Jialian Sheng

Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing

The theory of compressed sampling (CS) indicates that exact recovery of an unknown sparse signal can be achieved from very limited samples. For inversed synthetic aperture radar (ISAR), the image of a target is usually constructed by strong scattering centers whose number is much smaller than that of pixels of an image plane. This sparsity of the ISAR signal intrinsically paves a way to apply CS to the reconstruction of high-resolution ISAR imagery. CS-based high-resolution ISAR imaging with limited pulses is developed, and it performs well in the case of high signal-to-noise ratios. However, strong noise and clutter are usually inevitable in radar imaging, which challenges current high-resolution imaging approaches based on parametric modeling, including the CS-based approach. In this paper, we present an improved version of CS-based high-resolution imaging to overcome strong noise and clutter by combining coherent projectors and weighting with the CS optimization for ISAR image generation. Real data are used to test the robustness of the improved CS imaging compared with other current techniques. Experimental results show that the approach is capable of precise estimation of scattering centers and effective suppression of noise.

Wavenumber-Domain Autofocusing for Highly Squinted UAV SAR Imagery

Being capable of enhancing the flexibility and observing ability of synthetic aperture radar (SAR), squint mode is one of the most essential operating modes in SAR applications. However, processing of highly squinted SAR data is usually a challenging task attributed to the spatial-variant range cell migration over a long aperture. The Omega-k algorithm is generally accepted as an ideal solution to this problem. In this paper, we focus on using the wavenumber-domain approach for highly squinted unmanned aerial vehicle (UAV) SAR imagery. A squinted phase gradient autofocus (SPGA) algorithm is proposed to overcome the severe motion errors, including phase and nonsystematic errors. Herein, the inconsistence of phase error and range error in the squinted wavenumber-domain imaging is first presented, which reveals that even the motion error introduces very small phase error, it causes considerable range error due to the Stolt mapping. Based on this, two schemes of SPGA-based motion compensation are developed according to the severity of motion error. By adapting the advantages of weighted phase gradient autofocus and quality phase gradient autofocus, the robustness of SPGA is ensured. Real measured data sets are used to validate the proposed approach for highly squinted UAV-SAR imagery.

ISAR Cross-Range Scaling by Using Sharpness Maximization

This letter presents a new method of cross-range scaling in inverse synthetic aperture radar (ISAR) imaging. The effective rotational velocity (ERV), being the crucial factor for scaling, is generally unknown for noncooperative objects. By considering the degradation from target rotation, the proposed scheme estimates ERV based on image sharpness maximization. A range deviator induced by the center shift is also embedded in the estimation process. The cross-range scaling factor with an enhanced ISAR image can be obtained by an efficient Gauss-Newton method. The results acquired from both the simulations and real data experiments validate the effectiveness and robustness of the proposed method.

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

In general, conventional error correction for inverse synthetic aperture radarimaging consists of range alignment and phase adjustment, which compensate range shift and phase error, respectively. Minimum entropy-based methods have been proposed to realize range alignment and phase adjustment. However, it becomes challenging to align high-resolution profiles when strong noise presents, even using entropy minimization. Consequently, the subsequent phase adjustment fails to correct phase errors. In this article, we propose a novel method for translational motion correction, where entropy minimization is utilized to achieve range alignment and phase adjustment jointly. And, a coordinate descent algorithm is proposed to solve the optimization implemented by quasi-Newton algorithm. Moreover, a method for coarse motion estimation is also proposed for initialization in solving the optimization. Both simulated and real-measured datasets are used to confirm the effectiveness of the joint motion correction in low signal-to-noise ratio situations.

Minimum-Entropy-Based Autofocus Algorithm for SAR Data Using Chebyshev Approximation and Method of Series Reversion, and Its Implementation in a Data Processor

A novel autofocus method for synthetic aperture radar (SAR) image is studied. Based on a quadratic model for the phase error within each sub-area (narrow strip × sub-aperture) after a wide range swath is subdivided into narrow range strips and long azimuth aperture into sub-apertures, an objective function for estimation of the error is derived through the principle of minimum entropy. There is only one unknown variable in the function. With the Chebyshev approximation, the function is approximated as a polynomial, and the unknown is then solved using the method of series reversion. Curve-fitting methods are applied to estimate phase error for an entire scene of the full-swath by full-aperture. Through simulations, the proposed method is applied to restore the defocused SAR imagery that is well focused. The restored and original images are almost identical qualitatively and quantitatively. Next, the method is implemented into an existing SAR data processor. Two sets of SAR raw data at X- and Ku-bands are processed and two images are formed. Well-focused and high-resolution images from plain and rugged terrain are obtained even without the use of ancillary attitude data of the airborne SAR platform. Thus, the studied method is verified.

Performance improvement in multi-ship imaging for ScanSAR based on sparse representation

There is always a compromise between unambiguous wide-swath imaging and high cross-range resolution owing to the constraint of minimum antenna area for conventional single-channel spaceborne synthetic aperture radar (SAR) imaging. To overcome the inherent systemic limitation, multi-channel SAR imaging has been developed. Nevertheless, this still suffers from various problems such as high system complexity. To simplify the system structure, a novel algorithm for high resolution multi-ship ScanSAR imaging based on sparse representation is proposed in this paper, where the SAR imaging model is established via maximum a posterior estimation by utilizing the sparsity prior of multi-ship targets. In the scheme, a wide swath is generated in the ScanSAR mode by continuously switching the radar footprint between subswaths. Meanwhile, high cross-range resolution is realized from sparse subapertures by exploiting the sparsity feature of multi-ship imaging. In particular, the SAR observation operator is constructed approximately as the inverse of conventional SAR imaging and then high resolution SAR imaging including range cell migration compensation is achieved by solving the optimization. Compared with multi-channel SAR imaging, the system complexity is effectively reduced in the ScanSAR mode. In addition, enhancement of the cross-range resolution is realized by incorporating the sparsity prior with sparse subapertures. As a result, the amount of data is effectively reduced. Experiments based on measured data have been carried out to confirm the effectiveness and validity of the proposed algorithm.

Using Derivatives of an Implicit Function to Obtain the Stationary Phase of the Two-Dimensional Spectrum for Bistatic SAR Imaging

There are two square-root terms in the range history of a return signal from a bistatic synthetic aperture radar (BiSAR). The transfer function for imaging in the 2-D frequency or range Doppler domain using the principle of stationary phase cannot be analytically derived. To address this problem, we approximated the stationary phase of the 2-D spectrum with an expansion of the Taylor series on the azimuth frequency and called the approximation as the derivatives of an implicit function (DIF). After algebraic manipulation, the DIF and 2-D spectrum were obtained for a generally configured BiSAR. With the DIF method, we dissolved one square-root term out of the two for an azimuth-invariant BiSAR, which is particularly advantageous in the implementation of an imaging algorithm. Then, a modified range Doppler algorithm was developed to process the BiSAR data. Promising results were obtained.

Coherent processing for ISAR imaging with sparse apertures

To implement target detection, tracking and imaging in a multifunctional radar system, the wideband measurements for inverse synthetic aperture radar (ISAR) imaging are usually sparsely recorded. Considering the incoherence problem in such sparse-aperture ISAR (SA-ISAR) systems, we concentrate on the study of a coherent processing method in this work. Based on an all-pole model, the incoherence parameters between abutting sub-apertures can be effectively estimated. After coherence compensation, an optimization-based SAISAR imaging approach is provided from the view of statistics. Simulation and real data experiments validate the feasibility and effectiveness of the proposals.