Radar Ground Target Recognition Based On High Resolution Range Profiles

Due to the growing complexity of ground battlefield environment,it becomes increasingly difficult to detect target accurately with conventional radar signal and information processing.Thus the radar target recognition method has received intensive attention from the radar scholars.Radar high resolution range profile(HRRP)has been widely used for practical target recognition system for its easy acquisition and low storage requirement.Besides,HRRPs contain the detailed target structure signatures,such as strength and distribution of scatters,and target size,etc.So far,numerous efforts have been devoted to verify the advantage of HRRP-based target recognition.However,there are some problems to be further studied,such as the incomplete training samples of non-cooperative target,extraction of discriminative and low-dimension feature vector,design of discriminator and classifier,etc.Based on the practical requirements of precision attack for ground target in the complexity environment,this dissertation aims at the design of feature extraction and recognition method.The main contributions are summarized as follow:1.To solve the incomplete training samples of non-cooperative targets,a novel HRRP simulation method is proposed.First the exquisite scattering point model is constructed based on the mixture model and statistical distribution of scatters.Taking the covering influence into consideration,the constructed scattering model is further simplified by removing overlapped scatters on the radar line of sight.Then the high frequency approximate electromagnetic scattering computing method is used to obtain the backscattering intensity of scatters.Therefore,the simulated HRRP can be generated.By comparison,it can be seen that the simulated HRRP shares similar scattering and statistical characteristics with the real-measured HRRP.It is noted that the proposed recognition method is based on the template matching theory.In the recognition process,the simulated HRRPs serve as training samples while the measured HRRPs are utilized for testing.The experiment result verifies the effectiveness of the proposed method.2.To extract low-dimension and highly discriminative features from HRRP,a novel feature extraction method is designed,which is named as statistics kernel function discrimination analysis method.Based on statistical analysis,the statistical kernel functions are exploited with ideal and nonideal statistical model of HRRP range cells.Therefore the integrated target information can be obtained for target recognition with minimum information loss.In addition,a novel criterion function is constructed based on canonical correlation analysis and discrimination analysis.In this criterion function,the within-class correlation and between-class discrimination are maximized to guarantee high discrimination for feature vectors.Besides,the redundancy and dimensionality of the feature vectors are reduced by the fusion operation within the criterion function,which reduces time consumption for practical radar target recognition system.Experimental results with measured datasets validate the efficiency of the proposed method.3.To improve the HRRP-based target recognition performance under low signal-to-noise ratio(SNR),a target recognition method based on sparse and low-rank joint learning is proposed.Specifically,sparse representation(SR)and low-rank representation(LRR)are applied to extract the local and global characteristics of target HRRPs,respectively.To obtain the noise-robust and high-discriminative features of HRRPs,dictionary learning is involved.In the training stage,the discriminative dictionary is constructed based on Fisher discriminant criterion and support vector theory.Besides,in order to achieve more accurate feature space description,jointly discriminative analysis multiclass classifier weighted embedding dictionary learning method is used.Denoising dictionary optimization is implemented for noise suppression in the testing stage.Experimental results based on the measured HRRP data demonstrate that the proposed method can recover the original HRRPs and significantly improve the recognition performance under low SNR conditions.4.To identify the out-of-database targets in HRRP-based target recognition,an improved target identifier is designed based on clustering strategy and feature space distribution.In the training stage,a K-Means clustering strategy based on the pre-processing of correlation coefficient is utilized to divide the training feature space.Then each sub-space boundary is determined by support vector domain description(SVDD)based on the distribution of the feature space.Finally,the target category can be decided with the sub-space boundary and the weighted K-neighbors principle.This method can work without the template of out-of-database samples,which improves the effectiveness of target identification.Due to the fact that the feature space of different targets has the characteristic of non-uniform aggregation under different attitudes,a procedure of region partition is applied to training dataset.Thus the computational load is relieved by decreasing the searching operation of template matching.The requirement of real-time processing can be satisfied.Finally,the experiment results based on both simulation and real-data verify the effectiveness and efficiency of the designed identifier.5.For radar HRRP recognition,three aspects are of great importance to improve the performance,i.e.discrimination for outlier,classification for inner and accurate description for feature space.To tackle these issues,a novel target recognition method is designed,denoted by multiple support vectors method.First,a treble correlate support vector model is constructed to segment the feature space into two regions according to the density of feature vectors.Then the description and classification hyperplane for each region are obtained.Based on the support vector framework,the computation complexity can be reduced significantly for practical radar HRRP recognition.Finally,the experiment based on the measured data verifies the excellent performance of the proposed method.