High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

雷达目标特征提取的一种方法

将小波变换和马氏距离相结合，提出了一种基于ISAR成像的雷达目标特征提取方法。对ISAR成像数据进行正交二进Symlet小波分解和门限处理，对数据进行压缩，然后计算压缩后坐标点的马氏（Mahalanobis)距离，得到目标稳定的特征向量。由实验结果看出，此向量具有在一定范围内的不变性，能够用于雷达目标识别。     

ISAR Imaging of Fluctuating Ship Target Based on a Novel Approach for Cubic Chirplet Decomposition with Local Polynomial Wigner Distribution

基于局域多项式魏格纳分布的三次Chirplet分解新方法及其在舰船目标逆合成孔径雷达成像中的应用

王勇,高建军

(哈尔滨工业大学 电子与信息工程学院)

创新点说明：

1） 本文提出一种三次Chirplet分解的新方法。通过计算散射点回波信号的局域多项式魏格纳分布,可获得三次Chirplet基函数的参数估计。解决了传统参数估计方法存在的算法复杂、计算量大等问题。

2） 本文提出一种基于三次Chirplet分解的舰船目标逆合成孔径雷达成像新方法,解决了舰船目标复杂运动情况下,传统的成像方法成像质量下降的问题。本文方法可获得舰船目标高质量的成像结果。

研究目的：

本文的写作目的主要是为了解决舰船目标复杂运动情况下如何提高成像质量的问题。同时提出新的信号参数估计方法,解决传统方法存在的算法复杂、计算量大等问题。

研究方法：

1） 基于局域多项式魏格纳分布的三次Chirplet分解的新方法。通过峰值搜索,可获得三次Chirplet基函数三次相位系数的估计,进而通过解调频等技术获得其它信号参数的估计。对于多分量信号,可通过洁净技术逐一估计出不同信号分量的参数。

2） 舰船目标复杂运动情况下,散射点回波信号可用多分量多项式相位信号模型来进行刻画。通过对多项式相位信号进行三次Chirplet分解,可获得其内在结构信息。进而采用距离——瞬时多普勒成像算法,可获得舰船目标高质量的成像结果。

仿真数据和外场实测数据成像结果验证了本文方法的有效性。

结果：

1） 文中构造了三维摆动的舰船目标及其回波信号模型,如文中图2所示。相应的成像结果如文中图4所示。此时获得了舰船目标高质量的成像结果。

2）文中选取舰船目标的外场实测数据,相应的成像结果如文中图6和图7所示,进一步说明了本文算法的适应性。

结论：

基于局域多项式魏格纳分布的三次Chirplet分解的新方法,可获得三次Chirplet基函数的高精度参数估计结果。将该方法应用于复杂运动舰船目标的逆合成孔径雷达成像中,可获得舰船目标高质量的成像结果。

关键词：三次Chirplet分解;局域多项式魏格纳分布;逆合成孔径雷达成像

     

FPIR实时快视成像方法

一维全极化综合孔径辐射计(Full Polarization Interferometric Radiometer,FPIR)是一种轻量化的高空间分辨率的综合孔径微波辐射计系统。基于综合孔径辐射计成像原理及X波段FPIR原理样机,提出一种快视成像方法:(1)对原始数据进行抽取和平均,降低实时处理运算量;(2)简化误差处理过程,进而利用傅立叶变换法和G矩阵法反演亮温。通过对FPIR原理样机的地面实验,验证了该实时快视成像方法的有效性。     

一种基于SPWVD-WVD的高质量时频分析方法及ISAR成像应用

交叉项干扰抑制与高时频聚集度是准确反应信号的时频分布特征的重要因素。传统的魏格纳-维尔分布(Wigner-Ville Distribution,WVD)算法虽能获得较高的时频分辨特征,但分析多成分信号时存在严重的交叉项干扰问题,限制了其实用性;而平滑伪魏格纳-维尔分布(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)算法虽在一定程度上抑制交叉项干扰,但降低了时频聚集度。为了解决上述问题,提出了基于SPWVD-WVD的时频分析方法。该方法利用SPWVD与WVD之间的滤波互消效应,将SPWVD二值化结果与WVD结果进行矩阵运算,最终得到高质量的时频分析结果。实验结果表明,所提出的算法能够有效去除多分量信号的交叉项干扰,提高信号分析结果的时频聚集度,还原多分量信号的真实时频分布。最后将该算法成功应用于逆合成孔径雷达成像中。     

Structured Bayesian compressive sensing with spatial location dependence via variational Bayesian inference

In this paper, a novel non-parametric Bayesian compressive sensing algorithm is proposed to enhance reconstruction performance of sparse entries with a continuous structure by exploiting the location dependence of entries. An approach is proposed which involves the logistic model and location-dependent Gaussian kernel. The variational Bayesian inference scheme is used to perform the posterior distributions and acquire an approximately analytical solution. Compared to the conventional clustered based methods, which only exploit the information of neighboring pixels, the proposed approach takes the relationship between the pixels of the entire image into account to enable the utilization of the underlying sparse signal structure. It significantly reduces the required number of observations for sparse reconstruction. Both real-valued signal applications, including one-dimension signal and two-dimension image, and complex-valued signal applications, including single-snapshot direction-of-arrival (DOA) estimation of distributed sources and inverse synthetic aperture radar (ISAR) imaging with a limited number of pluses, demonstrate the superiority of the proposed algorithm.     

Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests

Disturbed forests may need decades to reach a mature stage and optically-based vegetation indices are usually poorly suited for monitoring purposes due to the rapid saturation of the signal with increasing canopy cover. Spaceborne synthetic aperture radar (SAR) data provide an alternate monitoring approach since the backscattered microwave energy is sensitive to the vegetation structure. Images from two regions in Spain and Alaska were used to analyze SAR metrics (cross-polarized backscatter and co-polarized interferometric coherence) from regrowing forests previously affected by fire. TerraSAR-X X-band backscatter showed the lowest sensitivity to forest regrowth, with the average backscatter increasing by 1-2 dB between the most recent fire scar and the unburned forest. Increased sensitivity (around 3-4 dB) was observed for C-band Envisat Advanced Synthetic Aperture (ASAR) backscatter. The Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) L-band backscatter presented the highest dynamic range from unburned to recently burned forests (approximately 8 dB). The interferometric coherence showed low sensitivity to forest regrowth at all SAR frequencies. For Mediterranean forests, five phases of forest regrowth were discerned whereas for boreal forest, up to four different regrowth phases could be discerned with L-band SAR data. In comparison, the Normalized Difference Vegetation Index (NDVI) provided reliable differentiation only for the most recent development stages. The results obtained were consistent in both environments.     

基于标志点的InSAR系统仿真性能评估方法

系统仿真实验对于天基InSAR系统的总体设计和关键技术验证意义重大,性能评估是其中的关键环节之一。建立了由三大类共8项指标组成的InSAR系统性能指标体系,提出了基于标志点的InSAR系统仿真性能评估方法。该方法通过在仿真地面场景中合理布设标志点,并以标志点作为评估样本点来计算各项指标,可达到与理想干涉量评估方法相近的指标计算精度,同时避免了理想干涉量评估方法稳健性不足的问题,且计算效率更高。仿真实验结果验证了评估方法的有效性。