In the current scenario of high-resolution inverse synthetic aperture radar (ISAR) imaging, the non-cooperative targets may have strong maneuverability, which tends to cause time-variant Doppler modulation and imaging plane in the echoed data. Furthermore, it is still a challenge to realize ISAR imaging of maneuvering targets from sparse aperture (SA) data. In this paper, we focus on the problem of 3D geometry and motion estimations of maneuvering targets for interferometric ISAR (InISAR) with SA. For a target of uniformly accelerated rotation, the rotational modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. In particular, a joint multi-channel imaging approach is developed to incorporate the multi-channel data and treat the multi-channel ISAR image formation as a joint-sparsity constraint optimization. Then, a modified orthogonal matching pursuit (OMP) algorithm is employed to solve the optimization problem to produce high-resolution range-Doppler (RD) images and chirp parameter estimation. The 3D target geometry and the motion estimations are followed by using the acquired RD images and chirp parameters. Herein, a joint estimation approach of 3D geometry and rotation motion is presented to realize outlier removing and error reduction. In comparison with independent single-channel processing, the proposed joint multi-channel imaging approach performs better in 2D imaging, 3D imaging, and motion estimation. Finally, experiments using both simulated and measured data are performed to confirm the effectiveness of the proposed algorithm.

Interferometric synthetic aperture radar (InSAR) images are corrupted by strong noise, including interferometric phase and speckle noises. In general, the scenes in homogeneous areas are characterized by continuous-variation heights and stationary backscattered coefficients, exhibiting a locally spatial stationarity. The stationarity provides a rational of sparse representation of amplitude and interferometric phase to perform noise reduction. In this paper, we develop a novel algorithm of InSAR image formation from Bayesian perspective to perform interferometric phase noise reduction and despeckling. In the scheme, the InSAR image formation is constructed via maximum a posteriori estimation, which is formulated as a sparse regularization of amplitude and interferometric phase in the wavelet domain. Furthermore, the statistics of the wavelet-transformed image is modeled as complex Laplace distribution to enforce a sparse prior. Then, multichannel imaging is realized using a modified quasi-Newton method in a sequential and iterative manner, where both the interferometric phase and speckle noises are reduced step by step. Due to the simultaneously sparse regularized reconstruction of amplitude and interferometric phase, the performance of noise reduction can be effectively improved. Then, we extend it to joint sparse constraint on multichannel data by considering the joint statistics of multichannel data. Finally, experimental results based on simulated and measured data confirm the effectiveness of the proposed algorithm.

Interferometric Inverse SAR (InISAR) is capable of acquiring three-dimensional image of the moving targets, which is much helpful to the target classification and identification. Meanwhile, multifunctional ISAR/InISAR system aims at maneuvering targets and only sparse aperture measurements are available for each target, which is a challenge to the conventional ISAR imaging algorithms. A joint sparsity-constraint InISAR 3-D imaging approaches is presented for maneuvering targets with sparse apertures. For a uniformly accelerated rotation target, the Doppler modulation in echo is formulated as chirp sensing code under a chirp-Fourier dictionary to represent the maneuverability. Then the joint multi-channel InISAR imaging approach is converted into a joint sparse constraint optimization. And a modified Orthogonal Matching Pursuit (OMP) algorithm is employed to solve the optimization. The 3-D target geometry is followed by using obtaining 2-D images and estimated chirp parameters. Finally, the experiment using measured data is performed to confirm the effectiveness of the proposed method.

Due to the problem of high grating-lobe caused by phase discontinuity for frequency bandwidth synthetic,a new method of frequency synthetic bandwidth is proposed.The cause for phase discontinuity of frequency spectrum is the relative motion between radar and scatterers.The phase error for every sub-pulse according to different forms of the slant range between radar and scatterers is compensated by this method.A new approach is put forward for step frequency signal processing after the frequency band is synthesized.First,the pulse is compressed and frequency band is synthesized.After the frequency band is synthesized,the second range compression and the range migration correction are implemented. Then,phase discontinuity due to the corrected range migration correction is avoided.Finally,Chirp Scaling is applied to obtain the squint two-dimension high resolution SAR(Synthetic Aperture Radar,SAR)image.In order to identify the availability of this method,simulation results are shown in the paper.Experiments on raw data and simulation show that the wide-band signal could be synthesized by the narrow-band stepped signal.

In high-resolution radar imaging, there is inevitable migration through range cells (MTRCs) in an inverse synthetic aperture radar (ISAR) image due to the manoeuvrability of the target. In the case of sparse aperture (SA) measurement, it is generally difficult to accurately correct MTRCs to degrade the imaging performance. An approach of SA-imaging jointly with MTRC correction for manoeuvring targets is presented. Under a chirp-Fourier dictionary by involving MTRCs, the ISAR image formation is treated as a sparse-driven optimisation to overcome the SA. Experiments based on the measured data are performed to confirm the effectiveness of the proposed algorithm.