Research On Airborne Radar STAP And SAR/GMTI Technologies In Heterogeneous Environments

Space-time adaptive processing (STAP) is a leading radar signal processing technique thatadaptively forms a two-dimensional filter with respect to the statistical characteristics of the clutterplus noise to effectively suppress the clutter component contained in the receiving data. It greatlyenhances the detection performance of airborne moving target indication (MTI) radar and syntheticaperture radar ground moving target indication (SAR/GMTI) system. However, for conventionalSTAP, a key step is to adaptively estimate the clutter plus noise covariance matrix, in which thetraining samples must be independent identically distributed (I.I.D) with the clutter plus noisecomponent in the cell under test. Unfortunately, in practical processing, the clutter environmentsalways present to be heterogeneous. That means the I.I.D assumption will never be valid, whichfinally leads to performance loss of STAP. Therefore, the major work in this dissertation focuses onthe research of STAP and SAR-STAP algorithms in nonhomogeneous environment and can besummarized as follow:The clutter model for airborne radar is studied and the clutter characteristic is analyzed.Fundamental theories such as optimal STAP weight vector as well as several criteria to evaluate theperformance are introduced. Classical reduced-dimension/rank algorithms such as3DT, minimumnorm eigencancerler (MNE), and loaded sample matrix inversion (LMSI) are investigated.Experimental results with respect to measured data collected by a three-channel airborne radar areemployed to verify the related theories and algorithms.The nonhomogeneous STAP algorithms are studied. Three nonhomogeneous algorithms, i.e., thenonhomogeneous detector (NHD), space-time autoregressive (STAR) filtering and the structuredSTAP are investigated, respectively. At first, the NHD is introduced into reduced-dimension STAPprocessing by employing a generalized inner product (GIP) based reiteratively censored (RC)3DTalgorithm to process the measured data, which is proved to be of great improvement. Then, aiming atthe failure of STAR in nonstationary clutter in slow-time, a new time-varying (TV) STAR algorithmthat based on the time-varying autoregressive (TVAR) model is proposed. The virtue of TV-STARrelative to STAR is verified by simulation as well as experimental results. At last, according to theperformance loss caused by the inaccuracy of the so called structured covariance matrix, a newstructured STAP algorithm is proposed. In the new proposed algorithm, the clutter plus noisecovariance matrix is formulated by knowledge-aided (KA) method and undergoes several adjustmentsvia tapering matrices. After that, the adjusted matrix is finally employed to calculate the space-time weight vector. The desirable detection performance is also verified by experimental results.The adaptive clutter suppression interferometer (ACSI) SAR technology is investigated. The socalled ACSI technique that introduces adaptive processing in CSI-SAR system cancels the cluttercomponent effectively by two receiving channels, and is considered to be the most valuableimplementation of SAR-STAP in practical processing. According to the performance loss caused byheterogeneous environment, two nonhomogeneous ACSI algorithms, i.e., the constraint ACSI andmedian canceller (MC) ACSI algorithm based on median estimation method are proposed and verifiedby the experimental results. Then, the parameter estimation methods for the detected moving target inCSI-SAR system are studied, which is also supported by simulation as well as experimental results.Finally, according to the practical engineering application, an entire CSI-SAR/GMTI signalprocessing scheme is designed for the three-channel SAR system and some related experimentalresults are provided.The image domain multi-channel SAR-STAP technology is studied. Based on the post-DopplerSAR-STAP method (MSAR) proposed by Ender, the image domain SAR-STAP technique isintroduced to detect moving target after SAR imaging. Then two nonhomogeneous SAR-STAPalgorithms, i.e., knowledge-aided SAR-STAP and modified FRACTA for multi-channel SAR, areproposed to operate in heterogeneous clutter environment and also verified by experimental resultswith respect to the three-channel SAR system. At last, the parameter estimation methods formulti-channel SAR system are investigated, the maximum likelihood estimation (MLE) as well as theadaptive monopulse method is introduced. The performance of each method is analyzed bysimulations and verified by experimental results.As a relatively independent part, the monopulse imaging technology is investigated at the end ofthis dissertation. A monopulse imaging algorithm is proposed to improve the definition of radar imageconcerning the airborne/missle-borne radar forward-looking area, which can be hardly achieved bySAR or Doppler beam sharpening (DBS) technique. Furthermore, based on the probability densityfunction of the monopulse ratio, three criteria, i.e., the image position distortion, resolution andsignal-to-noise ratio of the target to be imaged are proposed to evaluate the quality of monopulseimage. Factors determining the proposed criteria are analyzed afterwards, followed by the simulationand experimental results to verify the performance of the proposed algorithm.