Research On Multi-dimensional ISAR Imaging Based On Motion Parameter Estimation

With the transformation of modern warfare forms and civilian demand,inverse synthetic aperture radar(ISAR)has become one of the key technology for imaging and identification of non-cooperative targets in airspace,aerospace and sea.Due to its important role and practical value in military and civilian fields,ISAR has been developed in the direction of multi-dimension,multi-function,diversification and intelligence,in which the various working patterns and intricate target motions,as well as non-cooperation,greatly challenge the ISAR imaging system.In order to further improve the ISAR imaging capabilities of non-cooperative moving targets,several special researches are made,including the twodimensional ISAR imaging algorithm for targets with complex motions,cross-range scaling and three-dimensional ISAR imaging algorithm for maneuvering targets.The main work in this dissertation is summarized as follows:1.For the classical ISAR imaging algorithms for targets with complex motions,a new ISAR imaging technique based on the high-order ambiguity function-Lv's distribution(HAFLVD)is proposed.The received signal can be modeled as cubic phase signal(CPS),while the double lag parametric instantaneous autocorrelation function is defined based on the high-order ambiguity function and the Lv's Distribution.Then the scaled Fourier transform(SCFT)is applied to remove the linear coupling and fast Fourier transform(FFT)to achieve the energy accumulation.Finally,the non-searching parameter estimation and the ISAR imaging of targets with complex motions are accomplished by obtained HAF-LVD.Because of the introduction of the scaling factor,this algorithm can flexibly deal with more changeful and hostile ISAR environment without loss of the anti-noise performance and computation efficiency.Simulation results validate the effectiveness of the HAF-LVD and the ISAR imaging approach.2.A novel algorithm based on local polynomial ambiguity function(LPAF)for the CPS parameter estimation and the ISAR imaging of targets with complex motions is presented.For the CPS model in ISAR system,the chirp rate and the quadratic chirp rate will deteriorate the ISAR image quality due to the Doppler frequency shift.The parameter estimation algorithm for CPS based on the LPAF is defined,which can be easily implemented via the complex multiplication and fast Fourier transform(FFT).Compared with other multi-linear parameter estimation algorithms and the local polynomial Wigner Distribution(LPWD)for CPS,the proposed LPAF-based algorithm can achieve a better compromise between estimation performance and computational complexity.Then,the high-quality ISAR image can be obtained by the proposed ISAR imaging algorithm based on the LPAF.The results of the simulated data demonstrate the effectiveness of the proposed algorithm.3.According to the analyses of noncorrelation CPS parameter estimation algorithm and correlation CPS parameter estimation algorithm,a modified fast bilinear CPS parameter estimation algorithm based on integrated parametric cubic phase function(IPCPF)and reversing Wigner-Ville distribution(RWVD)processing is developed.This algorithm can be used to ISAR imaging for targets with complex motions and cross-range scaling in the low signal-to-noise ratio(SNR)environment.By utilizing the parametric cubic phase function(PCPF),the nonuniform fast Fourier transform(NUFFT)and the generalized scaled Fourier transform(GSCFT)based on Chirp-Z,the parameter estimation of CPS and the ISAR imaging of targets with complex motions can be achieved quickly.The bilinearity of the PCPF guarantees a higher anti-noise performance and a better suppression on cross-terms.Moreover,the brute-force searching is eliminated and the computational cost is reduced based on the NUFFT and the GSCFT.Compared with the classical multilinear CPS parameter estimation algorithm,the fast bilinear CPS parameter estimation algorithm greatly reduces the computation load and improves the noise immunity.It is very suitable for ISAR imaging at low SNR.Finally,a new cross-range scaling(CRS)method based on regression analysis is proposed for two-dimensional ISAR imaging.The dual experiments with the synthetic data and the real radar data demonstrate the effectiveness and the superiority of the proposed ISAR imaging algorithm.4.Two-dimensional ISAR imaging does not provide the three-dimensional position information due to the indeterminate relative motion between radar and target.To tackle this issue,an interferometric ISAR(InISAR)imaging algorithm based on the joint cross modified Wigner-Ville distribution(MWVD)is presented to form 3-D images of maneuvering targets.First,we form two orthogonal interferometric baselines with three receiving antennas to establish an InISAR imaging system.Second,the joint cross MWVD is used for all range bins of each antenna pair to generate the separation of the scatterer as well as preserve the phase information that contains position information of the scatterer.Finally,the target images can be directly reconstructed from the extracted phase information,and the accurate estimation of the effective rotation vector is realized.The algorithm can effectively reduce the computation cost and improves the imaging quality by using the high time-frequency focusing of WVD and the non-searching parameter estimation algorithm.Simulation results demonstrate the validity of the proposal.