Study On New Method Of High Resolution ISAR Imaging  Posted on:20150309  Degree:Doctor  Type:Dissertation  Country:China  Candidate:H C Liu  Full Text:PDF  GTID:1268330431462469  Subject:Signal and Information Processing  Abstract/Summary:  PDF Full Text Request  Inverse Synthetic Aperture Radar (ISAR) can acquire ISAR image ofnoncooperative target under the condition of all weather, all day and far distance,which enhances radar’s information inquisition capability dramatically. Therefore, inboth military and civilian application, ISAR imaging method has great importance.Recently, the problem of realtime, superresolution and crossrange scaling in ISARimaging has been studied deeply. In order to improve the imaging capability of ISAR,which is more suitable for radar automatic target recognition base on ISAR image, somenew fast and adaptive imaging ISAR imanging algorithms with limited pulses arestudied in this dissertation. The main work in this dissertation can be shown as follows.(1) Superresolution ISAR imaging based on sparse Bayesian learningIn Chapter3, ISAR imaging models of ridge target and micromotion target havebeen analyzed, and the process of superresolution ISAR imaging based on sparse signalprocessing is presented. The following works have been done to improve the ISARimage quality for realtime and adaptive ISAR imaging.An algorithm for superresolution ISAR imaging based on sparse Bayesianlearning (SBL) is proposed. Recently, compressive sensing (CS) has been successfullyused in ISAR imaging. Since the exact sparse reconstruction, i.e., L0norm constraint, isNP hard, L1norm relaxation is widely used at the cost of performance degradation inthe sparseness of the solution. Furthermore, the regularized factor in CSbased ISARimaging algorithms should be adjusted manually. This makes the existing algorithmsinconvenient to be used in practice. SBL adopts individual Gaussian prior, which retainsa preferable property of diversity measure and can give more sparse solution. Moreover,all the necessary parameters can be estimated using an efficient evidence maximizationprocedure, which can be easily used in practice. The validity of the superresolutionbased on SBL has been verified by measured data of airplane and ship.A novel ISAR imaging algorithm for micromotion target based on multiplesparse Bayesian learning (MSBL) is proposed. The signal model of micromotion targetis analyzed. The ISAR image can be divided into irregular image (rotating part) andstraight lines (main body). Since the signal of the main body has the property ofcommon profiles, MSBL can be used to obtain the signal of main body effectively. Theimage of micromotion parts can be obtained by substracting the image of main body.Finally, the clear ISAR image of main body and the micromotion parameter of rotating part can be obtained. The validity of the proposed ISAR imaging algorithm based onMSBL has been testified by simulated and measured data.(2) An adaptive ISAR imaging algorithm based on evidence frameworkIn Chapter4，An adaptive ISAR imaging algorithm based on evidenceframework is proposed. Based on the CS ISAR signal model and the approximation ofsparse constraint, i.e., L1norm, the closed forms of all necessary parameters areobtained using evidence framework. This algorithm iterates between sparse coding andparameter estimation until a fixed number of iterations is reached. This ISAR imagingalgorithm improves the adaptivity of ISAR imaging. Experiments based on simulatedand measured data are demonstrated to show the efficiency of the proposed ISARimaging algorithm.(3) Joint ISAR imaging and crossrange scaling using compressive sensing withadaptive dictionary (CSAD)In Chapter5, ISAR crossrang scaling is mainly studied. Firstly, the chirprateCSISAR signal model is presented, and the cost function is obtained by constructing theadaptive dictionary. Since a disturbed matrix with chriprate is added to original CSISAR imaging model, the sparse reconstruction method used in CS method is invalid.An alternative recursion algorithm can be used to reconstruct the sparse signal with highprobability and this has been confirmed by theories analysis and data demonstration.The recursion algorithm can be divided two stages:1) fix the value of chirprate, get thesuperresolution ISAR image by the algorithm of sparse reconstruction;2) fix thesuperresolution ISAR, get the value of chirprate by gradient algorithm. Finally, therotation rate can be obtained by least square method, and the range and corssrangeISAR image can be obtained by using the rotation rate to scale the ISAR image. Thevalidity of the proposed ISAR imaging algorithm based on CSAD algorithm has beentestified by measured data.(4) A range profile compensation algorithm for space target with uniformaccelerationIn Chapter6，Based on the model of wideband linear frequency modulation signal,the property of space target echo with uniform acceleration is analyzed. The target echocan be modeled as cubic phase signal, the CramerRao bound of velocity andacceleration is deduced. A compensation algorithm based on cubic phase function isproposed, which can estimate the velocity and acceleration of target and compensate thehigh resolution range profile under lower signal to noise ratio, then it is preferable to the following imaging and target recognition. The results of simulation data show that theproposed algorithm can effective compensate the motion of space target with uniformacceleration.  Keywords/Search Tags:  Inverse synthetic aperture radar (ISAR), Superresolution, motioncompensation, Sparse Bayesian learning (SBL), Compressive sensing (CS), Evidence framework, maximum posterior (MAP), Crossrange scaling  PDF Full Text Request  Related items 
 
