复AR双谱及在常规雷达目标分类中的应用

根据飞机喷气发动机调制(JEM)特征产生机理和参数模型及常规雷达回波特点,本文采用复AR双谱技术分析了常规雷达回波中的JEM特征,揭示了回波中JEM频率和径向Doppler频率之间的耦合关系,并从双谱切片中提取了相对不变的JEM周期特征。提出了基于JEM周期特征的目标分类方法。实测数据分类实验表明:JEM周期特征用于常规雷达飞机目标分类是有效的。     

基于小波分析的JEM特征提取与应用

根据对常规防空雷达飞机回波特性及其JEM(Jet Engine Modulation)特征的理论模型和实际信号分析与理解,本文采用小波分析处理方法从回波幅度分量提取JEM特征,用于螺旋桨飞机自动识别。对实测回波数据的分析提取到了比较精确而稳定的JEM特征,并表明该方法具有提取方便、误差较小的优点。     

基于分数布朗特征的低分辨雷达目标分类

低分辨雷达目标的JEM效应不仅体现在对目标回波的频率调制上,还体现在幅度调制上,以上2种调试特征是互补的关系,综合运用2种调制特征将会改善低分辨雷达对飞机目标的分类与识别能力。分数布朗运动模型是一种可以用来描述自然界中随机分形的统计模型,文中提出将分数布朗运动模型用于反映回波幅度的不规则程度或起伏程度,以及回波幅度起伏的快慢,分别提取回波时域和频域的分数布朗分形维,再采用支持向量机分类器进行分类,很好地将直升机与民航机2类目标区分开来。实测数据表明,综合分类正确率大于85!。     

防空雷达飞机目标调制周期特征的研究

基于飞机旋转部件对雷达电磁波调制机理,以及对调制参数模型和回波特点的分析理解,本文针对防空雷达,分析了几种飞机目标的调制周期特征,提出了几种调制周期特征分析和提取的方法.本文的主要贡献是揭示了防空雷达飞机回波复包络中的调制周期特征,分析了在不同飞机姿态、不同雷达工作状态下实际回波中调制周期特征的稳定性,并讨论了调制周期特征用于辨识螺旋桨飞机的可能性.实测数据分析结果表明:本文方法能从防空雷达螺旋桨飞机回波中提取调制周期特征,并用于螺旋桨飞机的辨识.     

基于旋翼结构微多普勒特征的目标识别方法

根据飞机目标旋转部件周期运动对电磁波的调制特性,建立了旋翼目标回波信号的数学模型;在分析雷达回波谱特性基础上提取了表征目标姿态及旋翼调制信息的能量特征、对称性特征,并用双谱法获取旋转部件回波(JEM)周期调制特征;最后,提出了一种新的飞机目标自动辨识方法,仿真证实了该方法具有较高综合辨识能力和正确辨识率.     

对空情报雷达自动辨识螺旋桨飞机

基于飞机旋转部件对雷达电磁波调制机理、调制参数模型以及回波特性，本文从实际对空情报雷达回波中提取了幅度、相位、周期3种调制特征，并分析讨论了3种调制特征物理意义及用于飞机分类与辨识的稳定性、有效性和局限性．在此基础上，提出了自动辨认特种军用螺旋桨飞机的文用方法一逐级最近邻特征综合分类法，并对实际回波进行了分类实验．分析与分类实验结果表明：3种调制特征具有相对不变性，对螺旋桨飞机的自动分类与辨识是有效的．     

基于时频图像处理的JEM分类识别方法

机载雷达探测飞机目标时,可通过提取JEM特征进行直升飞机、螺旋桨飞机、喷气式飞机分类识别,虽然其理论基础已相对成熟,但在机载杂波背景下对低SNR调制信号的检测与分类识别有待进一步提升,以改进工程化过程中分类识别的稳健性.文中在回顾JEM理论分析的基础上,分析了观测角度、叶片螺距、转子交调、RCS闪烁、雷达工作波形等因素...     

目标特征与雷达识别需求关系原理

根据实际涡喷JEM特殊的多压气级的特点,推导出多压气级JEM的理论参数模型,并由实际分析得出JEM的周期调制特征,提出利用JEM的周期调制特征对空目标进行分类的设想。提出了基于微多普勒特征进行目标识别所需要的雷达参数原理,并举例分析了利用微多普勒周期特征进行目标分类所需的参数。     

飞机回波FRFT域的多重分形特征与目标分类

低分辨雷达体制的限制和探测过程中背景杂波的影响,使得低分辨雷达飞机目标的分类识别较为困难。本文提出一种分数域多重分形方法,通过引入分数阶Fourier变换（FRFT）寻找飞机目标回波的最优FRFT域,并在最优分数阶FRFT域提取目标回波数据的多重分形特征,结合支持向量机进行飞机目标的分类识别。实验表明,FRFT可以增强飞机目标回波的多重分形特性;从最优FRFT域提取的多重分形特征具有较好的分类识别效果,FRFT域多重分形方法的飞机目标分类识别率要高于时域多重分形方法的飞机目标分类识别率;即使在低信噪比条件下,FRFT域多重分形方法仍可实现低分辨雷达飞机目标的粗分类。     

基于时域回波相关性特征的飞机目标分类方法

该文研究了常规窄带雷达体制下利用时域回波调制周期的差异实现直升机、螺旋桨飞机和喷气式飞机3类飞机目标的分类问题。首先分析3类飞机时域回波调制周期的差异;然后针对3类飞机目标时域回波调制周期的不同,基于时域回波相关性提取了2维特征向量;最后基于仿真数据和实测数据,利用支持向量机(SupportVector Machine, SVM)分类器的分类结果证明了在脉冲重复频率较低时,多普勒谱有一定程度混叠的情况下,时域相关性特征仍能表现出相对较好的分类性能      

基于分数阶傅里叶变换的窄带雷达飞机目标回波特征提取方法

该文研究了常规窄带雷达体制下,利用分数阶傅里叶变换扩展特征域,从而解决直升机、螺旋桨飞机和喷气式飞机3类飞机目标回波分类中的特征提取问题。在现代战场中,直升机、螺旋桨飞机和喷气式飞机具有不同的机动性能,并各自承担着重要的任务。因此,实现这3类飞机的分类具有重大的意义。该文针对3类飞机目标分类的传统特征数目少,包含信息量有限,导致分类性能不够好的问题,基于现有的特征提取方法引入分数阶傅里叶变换(Fractional Fourier Transform, FrFT),在经过FrFT后的分数域提取3类飞机目标回波的分数阶特征,弥补传统特征的不足。并利用线性相关向量机(Relevance Vector Machine, RVM)的特征选择功能对提取的分数阶特征进行特征选择并分类。基于仿真和实测数据的实验结果证明该文提出的分数阶特征的分类性能较传统时域、多普勒域特征有较大提升。     

散射中心的时频像特征研究

散射中心是高频区电磁散射的重要特征,其属性特征,如散射幅度、位置,对方位的依赖性对于雷达成像及目标识别具有重要意义。与其它雷达图像相比,时频图像能更完整地反映出散射中心的属性特征,但目前关于不同散射中心的时频像特征研究还不完整。该文首先基于散射中心模型,从理论上分析了各个散射中心时频像的特征,然后通过全波法电磁计算得到了典型结构目标的散射数据,从数值上验证了对时频图像特征的理论分析,最后总结了不同散射中心的时频像特征,此结论有助于从时频像中直观地判断目标的散射中心类型和其对应的物理结构特点,可为基于时频像的雷达目标特征提取与识别提供一定的理论参考。     

基于压缩感知和深度学习的分类识别技术

雷达对空飞机目标分类可实现雷达装备获得敌机属性和类别信息,对于现代战争其重要性显得尤为突出。针对复杂电磁环境下的飞机目标分类问题,结合防空雷达的特点建立3类（固定翼、螺旋桨和直升机）飞机旋转部件调制回波模型,并理论分析了不同类型飞机目标的微动特征差异。仿真分析在复杂电磁环境下干扰对微动频谱的影响。引入压缩感知方法进行干扰条件下的微动特征稀疏恢复,采用堆栈自编码学习(SAE)方法构建深层神经网络对目标进行自动特征提取和分类识别;实录数据验证表明,本文特征提取和识别方法在干扰比例41%时识别正确率能达到75%。     

High-resolution radar imagery of the Mirage III aircraft

High-resolution radar imagery has attracted increasing interest in recent years. As more radars are endowed with a high-resolution capability, target classification will become a regular system function. In order to classify an aircraft using radar, one must have an understanding of how the radar imagery relates to the physical aircraft. This paper illustrates the more important radar backscattering features on a typical fighter aircraft. Radar backscatter from an aircraft can occur through a variety of mechanisms. Although direct specular and diffractive mechanisms usually account for the majority of the scattering, indirect phenomena such as cavity scattering and creeping wave scattering are significant. This investigation finds that scattering from engine cavities is a particularly important radar backscatter mechanism for a fighter aircraft. Radar data of an actual Mirage aircraft is collected from a target turntable facility. This data is processed to obtain high-resolution range profiles (HRRP) and inverse synthetic aperture radar (ISAR) images, which indicate the prominent radar scatterers on the aircraft. The imagery is qualitatively examined, and its suitability for target classification is discussed     

Matched-field estimation of aircraft altitude from multipleover-the-horizon radar revisits

Over-the-horizon (OTH) radar uses the refractive properties of the ionosphere for wide-area surveillance of targets at long ranges. Currently, OTH radars can localize targets in latitude and longitude but have difficulty estimating target altitude, which is important for classification purposes. Methods that have been proposed for aircraft altitude estimation using OTH radar take advantage of micro-multipath returns due to ground reflections local to the aircraft and are typically limited in performance by the radar bandwidth and observation time. In previous work, electromagnetic matched-field processing was proposed for estimating aircraft altitude using a single dwell by exploiting the altitude dependence of unresolved multipath returns in complex range-Doppler space. However, the performance of the single dwell method suffers in situations where the coherent integration time (CIT) of the radar is short. To overcome this limitation, this paper presents a matched-field estimation approach that exploits the altitude dependence of dwell-to-dwell shape changes of the complex range-Doppler multipath return as the basis for multi-dwell maximum likelihood (ML) altitude estimation. Monte Carlo simulation results indicate that using a short CIT, moderate signal bandwidth, and 30-s revisit rate, multi-dwell matched field altitude estimation can achieve better than ±2500 ft accuracy after as few as four radar dwells. The results of processing actual radar data for both high-flying commercial aircraft and a low-flying twin engine aircraft are also presented and validated against aircraft altitude ground truth     

Noncooperative target classification using hierarchical modeling ofhigh-range resolution radar signatures

The classification of high-range resolution (HRR) radar signatures using multiscale features is considered. We present a hierarchical autoregressive moving average (ARMA) model for modeling HRR radar signals at multiple scales and use spectral features extracted from the model for classifying radar signatures. First, we show that the radar signal at a different scale obeys an ARMA process if it is an ARMA process at the observed scale. Then, an algorithm to estimate model parameters and power spectral density function at different scales using model parameters at the observed scale is presented. A feature set composed of spectral peaks is extracted from the estimated spectral density function using multiscale ARMA models. For HRR radar signature classification, multispectral features extracted from five different scales are used, and a minimum distance classifier with multiple prototypes is used to classify HRR data. The multiscale classifier is applied to two HRR radar data sets. Each data set contains 2500 test samples and 2500 training samples in five classes. For both data sets, about 95% of the radar returns are correctly classified