Comparison of two measurement fusion methods forKalman-filter-based multisensor data fusion

Currently there exist two commonly used measurement fusion methods for Kalman-filter-based multisensor data fusion. The first (Method I) simply merges the multisensor data through the observation vector of the Kalman filter, whereas the second (Method II) combines the multisensor data based on a minimum-mean-square-error criterion. This paper, based on an analysis of the fused state estimate covariances of the two measurement fusion methods, shows that the two measurement fusion methods are functionally equivalent if the sensors used for data fusion, with different and independent noise characteristics, have identical measurement matrices. Also presented are simulation results on state estimation using the two measurement fusion methods, followed by the analysis of the computational advantages of each method     

New track correlation algorithms in a multisensor data fusion system

In order to resolve the problem of track-to-track association in a distributed multisensor situation, this paper presents independent and dependent sequential track correlation algorithms based on Singer's and Bar-Shalom's algorithms. Based on sequential track correlation algorithm, the restricted and attenuation memory track correlation algorithms and sequential classic assignment rules are proposed. In this paper, these algorithms are described in detail. Then, the track correlation mass and multivalency processing methods are discussed as well. Finally, simulations are designed to compare the correlation performance of these algorithms with that of Singer's and Bar-Shalom's algorithms. The simulation results show that the performance of these algorithms proposed here is much better than that of the classical methods under the environments of dense targets, interfering, noise, track cross, and so on. Under the above situations, their correct correlation ratio is improved about 69 percent over the classical methods     

An efficient algorithm for multisensor track fusion

An efficient algorithm for track-to-track fusion by incorporating cross-covariance between tracks created by dissimilar sensors is described. An analytical solution of this problem is complicated if cross-correlation between sensors tracking the same target is taken into account. An explicit solution of the cross-covariance matrix at steady state is derived in terms of an integral. It is shown that solution of this integral involves inversion of a matrix whose elements are functions of parameters of individual trackers. Structure of this matrix is analyzed. An efficient analytical solution for inversion of this matrix is obtained. For fusion of similar sensors, it is shown that this matrix is reduced to the Routh-Hurwitz matrix which arises in the study of steady state stability of linear systems. Numerical results showing the amount of reduction of fused track covariance by taking into account the effects of cross-correlation between candidate tracks for fusion is also presented     

应用神经网络信息融合诊断航空发动机故障

研究了基于神经网络信息融合技术,同时结合模糊集合论对发动机气路部件进行故障诊断的方法,并以某型涡轴发动机为对象进行了仿真分析.研究结果表明该方法的故障诊断过程相对简单,对模型的精度要求不高,能够降低虚警、误报、漏报等情况的发生.      

Efficient multisensor fusion using multidimensional dataassociation

We present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (electronically scanned array (ESA) radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done so as to maximize the “time-depth” in addition to “sensor-width” for the number S of lists handled by the assignment algorithm. The standard procedure, which associates measurements from the most recently arrived S-1 frames to established tracks, can have, in the case of S sensors, a time-depth of zero. A new technique, which guarantees maximum effectiveness for an S-dimensional data association (S⩾3), i.e., maximum time-depth (S-1) for each sensor without sacrificing the fusion across sensors, is presented. Using a sliding window technique (of length S), the estimates are updated after each frame of measurements. The algorithm provides a systematic approach to automatic track formation, maintenance, and termination for multitarget tracking using multisensor fusion with multidimensional assignment for data association. Estimation results are presented for simulated data for a large scale air-to-ground target tracking problem     

Optimum multisensor fusion of correlated local decisions

A distributed detection system consisting of a number of local detectors and a fusion center is considered. Each detector makes a decision for the underlying binary hypothesis testing problem based on its own observation and transmits its decision to the fusion center where the global decision is derived. The local decision rules are assumed to be given, but the local decisions are correlated. The correlation is generally characterized by a finite number of conditional probabilities. The optimum decision fusion rule in the Neyman-Pearson sense is derived and analyzed. The performance of the distributed detection system versus the degree of correlation between the local decisions is analyzed for a correlation structure that can be indexed by a single parameter. System performance as well as the performance advantage of using a larger number of local detectors degrade as the degree of correlation between local decisions increases     

Covariance control for multisensor systems

As the profusion of different sensors improves the capabilities of tracking platforms, tracking objectives can move from simply trying to achieve the most with a limited sensor suite to developing the ability to achieve more specific tracking goals, such as reducing the uncertainty in a target estimate enough to accurately fire a weapon at a target or to ensure that a mobile robot does not collide with an obstacle. Multisensor manager systems that balance tracking performance with system resources have traditionally been ill-suited for achieving such specific control objectives. This work extends the methods developed in single-sensor management schemes to a multisensor application using an approach known as covariance control, which selects sensor combinations based on the difference between the desired covariance matrix and that of the predicted covariance of each target.     

Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Multi-fidelity Data Fusion(MDF) frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels. However, most existing MDF frameworks assume a uniform data structure between sampling data sources; thus, producing an accurate solution at the required level, for cases of non-uniform data structures is challenging. To address this challenge, an Adaptive Multi-fidelity Data Fusion(...     

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     

压敏漆图像数据处理技术

针对压敏漆工程应用的迫切需求,开发了压敏漆图像数据处理软件。采用了两种图像配准方法,并对二者的配准效果进行了对比。介绍了两种图像数据三维还原方法,采用直接线性变换将二维图像坐标与模型空间坐标联系起来,得到了模型表面的真实压力分布数据。引用作者在超声速风洞中完成的三角翼模型压敏漆试验的部分结果,验证了图像数据处理程序的有效性。     

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。     

量测数据自校准融合方法

提出了一种量测数据自校准融合方法,给出量测数据系统误差(未知输入)的自识别自校准公式与计算步骤,能够自动对量测数据中事先无法校准的系统误差进行识别、估计、补偿和修正,从而减小系统误差的影响。建立多传感器的数据融合公式与计算步骤,进而降低偶然误差的影响。分别对线性系统和非线性系统进行了详细讨论,并进行了大量实例计算和仿真模拟验证。从两个算例计算结果可以看出:该方法的相对误差至少要比传统方法减小45%,且计算简单,便于工程应用。      

Adaptive data fusion and sensor management for military applications

The military typically operates in demanding, dynamic, semi-structured and large-scale environments. This reality makes it difficult to detect, track, recognize/classify, and response to all entities within the volume of interest, thus increasing the risk of late (or non-) response to the ones that pose actual threat. A key challenge facing the military operators, in these contexts, is the focus of attention and effort, that is, how to make the most effective use of the available but scarce sensing and processing resources to gather the most relevant information from the environment and fuse it in the most efficient way. Adaptive Data Fusion and Sensor Management can aid this information gathering and fusion processes by automatically allocating, controlling, and coordinating the sensing and the processing resources to meet mission requirements. This paper presents results of a project initiated by Defence R&D Canada – Valcartier that aims at defining, developing, and demonstrating adaptive data fusion and sensor management concepts for distributed military surveillance operations.     

Optical fiber data bus modulation techniques for military avionics

In this paper, we propose several modulation techniques for use in military avionics optical fiber data buses, namely, Extended Manchester II Bi-Phase Coding with Beginning-Stopping Flags, Partial Trilevel Manchester II Bi-Phase Coding, Four-ary Pulse Width Modulation, Two-ary Pulse Width Modulation with Amplitude-Distinguished Sync Field, Two-ary Message Pulse Width Modulation and Two-ary Sync Pulse Amplitude Modulation, respectively. Compared with an existing modulation scheme of MIL-STD-1773 avionics data buses, the proposed techniques can be effectively used to overcome the difficulty in recognizing the correct operation states of an active transmitter at the output of optical receivers. The feasibility of proposed modulation schemes are discussed, and their performance is also evaluated     

MTI data clustering and formation recognition

Image exploitation technology approaches have generally focused on the detection and spatial analysis of stationary groups of objects on the ground using various sensors. While spatial arrangement is clearly necessary in analyzing military formations, it is usually not sufficient. Typically the arrangement must be examined within some context in order to interpret a pattern of deployment. For moving objects the spatial arrangement of the group relative to the direction of motion is key to recognizing the formation. By examining ground moving target indicator (MTI) radar data over time, motion can be inferred and used to establish a context for interpreting the spatial arrangement of the data. New techniques that exploit the multitemporal nature of MTI data are described. The first is a space-time clustering technique that locates compact groups of objects that persist in time. The technique Is an application of Marr and Hildreth's edge detection methodology to the dual problem of region segmentation, or more accurately, volumetric segmentation of space-time. The second technique is based on the use of the Hough transform for recognizing moving formations such as columns, wedges, and lines abreast by analyzing the shape of clustered MTI detections (specifically the orientation of linear arrangements within the group) with respect to their direction of motion. Preliminary results from simulated MTI data sets are presented     

数据融合技术的军事应用

随着战术和战略问题的复杂性化，数据融合技术已经广泛地应用于军事。本文主要介绍了传感器信息获取，以及军用数据融合-决策支持的体系结构。