Research On Target Detection And Its Related Topics Of Synthetic Aperture Radar

In recent years, Synthetic Aperture Radar (SAR) has been used in many fields. SAR technology can be used to detect radar targets of interesting, which embedded in strong ground clutter. Some researches and institutes have studied target detection and recognition for SAR. Lincoln Lab of MIT has produced much important influence in the field, which is support by DARPA (Defense Advanced Research Projects Agency) to study automatic target recognition (ATR). The project has been processing and finishing.This thesis has studied methods of target detection and its related technologies such as speckle suppressing and edge extracting for SAR by support of institute of science and technology of national defense. Some technologies and algorithms are proposed according to shortcoming of recent methods.1. Technology of speckle noise suppressing is studied, first new method of enhanced wavelet soft-threshold for speckle noise suppressing is proposed (1). This method combines wavelet soft-threshold and scene heterogeneity of SAR, because the different scene needs different filter method. Second new technology of enhanced wavelet Wiener for speckle noise suppressing is proposed (2), which combines wavelet transform and adaptive Wiener filter according to SAR image scene heterogeneity. It can better preserve clutter edge and point target. Method (2) has advantage because it adopts adaptive Wiener filter so the method don' t select wavelet threshold. Real SAR image testing satisfies the validities of these two methods and method (2) is better than method (1).2. Technology of Target detection for SAR is studied. A first new method of robust constant false alarm rate for target detection according to statistic of SAR image is proposed. The relationship between threshold coefficient and constant false alarm probability (CFAR) is obtained after SAR target detection is analyzed according to Gamma distribution. The theory and simple realizing approach of selecting threshold coefficient are proposed. The robust way of optimizing clutter' s mean is given, which has de-noising ability without processing of de-noising in target detection. The mean value is influenced due to speckle, the middle value has better performance in the field of anti-noise, but middle value is error estimate to mean, which is corrected by a coefficient. Another new method of target detection is proposed (4), in which enhanced speckle noise suppressing is applied, so multi-statistical has appeared, it' sGaussian distribution, Gamma distribution and high Gamma distribution. Different methods of target detection are applied for different statistical. The final result is obtained by area combining. Experiments show the two methods are valid and they have better behavior.3. A new robust edge extracting (5) is proposed after the edge extracting for SAR image is studied. Edge extracting is influenced by speckle noise, because mean need to estimate in the traditional ratio edge method. Speckle noise is harmful to edge extracting, traditional ratio edge fltethod isn' t better, and enhanced speckle suppressing has an edge suppose. Is a method for edge extracting existed, in which speckle is no harmful without suppressing speckle noise? A new middle value robust appeared according to result of chapter four. The validity of the methods is proved by experiments for real SAR image.4. Classification of Ground scene and target for SAR image is studied in the thesis. Method of classification is always a technology for SAR. Some one scale and two scale texture features are described and useful features are selected by testing relatively contribution according to much experiment result. Some classifier such as minimum distance classifier, maximum likelihood classifier (ML), and learning vector quantify (LVQ) classifier and c-mean classifier. Target' s describer by combining wavelet transform and constant false alarm rate is studied, wavelet transform is suitable to multi-distinguish analysis and its variance of wavelet field can represent signal. So that the...