Study On Broadband Radar Target Range Profile Recognition

The rise of modern radar technology provides a strong technical support for radartarget recognition. The high resolution range profile (HRRP) obtained by widebandradar shows the radial distance distribution details of target scattering centers along theradar line of sight, and contains more structure information than that of the target echoobtained by low resolution radar. Furthermore, the HRRP can be easily captured andprocessed, while potentially avoiding the complex motion compensation processing andtoo much imaging time-consuming, relative to the two or three dimensional imagery.Therefore, the target recognition based on HRRP has received extensive attention fromthe radar technology workers in recent years.Focused on the feature extraction and classification subprocesses, this dissertationprogressively deepens the research upon the theory and technology of radar multi-targetrecognition using HRRP. The main content and innovation are summarized as follows:(1) To reduce the eigen-decomposition operation burden of big scatter matrixes inclassical kernel methods, two new kernel-based methods are proposed by expanding thetwo-class discriminant units. By splitting and restructuring under the so called "oneagainst one" strategy, the two new methods both can divide a big scatter matrix into aseries of small ones, and then arrange the small ones by series or parallel. Theexperimental results show that the two new methods both can reduce the trainingtime-consuming effectively, improve the recognition performance, and are very suitablefor radar multi-target recognition.(2) In multi-target recognition, the quality and quantity of discriminant information(DI), which one is more important? Accompanied with this issue, three DI extractionmodels, i.e., passive recognition and general selection for among-class absolutediscriminant information (PGA) model, passive recognition and individual selection forbetween-class relative discriminant information (PIB) model, and active recognitionand individual selection for between-class discriminant information (AIB) model, aredesigned. Theoretical analyses indicate that the PGA model prefers to the DI qualitywhile the PIB and AIB models both prefer to the DI quantity. (3) Generlized discriminant analysis (GDA) is applied in the PIB and AIB models,and then two kernel-based methods, i.e., PIB-based GDA (PIB-GDA) and AIB-basedGDA (AIB-GDA), come forth for the DI quantity. Compared with GDA, PIB-GDA andAIB-GDA can not only reduce the training time-consuming greatly, but also improvethe recognition performance perfectly.(4) A summary and conclusion is given to the multi-agent technology, and then amulti-agent model is designed for radar HRRP target recognition. Also GDA is appliedfor model actualization, and then a new method, synthetic-GDA (S-GDA) algorithm,comes forth, which can be considered as the parallel combination of PIB-GDA andGDA. The experimental results indicate that S-GDA can realize the advantagecomplementary of PIB-GDA and GDA with respect to the recognition performance.(5) According to the three fundamental composite constructions, this dissertationproposes four composite kernel methods, i.e., parallel construction: kernel Fisherdiscriminant (KFD)-based multiclass synthetical discriminant analysis (KFD-MSDA)and gobal distributed KFD (G-DKFD), series construction: multi-KFD-based lineardiscriminant analysis (MKFD-LDA), mixed construction: kernel mixed discriminantanalysis (KMDA). The experimental results show that the recognition performance ofthe four composite kernel methods is labeled from high to low by KFD-MSDA,G-DKFD,KMDA and MKFD-LDA, among which the the recognition performance ofKMDA is very similar to that of GDA.