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 signal fusion of multiple frequency bands and multiple radars is an effective method to improve the resolution of target range profileWith the sparse band observation,the conventional phase compensation methods in signal fusion are invalid because the frequency bands are sparseA new super-resolution range imaging method based on the modified MUSIC(multiple signal classification) algorithm is presented with liner frequency modulated signalFirstly,the uniform B-splines is applied to resample the signals for obtaining the uniform signal sequencesSecondly,the signal model of multiple radars with multiple frequency bands is establishedConsequently,the phase compensation parameter and the scatter center parameters are estimated based on the uniform signal modelThe theoretical analysis and simulations results show the presented signal fusion imaging method can effectually increase signal bandwidth and obtain super-resolution range image under sparse frequency band observations conditions

The Super-Resolution Range Imaging Based on Sparse Band Multiple Frequency Bands Radars Signal Fusion

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

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

In practical bistatic inverse synthetic aperture radar (ISAR) imaging systems, the echo signals are modulated by non-ideal amplitude and phase characteristics of the transmitting and receiving channels, which seriously distorts image quality. However, the conventional channel calibration method based on a transponder is not applicable to bistatic ISAR imaging systems, since the baseline of the system is up to hundreds of kilometers. A channel calibration method only using calibration satellite echo information is proposed for the system, with a linear frequency modulation (LFM) waveform. Firstly, echoes of the calibration satellite are collected by tracking the satellite and multi-period echoes are aligned in the time domain, according to the pulse compression result. Then, the signal to noise ratio (SNR) is improved by accumulating multi-period echoes coherently in the time domain and the calibration coefficient is constructed based on the accumulated signal. Finally, spectrum of the echo signal is multiplied with the calibration coefficient to compensate the influence of channel characteristics. The effectiveness of the proposed method is verified by the simulation experiment with real satellite echoes.

A Novel Channel Calibration Method for Bistatic ISAR Imaging System

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