Zhijun Qiao

High-Resolution ISAR Imaging With Sparse Stepped-Frequency Waveforms

From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.

High-Resolution ISAR Imaging by Exploiting Sparse Apertures

Compressive sensing (CS) theory indicates that the optimal reconstruction of an unknown sparse signal can be achieved from limited noisy measurements by solving a sparsity-driven optimization problem. For inverse synthetic aperture radar (ISAR) imagery, the scattering field of the target is usually composed of only a limited number of strong scattering centers, representing strong spatial sparsity. This paper derives a new autofocus algorithm to exploit the sparse apertures (SAs) data for ISAR imagery. A sparsity-driven optimization based on Bayesian compressive sensing (BCS) is developed. In addition, we also propose an approach to determine the sparsity coefficient in the optimization by using constant-false-alarm-rate (CFAR) detection. Solving the sparsity-driven optimization with a modified Quasi-Newton algorithm, the phase error is corrected by combining a two-step phase correction approach, and well-focused image with effective noise suppression is obtained from SA data. Real data experiments show the validity of the proposed method.

A Robust Motion Compensation Approach for UAV SAR Imagery

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an essential tool for modern remote sensing applications. Owing to its size and weight constraints, UAV is very sensitive to atmospheric turbulence that causes serious trajectory deviations. In this paper, a novel databased motion compensation (MOCO) approach is proposed for the UAV SAR imagery. The approach is implemented by a three-step process: 1) The range-invariant motion error is estimated by the weighted phase gradient autofocus (WPGA), and the nonsystematic range cell migration function is calculated from the estimate for each subaperture SAR data; 2) the retrieval of the range-dependent phase error is executed by a local maximum-likelihood WPGA algorithm; and 3) the subaperture phase errors are coherently combined to perform the MOCO for the full-aperture data. Both simulated and real-data experiments show that the proposed approach is appropriate for highly precise imaging for UAV SAR equipped with only low-accuracy inertial navigation system.

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.

A general approach for getting the commutator representations of the hierarchies of nonlinear evolution equations

Abstract A general approach for generating the commutator representations of the hierarchies of nonlinear evolution equations (NLEEs) is presented. For this approach, three concrete examples are given.

A new two-component integrable system with peakon solutions.

A new two-component system with cubic nonlinearity and linear dispersion: mt=bux+12[m(uv−uxvx)]x−12m(uvx−uxv),nt=bvx+12[n(uv−uxvx)]x+12n(uvx−uxv),m=u−uxx,n=v−vxx,where b is an arbitrary real constant, is proposed in this paper. This system is shown integrable with its Lax pair, bi-Hamiltonian structure and infinitely many conservation laws. Geometrically, this system describes a non-trivial one-parameter family of pseudo-spherical surfaces. In the case b=0, the peaked soliton (peakon) and multi-peakon solutions to this two-component system are derived. In particular, the two-peakon dynamical system is explicitly solved and their interactions are investigated in details. Moreover, a new integrable cubic nonlinear equation with linear dispersion mt=bux+12[m(|u|2−|ux|2)]x−12m(uux∗−uxu∗),m=u−uxx,is obtained by imposing the complex conjugate reduction v=u* to the two-component system. The complex-valued N-peakon solution and kink wave solution to this complex equation are also derived.

Integrating Autofocus Techniques With Fast Factorized Back-Projection for High-Resolution Spotlight SAR Imaging

Back-projection (BP) is considered as an ideal methodology for the high-resolution synthetic aperture radar (SAR) imaging. However, applying conventional autofocus techniques to BP imagery requires a special consideration and is usually difficult to implement. In this letter, we present a scheme to compatibly blending a novel multiple aperture map drift (MAMD) algorithm with fast factorized back-projection (FFBP). Through an elaborate BP coordinate, we construct the Fourier transform relationship between FFBP sub-aperture (SA) images and the corresponding range-compressed phase history data. The phase error function is achieved by the MAMD within FFBP recursions, and well-focused imagery is obtained by phase correction on the range-compressed phase history data. The proposed scheme inherits the advantages of high precision and efficiency of the FFBP, and is suitable for high-resolution spotlight SAR imaging with raw data. Real data experiments guarantee the effectiveness of our proposed scheme.

A Fast BP Algorithm With Wavenumber Spectrum Fusion for High-Resolution Spotlight SAR Imaging

This letter presents the accelerated fast backprojection (AFBP) algorithm for high-resolution spotlight synthetic aperture radar (SAR) imaging. In conventional fast backprojection (FBP) algorithms, image-domain interpolation is employed in the subaperture (SA) fusion. However, in AFBP, by using a unified polar coordinate (UPC) system, the interpolation-based fusion is substituted by fusing the SA spectra in the wavenumber (WN) spectrum domain. The WN-domain SA fusion is efficiently implemented by fast Fourier transform and circular shifting. In this letter, an accurate impulse response function and the WN spectrum expression of the backprojected image in the UPC are explicitly derived, and furthermore, the implementations of AFBP are investigated in detail. Compared with conventional FBP algorithms, the AFBP can precisely focus on high-resolution SAR data with dramatically improved efficiency. Both simulation and real-measured data experiments validate the superiorities of AFBP by comparing it with the fast factorization backprojection (FFBP) algorithm.

Phase adjustment and isar imaging of maneuvering targets with sparse apertures

A multifunctional radar system can only acquire limited and discontinuous wideband pulses, which form sparse aperture (SA) observations of a target. To carry out radar activities (detection, tracking, and imaging) simultaneously for multiple targets, inverse synthetic aperture radar (ISAR) imaging exploiting these SA data is essential for multifunctional radar. In this paper, we study the phase adjustment and full-aperture (FA) reconstruction for SA-ISAR imaging of maneuvering targets. A modified eigenvector-based autofocus approach is proposed to correct phase errors within SA measurements of maneuvering targets. After phase correction, the FA data are reconstructed from SA measurements via sparse representation under a redundant chirp-Fourier dictionary. An efficient algorithm is developed to solve the sparse decomposition optimization, and ISAR images of the maneuvering target are obtained by adaptive joint time-frequency imaging approaches with the reconstructed data. Both simulated and real data sets are used to confirm the effectiveness of the proposed methods.

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.