Yuliang Qin

Orbital-Angular-Momentum-Based Electromagnetic Vortex Imaging

A novel radar imaging technique based on orbital angular momentum (OAM) modulation is presented. First, the generation of electromagnetic (EM) vortex wave, which carries the OAM, using incrementally phased uniform circular array (UCA) is introduced, and factors that affect the phase-front distribution are analyzed. Subsequently, echo signal models of both multiple-in-multiple-out and multiple-in-single-out modes are established. The target images are obtained using the fast Fourier transform (FFT) and back-projection methods. Simulation results demonstrate that orbital angular momentum has the prospect for acquiring the azimuth information of radar target. The signal of both OAM modulation and frequency modulation can be used to obtain two-dimensional radar target image. The work can benefit the development of novel information-rich radar based on orbital angular momentum, as well as radar target recognition.

Radar Coincidence Imaging: an Instantaneous Imaging Technique With Stochastic Signals

Motivated by classical coincidence imaging which has been realized in optical systems, an instantaneous microwave-radar imaging technique is proposed to obtain focused high-resolution images of targets without motion limitation. Such a radar coincidence imaging method resolves target scatterers based on measuring the independent waveforms of their echoes, which is quite different from conventional radar imaging techniques where target images are derived depending on time-delay and Doppler analysis. Due to the peculiar features of coincidence imaging, there are two potential advantages of the proposed imaging method over the conventional ones: 1) shortening the imaging time to even a pulse width without resolution deterioration so as to improve the performance of processing noncooperative targets and 2) simplifying the receiver complexity, resulting in a lower cost and platform flexibility in application. The basic principle of radar coincidence imaging is to employ the time-space independent detecting signals, which are produced by a multitransmitter configuration, to make scatterers located at different positions reflect independent waveforms from each other, and then to derive the target image based on the prior knowledge of this detecting signal spatial distribution. By constructing the mathematic model, the necessary conditions of the transmitting waveforms are analyzed for achieving radar coincidence imaging. A parameterized image-reconstruction algorithm is introduced to obtain high resolution for microwave radar systems. The effectiveness of this proposed imaging method is demonstrated via a set of simulations. Furthermore, the impacts of modeling error, noise, and waveform independence on the imaging performance are discussed in the experiments.

The Influence of Target Micromotion on SAR and GMTI

This paper analyzes the influence of typical target micromotions on synthetic aperture radar (SAR) images, azimuth resolution limit, SAR/ground moving target indication (GMTI), and MTI. According to the micromotion periods contained in the coherent processing interval, a new range model expansion and a generalized paired echo principle are proposed and applied to underlie the analysis. Several new kinds of image characteristics including gray strips, ghost points, and fences are reported, which are sheerly distinct from those of slow movers. Micromotion will also cause a prominent range cell migration even if its amplitude is far smaller than the range resolution. SAR/GMTI and MTI techniques will, in general, become invalid for micromotion targets. The influence is eventually demonstrated by the simulated data in the airborne single-channel geometry, and it can be used for SAR image interpretation as well as passive jamming.

Fast Raw-Signal Simulation of Extended Scenes for Missile-Borne SAR With Constant Acceleration

Fast raw-signal simulation is of considerable value for missile-borne synthetic aperture radar (SAR) algorithm development. On the basis of the two-dimensional (2-D) Fourier simulation method for stripmap SAR, we present a fast echo simulation method suitable for missile-borne SAR diving with constant acceleration. The analytical expression for the 2-D signal spectrum is derived and then converted to a stripmap one. Simulation results for a point target and a real scene demonstrate its validity and effectiveness.

Radar coincidence imaging in the presence of target-motion-induced error

Abstract. Radar coincidence imaging (RCI) is a new instantaneous imaging technique that does not depend on Doppler frequency for resolution. Such an imaging method does not require target relative motion and has an imaging interval that is even shorter than a pulse width. The potential advantages in processing both the relatively stationary and maneuvering targets make RCI provide a supplementary imaging approach for the conventional range Doppler imaging methods. The simulation experiments have preliminarily demonstrated the feasibility of the RCI technique. However, further investigations show that the imaging error arises for moving targets, and moreover, it is particularly related to target scattering maps. The paper analyzes the target-motion-induced error and points out that three factors are involved: target velocity, target scattering map, and the time-space independence of detecting signals. The current image-reconstruction algorithms of RCI, which are based on the least-square (LS) principle, are found to be seriously sensitive to the motion-induced errors and will be limited in practical imaging scenarios. Accordingly, the compressive sensing (CS) recovery algorithm is employed, which can utilize sparsity restriction to diminish the effect of the motion-induced error on image reconstruction. Simulations are designed to illustrate the three factors of the target-motion-induced error. The imaging performance of the LS and the CS methods in RCI image recovery are compared as well.

Electromagnetic Vortex Imaging Using Uniform Concentric Circular Arrays

Electromagnetic vortex imaging based on orbital angular momentum (OAM) modulation has the ability of azimuthal resolution without relative motion, which needs various OAM modes. However, the main-lobe directions of different OAM beams differ from each other so that the echo energy is limited. In this letter, several concentric circular arrays are employed to adjust the directivity, each array generating a beam carrying special OAM mode. With this scheme, the target can be illuminated by main lobes of all OAM beams simultaneously. Our proposal can benefit the image reconstruction in noisy environment. Moreover, this scheme can successfully reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution. Simulation results demonstrate the effectiveness of this scheme.

Sparse Auto-Calibration for Radar Coincidence Imaging with Gain-Phase Errors

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of relative motion between target and radar. The sparsity-driven approaches are commonly used in RCI, while the prior knowledge of imaging models needs to be known accurately. However, as one of the major model errors, the gain-phase error exists generally, and may cause inaccuracies of the model and defocus the image. In the present report, the sparse auto-calibration method is proposed to compensate the gain-phase error in RCI. The method can determine the gain-phase error as part of the imaging process. It uses an iterative algorithm, which cycles through steps of target reconstruction and gain-phase error estimation, where orthogonal matching pursuit (OMP) and Newton’s method are used, respectively. Simulation results show that the proposed method can improve the imaging quality significantly and estimate the gain-phase error accurately.

Generation of Orbital Angular Momentum Beams for Electromagnetic Vortex Imaging

The vortex electromagnetic (EM) wave, which carries orbital angular momentum (OAM), may provide extra rotational degree of freedom and can potentially be exploited in microwave staring imaging. Based on the concentric uniform circular array, the generation method of OAM beams for the EM vortex imaging is studied. The array radius and excitation signal for each ring are carefully designed to ensure that the main lobes of beams with different topological charges can simultaneously illuminate the area where the target may exist. Subsequently, the imaging model based on the linear frequency modulation signal is established. The fast Fourier transform (FFT) and other spectrum estimation methods are applied to obtain the cross-range profile of the target. Results demonstrate that the algorithms of power spectrum density estimation based on autoregressive model can achieve much higher resolution than the FFT method. Moreover, the imaging performance of the proposed method is robust against the noise influence. The work and results can benefit the development of remote sensing as well as the research on microwave staring imaging.

Radar Coincidence Imaging under Grid Mismatch

Radar coincidence imaging is an instantaneous imaging technique which does not depend on the relative motion between targets and radars. High-resolution, fine-quality images can be obtained using a single pulse either for stationary targets or for complexly maneuvering ones. There are two image-reconstruction algorithms used for radar coincidence imaging, that is, the correlation method and the parameterized method. In comparison with the former, the parameterized method can achieve much higher resolution but is seriously sensitive to grid mismatch. In the presence of grid mismatch, neither of the two algorithms can obtain recognizable high-resolution images. The above problem largely limits the applicability of radar coincidence imaging in actual imaging scenes where grid mismatch generally exists. This paper proposes a joint correlation-parameterization algorithm, which uses the correlation method to estimate the grid-mismatch error and then iteratively modifies the results of the parameterized method. The proposed algorithm can achieve high resolution with fine imagery quality under the grid mismatch. Examples are provided to illustrate the improvement of the proposed method.

The Geometry of Signal Detection with Applications to Radar Signal Processing

The problem of hypothesis testing in the Neyman–Pearson formulation is considered from a geometric viewpoint. In particular, a concise geometric interpretation of deterministic and random signal detection in the philosophy of information geometry is presented. In such a framework, both hypotheses and detectors can be treated as geometrical objects on the statistical manifold of a parameterized family of probability distributions. Both the detector and detection performance are geometrically elucidated in terms of the Kullback–Leibler divergence. Compared to the likelihood ratio test, the geometric interpretation provides a consistent but more comprehensive means to understand and deal with signal detection problems in a rather convenient manner. Example of the geometry based detector in radar constant false alarm rate (CFAR) detection is presented, which shows its advantage over the classical processing method.