Identification of ground targets from sequential high-range-resolution radar signatures

An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification results are presented for the ten-target MSTAR data set.     

Time-varying autoregressive modeling of HRR radar signatures

A time-varying autoregressive (TVAR) model is used for the modeling and classification of high range resolution (HRR) radar signatures. In this approach, the TVAR coefficients are expanded by a low-order discrete Fourier transform (DFT). A least-squares (LS) estimator of the TVAR model parameters is presented, and the maximum likelihood (ML) approach for determining the model order is also presented. The validity of the TVAR modeling approach is demonstrated by comparing with other approaches in estimating time-varying spectra of synthetic signals. The estimated TVAR model parameters are also used as features in classifying HRR radar signatures with a neural network. In the experiment with two sets of noncooperating target identification (NCTI) data, about 93% of samples are correctly classified     

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements     

基于改进支持向量机的油液在线监测系统

搭建油液在线监测实验平台进行磨粒分类识别实验,运用支持向量机和最近邻法相结合的方法对飞机发动机油液中的磨粒进行分类识别;其中基于支持向量机的磨粒分类器的输入为磨粒的主轴长度、纹理相关性、圆度等特征参数,输出为磨粒的分类结果;实验结果表明,基于支持向量机的磨粒分类器的分类准确率高达94％,并且由于最近邻法的使用,分类器的处理速度也提高了30％。     

High Range Resolution Profiles as Motion-Invariant Features for Moving Ground Targets Identification in SAR-Based Automatic Target Recognition

A quantitative model analysis is presented to justify the extraction of high range resolution (HRR) profiles from synthetic aperture radar (SAR) images as motion-invariant features for identifying moving ground targets. A comparative study is conducted to assess the effectiveness in the identification process between using HRR profiles and SAR images as target signatures. The results indicate that HRR profiles are just as viable as SAR image for identification. Furthermore, a score-level multi-look fusion identification method has been investigated. It is found that a correct accurate identification rate of greater than 99 percent, a low false alarm rate, and a high level of identification confidence can be achieved, providing very robust performance.     

民用飞机产品分类研究

民用飞机承载的零部件种类繁多、数量庞大,需要对飞机产品进行明确的分类,才能建立高效的飞机产品全生命周期技术管控机制。采用系统工程方法识别飞机产品分类相关利益攸关方,在分析总结关键利益攸关方需求的基础上,提出一种适用于民用飞机领域的基于产品来源的产品分类方案以及用以区分定制件和设备的判定准则,实现对民用飞机产品进行统一、规范及多维度的分类和定义,对某型号飞机产品分类案例进行验证。结果表明：基于产品来源的民用飞机分类方案,便于设计人员在实际工作中快速判定产品类别,保证产品分类的准确性和有效性,为民用飞机产品设计和技术管理提供了基础,较好地满足了民用飞机项目应用需求。     

基于深度学习的航空器异常飞行状态识别

飞行设备快速存取记录仪(Quick Access Recorder,以下简称QAR)保留了原始航班各类重要飞行参数在内的航行信息,使研究分析航空器实时状况和保障飞行质量成为可能。针对QAR数据高维大样本的特点,在如今大数据背景下,除了传统机理建模分析航空器飞行状态外,采用深度学习的方式建立基于数据驱动的航空器飞行状态识别模型,理论与实用意义兼具。通过对真实QAR飞行数据的研究,开发了基于深度稀疏受限玻尔兹曼机的异常飞行状态识别程序。首先利用小波降噪技术对原始飞行数据进行预处理清洗,在一系列典型飞行参数上提取经典时域特征以及小波奇异熵等信息熵特征构成特征集。在此基础上,分别利用经典的线性主元分析技术和深度稀疏玻尔兹曼机对特征集进行有效降维,最后采用四折交叉验证方式,通过高斯过程分类器实现对飞行状态的辨识。实验结果显示,基于深度受限玻尔兹曼机-高斯过程分类的飞行状态识别具有较高分类准确性。     

基于Mallat算法的飞机电网谐波检测系统

随着电力电子技术的广泛应用,谐波对飞机电网的污染越来越严重,检测、分析和抑制谐波已经成为重要的课题。小波变换具有时-频局部化特性,在电力系统的分析中具有广阔的应用前景。为实现对飞机电网谐波的快速检测、分析,将小波变换方法应用于飞机电气系统谐波检测中,提出了基于Mallat快速算法的谐波检测系统设计方案。实验结果表明,系统采用小波补偿的改进FFT算法进行谐波分析,可以将信号中不同频率的谐波快速有效地提取出来,并进行有针对性的分析,提高了谐波检测效率。     

基于支持向量机的飞机空中结冰严重程度识别

为了保证飞机的飞行安全,必须对飞机空中结冰的严重程度作出较准确的判断。针对飞机空中结冰状况的复杂性,提出将支持向量机与二分法相结合的飞机空中结冰严重程度识别的算法模型。仿真结果表明,虽然该训练样本较少且为多参量分类识别,但是由于建立了多支持向量机且采用二分法的概率抉择能找到最佳的建立支持向量机的分类方式,所以找到了最佳的分类方式,提高了分类准确率,而且可以较准确地识别飞机空中结冰的严重程度。可见该方法可以在训练样本较少的情况下对飞机空中结冰严重程度作出较好的识别效果。     

Fractal interpolation of radar signatures for detecting stationarytargets in ground clutter

The fundamentals of fractal geometry are reviewed, and its application to the millimeter-wave radar detection of stationary targets in a clutter background is described. First, high-range-resolution (HRR) profiles are used to determine the fractal interpolation functions needed to create fractal signatures. The fractal dimension is then determined for these signatures. On the basis of the value of the fractal dimension, the signature is declared to represent either a target of interest or clutter. The results of a CFAR (constant false alarm rate) simulation are presented to illustrate the performance of the method. They indicate that the fractal dimension feature used seems to be independent of amplitude. Thus, the fractal dimension information combined with traditional amplitude processing techniques will improve probabilities of detection     

一种商用飞机工程更改分类模型及管理策略

工程更改分类是构型控制的重要内容,为了满足日益突出的精细化工程更改管理需求,减少工程更改分类判断对人的依赖,需要引入新的分类和管理技术研究商用飞机工程更改分类模型及管理策略.首先,通过分析商用飞机研制过程工程更改的特点,分析工程更改影响力的组成因素,从“重要性”、“紧急性”和“复杂性”三个维度对工程更改影响力进行刻画,构建商用飞机更改影响评价指标体系.然后,采用综合模糊评价方法和坐标映射方法构建商用飞机更改分类模型,并基于八种分类给出与其密切相关的飞机更改管理策略和流程,实现对工程更改精细化管理.最后,以某商用飞机公司典型工程更改为例,讨论并验证更改分类模型和管理策略,结果表明模型有效.     

Super resolution feature extraction of moving targets

High range resolution (HRR) moving target indicator (MTI) is becoming increasingly important for many military and civilian applications such as the detection and classification of moving targets in strong clutter background. We consider the problem of extracting the HRR features of moving targets with very closely spaced scatterers in the presence of strong stationary clutter, where the range migration and Doppler frequency are taken into account. A relaxation-based algorithm, which is robust and computationally simple, is proposed to deal with the above problem. Numerical results have shown that the proposed algorithm exhibits super resolution and excellent estimation performance     

Comparison of Two Target Classification Techniques

It has been shown that radar returns in the resonance region carry information regarding the overall dimensions and shape of targets. Two radar target classification techniques developed to utilize such returns are discussed. Both of these techniques utilize resonance region backscatter measurements of the radar cross section (RCS) and the intrinsic target backscattered phase. A target catalog used for testing the techniques was generated from measurements of the RCS of scale models of modern aircraft and naval ships using a radar range at The Ohio State University. To test the classification technique, targets had their RCS and phase taken from the data base and corrupted by errors to simulate full-scale propagation path and processing distortion. Several classification methods were then used to determine how well the corrupted measurements fit the measurement target signatures in the catalog. The first technique uses nearest neighbor (NN) algorithms on the RCS magnitude and (range corrected) phase at a number (e.g., 2, 4, or 8) of operating frequencies. The second technique uses an inverse Fourier transformation of the complex multifrequency radar returns to the time domain followed by cross correlation. Comparisons are made of the performance of the two techniques as a function of signal-to-error noise power ratio for various processing options.     

基于ELM的飞机数字化装配定位运动模型

针对飞机装配中开敞性较差环境下的串联装配机构半闭环定位运动控制问题进行研究,提出了基于极限学习机(EML)算法的飞机数字化装配定位运动模型。通过分析飞机数字化装配串联定位机构的运动学模型特点及性能要求,提出了飞机数字化装配定位运动的单隐含层前馈神经网络模型,并基于极限学习机提出了装配定位运动的数据辨识模型,且最后给出了基于极限学习机算法的定位运动离线辨识方法。通过将某大型飞机机身壁板柔性预定位工装作为试验平台进行验证,结果表明,获得的定位运动模型使直接装配定位精度达到±0.25 mm,满足某大型飞机机身壁板长桁的装配定位精度要求±0.50 mm。试验系统涉及的若干关键技术已应用于某大型飞机的壁板组件装配预定位柔性工装系统。     

结构驾驶员模型与McRuer模型的仿真研究

根据驾驶员完成俯仰跟踪的人－机半物理实时仿真实验，分别对结构驾驶员模型和ＭｃＲｕｅｒ模型者了参数辨识，用两种模型和某电传操纵飞机纵向短周期等效系统模型构成人－机数值仿真系统，将模型仿真结果和实时仿真结果进行比较和分析，以便了解两种模型的特性，为深入研究闭环飞行品质提供参考依据。     

基于序列工作点的航空发动机过渡态气路分析参数选择研究

为了降低由多工作点分析(MOPA)方法的平均效应所产生的气路分析(GPA)系统误差,提出了基于航空发动机过渡工作过程的序列工作点分析(SOPA)技术,并以此为基础提出了一种系统的气路分析参数选择方法。该方法利用连续小波变换对时间信号的增强解析能力,提取待求健康参数在备选测量传感器上的参数特征,实现了在传感器安装受限条件下必要测量参数的选择。通过对SOPA子系统矩阵进行奇异值分解(SVD),获得了在过渡工作过程中不同时间片段上的健康参数可辨识性。针对大涵道比双轴分排涡扇发动机的参数分析结果表明:通过对待求健康参数的敏感性输出信号进行小波分析所确定的最简传感器布局,具备对全部待求健康参数的可辨识性;而以时间片段矩阵的条件数作为判据评估SOPA子系统的参数辨识能力,能够有效地确定具有高可靠性的SOPA时间片段位置,保证了对发动机气路部件健康状态的估计精度。     

航空器通信寻址报告系统数据处理技术研究

在实时追踪航空器动态的各类信息中,航空器通信寻址报告系统(ACARS)数据的精度和更新速度虽然无法和空管雷达相比,但其作用距离远、信息内容丰富的优点对空中交通管理尤为重要。由于目前国内民航采用的ACARS数据处理系统全部是引进美国ARINC公司的产品,极大地的限制了ACARS数据的应用。本文将尝试研究ACARS数据处理技术,以期对开发中国自己的数据处理系统做出贡献。     

民用发动机状态混沌预测算法

首先应用Haar小波和DB16小波对航空发动机排气温度的原始数据序列进行去噪处理,并且证明了处理后的数据序列具有混沌特征.其次应用混沌理论建立发动机状态预测算法,实现对排气温度的预测.通过检验排气温度预测值是否超过所规定的红线,以及该曲线是否平稳,从而进行发动机的健康状态排查.作为验证实例,使用一组某机型发动机实际飞行...     

Weighted marginal fisher analysis with spatially smooth for aircraft recognition

Due to limitations to extract invariant features for recognition when the aircraft presents various poses and lacks enough samples for training, a novel algorithm called Weighted Marginal Fisher Analysis with Spatially Smooth （WMFA-SS） for extracting invariant features in aircraft rec- ognition is proposed. According to the Graph Embedding （GE） framework, Heat Kernel function is firstly introduced to characterize the interclass separability when choosing the weights of penalty graph. Furthermore, Laplacian penalty is applied to constraining the coefficients to be spatially smooth in this algorithm. Laplacian penalty is able to incorporate the prior information that neigh- boring pixels are correlated. Besides, using a Laplacian penalty can also avoid the singularity of Laplacian matrix of intrinsic graph. Once compact representations of the images are obtained, it can be considered as invariant features and then be performed in classification to recognize different patterns of aircraft. Real aircraft recognition experiments show the superiority of our proposed WMFA-SS in comparison to other GE algorithms and the current aircraft recognition algorithm; the accuracy rate of our proposed method is 90.00% for dataset BH-AIR1.0 and 99.25% for dataset BH-AIR2.0.     

Optimum Integration of Aircraft Navigation Systems

A current problem in aircraft navigation is determining how to effect alow cost navigation system consistent with required mission operationswhich will render a high degree of accuracy and reliability. One wayto achieve this is through optimum integration of equipment,subsystems, and computer mechanizations. Consistent with this approach,the overall objectives of this paper are to show the advantages of anoptimally integrated aircraft navigation system, and to illustrate howto effect a low cost navigation system with high accuracy performance.An integrated aircraft navigation system employing a Kalman optimumestimation filter is configured and analyzed in detail. The results ofthe analysis clearly indicate how to achieve high accuracy performanceusing low cost subsystems; namely, via optimum systems integration.