仅有角度观测信息情况下目标机动自适应跟踪算法研究

针对光电目标跟踪问题中纯角度跟踪的特点,提出了纯角度目标的机动自适应跟踪算法.该算法采用机动目标“当前”统计模型描述目标的运动特性,根据强跟踪滤波器的思想通过实时检测滤波器的残差信息确定目标的机动变化情况,进而调整“当前”统计模型中表征目标机动特性的参数(机动频率和随机加速度分布的极值),使得运动模型更加符合目标的机动...     

一种水下被动目标运动分析与仿真

文章讨论了一种水下运动的目标和观测平台在不同平面的三维情形下,被动目标运动分析与仿真问题,文中从声呐测量的方位角、爷角和频率的数据入手,采用伪线性估计、最大似然估计两种不同的方法,对水下被动TMA问题进行了研究,并分析了两种不同方法的状态参数估计的误差特性。仿真结果表明：由于增加了观测的信息量,上述方法均能得到较好的参数估计结果,且伪线性估计方差向CRLB界接近。上述方法可推广到目标和观测平台处于同一平面的二维情形。     

光电探测中空间目标和恒星目标运动特性分析

地基大视场天文光电观测系统获取的序列图像中,空间目标和数目众多的恒星目标成像特性相似,两者的区别主要是其运动特性差异.以圆轨道空间目标为例,并忽略地球自转的影响,分析了低轨道空间目标在不同仰角处的运动特性;在建立地平坐标系和时角坐标系这两种天球坐标系的基础上,分析了恒星目标·在不同方位不同仰角处的运动特性;比较了空间目标和恒星目标在视场中运动特性的差异,结果表明恒星目标的运动体现为慢速运动,而空间目标表现为明显的运动特性,且在短时间内可以认为是匀速直线运动.     

声纳目标运动分析的实现方法

目标运动分析是声纳的一个重要问题。为了能够有效地估计目标运动状态,选择适当测度集合是第一步。开发出有力的估计算法是解决目标运动分析的另一个关键步骤。本文简练地回顾了声纳目标运动分析的实现方法。在本文中,目标运动分析被松散地分成两大类。一类是经典的目标跟踪方法的一部分,它把数据融合作为整个估计过程的不可分割的一部分;另一类在估计目标状态时并没有利用数据融合。     

多亮点各向异性目标回声特征的神经网络提取

如何从回声信号中抽取特征,是目标识别或无损检测的前提。实际物体,从其反射声学特性考察,大多呈多亮点结构并表现出各向异性,其回声特性主要随目标姿态角或声渡入射角的变化而呈混沌态变化。常规方法提取的回声特征,往往随入射角的变化出现严重的类内弥散与类间交迭,从而导致决策界面过分复杂、甚至完全失效。因此,在目标姿态以未知方式自然变化的情况下,如何提取适当的回波特征一直是国内外学者广泛关注的瓶颈问题。本文提供一种趋向不变识别的回声特征的神经网络自动提取方法和新的可分性测度Js与Jr。研究表明,它能直接利用回波信号自动生成具有最大可分性的紧致特征集。给出了典型的实验结果,并着重剖析了神经网络特征提取的信息蕴含及其物理意义。实验结果与理论分析的结论相一致。可望在自动目标识别、水下机器人视觉、图像处理、机械故障诊断、数据压缩、以及生物医学工程等领域获得应用。     

一种基于小波变换的目标融合跟踪方法

本文在分析小波变换的基础上，将小波分析应用到目标图像的融合跟踪技术上，利用小波的多尺度和多分辨特性，不仅能够获得不同分辨力下的图像序列，进行目标图像融合；还能有效地从信号中提取突变信号。对函数或信号进行多尺度的细化分析。图像边缘用小波变换进行处理和提取并对图像形心进行计算。能够得到较好的轮廓提取效果和形心定位精度，进而说明了小波变换可能成为目标跟踪中较好的数学方法。     

目标地震动信号的特征提取及识别研究

地面目标的地震动信号是目标识别的关键,本文研究了机动目标地震动特性,总结出目标的地震动信号的特征提取规律,并将神经网络方法用于目标的地震动信号的分类识别中。给出了识别结构及改进的BP算法,并将改进的BP算法用于实际目标的地震动信号的分类识别,得到令人满意的结果。     

复杂场景条件下的运动目标检测算法

针对复杂场景条件下运动目标检测方法存在的局限性,提出了一种基于运动检测和静止图像分割相融合的算法。采用相邻帧差法结合建立的假设检验模型进行自适应的运动目标检测;为消除孔径效应和噪声的影响,根据运动目标检测的结果,在当前帧利用区域增长法融合运动分割的结果。试验结果表明,算法能从复杂场景的图像序列中有效地检测和提取出运动目标,并有很强的鲁棒性。     

方位时延的TMA算法及其可观测性研究

设计了一种新型的水下被动目标运动分析系统,以测得的方位序列和到达不同传感器的时间延迟为基础,建立了状态方程和观测方程。利用非线性系统的可观测性定理分析了该系统,得出了基于方位时延量测水下被动TMA系统的可观测性结论并分析了可观测度。运用扩展卡尔曼滤波算法,对被动定位性能进行了分析。通过计算机仿真实验,验证了得到的可观测性结论。仿真结果同时也表明,该水下被动TMA系统具有收敛速度快、精度高、稳定性好等优点,有着良好的应用前景。     

杂波环境下机动目标跟踪算法研究

杂波环境下的机动目标跟踪问题具有非线性、非高斯、不完全观测的特点，其难点在于观测值与目标的对应关系及每一时刻的运动模式均呈现高度不确定性。文中将多模型理论和辅助粒子滤波算法相结合，提出了一种新的杂波环境下机动目标跟踪算法——多模型辅助粒子滤波算法（MMAPF）。仿真结果表明，该算法与传统的交互多模型——扩展卡尔曼滤波算法、辅助粒子滤波算法相比，在相同的情况下，具有更高的滤波精度和较好总体性能。     

Three dimensional passive underwater target motion analysis using correlated data fusion

In this paper a new method of passive underwater TMA (target motion analysis) using data fusion is presented. The findings of this research are based on an understanding that there is a powerful sonar system that consists of many types of sonar but with one own-ship, and that different target parameter measurements can be obtained simultaneously. For the analysis 3 data measurements, passive bearing, elevation and multipath time-delay, are used, which are divided into two groups: a group with estimates of two preliminary target parameter obtained by dealing with each group measurement independently, and a group where correlated estimates are sent to a fusion center where the correlation between two data groups are considered so that the passive underwater TMA is realized. Simulation results show that curves of parameter estimation errors obtained by using the data fusion have fast convergence and the estimation accuracy is noticeably improved. The TMA algorithm presented is verified and is of practical significance because it is easy to be realized in one ship.     

Simulation study of passive target motion analysis

In this paper, the problem of underwater passive target motion analysis (TMA) in three dimensions is discussed using the measurements of passive bearings and elevation and frequency on the condition that acoustic source and observer are in different horizontal planes. Simulation results with both of the PLE (pseudo-linear estimation) and MLE (Maximum likelihood estimation) show that the TMA method is effective in oceanic environment. Its error covariance curves tend to its Cramer-Rao lower bounds.     

Model selection and optimal sampling in high-throughput experimentation

The practical difficulties encountered in analyzing the kinetics of new reactions are considered from the viewpoint of the capabilities of state-of-the-art high-throughput systems. There are three problems. The first problem is that of model selection, i.e., choosing the correct reaction rate law. The second problem is how to obtain good estimates of the reaction parameters using only a small number of samples once a kinetic model is selected. The third problem is how to perform both functions using just one small set of measurements. To solve the first problem, we present an optimal sampling protocol to choose the correct kinetic model for a given reaction, based on T-optimal design. This protocol is then tested for the case of second-order and pseudo-first-order reactions using both experiments and computer simulations. To solve the second problem, we derive the information function for second-order reactions and use this function to find the optimal sampling points for estimating the kinetic constants. The third problem is further complicated by the fact that the optimal measurement times for determining the correct kinetic model differ from those needed to obtain good estimates of the kinetic constants. To solve this problem, we propose a Pareto optimal approach that can be tuned to give the set of best possible solutions for the two criteria. One important advantage of this approach is that it enables the integration of a priori knowledge into the workflow.     

Detecting drug targets with minimum side effects in metabolic networks

High-throughput techniques produce massive data on a genome-wide scale which facilitate pharmaceutical research. Drug target discovery is a crucial step in the drug discovery process and also plays a vital role in therapeutics. In this study, the problem of detecting drug targets was addressed, which finds a set of enzymes whose inhibition stops the production of a given set of target compounds and meanwhile minimally eliminates non-target compounds in the context of metabolic networks. The model aims to make the side effects of drugs as small as possible and thus has practical significance of potential pharmaceutical applications. Specifically, by exploiting special features of metabolic systems, a novel approach was proposed to exactly formulate this drug target detection problem as an integer linear programming model, which ensures that optimal solutions can be found efficiently without any heuristic manipulations. To verify the effectiveness of our approach, computational experiments on both Escherichia coli and Homo sapiens metabolic pathways were conducted. The results show that our approach can identify the optimal drug targets in an exact and efficient manner. In particular, it can be applied to large-scale networks including the whole metabolic networks from most organisms.     

Using neural networks for 3D measurement in stereo vision inspection systems

This paper presents a stereo vision inspection process which derives precise 3D measurements. Two artificial neural networks are used to facilitate the whole measurement process. At first, a simple camera calibration process is developed to derive the focal lengths and the relative information. A Hopfield neural network is used to solve the stereo matching problem, which has been constructed as an energy function. By means of a recursive process, the disparities of extracted feature points are obtained. In addition, a backpropagation neural network-based measurement error correction model for 3D measurement is proposed. It reduces the errors of 3D measurement associated with a part's orientation, position, magnitude and distance between the object and cameras. Four procedural processes are designed to implement this model. Our laboratory experiments demonstrate that the proposed measurement process has a satisfactory measurement result.     

Optimal confidence intervals on the variation for operators and gauge in manufacturing process with repeated measurements

It is necessary to measure the attributes of the parts in any manufacturing process. It is also important to monitor measurement system in the manufacturing process because repeated measurements of the attributes include variability as well as target value. This paper considers variabilities due to repeated measurements, operators, and gauge in a measurement system. The measurement system is statistically modeled as a two-factor mixed model with one covariate and interaction. That is, this model employs J operators randomly chosen to conduct measurements on I randomly selected parts from a manufacturing process. In this experiment each operator measures each part K times. This paper aims to provide engineering practitioners with statistically optimal confidence intervals on the variation due to operators and gauge resulted from a measurement system statistically modeled. The optimal confidence intervals are based on a moderate large sample method (MLS) and a generalized p-value method (GEN). The confidence intervals proposed can be useful tools to determine whether a manufacturing process is adequate for monitoring a measurement system.     

A Linear Time-Invariant Filtering Approach for Ultrasonic Transducer Normalization

The increased use of automatic defect detection and characterization systems of the self-learning type has created a demand for means capable of normalizing signals from ultrasonic transducers. Measurements obtained using different measurement setups should be normalized with reference to a standard transducer. It is usually an unfeasible task to optimize characterization procedures for all combinations of measurement parameters that are usually available in a modern complex measurement system. For instance, a change of transducer or only a change in cable length may result in substantial differences in measured data. We propose a linear filtering approach for normalizing ultrasonic pulse-echo measurements as a preprocessing step before presenting the data to a characterization system. The approach requires two data sets: one for the reference transducer and one for the transducer to normalize. We formulate the normalization problem as a general linear approximation problem and derive an optimal linear transformation for an ideal situation with known transducer and noise characteristics. Due to the properties of the optimal linear transformation, a close approximation of this transformation can be implemented using a linear time-invariant filter. We verify by simulations that the filter approximation is valid, and we also examine some properties concerning the accuracy of the estimates obtained using the filter approximation. The filter is obtained using the output error method, one of the standard system identification methods. The proposed method is tested on real ultrasonic data obtained from carbon-fiber—reinforced epoxy composites. The results of experiments with real data, illustrating one of the possible applications, are used to point out some practical considerations that have to be taken into account when implementing the proposed method.     

基于改进有序聚类法的立式加工中心进给系统温测点优化

为解决立式加工中心热误差补偿关键技术中温测点难选取的问题，提出了一种基于改进有序聚类法的机床进给系统温测点优化方法。首先，结合试验数据计算反映温测点温度变量与热误差相关性的互信息值，初步筛选机床各部件的温测点，消除测点间的耦合性；然后，根据筛选出的温测点，通过建立类直径矩阵和计算各类的最小误差函数，获得温度变量分类；最后，基于多元线性回归建立包含多个不同温测点的热误差模型，并对模型进行统计学综合分析，确定了最佳聚类数和最佳温测点。结果表明：在不同加工条件下采用改进有序聚类法建立的热误差模型的均方根误差和平均残差分别降至1.05 μm和1 μm以下，相较于采用传统有序聚类法和灰色关联度模糊聚类法建立的热误差模型，它具有更高的热误差预测精度和更好的鲁棒性。所提方法在中小型加工中心进给系统的温测点研究中具有广阔的应用前景。