Computer Simulation of Synthetic Aperture Radar Images of Three-Dimensional Objects

A digital simulation of coherent synthetic aperture radar (SAR) images of three-dimensional objects is described. The simulation is intended to produce representative SAR images that would be suitable for image analysis and pattern recognition studies. The procedure involves a modeling of the object using a combination of three-dimensional quadratic shapes yielding a smooth surface representation. The radar images of these models are then computed using physical optics scattering theory. Finite resolution both in range and cross-range direction is incorporated via a theoretical analysis which results in a simple Fourier transform representation of an equivalent "offset" window filter. Examples of the computer simulation for both infinite resolution and blurred or finite resolution are given for a KC-135 aircraft model.     

基于边缘区域增长法的飞机图像目标分割研究

对边缘区域增长算法进行了理论分析,介绍了用边缘区域增长法对飞机图像进行目标分割的主要步骤,并将该算法对飞机图像进行目标分割的效果与Otsu算法进行了对比。结果表明,该算法所分割的图像目标边缘和实际目标边缘一致性较好,边界精确、清晰。     

基于小波域贝叶斯估计模糊萎缩的SAR图像降斑算法

提出了基于小波域高斯混合模型贝叶斯估计模糊萎缩的SAR图像降斑算法.该算法分析了SAR图像在平稳小波变换(SWT)域中的统计模型,并用高斯混合模型对其进行描述,推导出基于贝叶斯估计的信号最小均方误差(MMSE)的模糊萎缩因子.籍此再根据小波域相邻尺度间小波系数的相关性,采用分区域模糊萎缩思想,很好地得到无斑点真实信号小波系数的估计值.仿真结果表明该算法在大大抑制斑点噪声的同时,有效地保持了边缘,其性能优于改进Lee滤波、小波软阈值和SWT萎缩降斑算法.     

Image texture synthesis-by-analysis using moving-average models

A synthesis-by-analysis model for texture replication or simulation is presented. This model can closely replicate a given textured image or produce another image that although distinct from the original, has the same general visual characteristics and the same first and second-order gray-level statistics as the original image. The texture synthesis algorithm, proposed contains three distinct components: a moving-average (MA) filter, a filter excitation function, and a gray-level histogram. The analysis portion of the texture synthesis algorithm derives the three from a given image. The synthesis portion convolves the MA filter kernel with the excitation function, adds noise, and modifies the histogram of the result. The advantages of this texture model over others include conceptually and computationally simple and robust parameter estimation, inherent stability, parsimony in the number of parameters, and synthesis through convolution. The authors describe a procedure for deriving the correct MA kernel using a signal enhancement algorithm, demonstrate the effectiveness of the model by using it to mimic several diverse textured images, discuss its applicability to the problem of infrared background simulation, and include detailed algorithms for the implementation of the model     

Surface movement radar data processing methods for airportsurveillance

An airport surveillance function operating on surface movement radar (SMR) images is proposed and evaluated. The main contributions presented are the statistical error models of the target centroid and attributes extracted from radar images, developed and applied to the design of its main data processing blocks. Besides a multihypothesis image-to-tracks assignment method, a tracking filter using the extracted orientation and a classification scheme based on target attributes is detailed. The error models confidence and processing methods performance are demonstrated through simulation in representative scenarios     

Mixture Reduction Algorithms for Point and Extended Object Tracking in Clutter

The Bayesian solution to the problem of tracking a target with measurement association uncertainty gives rise to mixture distributions, which are composed of an ever increasing number of components. To produce a practical tracking filter, the growth of components must be controlled by approximating the mixture distribution. Two mixture reduction schemes (a joining algorithm and a clustering algorithm) have been derived for this purpose. If significant well spaced mixture components are present, these techniques can provide a useful improvement over the probabilistic data association filter (PDAF) approach, which reduces the mixture to a single Gaussian component at each time step. For the standard problem of tracking a point target in uniform random clutter, a Monte Carlo simulation study has been employed to identify the region of the problem parameter space where significant performance improvement is obtained over the PDAF. In the second part of this paper, the formal Bayesian filter is derived for an extended target consisting of an array of measurement sources with association uncertainty. A practical multiple hypothesis filter is implemented using mixture reduction and simulation results are presented.     

信赖域滤子算法在航空发动机在线优化中的应用

主要研究了航空发动机在线优化问题.以非线性发动机部件级模型为优化对象,将信赖域滤子算法应用于航空发动机在线优化,相比基本信赖域算法,该算法由于采用非单调的滤子算法和松弛重置,兼顾了算法在目标函数值下降与可行性保持两方面的品质,通过松弛重置避免子问题的不可行性,滤子算法则保证了算法收敛到全局最小解.最后,基于信赖域滤子算法,以涡扇发动机最小油耗寻优控制为仿真算例,验证了该算法的优越性.      

一种滤波SST方法在翼型深失速模拟中的应用

 为了提高原始剪切应力输运(SST)湍流模型对于分离流动的求解精度,将大涡模拟(LES)中的滤波因子和SST方程相结合构造出一种滤波SST方法,利用湍流尺度对流场求解区域进行划分,近壁面附近的稳态流动由湍流模型控制,远壁面采用LES方法进行模拟。与传统混合RANS/LES方法相比,该方法的特点是:滤波因子的选取不再依赖于网格尺度,可以有效地降低网格诱导分离现象发生的概率。采用该方法对NACA0021翼型深失速特性进行了仿真研究,对比了非定常雷诺平均Navier-Stokes(URANS)方法和SST-DES方法,从仿真结果可以看出滤波SST方法有效地激活了分离区域的脉动,充分展现了分离的三维特性;同时算例求解结果证明该方法的精度高于URANS方法,与试验结果吻合较好,显示其具有一定的工程应用价值。     

Multiple-Model Probability Hypothesis Density Filter for Tracking Maneuvering Targets

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. The problem becomes even more complicated when the number of targets varies, in which case the dimensionality of the state space itself becomes a discrete random variable. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment (the PHD) of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems with a varying number of targets. The integral of PHD in any region of the state space gives the expected number of targets in that region. With maneuvering targets, detecting and tracking the changes in the target motion model also become important. The target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-dependent estimates in a manner similar to the one used in the interacting multiple model (IMM) estimator. This paper propose a multiple-model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using sequential Monte Carlo (SMC) methods. The resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over single-model PHD filters.     

Synergism of binocular and motion stereo for passive ranging

Range measurements to objects in the world relative to mobile platforms such as ground or air vehicles are critical for visually aided navigation and obstacle detection/avoidance. An approach is presented that consists of a synergistic combination of two types of passive ranging method: binocular stereo and motion stereo. We show a new way to model the errors in binocular and motion stereo in conjunction with an inertial navigation system (INS) and derive the appropriate Kalman filter to refine the estimates from these two stereo ranging techniques. We present results using laboratory images that show that refined estimates can be optimally combined to give range values which are more accurate than any one of the individual estimates from binocular and motion stereo. By incorporating a blending filter, the approach has the potential of providing accurate, dense range measurements for all the pixels in the field of view (FOV)     

Multiple model nonlinear filtering for low signal ground target applications

The design and implementation of a multiple model nonlinear filter (MMNLF) for ground target tracking using ground moving target indicator (GMTI) radar measurements is described. Like the well-known interacting multiple model Kalman filter (IMMKF), the MMNLF is based on the theory of hybrid stochastic systems. However, since it models the probability distribution for the target in a region, rather than just the distribution's first and second moments, a nonlinear filter is able to capture more fine-grained detail of the target motion and requires fewer models than typical IMMKF implementations. This is illustrated here with a two-model MMNLF in which one motion model incorporates terrain constraints while the second is a nearly constant velocity (CV) model. Another feature of the MMNLF is that it enables incorporation of prethresholded measurements. To implement the filter, the target state conditional probability density is discretized on a set of moving grids and recursively updated with sensor measurements via Bayes' formula. The conditional density is time updated between sensor measurements using alternating direction implicit (ADI) finite difference methods, generalized for this hybrid application. In simulation testing against low signal-to-interference-plus-noise ratio (SINR) targets, the MMNLF is able to maintain track in situations where single model filters based on either of the component models or filters that use thresholded data fail. Potential applications of this work include detection and tracking of foliage-obscured moving targets.     

Recursive 3-D motion estimation from a monocular image sequence

Consideration is given to the design and application of a recursive algorithm to a sequence of images of a moving object to estimate both its structure and kinematics. The object is assumed to be rigid, and its motion is assumed to be smooth in the sense that it can be modeled by retaining an arbitrary number of terms in the appropriate Taylor series expansions. Translational motion involves a standard rectilinear model, while rotational motion is described with quaternions. Neglected terms of the Taylor series are modeled as process noise. A state-space model is constructed, incorporating both kinematic and structural states, and recursive techniques are used to estimate the state vector as a function of time. A set of object match points is assumed to be available. The problem is formulated as a parameter estimation and tracking problem which can use an arbitrarily large number of images in a sequence. The recursive estimation is done using an iterated extended Kalman filter (IEKF), initialized with the output of a batch algorithm run on the first few frames. Approximate Cramer-Rao lower bounds on the error covariance of the batch estimate are used as the initial state estimate error covariance of the IEKF. The performance of the recursive estimator is illustrated using both real and synthetic image sequences     

A Modified Karhunen-Loeve Model of Gravity Disturbance Vector

A modified anisotropic Karhunen-Loeve (KL) model for the gravity disturbing potential is proposed. This model, unlike the previous KL model of Bose, does not vanish on the boundaries of a local region. Laplace's equation for the gravity disturbing potential and flatearth Vening-Meinesz relationships of the gravity disturbance vectorcomponents (?,?,?g) when the model is isotropic, are exploited to determine the unknown parameters of the model. The resulting model, in addition to having random boundary values, has three more parameters than those found in Bose's KL model. This should provide extra flexibility in fitting the model to the noisy data available in a local finite region.     

基于分形理论的红外图像港岸背景抑制方法

为了实现港岸背景的抑制,提出了一种基于分形理论和小波变换的背景抑制方法。首先,在高斯去噪的基础上,使用Otsu方法对图像进行粗分割;然后,对粗分割得到的背景图像进行分形降维处理,选定序列图像的比较区域;最后,利用小波变换的奇异值检测特性,在比较区域寻找序列图像比较的基准点,从而确定序列图像的移动范围,进而实现序列图像背景抑制。通过对实际港岸背景红外图像的实验,验证了所提方法的可行性和有效性。     

AFIT neural network research

A brief summary of research done at the Air Force Institute of Technology (AFIT) in the area of neural networks is provided. It has been shown that backpropagation, used for feedforward artificial neural networks, is just a degenerate version of an extended Kalman filter, and that networks can do about as well as the optimum statistical classification technique. A method of finding the importance of features for use by a neural network classifier has been determined. Techniques for using neural networks for image segmentation have been developed. In optical pattern recognition, techniques that allow the processing of real FLIR (forward-looking infrared) images with existing binary spatial light modulators have been devised. An optical direction of arrival detector applicable to laser illumination direction determination has been designed and tested; the design is similar to a fly's eye. Coated mirrors for the optical confocal Fabry-Perot interferometer have been designed, specified, fabricated, and installed. Significant progress has been made in the use of neural networks for processing multiple-feature sets for speech recognition     

基于塔形形态算法的SAR图像相干斑抑制

提出将塔形形态算法用于抑制SAR图像中相干斑处理，为减少塔形处理中由于下采样造成的信息损失，从并行处理的角度出发，对塔形形态滤波器提出了改进，将下采样中的各子图同时进行形态滤波处理，在上采样中将各子图合并。经分析和处理表明这两种算法用于SAR图像滤波都能有效地去除相干斑噪声。与塔形形态滤波、Lee滤波器算法比较，采用并行算法处理后图像的各项指标有明显的改善。     

基于对偶数的航天器多特征融合相对导航算法

针对基于视觉的航天器相对导航问题,利用对偶数推导并给出了航天器相对耦合动力学方程,该方程一体化描述了追踪航天器相对于目标航天器的姿态运动和轨道运动,且考虑了由非质心点引起的相对姿态与相对轨道之间的耦合影响。在对偶代数的框架内,统一描述了目标航天器上的特征点和特征线,并基于特征点、线在像平面的投影建立了多特征融合的单目视觉测量模型。最后通过对系统状态方程以及测量方程的线性化,应用迭代扩展卡尔曼滤波(IEKF)算法对非质心点的相对运动状态进行了估计。仿真结果表明,本文的算法能够对航天器非质心点的相对运动状态进行较高精度的估计。     

New approach for suppression of FM jamming in GPS receivers

A new approach is proposed for the suppression of FM jammers in C/A code GPS receivers. This approach is based on the cascading of the augmented-state approximate conditional mean (ASACM) filter and the discrete wavelet transform (DWT) filter. An ASACM filter for single interference suppression in spread-spectrum systems has been reported in the recent literature. However, the formulation of the ASACM filter and its performance analysis is lacking in the case of suppression of multiple FM interferences. The ASACM filter is formulated here for the suppression of multiple FM jammers. Further, a DWT filter using biorthogonal wavelet is suggested for the suppression of FM jammers. Finally, these two filters are cascaded to get an optimum performance for higher jammer to signal ratio, which is of importance in the GPS, for the case of suppression of multiple FM jammers. The performance of the proposed filters is analyzed through simulation examples for the suppression of single and multiple FM jammers in GPS receivers     

A track filter for reentry objects with uncertain drag

A filter was developed for maintaining track on ballistic missiles whose drag profiles are unknown or deviate significantly from prior predictions. The filter employs an innovative form of a seven-state Kalman filter in which object drag is included as a state to be estimated. Using measurements of range, azimuth, and elevation, the filter can track endo- and exo-atmospheric targets on a wide variety of trajectories without requiring a priori tuning to account for variations in reentry angle, drag history, measurement signal-to-noise ratio, etc. The filter was designed to be implemented at the millimeter wave (MMW) radar (a high-range-resolution, narrow beamwidth, Ka-band radar) located at Kwajalein Missile Range (KMR) in the Marshall Islands. Extensive testing and comparisons using a high fidelity simulation showed the new filter to be robust to a wide variety of trajectories and substantially better than track filters presently used at KMR. The filter was coded to run efficiently in real time, installed at the MMW radar, and successfully used to track an intercontinental ballistic missile (ICBM) with varying drag characteristics through exo-atmospheric and reentry phases. The filter yielded a more accurate and responsive track than possible with the previously used filter on a similar trajectory     

基于图像的发动机滑油滤磨屑定量分析技术

定量分析滑油滤磨屑是提高航空发动机内部磨损故障诊断成功率的有效技术途径.为满足某型航空发动机使用维护中滑油滤磨屑快速定量分析和磨损状态诊断的实际需求,设计制造了油滤磨屑自动清洗收集装置,采用电荷耦合器件(CCD)获取油滤磨屑数字图像,运用二维最大熵遗传算法对磨屑图像进行阈值分割后提取磨屑目标的特征量,将磨屑特征量与反映...