate that these methods are valid, the small target can be detected precisely with no prior knowledge of the small target and ocean environment conditions.5. Since the real-life sea clutter data contains radar measurement noise and dynamic noise from rough surface which may influence the analysis of the intrinsic characteristics of sea clutter, so Averaging, Median, Wavelet and Empirical Mode Decomposition (EMD) algorithm are used for denoising. Wavelet thresholding method and EMD algorithm for denoising is the major investigation in this dissertation. Experiments indicates that db2 wavelet with Hyperbolic Thresholding function and HeurSure threshold and EMD algorithm for denoising of sea clutter are excellent, since EMD-based signal denoising is full data driven, so EMD algorithm denoising is optimist a little better than Wavelet thresholding denoising method.