Synthetic Aperture Radar Target Recognition And Imaging Simulation

Synthetic Aperture Radar (SAR) can observe all-weather, all-day and penetrate some objects. Therefore, it is widely applied in many areas, such as military objective reconnaissance, natural disaster monitoring, resource survey and so on. The information abstraction from the SAR images has been studied all the while, which is the key step of the SAR image interpretation and plays the important roles in the detection, imaging and SAR image interpretation. But it is difficult for the interpretation of SAR image to meet to the rapid growth of the data collection capability。This dissertation focuses on the study of the target feature extraction, feature simulation and automatic target recognition (ATR), such as a marine, an aircraft, a vehicle and so on.Following the processes of target feature extraction and SAR ATR, and on the basis of previous studies of many pioneers in this field, some investigations are done in this paper as follows:1) The basic characteristic of SAR image and the features of some typical objects were studied first, especially the texture information in SAR image and some common texture analysis methods.2) The feature of speckle noise in SAR image is analyzed, several speckle removal methods are carried out to reduce the speckle which can corrupt the useful information in the SAR image. The wavelet thresholding method was used to remove speckle noise in the SAR images. In this paper, Daubechies base4 is chosen to make SAR images 2D wavelet transformation. A tradeoff method between soft-thresholding and hard-thresholding was used and the threshold values were calculated at different levels. The experimental result of removing speckles in SAR images was satisfied, and a lot of useful detail information such as edges can be retained.3) A Gabor filter bank in different scales and directions was constructed based on detailed analysis of the structure of the Gabor filter and the relations between the filter parameters. A series of filtered images were obtained by the Gabor bank and the local maximum points were extracted from the filtered images. The feature points were characterized by the sum of the energy values in their small neighboring area around them. This method is an attempt at a marine recognition using high resolution SAR imagery. The differences between the feature points extracted from different objects are obvious, and they can be used as important features for distinguishing one object from the other objects. The Gabor texture feature can be applied to object classification and recognition in some situations.4) The theory of attributed scattering centers based on the Physics Optics(PO) and the Geometric Theory of Diffraction(GTD) is analyzed and discussed. The detailed formula is derived. The parameters of the model are been well estimated on regions of the SAR image. Leading surface estimation (orientation of the target estimation) and Delaunay walk are introduced to improve performance of the SAR ATR system. The recognition results using MSTAR show that the algorithms for estimating the parameters are efficient and the theory of attributed scattering centers is practical.5) The method of radar target modeling, electromagnetic scatter calculation and radar imaging simulation is described. First, it models the object with triangular element by using AutoCAD, reverse ray tracing technique is used to find object shadow, radar scattering character is calculated with the Physics Optics theory, then a high-resolution ISAR image of the target is obtained. The results show that the algorithms for calculating RCS are efficient and the ISAR imaging simulation system is practical.6) Based on the conventional ionospheric data obtained by European incoherent scatter radar, the "mutation" phenomenon of the sounding data power was studied. Generally, the "mutation" power in more than 500km is not produced as geophysical effects, but due to satellites or space debris. Matched filtering method was used to achieve the space debris detection and the debris's character extraction, the signal to noise ratio is improved by using coherent accumulation method. A total of 363 pieces were captured, and the distribution of debris with height and time has been gotten.