ISAR cross-range scaling based on the MUSIC technique

Cross-range scaling plays an important role in the inverse synthetic aperture radar(ISAR) imaging. Many of the published cross-range scaling algorithms are based on the fast Fourier transformation(FFT). However, the FFT technique is resolution limited, so that the FFT-based algorithms will fail in the rotation velocity(RV) estimation of the slow rotation target. In this paper,we propose an accurate cross-range scaling algorithm based on the multiple signal classification(MUSIC) method. We first select some range bins with the mono-component linear frequency modulated(LFM) signal model. Then, we dechirp the signal of each selected range bin into the form of sinusoidal signal, and utilize the super-resolution MUSIC technique to accurately estimate the frequency. After processing all the range bins, a linear relationship related to the RV can be obtained. Eventually, the ISAR image can be scaled. The proposal can precisely estimate the small RV of the slow rotation target with low computational complexity. Furthermore, the proposal can also be used in the case of cross-range scaling for the sparse aperture data. Experimental results with the simulated and raw data validate the superiority of the novel method.

CROSS-RANGE RESOLUTION OF SYNTHETIC APERTURE RADAR BASED ON DIVING MODEL

This paper concentrates on the cross-range resolution of Synthetic Aperture Radar(SAR) based on diving model.In comparison to the azimuth resolution,the cross-range resolution can manifest the two-dimensional resolution ability of the imaging sensor SAR correctly.The diving model of SAR is an extended model from the conventional stripmap model,and the cross-range resolution expression is deduced from the equivalent linear frequency modulation pulses' compression.This expression points out that only the cross-range velocity component of the horizontal velocity contributes to the cross-range resolution.Also the cross-range resolution expressions and the performance of the conventional stripmap operation,squint side-look operation and beam circular-scanning operation are discussed.The cross-range resolution expression based on diving model will provide more general and more accurate reference.

ISAR Imaging and Cross-Range Scaling Based on Image Rotation Correlation

With the rapid advancement of technology,not only do we need to acquire a clear in-verse synthetic aperture radar(ISAR)image,but also the real size of the target on the imaging plane,so it’s particularly important for the ISAR to rescale the images.That is,the ISAR image which is in the range-Doppler domain is converted into the range-azimuth domain.Actually,the key point to solving the problem is to estimate the rotation parameters.In this paper,a new scheme to rescale the images is proposed.For the sake of solving the problem of two-dimensional image blur and target high-speed,the instantaneous range instantaneous Doppler(IRID)method is used to obtain ISAR images,and the rotation parameters are estimated by comparing the rotation correlation of the two images.Using this method,the error of the estimated rotation parameters is greatly reduced,so that the target can be rescaled accurately.The simulation results verify the ef-fectiveness of the proposed algorithm.

目标二维RCS成像仿真与实验研究

对目标开展了二维RCS成像仿真与实验研究,分别采用SBR方法与解析方法计算目标的散射特性并提取相应的二维RCS成像信息,基于时域加窗技术对采样信号进行加权处理抑制旁瓣电平以提高成像质量,通过Cross-Range处理方法获取目标二维RCS成像结果以表征目标散射的强弱分布,设计了斜置平板以及3个小球这两个典型案例,展开相应的二维RCS成像仿真与实验分析研究。结果表明:二维RCS成像仿真结果与测试结果吻合良好,有效验证了二维RCS成像仿真方法的有效性。

Rotational parameters estimation of maneuvering target in ISAR imaging

The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR （inverse synthetic aperture radar）, which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM （frequency modulation） parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT （Radon-Wigner transform）. The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.

THREE ASPECTS OF ISAR SIGNAL PROCESSING

Inverse synthetic aperture radar (ISAR) is a high resolution imaging radar of non-cooperative target. Its signal processing involves two links,i. e. motion compensation and image formation. In this Paper, three critical problems of ISAR signal processing are investigated.They are: (1) superresolution ISAR imaging with multilayer neural network ; (2) motion compensation of ISAR in frequency domain and (3) cross-range scaling of ISAR. The proposed approaches are used to process the real data of model B-52 collected in a microwave anechoic chamber. The reconstructed images show that the proposed approaches are correct and effective.

Staring-imaging satellite pointing estimation based on sequential ISAR images

Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and surveillance.However,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation methods.Especially,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this task.To address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite’s main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for investigation in this article due to its unique structure.Specifically,considering the kinematic characteristic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted axes.Based on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this problem.Finally,the pointing of targets and the cross-range scaling factors of ISAR images are obtained.Simulation experiments based on actual satellite orbital parameters verify the effectiveness of the proposed method.This work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.