Study On Radar Clutter's Characteristic And Signal Processing Method Based On Statistical And Complexity Theory

The radar signal processing in clutter becomes a vital problem in the information based modern war because of the complex and variable battlefield and the increasing demand on the information processing ability of radar.Moreover,the characteristic of radar clutter has changed as the resolution of radar increases,and this puts forward a new challenge for modern radar signal processing methods.Evoked by the practical demand of several projects, we carry out research on the clutter characteristic of high range resolution radar and the signal processing method under clutter.The work contains four parts:The second chapter analyzes the statistical characteristic of some measured sea clutter data.Firstly,the fundamental theory and method for analyzing the statistical characteristic of radar clutter is introduced.Different methods for estimating the model parameters of K-distributed clutter are compared and a new estimation method based on particle filter is proposed.Secondly,the statistical characteristic of the measured sea clutter data in two special projects is analyzed,including distribution fitting,time-space correlation analysis and spectrum analysis.The simulation method of spatial-temporal correlated clutter is realised based on SIRP and ZMNL.The third chapter studies the complex characteristic of high range resolution sea clutter based on the complexity theory.Firstly,the evolution and principle of the complexity theory is briefly introduced,and the complexity of sea clutter is analyzed.Secondly,the nonlinearity of the sea clutter in time domain is testified by phase space reconstruction and surrogate data method;the predictability and deterministic of high resolution sea clutter is analyzed using recurrence plot;the complexity of clutter and target echo is analyzed quantificationally using complexity measuring theory.Thirdly,the multi-fractal of sea clutter is tested experimentally; the varying property of the multi-fractal is discussed;and the simulation method of sea clutter based on wavelet-multi-fractal model is proposed.Lastly,we use a new time-frequency analysis method,i.e.,the Hilbert-Huang Transform(HHT),to compare the decomposed characteristic of clutter and target echo.A new way for target-clutter discrimination is proposed based on the different HHT energy characteristic of the target and the clutter.Chapter 4 focuses on the detection of small targets in non-Gaussian clutter.Several new detection methods are studied:â‘ Detection method based on RBF neural network.We propose an improved algorithm which has a denoising step using wavelet decomposition and trains the network by target bins.â‘¡Small target detection method in K-distributed clutter based on stochastic resonance.The optimal detector under K-distributed clutter is deduced.A sub-optimal detector based on the quantizers is also proposed by comparing the optimal detector with the multilevel quantizers.â‘¢A detector based on the proposed principle of fractional lower order cyclostationary sigal is used to detect and estimate the LFM signals in alpha-stable clutter.Chapter 5 studies the parameter estimation problem in clutter,i.e.,estimating scattering center parameters from high resolution range profiles and estimating the motion parameter of targets in clutter.Firstly,the joint model selection and parameter estimation problem of GTD model under heavy noise is studied and a new method based on Bayes principle and RJ-MCMC algorithm is proposed.Secondly,a robust M-estimator is proposed to suppress the spiky data in non-Gaussian clutter when estimating scattering center parameters and the CPSO(co-operative particle swarm optimization) algorithm is used in the optimization process of the M-estimator.Lastly,the motion parameter estimation problem of targets in non-Gaussian clutter is studied and an improved algorithm based on the particle filter for the sequential estimation of the "static parameters" is proposed.A systematic conclusion together with some further discussion on future work is given at the end of this dissertation.