Study Of Radar Target Recognition Method Based On High Range Resolution Profile

With the new requirement for radar to get more information from the battlefield, radar automatic target recognition (ATR) as a new research area appeared in the 1960s. The development of high range resolution radar has given strong support to RATR. High range resolution profile (HRRP) contains the target structure signatures and is easy to be acquired, which makes the HRRP ATR to be received intensive attention. The work of this dissertation is focused on feature extraction and target recognition of high range resolution profile. The main content is summarized as follows.Starting from the scatters model of high-resolution radar target, the property of HRRP and several key problems of HRRP recognition is discussed. The range profiles obtained by conventional inverse fast Fourier transform (IFFT) is compared with that obtained by the RELAX and multiple signal classification (MUSIC) super- resolution technique. Through simulations, we detail the problems such as the target orientation, the number of scatters and the signal-to-noise ratio sensitivity which should be paid attention to when using MUSIC super-resolution range profile for radar automatic target recognition.The dimensionality reduction of power spectrum feature is concerned in HRRP recognition. The stronger correlation between the low frequency bins is shown in simulation results. A concise overview of the linear feature compress methods is given. With the statistical property of power spectrum discussed, a new fisher discriminant analysis feature compress method, which is based on the standard data, is presented for dimensionality reduction of power spectrum feature. And the performance improvement mechanism of the proposed method is discussed. The experimental results based on the measured data show that the proposed technique achieves robust good recognition performance with low feature dimension.HRRP recognition based on translation invariant feature—the central moments feature vector is concerned. To handle the target aspect sensitivity of moments feature, the average HRRPs associated with different target aspect sectors are used to extract the central moments feature vector for further recognition. A multi-class support vector machine (SVM) classifier with better generalization is designed to classify airplane objects. Comparing with the moments based MCC method, and the HRRP based MCC, the proposed approach can achieve better recognition performance and reduce the computation complexity and storage requirement. Experiment results based on the measured data are given to show the efficiency of the proposed method. The multiples HRRPs recognition problem is concerned. Generally, using single profile can not achieve good recognition performance, because of HRRP's target aspect sensitivity. Actually, a sequence of independent HRRPs can be obtained in many radar systems. The average range profile and the variance profile are extracted together as the feature vectors for both training data and test data representation. A decision rule is established for HRRP sequence recognition based on the minimum Kullback-Leibler (K-L) distance criterion. And the same criterion is used for time-shift compensation of HRRP, with fast algorithm proposed. Comparing with the maximum likelihood criterion and adaptive Gaussian classifier (AGC), the propose method is much more computational efficient but with comparable recognition performance. Experimental results based on both the measured and the simulation data show that the minimum K-L distance classifier is effective.Radar High Range Resolution Profile (HRRP) is very sensitive to target aspect variation. To deal with this problem, usually, multiple statistical models are built for different target aspect sector when using HRRP for target recognition. Therefore, how to determine target aspect sector number and how to divide target aspect sector play an important role in classifier training. A data driven adaptive learning algorithm is proposed in this paper, which determines the target aspect sector boundary based on a multivariate Gaussian statistical data model and an iteration algorithm, and the target aspect sector number can be determined simultaneously. Comparing with the traditional equal interval target aspect partition approach, the proposed approach can achieve better recognition performance with lower computation complexity. Experimental results based on the measured data show the efficiency of the proposed method.