Study On Signal Modeling And Imaging Algorithm For Airborne/spaceborne SAR With Nonlinear Trajectory

The existing and future synthetic aperture radars(SARs)cannot always work at an ideally linear trajectory.Their radar platforms may passively work at a nonlinear trajectory,or the radar platform adjusts its movement with the consideration of some actual requirements and,thus may cause a nonlinear trajectory.The available signal modeling and imaging processing algorithms for the conventional linear trajectory and the nonlinear trajectory may be of low accuracy,low efficiency and low imaging performance.This dissertation develops the research on nonlinear trajectory signal modeling and imaging algorithms surrounding some key problems.The main content is summarized as follows.1.The second chapter studies several slant range models for the nonlinear trajectory signal modeling and imaging processing.The original double slant range model can accurately describe the relative movement between the radar platform and the observation region,which is used as the foundation for the evaluation of imagery geometric resolution.Then,considering that the original double slant range model may complicate the analysis of Doppler characteristics,a center time equivalent slant range model is introduced,in which the bistatic configuration is equivalent to the monostatic configuration.Since the above two slant range models have no analytically mathematical expression and thus may complicate the subsequent imaging processing,a high-order polynomial slant range model is introduced,which is used as the foundation for the design of imaging algorithms.Finally,a slant range model based on the motion compensation(MOCO)principle is presented,which is used as the foundation for the design of MOCO algorithms.2.For the evaluation of imagery geometric resolution in nonlinear trajectory SAR,the third chapter develops two analytical approaches for the resolution evaluation.Through introducing a quartic polynomial to approach the form of ambiguity function(AF)around the resolution,the approach based on the local approximation of AF acquires the mathematical expression to analytically evaluate the imagery geometric resolution.The approach has the advantages of high accuracy,high efficiency and great generality.The other one is based on the resolution ellipse,in which the resolution ellipse is used to approximately model the imagery geometric resolution.Firstly,the expression of resolution ellipse in slant range plane is established.Then,the resolution ellipse in slant range plane is projected to the ground plane and,thus the expression resolution ellipse in ground plane is obtained.The approach is capable of analytically describing the coupling characteristics between the range bandwidth and synthetic aperture time.Therefore,the approach can analytically optimize these two system parameters.3.For the large Doppler centroid in MEO/GEO-SAR,a two-dimensional beam steering method to simultaneously consider Doppler centroid and ground observation is studied,in which a concept of “Minimum-Doppler plane” is introduced.The minimum-Doppler plane minimizes the Doppler centroid and meanwhile guarantees that the beams after the steering can illuminate the Earth surface.Compared to the traditional two-dimensional beam steering methods,the beams using the proposed method will not illuminate outside the Earth surface.In addition,from the point of view of the system performance enhancement and with two aspects of system design,three main benefits of the yaw steering for the MEO/GEO-SAR are clarified and demonstrated: 1)makes the two-dimensional side-lobes more orthogonal,and improves the imagery quality;2)largely reduces the synthetic aperture time,and decreases the computation burden of imaging processing;and 3)substantially mitigates the azimuth-variation of raw data,and simplifies the imaging processing.4.In the fifth chapter,for the severely two-dimensional space-variance in the high-resolution imaging of space-borne nonlinear trajectory SAR,this chapter first develops a “TSVD-NCS” imaging algorithm for GEO-SAR,in which the mathematical tool of singular value decomposition(SVD)is introduced to analyze the azimuth-varianct characteristics of the two-dimensional spectrum.It is found that the two-dimensional spectrum should be accurately expressed by two feature components.The proposed algorithm innovatively uses the SVD operation twice to compensate for the two feature components one by one.The synthetic aperture time can be 1,000 s,and the imaging scene is 130 km×150 km(range×azimuth).Then based on the “TSVD-NCS” algorithm,a GEO-SAR imaging algorithm based on 2-D SVD and optimal LRWC preprocessing is studied.Before the correction of 2-D space-variance,an azimuth preprocessing of optimal LRWC is introduced to minimize the azimuth-variance,which can facilitate the azimuth-variance correction or improve the imaging performance of existing algorithms.In addition,the SVD processing strategy is used in both the range and azimuth dimension,which can simplify the flow path of imaging processing to a certain extent.Moreover,the accuracy of SVD-based algorithm should be higher than that of the NCS algorithms.The synthetic aperture time can be 1,000 s,and the imaging scene is 500 km×200 km(range×azimuth).5.The sixth chapter is the research on the MOCO algorithms for the airborne/spaceborne nonlinear trajectory SAR.Firstly,based on the MOCO principle,this chapter studies the MOCO imaging algorithm for MEO-SAR.However,the conventional two-step MOCO may causes severe expansion of azimuth spectrum.For this problem,a MEO-SAR MOCO imaging algorithm based on an ASE-Velocity model is studied.The algorithm overcomes the azimuth spectrum expansion through modifying the conventional two-step MOCO and introducing an ASE-Velocity model.Meanwhile,the algorithm in azimuth processing and the azimuth TSVD algorithm in the fifth chapter combine to correct the azimuth-variance,which can further enlarge the imaging scene in azimuth.Secondly,for the 2-D space-variance of motion error in the motion estimation and compensation of ultrahigh resolution airborne SAR,a 2-D space-variant motion error estimation and compensation algorithm for ultrahigh resolution airborne SAR is developed,in which the error estimation method removes the effect of the azimuth-variant error on the estimation of range-variant error before the estimation of range-variant error.The method simultaneously considers the range-variant and azimuth-variant error and therefore improves the estimation precision.Compared to the conventional two-step MOCO methods,the proposed MOCO algorithm uses the range block-based CZT to compensate for the range-variant envelope error,which enlarges the range swath.