Data fusion system engineering

The paper reports on methods for the cost-effective development and integration of multi-sensor fusion technology. The methods presented extend the Project Correlation Data Fusion Engineering Guidelines with significant evolution. The key new insight is in formulating the system engineering process as a resource management problem; allowing the application of the Bowman's model of the duality between data fusion and resource management     

Data fusion of decentralized local tracker outputs

An approach for fusing offboard track-level data at a central fusion node is presented. The case where the offboard tracker continues to update its local track estimate with measurement and system dynamics models that are not necessarily linear is considered. An algorithm is developed to perform this fusion at a central node without having access to the offboard measurements, their noise statistics, or the location of the local estimator. The algorithm is based on an extension of results that were originally established for linear offboard trackers. A second goal of this work is to develop an inequality constraint for selecting the proper sampling interval for the incoming state estimates to the fusion node. This interval is selected to allow use of conventional Kalman filter algorithms at the fusion node without suffering error performance degradation due to processing a correlated sequence of track state estimates     

Data fusion of multiradar system by using genetic algorithm

Detection system with distributed sensors and data fusion. are increasingly being used by surveillance systems. There has been a great deal of theoretical study on decentralized detection networks composed of identical or non-identical sensors. To solve the resulting nonlinear system, exhaustive search and some crude approximations are adopted. However, those methods often cause either the system to be insensitive to some parameters or the suboptimal results. In this paper, a novel flexible genetic algorithm is investigated to obtain the optimal results on constant false alarm rate data fusion. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. The simulation results show that adopting the proposed approach, one can achieve better performances than the reported methods and results     

生物识别技术在机场安保领域的应用

美国9·11事件使航空保安领域成为人们关注的焦点,各国政府纷纷增加科技投入,提高航空保安领域的科技水平,以保证航空运输的安全、高效,从而使以生物识别技术为代表的一批高科技产品在航空保安领域获得了广泛关注和应用。本文就生物识别技术在国内外机场安保领域的应用现状进行了分析、总结,并对生物识别技术的发展前景进行了展望。     

Smart card information and operations using biometrics

Nowadays many systems need a portable media to store some sensible data, such as smart cards. The information can be protected by the user with his Personal Identification Number (PIN), or through biometrics. Unfortunately, there is not a smart card today that can verify the biometric template inside it, performing this task in the terminal. The author has developed the algorithms and data structures needed to solve this problem. Therefore, he has created a smart card with user biometric authentication, based on an Open Platform smart card (in this case, a JavaCard). To achieve these results, different biometric techniques have been studied: speaker verification, hand geometry and iris recognition. Experimental results are given to show the viability of the prototype developed     

Waveform fusion in sonar signal processing

In active sonar systems, proper selection of the transmitted waveform is critical for target detection and parameter estimation, especially with the existence of clutter (reverberation). Two commonly used waveforms (constant frequency (CF) and linear frequency modulated (LFM)) are studied. Their characteristics are complementary both with respect to their accuracies and their sensitivity to the blind zero-Doppler ridge. Several fusion schemes of the two kinds of waveforms are explored and fusion results are studied both analytically and from simulation. It is concluded that fusion of the information of different waveforms can be not only more robust, but in some cases outright preferable, in term of detection probability and estimation accuracy.     

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     

On optimal track-to-track fusion

Track-to-track fusion is an important part in multisensor fusion. Much research has been done in this area. Chong et al. (1979, 1986, 1990) among others, presented an optimal fusion formula under an arbitrary communication pattern. This formula is optimal when the underlying systems are deterministic, i.e., the process noise is zero, or when full-rate communication (two sensors exchange information each time they receive new measurements) is employed. However, in practice, the process noise is not negligible due to target maneuvering and sensors typically communicate infrequently to save communication bandwidth. In such situations, the measurements from two sensors are not conditionally (given the previous target state) independent due to the common process noise from the underlying system, and the fusion formula becomes an approximate one. This dependence phenomena was also observed by Bar-Shalom (1981) where a formula was derived to compute the cross-covariance of two track estimates obtained by different sensors. Based on this results a fusion formula was subsequently derived (1986) to combine the local estimates which took into account the dependency between the two estimates. Unfortunately, the Bayesian derivation made an assumption that is not met. This work points out the implicit approximation made and shows that the result turns out to be optimal only in the ML (maximum likelihood) sense. A performance evaluation technique is then proposed to study the performance of various track-to-track fusion techniques. The results provide performance bounds of different techniques under various operating conditions which can be used in designing a fusion system.     

Optimal distributed decision fusion

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions about the same hypothesis to a fusion center that combines them into a final decision. Assuming that the sensor decisions are independent of each other for each hypothesis, the authors provide a general proof that the optimal decision scheme that maximizes the probability of detection at the fusion for fixed false alarm probability consists of a Neyman-Pearson test (or a randomized N-P test) at the fusion and likelihood-ratio tests at the sensors     

CPC环境下协作企业间数据及其管理

针对CPC环境下协作企业间数据及其管理问题,给出了协作企业数据分类模型和产品数据多视图表达模型,在此基础上建立了支持CPC的数据管理系统体系结构,为CPC环境下协作企业间的数据管理和数据共享提供了良好的信息基础环境。     

多传感器数据融合中的数据配准研究

数据配准是多传感器数据融合的先期处理过程。本文在研究了传统的最小二乘算法的基础上,提出了基于三维坐标系中的极大似然配准算法,解决了最小二乘配准算法对于传感器相距较远时无法解决的问题。最后通过计算机仿真证明了此算法的有效性。     

Problem characterization in tracking/fusion algorithm evaluation

The performance of a tracking/fusion algorithm depends very much on the complexity of the problem. This paper presents an approach for evaluating tracking/fusion algorithms that consider the difficulty of the problem. Evaluation is performed by characterizing the performance of the basic functions of prediction and association. The problem complexity is summarized by means of context metrics. Two context metrics for characterizing prediction and association difficulty are normalized target mobility and normalized target density. These metrics should be presented along with the performance metrics. The context metrics also support more efficient generation of input data for performance evaluation. Simple tests for evaluating basic tracking algorithm functions are presented     

数据融合技术在空气动力学研究中的应用

气动力数据通常通过风洞试验、数值计算和飞行试验三种途径获得,三者各有优缺点,为了得到更加完善准确的气动数据,可采用数据融合技术对不同来源的数据进行深加工和利用。针对数据融合技术在空气动力学中的应用进行讨论和探索,首先介绍了数据融合技术在空气动力学中的应用背景、发展现状及数据融合的基本思想,此后综合提出了建立气动数据融合准则的基本思路和两种具有应用价值的融合算法:基于不确定度的数据融合方法和基于气动力建模的数据融合方法,并给出了部分研究应用结果。最后,文章对气动数据融合技术在气动领域的应用前景进行了展望。研究发现,采用数据融合技术后,可以进一步提高气动数据的完整性和准确性,但由于气动数据融合工作不仅需要进行算法研究,同时还需要工作人员的大量经验,融合结果包含较多的人为因素,因此具体采用何种融合方法要根据具体情况而定。     

Track-to-track fusion with dissimilar sensors

An analysis is described of a kinematic state vector fusion algorithm when tracks are obtained from dissimilar sensors. For the sake of simplicity, it is assumed that two dissimilar sensors are equipped with nonidentical two-dimensional optimal linear Kalman filters. It is shown that the performance of such a track-to-track fusion algorithm can be improved if the cross-correlation matrix between candidate tracks is positive. This cross-correlation is introduced by noise associated with target maneuver that is common to the tracking filters in both sensors and is often neglected. An expression for the steady state cross-correlation matrix in closed form is derived and conditions for positivity of the cross-correlation matrix are obtained. The effect of positivity on performance of kinematic track-to-track fusion is also discussed     

Hypothesis management for information fusion

The efficient management of large collections of fusion hypotheses presents a critical challenge for scaling high-level information fusion systems to solve large problems. We motivate this challenge in the context of two Alphatech research projects, and discuss several partial solutions. A recurring theme is the exploitation of space-efficient, factored representations of multiple hypotheses to enable an efficient search for good hypotheses.     

A statistical overview of recent literature in information fusion

The objective of this paper is to make a picture of the recent articles published on information fusion. Indeed, a great number of documents dealing with this technique are available in the literature. A classification scheme including application fields, fusion goals, fusion system architecture and mathematical tools is proposed. This overview of the last three years allows us to compute the article distribution into each class. Finally, some elements of preliminary analysis of this classification are drawn     

Distributed decision fusion in the presence of link failures

The problem of optimal data fusion involving detector unit communication link failures is considered. Two strategies for decision making in presence of link failures are examined and an optimal decision making scheme in the sense of the Neyman-Pearson (N-P) test is proposed. The performance of q+1 reliable links versus q reliable links are examined theoretically, as well as, numerically using the receiver operating characteristics (ROCs)     

斜面检测中的数据处理

介绍了斜面的测量和计算机处理数据的方法,主要研究最小二乘原理下的数据处理,应用C语言编程处理,大大提高了斜面测量的精度和运算速度。同时提出基于最小二乘法处理的数学模型,给出相应的程序框图。     

Voting fusion adaptation for landmine detection

This paper presents a voting fusion application for use with a remotely controlled multisensor vehicle platform for antitank landmine detection. Data from three landmine detection sensors mounted at the front of the vehicle enhance the probability of detection and, when combined via data fusion, limit the false alarm density to practical levels. The performance of the voting fusion scheme presented in this paper is contrasted with a heuristic data fusion approach developed by General Dynamics Canada.