基于联合互时频分布的InISAR三维成像方法

 常规InISAR通常采取先进行各天线的二维ISAR成像再进行图像间干涉处理的三维成像步骤.与此不同,本文提出了一种基于联合互时频分布的InISAR三维成像方法,通过构造干涉天线接收回波之间的联合互时频分布,在一次时频变换中能同时实现同一距离单元内各散射点的横向分辨和相位干涉处理,并利用Radon变换有效抑制了互时频分布的交叉项.该方法不仅具有Cohen类时频变换的高时频聚集性的特点,而且对0阶平稳运动和1阶机动目标都能进行三维成像.仿真结果验证了该方法的有效性.     

一种基于时域分解的ISAR图像理解与处理方法

逆合成孔径雷达(ISAR)图像的自动理解是应用其进行自动目标识别(ATR)的基础。文中提出一种基于改进的自适应高斯表示(AGR)的ISAR图像理解与处理方法。该方法对目标ISAR图像中点散射与非点散射效应并存的现象给以有效的参数化的描述,进而消除非点散射在图像中造成的模糊展布,并提取到可用于目标识别的成像目标散射中心特征。基于飞机目标实测数据的实验验证了该方法的有效性。     

ISAR Imaging of Maneuvering Targets Based on the Range Centroid Doppler Technique

A new inverse synthetic aperture radar (ISAR) imaging approach is presented for application in situations where the maneuverability of noncooperative target is not too severe and the Doppler variation of subechoes from scatterers can be approximated as a first-order polynomial. The proposed algorithm is referred to as the range centroid Doppler (RCD) ISAR imaging technique and is based on the stretch Keystone-Wigner transform (SKWT). The SKWT introduces a stretch weight factor containing a range of chirp rate into the autocorrelation function of each cross-range profile and uses a 1-D interpolation of the phase history which we call stretch keystone formatting. The processing simultaneously eliminates the effects of linear frequency migration for all signal components regardless of their unknown chirp rate in time-frequency plane, but not for the noise or for the cross terms. By utilizing this novel technique, clear ISAR imaging can be achieved for maneuvering targets without an exhaustive search procedure for the motion parameters. Performance comparison is carried out to evaluate the improvement of the RCD technique versus other methods such as the conventional range Doppler (RD) technique, the range instantaneous Doppler (RID) technique, and adaptive joint time-frequency (AJTF) technique. Examples provided demonstrate the effectiveness of the RCD technique with both simulated and experimental ISAR data.     

压缩感知机动目标ISAR成像新方法

基于Fourier基的压缩感知（Compressed Sensing,CS）算法已被成功应用于平稳运动目标的逆合成孔径雷达（Inversed Synthetic Aperture Radar,ISAR）成像。但由于建模时对ISAR回波方位相位高次项的忽略,Fourier基矩阵对机动目标回波数据方位信息的稀疏表示失效,导致对机动目标的成像在方位向模糊。鉴于时频分析技术良好的时频局部化特性,将其引入到雷达回波方位向分析中,以改进用于表示雷达回波数据的稀疏基,实现对选定时间切片内回波数据多普勒频率的稀疏表示。改进后的基矩阵在通过CS技术解析回波在时间切片内方位信息的同时,又保证了利用有限数据成像的分辨率。与基于Fourier基CS成像等现有方法相比较,新方法在方位向的成像质量上有较大改进。仿真实验验证了算法的有效性。     

An Algorithm to Detect the Presence of 3D Target Motion from ISAR Data

We present an algorithm to detect the presence of 3D target motion from ISAR data. Based on the 3D point scatterer model, we first examine the effect of 3D motion on ISAR imaging. It is shown that existing motion compensation algorithms cannot properly focus targets exhibiting 3D motion during the imaging interval. An algorithm is then derived to blindly detect the degree of 3D target motion from raw radar data. It is based on measuring the linearity of phases between two or more point scatterers on the target. The phase estimation is implemented using the adaptive joint time-frequency technique. Examples are provided to demonstrate the effectiveness of the 3D motion detection algorithm with both simulation and real ISAR data. The detection results are corroborated with the truth motion data from on-board motion sensors and correlated with the resulting ISAR images.     

Adaptive ISAR image construction from nonuniformly undersampleddata

An adaptive approach is proposed to construct ISAR images from nonuniformly undersampled data in the angular domain. The algorithm uses an adaptive scattering feature extraction engine in place of the Fourier transform in the image construction procedure. The algorithm entails searching and extracting out individual target scattering features one at a time in an iterative fashion. The interference between different target scattering features is thus avoided and a clean ISAR image without the aliasing effect can be obtained. The algorithm is verified by constructing the ISAR image from the chamber measurement data of the model VFY-218 airplane     

Efficient radar target classification using adaptive jointtime-frequency processing

This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substantial savings of computational resources, preserving as much essential information for classifying targets as possible. Next, a principal component analysis is used to further reduce the dimension of feature space, and the resulting feature vectors are passed to the classifier stage based on the multilayer perceptron neural network. To demonstrate the performance of the proposed scheme, various thin-wire targets are identified. The results show that the proposed technique has a significant potential for use in target recognition     

Scattering center parameterization of wide-angle backscattered datausing adaptive Gaussian representation

We present a scattering center extraction algorithm to parameterize the backscattered data from complex targets collected over large angular apertures. This parameterization is based on a scattering center model of the target, but includes an aspect-dependent amplitude function for each scattering center. A two-dimensional (2-D) adaptive Gaussian representation (AGR) algorithm is used to extract the position and the amplitude function associated with each scattering center. The algorithm is tested with data generated by the Xpatch radar simulation code as well as chamber measurement data. The results show that a very good compression ratio can be achieved, resulting in a compact scattering center model of the target. Once such model is available, we can easily reconstruct range profiles and ISAR images at any aspect on the same plane with good accuracy     

Joint time-frequency analysis for radar signal and image processing

The Fourier transform has been widely used in radar signal and image processing. When the radar signals exhibit time- or frequency-varying behavior, an analysis that can represent the intensity or energy distribution of signals in the joint time-frequency (JTF) domain is most desirable. In this article, we showed that JTF analysis is a useful tool for improving radar signal and image processing for time- and frequency-varying cases. We applied JTF analysis to radar backscattering and feature extraction; we also examined its application to radar imaging of moving targets. Most methods of JTF analysis are non-parametric. However, parametric or model-based methods of time-frequency analysis, such as adaptive Gaussian and chirplets, are more suitable for radar signals and images     

基于时频分析的ISAR成像

传统的逆合成孔径雷达(ISAR)成像算法用傅里叶变换进行频谱分析,对机动目标的成像会导致成像模糊。本文应用短时傅里叶变换(STFT)、魏格纳分布(WVD)、平滑伪魏格纳分布(SPWVD)几种时频分析方法对机动目标进行瞬时成像。仿真和实验结果表明,通过该方法能得到清晰的目标的距离-瞬时多普勒像。     

弹道中段目标多普勒成像算法研究

弹道中段目标成像技术是弹道导弹防御系统的核心技术。利用传统的宽带ISAR雷达对弹道中段目标成像无法利用统一的相位补偿函数进行相位补偿,导致成像结果的模糊。该文提出一种提取时频骨架线的信号处理算法,算法通过对提取的弹道中段目标窄带回波的时频多普勒骨架线进行幅度增强,提高目标多普勒成像分辨率,仿真数据和暗室测量数据试验表明,该算法能够实现基于窄带雷达回波多普勒信息的弹道中段目标二维成像。     

含旋转部件的动态目标ISAR成像及微多普勒特征提取

含旋转部件的动态目标ISAR成像中,受旋转部件微多普勒调制影响,导致目标成像质量下降,给目标识别带来巨大困难.抑制或消除旋转部件回波是处理带有旋转部件目标ISAR成像的重要内容.现有的算法在处理旋转部件时采用了单个旋转点的假设,虽然取得了较好的效果,但是对实测数据分离效果不明显.为此,本文基于含旋转部件目标主体和旋转部...     

Real-time motion compensation, image formation and image enhancement of moving targets in ISAR and SAR using S-methodbased approach

The commonly used technique for inverse synthetic aperture radar (ISAR)/synthetic aperture radar signal analysis is a two-dimensional Fourier transform (FT), which results in an image of the target's reflectivity mapped onto a range and cross-range plane. However, in cases where the line-of-sight projections of the target's point velocities change or there is uncompensated movement within the coherent integration time, the FT produces blurred images. For target recognition applications, mainly those in military surveillance and reconnaissance operations, a blurred ISAR image has to be refocused quickly so that it can be used for real-time target identification. Two standard techniques used for improvement of blurred ISAR images are motion compensation and the use of quadratic time-frequency representations. Both are computationally intensive. The authors present an effective quadratic time-frequency representation, the S-method. This approach performs better than the Fourier transform method by drastically improving images of fast manoeuvring targets and by increasing the SNR in both low and high noise environments. These advantages are a result of the S-method's ability to automatically compensate for quadratic and all even higher-order phase terms. Thus, targets with constant acceleration will undergo full motion compensation and their point scatterers will each be localised. It should be noted that the source of the quadratic term can come not only from acceleration, but also from non-uniform rotational motion and the cosine term in wide-angle imaging. The method is also computationally simple, requiring only slight modifications to the existing FT-based algorithm. The effectiveness of the S-method is demonstrated through application to simulated and experimental data sets.     

Reconstruction from projections under time-frequency constraints

Low-pass filtering computed tomography (CT) images to reduce noise may smooth or modify image features which are very important to the physician. Image features are often more easily identified and processed in the time-frequency plane. The authors use time-frequency distributions for spatially varying filtering of noisy CT images, constraining time-frequency representation coefficients of the projection data or of the reconstructed image to be zero in certain regions of the time-frequency plane. The authors consider two different applications: 1) filtering the projection data and then performing image reconstruction; and 2) filtering the reconstructed image directly. Criteria minimized, subject to constraints, may be either a deterministic minimum weighted perturbation of the given projection data or a stochastic minimum mean-square error in colored Gaussian noise. Results show improvement over processing the image with a linear spatially invariant filter.     

基于压缩感知的二维联合超分辨ISAR成像算法

在ISAR成像中,距离和方位分辨率分别受发射信号带宽和成像积累角的限制。基于压缩感知(CS)理论,该文提出了一种2维联合超分辨ISAR成像算法。首先建立ISAR观测信号模型并构造2维超分辨字典,然后利用ISAR图像的稀疏先验信息将2维联合超分辨成像建模为最小l1范数的优化问题,最后提出一种快速算法求解该优化问题。该方法进行距离维和方位维的联合处理,有效利用了回波数据的2维耦合信息;通过共轭梯度(CG)运算,快速傅里叶变换(FFT),Hadamard乘积等操作,有效提高了算法的实现效率。仿真和实测实验验证了该算法的有效性。     

基于稀疏表示的被动毫米波L-R成像算法

在被动毫米波的图像恢复中,L-R算法是一种简单而有效的非线性方法。但当噪声不可忽略时,L-R算法难以获得较好的复原结果。自适应稀疏表示,作为一种新的信号处理方法,具有表达信号灵活的特点,能够在保持目标特征的同时有效地去除噪声。该文提出一种基于自适应稀疏表示的L-R算法。首先采用稀疏信号表示的方法进行去噪,然后使用L-R算法进行图像恢复。这种改进算法通过使用基于自适应稀疏表示的去噪算法有效地减少了噪声对L-R算法的影响。实验数据的成像结果表明：该文的改进算法提高了L-R算法的性能,可用于低信噪比的图像复原。     

Gabor expansion for adaptive echo cancellation

A good echo cancellation algorithm should have a fast convergence rate, small steady-state residual echo, and less implementation cost. The normalized least mean square (NLMS) adaptive filtering algorithm may not achieve this goal. We show that using the Gabor expansion is a way to achieve this goal. For direct digital signal processing compatibility the Gabor expansion introduced in this paper is for discrete-time signals, although the Gabor expansion also can be used for continuous-time signals. The Gabor expansion can be defined as a discrete-time signal representation in the joint time-frequency domain of a weighted sum of the collection of functions (known as the synthesis functions). There are several design issues in the echo canceller based on the Gabor expansion: the design of the analysis functions for the far-end speech, the design of the analysis functions for the near-end signal containing the echo plus the near-end speech, the design of the adaptive filters in the subsignal path, and the design of the synthesis functions. All the adaptive filters are designed using identical NLMS adaptive filtering algorithms     

基于HH变换的非平稳运动目标的ISAR成像

ISAR时频分析法成像与传统的成像处理算法相比,将二维的距离-多普勒矩阵变成了三维的时间-距离-多普勒矩阵,在一定程度上改善了成像质量,但对于非平稳运动目标的成像效果并不非常显著.应用Hilbert-Huang变换,对非平稳运动ISAR目标进行成像处理,并与经典的Wigner-Ville分布算法所得图像进行比较.成像试验结果表明:该算法明显改进了图像质量.     

基于SVA算法和时频分析的ISAR成像

研究了时频分析在ISAR成像中的运用,论述了一维SVA算法和改进的二维SVA算法在提高图像分辨率、降低信号自身旁瓣电平方面的优势。在此基础上,提出将两者联合运用到机动目标的逆合成孔径雷达成像中,改善了传统的距离-多普勒算法对机动目标成像的模糊现象。实验结果证明了此方法的正确性和有效性。     

压缩感知在ISAR谱图压缩重构中的应用

针对天基逆合成孔径雷达（Space-borne ISAR）回波信息传输到地面过程中数据量大的问题,本文提出了一种基于压缩感知的ISAR数据压缩与重构方法。该方法是将目标回波变换到谱图域（即距离-慢时间域）后,再利用压缩感知技术将目标谱图压缩后传输,大幅减少了传输数据量。在地面接收端,利用基于正交匹配追踪法的谱图重构方法恢复谱图信息,并重构最终的ISAR像。仿真结果表明,利用本文方法恢复的谱图与压缩前的谱图相比相当逼近,而且重构出了高质量的ISAR像。