Study On Wideband Radar Target Recognition

Inverse synthetic aperture radar (ISAR) images represent the projection of the target onto the range-Doppler plane, which contain the informative 2-D target structure signatures. The target recognition using radar target ISAR images is an important field of the wideband radar automatic target recognition (RATR). Polarization is an indispensable component of the description of the target electromagnetic characteristic. Wideband polarization RATR has received more and more attentions. This dissertation provides our researches for ISAR and polarization target recognition. Interferometric ISAR (InISAR), InISAR target recognition, multi-polarized high resolution range profile (HRRP) target recognition and polarization scatter matrix (PSM) target recognition have been developed in this dissertation. Details are described as follows.1. The theory of InISAR is introduced briefly. Using the characteristic of the higher SNR at stronger pixels, a novel method of InISAR imaging based on the dominant scatterers is proposed. This method can deal with the low signal-to-noise ratio and phase wrapping in InISAR imaging. InISAR imaging in squint model is also researched.2. The issues of InISAR 3-D imaging are discussed. The first issue is about the antenna array and the baseline. Two kinds of the antenna array, the relationship of the azimuth resolution to baseline, the baseline decorrelation and the optimal baseline are presented. The second issue is angle glint. The theory and the suppression are analyzed. The applications of super-resolution in InISAR imaging is the last issue. Capon and Relax method are discussed and compared with FFT method.3. A method of the InISAR target recognition is proposed. The InISAR preprocessing is presented. The features are extracted from the polar image that is obtained from InISAR image by the polar mapping. The extracted features have invariance with respect to rotation and scale. The effects of the four important parameters (elevation, speed, baseline and range) on imaging and recognition are discussed, the results of four experiments prove the theory analysis.4. The multiple-polarized HRRP includes much more target information than single-polarized radar HRRP dose, so using multiple classifiers to combine multiple-polarized information can enhance the radar target recognition performance. Two methods of combining multiple classifiers are proposed, which are the weighted average algorithm and the weighted voting algorithm. Employing the two different combining rules can fuse and classify the multi-polarization radar HRRP. The good recognition performances are achieved.5. Aiming at the great quantity of multi-polarized HRRP, the complexity of the data distribution and the recognition algorithm, the methods based on kernel methods are proposed. Two kernel functions based on the multi-polarization HRRP are defined, then two kernel functions are employed to the kernel principal component analysis (KPCA) respectively. The multi-polarized radar HRRP can be recognized as a whole one in the proposed methods, so the complexity of the recognition algorithm is reduced.6. Aiming at the difficulty of the feature extraction from the polarization scatter matrix (PSM), the kernel methods based on PSM are proposed. Two kinds of kernel functions based on PSM are defined, and they are employed to KPCA respectively. The proposed methods achieve good recognition performance. The proposed two kinds of kernel functions based on PSM are employed to the kernel optimization. Using a data-dependent kernel, an optimized kernel is obtained by maximizing the kernel Fisher criterion. The KPCA features are used as the input of the classifier to classify the targets.