Radar Target Recognition Algorithms Based On ISAR Image

Inverse Synthetic Aperture Radar(ISAR)can offer the shape and structure features of targets,ISAR image is not sensitive to attitude angle,and many image recognition methods can provide reference for ISAR recognition.So radar target recognition based on ISAR image is very important and has good development prospects.In this dissertation,some research works have been made on feature extraction of ISAR image target.The main contributions are illustrated as follows:1.The transformation feature extraction method of ISAR image is studied.The negative decomposition coefficient is not allowed in subspace feature extraction method.So,non-negative matrix factorization algorithm is studied to solve it.A novel ISAR image recognition algorithm based on block two-directional and two-dimensional non-negative matrix factorization with projected gradient(B(2D)2PGNMF)is proposed.Based on the two-dimensional non-negative matrix factorization with projected gradient,the same kind of training samples form a small matrix,row non-negative matrix factorization with projected gradient and column non-negative matrix factorization with projected gradient are used in each small matrix.The coefficient matrix of the sample mean of each type of training samples is calculated as standard feature samples by the obtained base matrix.According to the minimum distance of the coefficient matrix of the test sample and the sample mean of each class,five-type aircraft models are classified.2.The multi-scale feature extraction method of ISAR image is studied.The existing feature extraction methods analyse ISAR image on single scale,which can not reflect the intrinsic multi-scale characteristics of targets.A novel ISAR image recognition algorithm based on Multi-scale Block Local Binary Patterns(MB-LBGP)is proposed.Firstly,the corresponding Gabor magnitude maps(GMMs)are obtained by convolving the enhanced ISAR image with multi-scale and multi-orientation Gabor filters.Then,each GMM is divided into small regions from which multi-scale block local binary pattern is used to extract histogram features.At last,five-type aircraft models are classified by using a nearest neighbor classifier with Chi square as a dissimilarity measure in the computed feature space.3.The fusion feature extraction method of Gabor Magnitude and phase is studied.SAR,ISAR and the most recognition algorithms in other areas based on Gabor feature only use the amplitude information.Gabor phase information is not used.In order to solve it,a novel ISAR target recognition method based on the fusion feature of Gabor Magnitude and phase is proposed in this paper.Firstly,the corresponding Gabor magnitude maps(GMMs)and Gabor phase information are obtained by convolving the ISAR image with multi-scale and multi-orientation Gabor filters.Secondly,each GMM is divided into several nonoverlapping rectangular units,and the histogram of unit is computed and combined as the magnitude histogram features.Thirdly,the local Gabor phase pattern is used to extract phase histogram features.Then,the fusion features of the Gabor magnitude features and Gabor phase features is used as the features of ISAR image.At last,five-type aircraft models are classified by using a nearest neighbor classifier with Chi square as a dissimilarity measure in the computed feature space.4.ISAR image recognition algorithm based on improved neural network and view-aspect is studied.The template-based method in inverse synthetic aperture radar(ISAR)image recognition needs a set of target templates in different gestures.It results in the great consumption of storage and computation.So,a novel method is proposed to construct the simplified prototype template library to replace the conventional library,and the improved pulse coupled neural network(PCNN)is introduced to extract the features of radar targets.Firstly,the features of ISAR images are extracted to construct the template library of feature parameters by the improved PCNN which is a fusion algorithm based on PCNN and mathematical morphology.Secondly,depending on the concept of view-aspect in computer vision,the template library of feature parameters is partitioned into different view-aspects by iterative self-organizing data analysis algorithm(ISODATA).Thirdly,the parameters of view-aspect are extracted to construct the prototype template library.Finally,the testing image is classified by matching the prototype template library.5.The multiple classifier fusion of ISAR image is studied.B(2D)2PGNMF,MB-LGBP,the fusion of Gabor Magnitude and phase and the improved PCNN are compared in the different classifers.From the perspective of multiple classifier fusion,three kinds of features,which include B(2D)2PGNMF,the fusion of Gabor Magnitude and phase and the improved PCNN,are combined with three kinds of classifiers,which include the nearest neighbor classifier,decision tree classifier and cosine classifier,to realize the multiple features and multiple classifiers fusion.Naive bayes and weighted voting are adopted to carry out the fusion.