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Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Johnson, JL Schamschula, MP Inguva, R Caulfield, HJ USA AMCOM Redstone Arsenal AL 35809 USA

ISBN: (纸本)0819428256

Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex - the PCNN or Pulse Coupled Neural Network - performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the three-dimensional PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting two-dimensional (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter-term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single two-dimensional pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.

关键词： Image fusion Neurons sensors Data modeling RGB color model Neural networks Modulation Pulse generators sensor fusion Image processing

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Optimal features in feature out (FEI-FEO) fusion for decisions in multisensor environments

Optimal features in feature out (FEI-FEO) fusion for decisio...

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Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Belur, SV Dasarathy, BV Comp Sci Corp Dept Appl Res Syst Sci Div Lanham MD 20706 USA

ISBN: (纸本)0819428256

The study presents a formal methodology to the problem of feature level fusion, that had been previously addressed in the literature mostly in an ad hoc manner on a case by case basis only. The input set of features extracted from multiple sensors (data sources) are optimally fused to derive a synthetic feature so as to enhance the effective discrimination potential among the defined set of decision classes. This features in - feature out (FEI-FEO)' fusion process, unlike most other fusion schemes reported in the literature, is designed through a formal learning phase in which an optimal mapping from the multisensor derived feature space to a single unified feature is developed. This learning, accomplished through a new composite random and deterministic search based optimization teal, defines the transformation for the FEI-FEO process. This transformation is applied to the multi-sensor generated feature sets in the operational phase to derive the fused feature values corresponding to the objects under observation. The corresponding classification decisions are made on the basis of relative closeness of these feature values to the different class mean values in the transformed single dimensional feature space. The new methodology has been implemented in MATLAB which, being a vector/matrix oriented language, is an ideal candidate for solving problems in patter recognition and learning. The method is applied to well-known data sets available on the web for testing pattern recognition algorithms to assess its effectiveness relative to the traditional classification methods from both conceptual as well as computational view points.

关键词： feature level fusion feature optimization features in - feature out fusion feature synthesis

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Adaptive local fusion systems for novelty detection and diagnostics in condition monitoring

Adaptive local fusion systems for novelty detection and diag...

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Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Taylor, O MacIntyre, J Univ Sunderland Sch Comp & Informat Sci Ctr Adapt Syst Sunderland SR6 0DD England

ISBN: (纸本)0819428256

This paper describes the application of Kohonen Self Organising Maps in a dynamic machine condition monitoring application which learns fault conditions over time. The authors describe the implementation of a novelty detection and adaptive diagnostic system which forms a modular component of a larger on-line condition monitoring system. NEURAL-MAINE, a project under the European Union's EUREKA programme, aims to advance on-line condition monitoring applications by the use of neural networks, data fusion and multiple sensor technology. NEURAL-MAINE aims to implement a system on two levels: at the first,, Local fusion Systems (LFS) are used to model individual machine components, such as high pressure turbines;at the second, a larger 'Overseer' system sits above the LFS and takes in their input, as well as plant operating parameters, in order to give a global view of the condition of the plant. This paper describes the implementation and testing of the two major components of the Local fusion System, namely the novelty detection and the adaptive diagnostic systems. Novelty detection works by using a Kohonen-based neural network(1) to learn the normal operating state of the component that is being modeled, with new values being passed into the network to see if it is similar to the learned normal states of operation. If the new fused sensor values are unlike the values which the Kohonen neural network represents, this is nagged as novel, and the values are passed to the adaptive diagnostic system. The adaptive diagnostic system takes as its input the values that the novelty detection system identified as novel and passes these into the Kohonen-based diagnostic networks. If the diagnostic networks recognize the values then a local diagnosis is given and passed to the Overseer system. If the diagnostic networks do not recognize the values, then these patterns are dynamically learned as the new fault condition occurs, and the diagnostic networks are updated. This system a

关键词： neural networks data fusion novelty detection adaptive diagnostic system

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A trainable decisions in-decision out (DEI-DEO) fusion system

A trainable decisions in-decision out (DEI-DEO) fusion syste...

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sensor fusion: architectures, algorithms and applications ii

作者： Dasarathy, Belur V. Dynetics Inc. P. O. Box 5500 Huntsville AL 35814-5500 United States

Most of the decision fusion systems proposed hitherto in the literature for multiple data source (sensor) environments operate on the basis of pre-defined fusion logic, be they crisp (deterministic), probabilistic, or fuzzy in nature, with no specific learning phase. The fusion systems that are trainable, i. e., ones that have a learning phase, mostly operate in the features-in-decision-out mode, which essentially reduces the fusion process functionally to a pattern classification task in the joint feature space. In this study, a trainable decisions-in-decision-out fusion system is described which estimates a fuzzy membership distribution spread across the different decision choices based on the performance of the different decision processors (sensors) corresponding to each training sample (object) which is associated with a specific ground truth (true decision). Based on a multi-decision space histogram analysis of the performance of the different processors over the entire training data set, a look-up table associating each cell of the histogram with a specific true decision is generated which forms the basis for the operational phase. In the operational phase, for each set of decision inputs, a pointer to the look-up table learnt previously is generated from which a fused decision is derived. This methodology, although primarily designed for fusing crisp decisions from the multiple decision sources, can be adapted for fusion of fuzzy decisions as well if such are the inputs from these sources. Examples, which illustrate the benefits and limitations of the crisp and fuzzy versions of the trainable fusion systems, are also included.

关键词： sensor data fusion

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Estimation of subpixel-resolution motion fields from segmented image sequences

Estimation of subpixel-resolution motion fields from segment...

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Conference on sensor fusion - architectures, algorithms, and applications ii

作者： Schultz, RR Stevenson, RL Univ N Dakota Dept Elect Engn Grand Forks ND 58202 USA

ISBN: (纸本)0819428256

Super-resolution enhancement algorithms are used to estimate a high-resolution video still (HRVS) from several low-resolution frames, provided that objects within the image sequence move with subpixel increments. However, estimating an accurate subpixel-resolution motion field between two low-resolution, noisy video frames has proven to be a formidable challenge. Up-sampling the image sequence frames followed by the application of block matching, optical flow estimation, or Bayesian motion estimation results in relatively poor subpixel-resolution motion fields, and consequently inaccurate regions within the super-resolution enhanced video still. This is particularly true for large interpolation factors (greater than or equal to 4). To improve the quality of the subpixel motion fields and the corresponding HRVS, motion can be estimated for each object within a segmented image sequence. First, a reference video frame is segmented into its constituent objects, and a mask is generated for each object which describes its spatial location. As described previously, subpixel-resolution motion estimation is then conducted by video frame up-sampling followed by the application of a motion estimation algorithm. Finally, the motion vectors are averaged over the region of each mask by applying an a-trimmed mean filter to the horizontal and vertical components separately. Since each object moves as a single entity, averaging eliminates many of the motion estimation errors and results in much more consistent subpixel motion fields. A substantial improvement is also visible within particular regions of the HRVS estimates. Subpixel-resolution motion fields and HRVS estimates are computed for interpolation factors of 2, 4, 8, and 16, to examine the benefits of object segmentation and motion field averaging.

关键词： interpolation motion estimation segmentation sensor fusion super-resolution enhancement

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A non-ad-hoc decision rule for Dempster-Shafer method of evidential reasoning

A non-ad-hoc decision rule for Dempster-Shafer method of evi...

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sensor fusion: architectures, algorithms and applications ii

作者： Cheaito, Ali Lecours, Michel Bossé, Éloi Electrical and Computer Eng. Dept. Universite Laval Laval Que. G1K 7P4 Canada

This paper is concerned with the fusion of identity information through the use of statistical analysis rooted in Dempster-Shafer theory of evidence to provide automatic identification aboard a platform. An identity information process for a baseline Multi-Source Data fusion (MSDF) system is defined. The MSDF system is applied to information sources which include a number of radars, IFF systems, an ESM system, and a remote track source. We use a comprehensive Platform Data Base (PDB) containing all the possible identity values that the potential target may take, and we use the fuzzy logic strategies which enable the fusion of subjective attribute information from sensor and the PDB to make the derivation of target identity more quickly, more precisely, and with statistically quantifiable measures of confidence. The conventional Dempster-Shafer lacks a formal basis upon which decision can be made in the face of ambiguity. We define a non-ad hoc decision rule based on the expected utility (EUI) interval for pruning the "unessential" propositions which would otherwise overload the real-time data fusion system. An example has been selected to demonstrate the implementation of our modified Dempster-Shafer method of evidential reasoning.

关键词： sensor data fusion

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Multisensor data fusion

GEC JOURNAL OF TECHNOLOGY

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GEC JOURNAL OF TECHNOLOGY 1998年第1期15卷 27-37页

作者： Grossmann, P GMRCB Borehamwood England

Growing demand for better sensing and monitoring systems and for more effective means of processing the increasing flow of information has led to the wider implementation of multisensor data fusion techniques. With the recent rapid developments in sensor technology and in the advanced methods of signal and data processing, multisensor data fusion is becoming one of the most important new techniques to be used in many military, industrial and commercial applications. This article offers a brief overview of the fusion area illustrated by several examples of established techniques, their limitations, proposed new solutions and other new developments.

关键词： multisensor multisource data fusion tracking classification situation assessment algorithms architectures

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Multispectral simulation environment for modeling low-light-level sensor systems

Multispectral simulation environment for modeling low-light-...

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Conference on Image Intensifiers and applications;and Characteristics and Consequences of Space Debris and Near-Earth Objects

作者： Ientilucci, EJ Brown, SD Schott, JR Raqueño, RV Rochester Inst Technol Digital Imaging & Remote Sensing Lab Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (纸本)0819428892

Image intensifying cameras have been found to be extremely useful in low-light-level (LLL) scenarios including military night vision and civilian rescue operations. These sensors utilize the available visible region photons and an amplification process to produce high contrast imagery. It has been demonstrated that processing techniques can further enhance the quality of this imagery. For example, fusion with matching thermal LR imagery can improve image content when very little visible region contrast is available. To aid in the improvement of current algorithms and the development of new ones, a high fidelity simulation environment capable of producing radiometrically correct multi-band Imagery for low-light-level conditions is desired. This paper describes a modeling environment attempting to meet these criteria by addressing the task as two individual components: (i) prediction of a low-light-level radiance field from an arbitrary scene, and (ii) simulation of the output The a low-light-level sensor for a given radiance field. The radiance prediction engine utilized in this environment is the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model which is a first principles based multi-spectral synthetic image generation model capable of producing an arbitrary number of bands in the 0.28 to 20 mu m region. The DIRSIG model is utilized to produce high spatial and spectral resolution radiance field images. These images are then:processed by a user configurable multi-stage low-light-level sensor model that applies the appropriate noise and modulation transfer function (MTF) at each stage in the image processing chain. This includes the ability to reproduce common intensifying sensor artifacts such as saturation and "blooming". Additionally, co-registered imagery in other spectral bands may be simultaneously generated. for testing fusion and exploitation algorithms. This paper discusses specific aspects of the DIRSIG radiance prediction for low-light-lev

关键词： DIRSIG image intensifier low light level (LLL) synthetic image generation image simulation

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Fault detection and isolation in multisensor train navigation systems

Fault detection and isolation in multisensor train navigatio...

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UKACC International Conference on Control 98

作者： Mirabadi, A Mort, N Schmid, F Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

ISBN: (纸本)085296708X

In new Automatic Train Control (ATC) and Automatic Train Protection (ATP) systems, the onboard navigation system plays a vital role. Applying multisensor fusion algorithms in the vehicle navigation field has made it possible to make inexpensive and accurate positioning systems which will satisfy the requirements of railway systems. Kalman filtering, as one of the well known state estimation methods, has proved to be the most effective technique in multisensor data fusion for navigation applications. On the other hand reliability of the navigation system is of great concern which necessitates the design of a fault tolerant navigation system in which the detection and isolation of the faults is possible. This paper will first address different multisensor integration methods and later will discuss some of the fault detection methods for navigation systems using Kalman filtering and some simulation results will be presented. Fault isolation using the proposed sensor architecture is another topic which will be discussed in more detail.

关键词： multisensor fusion Kalman filter navigation railways fault detection

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Distributed fusion architectures and algorithms for target tracking

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PROCEEDINGS OF THE IEEE 1997年第1期85卷 95-107页

作者： Liggins, ME Chong, CY Kadar, I Alford, MG Vannicola, V Thomopoulos, S BOOZ ALLEN & HAMILTON INC SUNNYVALECA 94089 GRUMMAN CORP ADV TECHNOL & DEV CTRBETHPAGENY 11714

Modem surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets, In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow is distributed fusion systems and for developing algorithms. fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.

关键词： Distribute ARCHITECTURE data association sensor Data algorithms fusion (melting) Target tracking fusion fusion reactions fusion, biological SURVEILLANCE SYSTEMS

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