Coherent compensation and high‐resolution technology of multi‐band inverse synthetic aperture radar fusion imaging

Bistatic inverse synthetic aperture radar (Bi-ISAR) can obtain complementary information of moving targets and overcome the inherent imaging limitations of monostatic ISAR. However, the complex motion of maneuvering targets invalidates the assumption that the imaging projection plane (IPP) is constant in conventional Bi-ISAR imaging. The 2-D spatial variant phase errors would be induced. Moreover, the phase errors have a high-order form due to the time-varying bistatic angle and the high maneuvering characteristics of the target. Meanwhile, the linear geometric distortion induced by the bistatic configuration seriously challenges target identification and classification. In this paper, we propose a novel method to compensate for the 2-D spatial variant phase errors and correct the geometric distortion simultaneously for Bi-ISAR imaging based on parametric minimum entropy optimization. First, the signal mode for maneuvering target in the bistatic configuration is developed. Second, based on the developed signal model, we analyze the coupling relationship between the 2-D high-order spatial variant phase errors and the bistatic angle, and establish a parametric minimum entropy optimization model for high-order spatial variant phase errors compensation. Then, an efficient Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is adopted to obtain the optimal solution of spatial variant coefficients. Finally, with the estimated optimal parameters, the integrated processing of 2-D spatial variant phase errors compensation and distortion correction can be realized. This method can simultaneously obtain well-focused and restored Bi-ISAR images of maneuvering targets without selecting prominent scatterers. Experiments based on scattering point simulation data and electromagnetic data verify the effectiveness of the proposed method. 

Joint Motion Compensation and Distortion Correction for Maneuvering Target Bistatic ISAR Imaging Based on Parametric Minimum Entropy Optimization

The performance of both autofocusing and imaging resolution degrades using the traditional autofocusing and range–Doppler algorithm for bistatic inverse synthetic aperture radar (Bi-ISAR) with sparse apertures. A Bi-ISAR sparse imaging algorithm based on complex Gaussian scale mixture (CGSM) prior is proposed to jointly achieve the high-resolution imaging and autofocusing. First, a sparse basis matrix with the time-varying bistatic angle is constructed to represent the sparse echo data and the Bi-ISAR joint with autofocusing imaging model is established based on compressed sensing from sparse apertures. Second, the elements of the target image and the noise are assumed to be a CGSM prior with Gaussian distribution, respectively. Finally, the sparse image reconstruction and phase autofocusing are accomplished by the variational Bayesian expectation maximisation method. The proposed algorithm with the full Bayesian inference can obtain a well-focused image without manual adjustments of regularisation parameters. Meanwhile, it can avoid the local minimum and structural errors, due to utilising the statistical information of a posterior. Simulated results of electromagnetic numerical data verify the superiority of the algorithm in autofocusing, sparse imaging and noise suppression performance.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2019.0296

Bi-ISAR sparse imaging algorithm with complex Gaussian scale mixture prior

Images from high-resolution inverse synthetic aperture radar (ISAR) can provide more information about the targets. Multiband fusion imaging techniques can achieve higher range resolution without increasing hardware costs. A multiband fusion imaging algorithm based on variational Bayesian inference (VBI) is proposed to improve the range resolution of ISAR images. First, a multiband fusion ISAR imaging model is established based on sparse representation. Second, the scattering coefficients and noise are assumed to be the Laplacian scale mixture distribution and the complex Gaussian distribution, respectively. Finally, the fusion image is directly reconstructed in the complex domain by the VBI based on Laplace approximation method. The effectiveness and robustness of the proposed algorithm are verified by the experimental fusion results of one-dimensional signals and two-dimensional ISAR images.

Multiband fusion inverse synthetic aperture radar imaging based on variational Bayesian inference

Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint of image quality optimization and sparsity is proposed. First, the proposed algorithm establishes the Bi-ISAR sparse aperture self-focusing signal model, reconstructs images through fast sparse Bayesian learning (FSBL), uses the minimum Tsallis entropy and constraints the reconstruction process, iteratively updates the phase error, and performs self-focusing to realize the initial phase correction of Bi-ISAR images. Simulation results show that the proposed algorithm has a fast convergence speed, strong robustness to noise, and high accuracy in reconstructing images. 

Bistatic inverse synthetic aperture radar sparse aperture self-focusing algorithm based on the joint constraint of compressed sensing and minimum Tsallias entropy

The linear geometry distortion caused by time variant bistatic angles induces the sheared shape of the bistatic inverse synthetic aperture radar (bistatic-ISAR) image. A linear geometry distortion alleviation algorithm for space targets in bistatic-ISAR systems is presented by exploiting prior information. First, we analyze formation mathematics of linear geometry distortions in the Range Doppler (RD) domain. Second, we estimate coefficients of first-order polynomial of bistatic angles by least square error (LSE) method through exploiting the imaging geometry and orbital information of space targets. Third, we compensate the linear spatial-variant terms to restore the linear geometry distortions. Consequently, the restored bistatic-ISAR image with real shape is obtained. Simulated results of the ideal point scatterers dataset and electromagnetic numerical dataset verify the performance of the proposed algorithm.

https://www.mdpi.com/2079-9292/8/5/560/pdf

Bistatic-ISAR Linear Geometry Distortion Alleviation of Space Targets

The linear-geometry distortion and image defocus are induced by the time-varying bistatic angle in a bistatic inverse synthetic aperture radar (Bi-ISAR) system. Here, a distortion mitigation algorithm of a space target is proposed via exploiting the orbital prior information. First, the linear/quadratic distortions are analysed in the Range-Doppler (RD) image formation mathematics. The coefficients of the time-varying bistatic angle are estimated by means of the orbit information of the target and the imaging geometry. Second, the equivalent rotation centre (RC) is estimated iteratively based on the maximum image contrast criterion. Third, the linear-geometry distortion and quadratic distortion related with the range position are restored by the compensation of the spatial-varying phase terms. Finally, the well-focused image is achieved by applying the matching Fourier transform (MFT) along the cross-range direction. Experimental results based on both the ideal point scatterers model and numerical electromagnetic (EM) data are provided to support the algorithm.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2018.5605

Bistatic ISAR distortion mitigation of a space target via exploiting the orbital prior information

Due to the large size of space targets, migration through resolution cells (MTRC) are induced by a rotational motion in high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) systems. The inaccurate correction of MTRC degrades the quality of Bi-ISAR images. However, it is challenging to correct the MTRC where sparse aperture data exists for Bi-ISAR systems. A joint approach of MTRC correction and sparse high-resolution imaging for Bi-ISAR systems is presented in this paper. First, a Bi-ISAR imaging sparse model-related to MTRC is established based on compress sensing (CS). Second, the target image elements and noise are modeled as the complex Laplace prior, and the Gaussian prior, respectively. Finally, the high-resolution, well-focused image is obtained by the full Bayesian inference method, without manual adjustments of unknown parameters. Simulated results verify the effectiveness and robustness of the proposed algorithm.

High-Resolution Bistatic ISAR Imaging of a Space Target with Sparse Aperture

The invention is applicable to the technical field of radars, and provides a radar device simulation training system, which comprises a main control device, an entity control device, a virtual terminal and a radar antenna dynamic simulation device, wherein the main control device acquires a training item and a corresponding working mode; when the working mode is an autonomous mode, the training item is sent to a virtual terminal; when the working mode is a controlled mode, the training item is sent to the entity control device; the entity control device displays a first radar training scene according to the training item, and acquires and sends first operation information to the main control device; the main control device generates an antenna movement instruction according the first operation information to control the attitude of the radar antenna; and the virtual terminal displays a second radar training scene according to the training item, and generates and displays a virtual action according to the acquired second operation information. The system achieves the function complementation of the virtual terminal and entity control device, thereby effectively improving the radar simulation training effect of the trainee and improving the radar operation capability of the trainee.

Radar device simulation training system

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