Research On The Azimuth Sensitivity And Recognition Algorithm Of Radar High Resolution Range Profile

High resolution range profile (HRRP) plays an important part in the radarautomatic target recognition community for the simplicity in acquiring and processing,the feasibility in engineering, and so on. However, the HRRP recognition performancebased on the popular template database and recognition algorithms is not alwayssatisfying for some factors, such as the constraint at the application background and thedevelopment in electromagnetic calculation. In allusion to the requirement in battlefieldreconnaissance and situation awareness, this dissertation focuses on the research on theazimuth sensitivity and recognition algorithms of HRRP and provides some technicalsupports for the engineering application of HRRP recognition algorithms.Chapter1presents the background of this dissertation. We review the developmentof radar automatic target recognition and summarize some key techniques in HRRPrecognition, which are the azimuth sensitivity, shift sensitivity, preprocessing, featureextraction and classifier selection. We also point out the disadvantage of abovetechniques.Chapter2introduces the basic theory of HRRP. Based on the scattering centremodel, we investigate the acquiring processing and mathematical model. Then, wemake profound analysis about the azimuth sensitivity. Based on the mathematical modelof HRRP, we conclude the resolution ability of HRRP for two ideal point targets andconsummate the definition of functional resolution. The functional resolution isessentially induced by the limit bandwidth of radar transmit signal, which makes theideal point target spread as the sinc function in HRRP. Also, we demonstrate the speckleof HRRP and provide the theoretical basis for the work in next chapter.Chapter3focuses on the azimuth sensitivity of HRRP. Firstly, we announce thespeckle of HRRP and the “spurious dual peaks” feature of speckled HRRP. Weestablish the theoretical model of speckle and conclude the occurrence condition ofspeckle. Thereby, we obtain the relationship between the speckle probability in HRRPand the parameters of radar and the target. The experiment in an anechoic chamber isused to verify all the analyses about the speckle. Secondly, we conclude the matchingscore between speckled HRRP and average HRRP. Based on the conclusion, we studyexperimentally the influence of the speckle on the number of average HRRPs, which arerespectively obtained according to the constant angular production and the adaptiveangular production. The obtained results can provide some theoretical supports for thetemplate database forming. Thirdly, we do some research on the influences of thespeckle on the structural features, such as the predominant scatterers and the rangelength. And, the influences of the speckle on some spectral features are measured by thematching score. These works are meaningful for the selection of HRRP feature. Chapter4studies the application of the azimuth sensitivity of HRRP. Based on themathematical model of high resolution radar echo, we demonstrate that the fluctuationof echo power with the low time can be deemes as the summation of several sincfunctions. And, the “partial” cross-range length of the target can be extracted from thespectrum of the curve of echo power-low time. Then, making use of the range resolutionof HRRP, we propose a fast algorithm for estimating the cross-range length of radartarget using the fluctuation of HRRP. Compared with the conventional method based onISAR imaging, the proposed algorithm avoids some complicate steps and takes only oneFast Fourier Transform at the cross-range. The experimental results demonstrate theeffectiveness of the proposed algorithm and its low computational complexity.Chapter5studies the BOX-COX transformation of HRRP. Based on the statisticalcharacteristic of Gaussian distribution, we demonstrate a method to estimate theBOX-COX transformation parameter using the skewness and kurtosis normal test. Then,we make a deep insight into the BOX-COX transformation characteristics of three typesof typical echo. Considering with the high vector nature and azimuth sensitivity ofHRRP, we propose a new algorithm to perform the BOX-COX transformation forHRRP. The propose algorithm adaptively adjusts the parameter of BOX-COXtransformation for each angular frame of each target and each range cell of HRRP.Therefore, it can improve the normality performance of HRRP and inspire the ability ofthe classifier with the linear discriminant function. Experimental results for MSTARdata show that the proposed algorithm can improve the recognition performancesignificantly.Chapter6studies the feature extraction of HRRP. Based on the difference betweenrange cells, we propose a new weighted HRRP which is more immune from the azimuthof the target. Considering that HRRPs are located on the unit hypersphere withclustering only in some areas, we establish the template of center-affinity sphere. Then,we define a new distance according to the affinity sphere and propose an algorithmbased on the affinity among HRRP samples. The proposed algorithm utilizes the spatialcharacteristic of HRRPs and can improve the recognition performance significantly.Experimental results for MSTAR data show the validity of the proposed algorithm.Chapter7studies the classifier of HRRP. Based on the sparse characteristics andazimuth sensitivity of HRRP, we define the residual of the scattering center model.Then, we design the classifier called the scattering centre model and sparserepresentation-based classifier (SCM-SRC). SCM-SRC can avoid the overfittingphenomenon when the typical SRC are applied to HRRP recognition. Experimentalresults for MSTAR data show the validity of the proposed algorithm.Chapter8summarizes the dissertation and discusses the future work to beresearched.