Study On High Resolution SAR/ISAR Imaging And Error Correction

High resolution synthetic aperture radar and inverse synthetic aperture radar (SAR/ISAR)imaging technique has the ability of well-weather, day/night and long range applications,which dramatically enhance the capability of information acquisition of modern radar.Therefore, SAR/ISAR technique plays an essential role in many military and civilian fields.In SAR/ISAR imaging, high resolution is very important to represent the detailedcharacteristic of the target. Range resolution relies on the bandwidth of the transmitted signal,and azimuth resolution depends on the synthetic aperture size. Two-dimension resolution islimited by not only the radar system constraints both also the manifold accuracy of thesynthetic aperture. Generally, with the increase of azimuth resolution, focusing performancewould be very sensitive to the systemetic errors, such as motion errors, demanding precisecompensation schemes. By arranging the radar frequency and time resource suitably andcombining with phased array technique, modern multi-function ISAR accomplishes multiplytasks simutantaneously, such as wide-swath surveillance, multi-target tracking and imaging.However, in this case, the frequency band and aperture for a single target is limited and sparse,which should be accounted in the imaging processing. Owing to its fleasibility andmaneuverability, compact SAR mounted on small platforms, such unmanned aerial vehicleand missile, is very important for modern battlefield survelliance. However, because of itssmall size and light-load capability, it is sensitive to the atmospheric turbulence, andfurthermore, the high-precision inertia navigation system is usually unavailable due to loadcapability constraint of the platforms. Therefore, prcesise and robust motion compensationbased on raw data is desiderated.This dissertation studies new techniques to improve the resolution, operational swath,feasibility and robustness of SAR/ISAR from four key aspects, i.e. resolution enhancementwith sparse representation, high resolution imaging from sparse frequency bands andapertures, precise and robust motion compensation based on raw data, andhigh-resolution-wide-swath imaging with azimuth multi-channel SAR. The relevant work issupported by by the National Basic Research Program of China （973Program, No.2010CB731903）, National Science Foundation of China (No.60802081and No.60890072)and the National High Technology Research and Development Program of China (No.2008AA8080402).The main content of this dissertation is summarized as follows.The first part focuses on the super-resolution imaging based on sparse representation. A general compressive sensing (CS)-based imaging scheme is built. Some related factorsaffecting the performance of the algorithm are analyzed in detail, based on which we alsodevelop the approach to estimating the related parameters. Accounting for the strong noise,the signal and noise supports are distigushed via introducing optimal weigths, and theimproved CS super-resolution imaging method is proposed. Developed from Bayesiancompressive sensing (BCS), we build the norm1-regularition-based super-resolution imagingscheme, and the weighting parameter for norm1term and its maximum likelihood estimationare derivated mathematically. Non-identical statistics model is extended to the BCS-basedoptimization, and two super-resolution algorithms, Bayesian super-resolution and improvedBayesian super-resolution (BSR and IBSR), are developed. A stage-by-stage procedure isdeveloped to jointly estimate statistics parameters and reconstruct the super-resolution image.Combining with fast Fourier transform, we also propose a modified Quasi-Newton solver toBSR and IBSR optimizations. Some applicable approaches for short-aperture ISAR imagingand maneuvering target imaging are also developed. The validity of the proposed methods isproved by several sets of real-measured data.The second part studies high resolution radar imaging by exploiting sparse frequencybands and apertures. High resolution range profile synthesis by sparse representation and therevelant parameter selection are developed. For ISAR imaging with sparse stepped-frequencywaveforms, precision motion compensation procedure by combining optimal range alignment,autofocusing and parameter estimation with multi-frequency diversity is developed. Based onBayesian compressive sensing, high resolution imaging with exploiting sparse apertures isdeveloped. In the algorithm, the discontinuous phase error function is overcome by sparseaperture coherence processing, which can be jointly implemented in the imaging optimization.In terms of high precision and efficiency, a pre-processing for phase error correction ispresented. Extending from the all-pole model, we develop a novel coherent processing tocorrect the linear and constant phase difference between sub-apertures. Real data sets areutilized to confirm the validation of the proposed methods.The third part presents the SAR motion compensation (MOCO) based on the extendedphase gradient autofocus (EPGA). In this part, we develop the local maximumlikelihood-weighted phase grandient autofocus (LML-WPGA) algorithm, which is capable ofprecsion estimation of range-dependent phase errors. In terms of precise MOCO for thestrip-map SAR, a procedure implemented by weighted phase gradient autofocus andLML-WPGA is proposed, which corrects nonsystematic range migraition, nonspatial-variantand spatial-variant phase errors sequently. Subaperture overlapping and adaptive filtering areutilized to construct full-aperture motion error function from raw data. The MOCO scheme is extended to highly squinted and spotlight SAR imaging. And LML is also introduced into twoautofocuse PWE-PGA and WPCA. Therefore, LML-PWE-PGA and LML-WPCA aredeveloped, which have the propertities of high precision and efficiency. The proposedalgorithms are validated by using a number of real SAR sets.The last part focuses on high-resolution-wide-swath (HRWS) imaging withmulti-channel SAR and adaptive channel calibration. Based on detailed analysis on theDoppler ambiguity resolving with spatial filtering beamforming, the robust beamformingtechniques are introduced into multi-channel SAR imaging. Comparing with conventionalDoppler resolving method, robust beamforming-based method can not only suppressambiguity components adaptively, but also precise reconstruct signal component by arrayvector estimation. By the robust beamforming technique, the performance of multi-channelSAR imaging is enhanced effectively. A two-step channel calibration is proposed based on thefact that the amplitude and phase errors between channels are usually uncoupling in range andazimuth directions. Subspace-based calibration is developed to correct the channel mismatch,whose performance and robustness are ensured by using multiply Doppler bins and weightingsubspace projection. Two sets of real measured data are utilized to confirm the effectivenessof the proposed methods.