Research On Heavy-tailed Radar Clutter Model And Range-Spread Target Detection Algorithms

Both the radar target characteristic and the statistical property of the radar clutter have undergone profound changes as the resolution becomes higher, which has put forword more requirements for the radar target detection. In this dissertation, aiming at the range-spread target detection in the high-resolution radar clutter, some key techniques, such as heavy-tailed radar clutter model and its parameter estimation, bi-demensional correlated heavy-tailed radar clutter simulation, range-spread target detection based on high resolution profile and SAR imagery, are systematically studied by theoretic analysis and experimental validation.In Chapter 1, the research background and significance of range-spread target detection in high resolution radar clutter are addressed. Then the research status and trends of the modeling and simulation of the radar clutter and the range-spread target detection are reviewed briefly. Finally, the main work of this dissertation is summarized.The modeling of the radar clutter is studied in Chapter 2. Firstly, the common radar clutter models are summarized and classified. Then the characteristics of an outstanding clutter model are summarized according to the demands of practical applications. Finally, by analyzing and comparing, it is pointed out that the KK distribution and the G0 distribution derived from the multiplicative model have broad application prospects.In Chapter 4, the parameter estimation of the heavy-tailed clutter model is studied. The common estimators for the radar clutter model are firstly summarized, and their advantages and disadvantages are pointed out based on theoretic analysis and experimental validation. Then a parameter estimation algorithm based on particle swarm optimization is proposed. This algorithm takes the discrepancies between the histogram of the clutter data and probability density function of KK distribution as the cost function to search the optimal parameters of KK using the particle swarm optimization algorithm. The main factors which affect the accuracy of the parameter estimation are analyzed theoretically. Finally, this new algorithm is applied to both the simulation clutter data and some real synthetic aperture radar clutter data. The simulation results clearly show the good performance of this algorithm.The simulation of the bi-demensional correlated heavy-tailed radar clutter is studied in Chapter 4. Firstly, it is proposed to divide the cumulative density function of the target distribution to improve the accuracy of the nonlinear transform, and the generation of the bi-demensional correlated Gaussian radar clutter with any autocorrelation function is studied. Then the method to generate bi-demensional correlated KK distributed and G0 distributed radar clutter based on memeryless nonlinear transform is proposed. Then the spherically invariant random process characteristic functions of the KK distribution and G0 distribution are derivated and the simulation method based on SIRP is studied. Finally, it can be seen from the simulation results that both the method are effective.In Chapter 5, the range-spread target detection based on high resolution profile in the heavy-tailed radar clutter is studied. Firstly, the range-spread target detection problem is formulated and the main technique involved in this problem is summarized. Then the optimal detector, the GLRT detector and the OS-GLRT detector are derived both in the KK distributed and G0 distributed radar clutter. The performance of these three detectors is assessed and compared, and then it is pointed out that the OS-GLRT is a more applicative one. Finally, its performance under different simulation conditions is analyzed.The target detection based on SAR imagery in the heavy-tailed radar clutter is studied in Chapter 6. Firstly, the target detectors in SAR are summarized and classified, and the development directions of the CFAR detectors are analyzed. Then a global CFAR detector based on KK distribution is proposed and its better performance is validated compared with the one based on G0 distribution and the CFAR algorithm based on automatic censoring in the real high resolution SAR imagery.Chapter 7 concludes the research of this dissertation. Some problems and interesting area for future research are pointed out.