主被动多传感器多目标状态信息融合

研究了主被动多传感器多目标状态信息融合问题。针对被动式跟踪的特点,借助主动跟踪的距离通道值,提出类主动的被动式跟踪。在此基础上提出主被动串联状态信息融合和并联状态信息融合算法。仿真结果表明两种状态信息融合方法都可以大大提高跟踪精度,同时还可以提高系统的可靠性。     

基于神经网络的机动目标信息融合与并行自适应跟踪

基于“当前”统计模型和 BP神经网络 ,提出一种新的机动目标神经网络信息融合与并行自适应跟踪算法 ( NIFPAT)。该算法采用双滤波器并行结构 ,利用全状态反馈 ,通过 BP网络调整系统方差以适应目标的运动变化 ,具有对目标各种运动状态的良好自适应跟踪能力     

综合导航系统信息融合模拟仿真平台设计CSCD

为解决综导系统的信息融合问题,设计了一种新型的综导系统信息融合模拟仿真研究平台。平台包括综导信息融合模拟评估导调台、惯性类导航设备模拟器、非惯性类导航设备模拟器、综合导航模拟器、电子海图系统和数据分析服务器六大模块。导调台采取一种新的船舶运动参数发生器模拟生成多种海况条件下船舶运动参数;综合导航模拟器采取多种融合算法并行运行方式,以方便比较不同算法的性能;数据分析服务器采用高性能计算机模拟公共计算环境,可研究适应未来船舶软硬件计算资源的信息融合方法。系统测试表明,基于平台的导航信息融合流程和效果满足要求,所设计的平台系统功能正常,可直接用于舰船信息融合算法的设计、分析、测试评估等工程研发工作。     

Multi-sensor optimal data fusion for INS/GPS/SAR integrated navigation system

INS/GPS/SAR integrated navigation system represents the trend of next generation navigation systems with the high performance of independence, high precision and reliability. This paper presents a new multi-sensor data fusion methodology for INS/GPS/SAR integrated navigation systems. This methodology combines local decentralized fusion with global optimal fusion to enhance the accuracy and reliability of integrated navigation systems. A decentralized estimation fusion method is established for individual integrations of GPS and SAR into INS to obtain the local optimal state estimations in a parallel manner. A global optimal estimation fusion theory is studied to fuse the local optimal estimations for generating the global optimal state estimation of INS/GPS/SAR integrated navigation systems. The global data fusion features a method of variance upper finiteness and a method of variance upper bound to ensure that the global optimal state estimation can be achieved under a general condition. Experimental results demonstrate that INS/GPS/SAR integrated navigation systems achieved by using the proposed methodology have a better performance than INS/GPS integrated systems.     

The Omnibus model: a new model of data fusion?

Over the last two decades there have been several process models proposed (and used) for data and information fusion. A common theme of these models is the existence of multiple levels of processing within the data fusion process. In the 1980's three models were adopted: the intelligence cycle; the JDL model; and the Boyd control. The 1990's saw the introduction of the Dasarathy model and the Waterfall model. However, each of these models has particular advantages and disadvantages. A new model for data and information fusion is proposed. This is the Omnibus model, which draws together each of the previous models and their associated advantages whilst managing to overcome some of the disadvantages. Where possible, the terminology used within the Omnibus model is aimed at a general user of data fusion technology to allow use by a distributed audience     

基于数学模型的气动力数据融合研究

气动力误差来源复杂,气动力真值通常未知,气动力误差传递数学模型的建立非常困难,因此直接将信息融合技术应用于气动力数据融合目前还不可行。迄今为止,未见气动力数据融合的公开报道。本文首次提出了基于数学模型的气动力数据融合准则和方法。首先建立反映气动数据变化规律的数学模型,然后基于气动力数学模型,根据无论计算数据、风洞实验数据、还是飞行试验数据都必须满足气动力变化规律的原则,以气动力数据满足气动力变化规律的程度为依据,建立气动力数据融合准则和融合方法。算例表明,所建立的融合准则和方法是可行的。     

靶场测控通信系统可靠性分析

首先确定了测控通信系统可靠性分析框架，然后针对系统构型为串并联系统、试验信息为成败型数据／寿命型数据的情况，研究了可靠性信息融合的Bayes方法，最后讨论了系统可靠性的Bayes统计推断问题，包括点估计、置信估计和假设检验。     

低速风洞绳牵引并联支撑系统的模型气动试验研究(英文)

根据绳牵引并联机构的优点,建造了一种新型的用于低速风洞试验的飞机模型绳牵引并联支撑系统,研究该系统中模型的空气动力参数的测量与计算方法。文中对机构进行了静力学分析,建立了描述实验模型气动载荷的数学模型,提出了通过测量绳系拉力求得模型空气动力参数的解算方法;设计并构建了绳系拉力测量及数据采集系统;将该系统置于开口式回流低速风洞中进行了吹风试验,采集了模型在不同姿态和不同风速下的各牵引绳的拉力数据,并对数据进行了处理分析,通过解算得到了不同吹风条件下模型的气动载荷参数曲线。研究结果表明,绳牵引并联机构用于低速风洞试验的支撑系统是可行的。     

Mini-UAV altitude estimation using an inertially stabilized payload

A novel method is introduced for autonomous attitude estimation of a mini unmanned aerial vehicle (UAV) carrying an inertially stabilized payload. The method is based on utilizing the outputs of rate gyros normally used to inertially stabilize the payload, and other data that is normally available from conventional aircraft-mounted sensors. A decentralized estimation algorithm is developed, which uses the aircraft/payload mathematical models to bound the estimation errors. Exploiting modern multiprocessor computer technology, the new estimation algorithm comprises two parallel extended Kalman filters (EKFs) and a data fusion algorithm. Real-time experimental tests, incorporating a payload model with real rate gyros mounted on a three-axis flight table, have validated the feasibility of the concept. The theoretical and experimental investigation demonstrates that the estimation algorithm is capable of estimating the attitude angles with an estimation error not exceeding 1 deg, at output rates of 13 Hz, thus constituting a viable substitute for the conventional vertical gyroscope     

空管多雷达数据融合系统的设计与实现

现代空管多采用多雷达联网技术对飞机进行监视,以扩大监视空域的覆盖面积并提高对飞机的监视精度。本文设计了一套分布式空管多雷达数据融合系统的体系结构,对数据融合流程中的各关键步骤进行了详细介绍,并结合工程实际和具体的开发环境,对系统的工程实现进行了详细的阐述,其中包括系统的主要功能模块划分,以及各模块的内部流程结构和相互关系,最后给出了系统实现的结果和结论。     

Decision fusion rules in multi-hop wireless sensor networks

The decision fusion problem for a wireless sensor network (WSN) operating in a fading environment is considered. In particular, we develop channel-aware decision fusion rules for resource-constrained WSNs where binary decisions from local sensors may need to be relayed through multi-hop transmission in order to reach a fusion center. Each relay node employs a binary relay scheme whereby the relay output is inferred from the channel impaired observation received from its source node. This estimated binary decision is subsequently transmitted to the next node until it reaches the fusion center. Under a flat fading channel model, we derive the optimum fusion rules at the fusion center for two cases. In the first case, we assume that the fusion center has knowledge of the fading channel gains at all hops. In the second case, we assume a Rayleigh fading model, and derive fusion rules utilizing only the fading channel statistics. We show that likelihood ratio (LR) based optimum decision fusion statistics for both cases reduce to respective simple linear test statistics in the low channel signal-to-noise ratio (SNR) regime. These suboptimum detectors are easy to implement and require little a priori information. Performance evaluation, including a study of the robustness of the fusion statistics with respect to unknown system parameters, is conducted through simulations.     

基于Event的侧向碰撞风险改进模型

Event模型是一种基于长方体碰撞模板的碰撞风险评估模型,用该模型评估所得的碰撞风险概率过于保守。将碰撞模板改为圆柱体,建立了基于碰撞圆柱体的Event模型。在相同条件下运用Event模型和改进的Event模型对平行航路的侧向碰撞风险进行了评估计算,后者的碰撞风险是原Event模型碰撞风险的86.9％,仿真结果表明改进模型碰撞风险评估更精确,能够为中国平行航路的开辟进行安全评估计算提供理论依据。     

多传感器融合目标跟踪

分析了基于成象和雷达两种传感器对目标状态的测量模型及其融合模型。针对两种传感器之间测量信息的不同步问题,给出了一种基于最小二乘法的不同步信息之间的时间配准和融合方法,并设计了跟踪滤波器。     

基于灰色理论的一种数据融合方法

在灰色关联分析的基础上,对斜关联度进行了修正,引出了点、斜修正关联度分析的概念.通过对影响目标属性识别的各种因素进行分析,结合战术思想利用灰色点、斜修正关联度分析及多目标优化方法建立了数据融合模型,提出了一种基于灰色理论的多传感器数据融合方法.计算多传感器测量数据的灰色关联矩阵,进行灰色优势分析,然后进行数据融合.此方法考虑了各传感器测量数据的精确度,而且删除掉了测量比较差或测量不到的数据.仿真结果表明,应用该方法可进一步提高多传感器的测量精度和可靠性,适用于多传感器的数据融合.     

Processing requirements for multisensor low-cost brilliantmunitions

A generic mission for an autonomous brilliant munition is presented and used to identify the functions that embedded signal processors must perform. Based on these functions and other operational factors such as weather, countermeasures, larger search areas, reduced false alarm rates, and increased munition maneuverability, the processing loads in bits/second messages/second, operations/second, and instructions/second are derived. An evaluation of general implementation issues such as the requirements for data fusion, distributed and parallel processing architectures, reliable software, and low-cost hardware is presented     

机场近距平行跑道一起一降模式下的容量计算

通过对传统跑道容量模型的修正并结合相应的飞行程序，得出了近距平行跑道在一起一降运行模式下起飞和到达容量独立计算的数学模型，并且用中国上海浦东机场运行数据进行了验证，表明容量模型具有较好的实用性，从而在实际应用中方便了近距平行跑道容量的计算，同睁也提高了近距平行跑道的利用率。     

Decentralized CFAR signal detection

The authors develop the theory of CA-CFAR (cell-averaging constant false-alarm rate) detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA-CFAR detector to the data fusion center. The overall decision is obtained at the data fusion center based on some k out of n fusion rule. For a Swerling target model I embedded in white Gaussian noise of unknown level, the authors obtain the optimum threshold multipliers of the individual detectors. At the data fusion center, they derive an expression for the overall probability of detection while the overall probability of false alarm is maintained at the desired value for the given fusion rules. An example is presented showing numerical results     

航空发动机整机振动故障模糊信息熵诊断方法

为了获得航空发动机整机振动故障征兆与故障原因之间难以确定的复杂隶属关系,提高故障诊断的准确率,基于模糊数学理论和信息熵理论,提出了基于模糊信息熵的发动机整机振动故障融合诊断方法。通过建立模糊信息熵融合诊断的数学模型,使定量分析与专家经验和定性分析相结合,并应用于某型航空发动机整机振动故障诊断。实际应用表明,基于模糊信息熵融合的诊断模型较单一的模糊诊断模型准确率明显提高,验证了该方法用于航空发动机整机振动故障诊断是有效的。     

Asynchronous distributed detection

Binary parallel distributed-detection architectures employ a bank of local detectors to observe a common volume of surveillance, and form binary local decisions about the existence or nonexistence of a target in that volume. The local decisions are transmitted to a central detector, the data fusion center (DEC), which integrates them to a global target or no target decision. Most studies of distributed-detection systems assume that the local detectors are synchronized. In practice local decisions are made asynchronously and the DFC has to update its global decision continually. In this study the number of local decisions observed by the central detector within any observation period is Poisson distributed. An optimal fusion rule is developed and the sufficient statistic is shown to be a weighted sum of the local decisions collected by the DFC within the observation interval. The weights are functions of the individual local detector performance probabilities (i.e., probabilities of false alarm and detection). In this respect the decision rule is similar to the one developed by Chair and Varshney for the synchronized system. Unlike the Chair-Varshney rule, however, the DFC's decision threshold in the asynchronous system is time varying. Exact expressions and asymptotic approximations are developed for the detection performance with the optimal rule. These expressions allow performance prediction and assessment of tradeoffs in realistic decision fusion architectures which operate over modern communication networks     

A novel combination belief rule base model for mechanical equipment fault diagnosis

Due to the excellent performance in complex systems modeling under small samples and uncertainty, Belief Rule Base(BRB) expert system has been widely applied in fault diagnosis. However, the fault diagnosis process for complex mechanical equipment normally needs multiple attributes, which can lead to the rule number explosion problem in BRB, and limit the efficiency and accuracy. To solve this problem, a novel Combination Belief Rule Base(C-BRB) model based on Directed Acyclic Graph(DAG) structu...