Research On Radar Target Recognition Method From HRRP Based On Kernel Method

High-resolution range profile(HRRP) is the vectorial sum of returns from thetarget’s scattering centers projected onto the radar line of sight, which represents theradial distribution of a target’s scattering centers and contains important target structuresignatures. Therefore, HRRP target recognition keeps drawing great attention from theradar target recognition community. Kernel methods, as effective approachs on dealingwith nonlinear problem, have being a hot spot of research in the machine learning fieldin the latest decade. Owing to the complicated nonlinear relationship among HRRPs ofdifferent targets, kernel methods have been successfully applied in HRRP targetrecognition in recent years, obtaining excellent performance. Therefore, from the threeaspects, i.e., kernel feature extraction, kernel classifier designing and kernel methodbased HRRP online recognition, the theory and techniques for HRRP target recognitionare deeply studied in this dissertation. The main content contains four parts:1. The nonlinear feature extraction of HRRP is studied. Firstly, it is clarified thatthe linear discriminant analysis(LDA) and the local mean discriminant analysis(LMDA)belong to the linear feature extraction algorithms in essence, which can’t extractnonlinear features of targets to describe the nonlinear relationship among differenttargets. Secondly, as nonlinear separability is the main relationship among HRRPs ofdifferent targets, it is difficult for LMDA to obtain excellent recognition performance inthe complicated HRRP target recognition. Accordingly, in this dissertation, consideringthe high efficiency of kernel methods in dealing with nonlinear problem, the kernellocal mean discriminant analysis(KLMDA) algorithm is proposed. The experimentalresults indicate that, compared with the classical linear and nonlinear feature extractionalgorithms, the proposed algorithm can enhance the separability of different targets andimprove the recognition performance.2. For sufficient samples of the target and multiplex samples of nontargets, theissue of classification is addressed. The main work concerns the following two aspects.1) By analyzing the distribution characteristic of HRRPs in the support vector datadescription(SVDD) hypersphere space, it is theoretically confirmed that the optimalgeneralization performance of SVDD is unachievable while directly utilizing thetraining radius of SVDD for classification. Therefore, the concept of second training isdefined and the receiver operating characteristic(ROC) curve is employed to obtain theoptimal hypersphere radius. Thereafter, a method of radar HRRP classification based onSVDD is proposed, which is referred to as classical SVDD classification method.2) It isproved that the anti-noise capability of the classical SVDD classification method isinsufficient, whose main causes are explored by analyzing the variation of probabilitydensity distribution versus SNR. Then, an adaptive model of optimal hypersphere radius versus SNR is constructed. Consequently, a method of radar HRRP classification basedon adaptive SVDD is proposed, which can greatly improve the classificationperformance under low SNR conditions.3. The recognition algorithm designing of mutli-targets is focused on. Firstly, bydividing the SVDD hypersphere space into the inner space and the extended space, thedifferent ascription characteristics of HRRPs in the dual spaces are analyzed, and thedeficiency of the single space SVDD recognition method is revealed. Secondly, in orderto using the sufficient prior information of extended samples, the prior samples aredivided into two parts, i.e., model training samples and generalization samples.Afterward, three models are respectively employed to describe the sample distributioncharacteristic in the extended space, which can reflect the memberships of extendedsamples to the target. In accordance with test HRRPs’ multi-space distributingcharacteristics in multi-target hypersphere spaces, they are divided into two types, i.e.,shrink sample and slack sample, which can be determined by two different discriminantrules, respectively. Ultimately, a radar HRRP recognition method based on dual spaceSVDD is proposed. Compared with the recognition method based on single spaceSVDD, as long as the extended distribution model is appropriate, the proposed methodcan improve the recognition performance evidently.4. Considering the important requirement of online recognition in radar targetrecognition, under the condition of small scale training samples, the HRRP onlinerecognition issue is deeply researched. The main work includes:1) By analysing thegeneralization performance of SVDD to incremental samples, a series of importantconclusions are obtained, which have verified the feasibility of incremental learning onSVDD.2) The principle of samples’ coefficient adjustment is provided, and then thedivision of the set composed of online-updating incremental sample and existed samplesis analyzed in detail. Afterward, an incremental SVDD(ISVDD) algorithm is proposedfor online learning.3) In practical application of radar target recognition, incompletedatabase always exists. Thereby data enrolling, learning and modeling interactively andconcurrently is the primary approach to achieve target recognition. For the HRRP onlinerecognition with small scale training samples, a method of HRRP online recognitionbased on ISVDD is proposed. Compared with the HRRP online recognition methodbased on SVDD, in virtue of the application of ISVDD, the proposed method not onlyreduces the training time of incremental samples but also achieves excellent recognitionperformance. Moreover, it can avoid the requirement of large scale training samples.