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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications VI"

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Quantisation for probability-level fusion on a bandwidth budget

Quantisation for probability-level fusion on a bandwidth bud...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Black, JV Bedworth, MD Def Evaluat & Res Agcy Malvern Malvern WR14 3PS Worcs England

ISBN: (纸本)0819428256

Results are established for a simulated data fusion architecture featuring a synthetic two-class Gaussian problem, with Bayesian recognisers. The recognisers output posterior probabilities for each class. The probabilities from two or more recognisers of identical error rate are quantised using the nearest-neighbour coding rule. The coded values are decoded at a fusion centre and fused. A decision is made from the fused probabilities. The performance of the architecture is examined experimentally using code values that are uniformly distributed and code values that are produced using the Linde-Buzo-Grey (LEG) algorithm. Results are produced for two to six sensors and two to 32 code values. These results are compared to fusing probabilities represented using 32 bit floating-point numbers. Using 32 uniform or LEG-produced code values, produces results that are at most only 1% worse than fusing the uncoded probabilities.

关键词： data fusion quantisation architecture

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Intelligent processing techniques for sensor fusion

Intelligent processing techniques for sensor fusion

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Byrd, K Smith, B Allen, D Morris, N Bjork, C Deal-Giblin, K Nichols Res Corp Huntsville AL 35815 USA

ISBN: (纸本)0819428256

Intelligent processing techniques which can effectively combine sensor data from disparate sensors by selecting and using only the most beneficial individual sensor data is a critical element of exoatmospheric interceptor systems. A major goal of these algorithms is to provide robust discrimination against stressing threats in poor a priori conditions, and to incorporate adaptive approaches in off-nominal conditions. This paper summarizes the intelligent processing algorithms being developed, implemented and tested to intelligently fuse data from passive infrared and active LADAR sensors at the measurement, feature and decision level. These intelligent algorithms employ dynamic selection of individual sensor features and the weighting of multiple classifier decisions to optimize performance in good a priori conditions and robustness in poor a priori conditions. Features can be dynamically selected based on an estimate of the feature confidence which is determined from feature quality and weighting terms derived from the quality of sensor data and expected phenomenology. Multiple classifiers are employed which use both fuzzy logic and knowledge based approaches to fuse the sensor data and to provide a target lethality estimate. Target designation decisions can be made by fusing weighted individual classifier decisions whose output contains an estimate of the confidence of the data and the discrimination decisions. The confidence in the data and decisions can be used in real time to dynamically select different sensor feature data or to request additional sensor data on specific objects that have not been confidently identified as being lethal or non-lethal, The algorithms are implemented in C within a graphic user interface framework. Dynamic memory allocation and the sequential implementation of the feature algorithms are employed. The baseline set of fused sensor discrimination algorithms with intelligent processing are described in this paper. Example results from

关键词： sensor fusion intelligent processing discrimination feature extraction infrared laser radar fuzzy logic

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Analysis of the functional and real-time requirements of a multi-sensor data fusion (MSDF) situation and threat assessment (STA) resource management (RM) system

Analysis of the functional and real-time requirements of a m...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Duquet, JR Bergeron, P Blodgett, D Couture, J Lockheed Martin Corp R&D Dept Montreal PQ H4P 1K6 Canada

ISBN: (纸本)0819428256

The Research and Development group at Lockheed Martin Canada, in collaboration with the Defence Research Establishment Valcartier (DREV), has undertaken a research project in order to capture and analyze the real-time and functional requirements of a next generation Command and Control System (CCS) for the Canadian Patrol Frigates (CPF), integrating Multi-sensor Data fusion (MSDF), Situation and Threat Assessment (STA) and Resource Management (RM). One important aspect of the project is to define how the use of Artificial Intelligence (AI) may optimize the performance of an integrated, real-time MSDF/STA/RM system. A closed-loop simulation environment is being developed to facilitate the evaluation of MSDF/STA/RM concepts, algorithms and architectures. This environment comprises a) a scenario generator, b) complex sensor, hardkill and softkill weapon models, c) a realtime monitoring tool d) a distributed Knowledge-Base System (KBS) shell. The latter is being completely designed and implemented in-house since no commercial KBS shell could adequately satisfy all the project requirements. The closed-loop capability of the simulation environment, together with its "simulated real-time" capability, allows the interaction between the MSDF/STA/RM system and the environment targets during the execution of a scenario. This capability is essential to measure the performance of many STA and RM functionalities. Some benchmark scenarios have been selected to demonstrate quantitatively the capabilities of the selected MSDF/STA/RM algorithms. This paper describes the simulation environment and discusses the MSDF/STA/RM functionalities currently implemented and their performance as an automatic CCS.

关键词： Intelligent Decision Aids Data fusion Real-Time Adaptive Planning Situation and Threat Assessment Resource Management Command and Control System (CCS) Artificial Intelligence (AI)

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Morphological filters and wavelet based image fusion for concealed weapons detection

Morphological filters and wavelet based image fusion for con...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Ramac, LC Uner, MK Varshney, PK Alford, M Ferris, D Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819428256

When viewing a scene for an object recognition task, one imaging sensor may not provide all the information needed for recognition. One way to obtain more information is to use multiple sensors. These sensors should provide images that contain complementary information about the same scene. After preprocessing the source images, we use image fusion to combine the information from the different sensors. The images to be fused may have some details such as shadows, wrinkles, imaging artifacts, etc., that are not needed in the final fused image. One application of morphological filters is to remove objects of a given size range from the image. Therefore, we use morphological filters in conjunction with wavelets to improve the recognition performance after fusion. After morphological filtering, wavelets are used to construct multiresolution representations of the source images. Once the source images are decomposed, the details are combined to form a composite decomposed image. This method allows details at different levels to be combined independently so that important information is maintained in the final composite image. We are developing image fusion algorithms for concealed weapon detection (CWD) applications. fusion is useful in situations where the sensor types have different properties, e.g., IR and MMW sensors. Fusing these types of images results in composite images which contain more complete information for CWD applications such as detection of concealed weapons on a person, In this paper we present our most recent results in this area.

关键词： image fusion mathematical morphology wavelets

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The minimum sample SPRT for sensor or operating point selection in a single or multisensor environment

The minimum sample SPRT for sensor or operating point select...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Pawlak, RJ USN Ctr Surface Warfare Dahlgren Div Self Def Sensors Branch Dahlgren VA 22448 USA

ISBN: (纸本)0819428256

This paper describes a technique for employing the sequential probability ratio test (SPRT) in a single or multisensor environment. The technique minimizes the number of sensor decisions required to declare the null or alternative hypothesis when there is a choice of different sensors or sensor operating points. Thus the technique will be dubbed the Minimum-Sample SPRT (MS-SPRT). The first step of the MS-SPRT requires an off-line optimization of the choice of sensors across all possible values of the alternative hypothesis probability. The second step of the technique involves the application of two Kalman filters to estimate the probability of the alternative hypothesis and to optimize a set of sensor probabilities. The sensor probabilities determine the optimal sensor choice that minimizes the expected number of samples before a decision is made. Three examples are given using simulated data. In the first example, it is shown that the MS-SPRT is not necessary. The second example shows the usefulness of the MS-SPRT when there is a step discontinuity in the null/alternative hypothesis probabilities. In the third example, the MS-SPRT facilitates the use of the proper sensor for a probabilistic variation in the hypothesis probabilities.

关键词： SPRT sensor fusion MS-SPRT Kalman

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Multi-agent data fusion workstation (MADFW) architecture

Multi-agent data fusion workstation (MADFW) architecture

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Shahbazian, E Bosse, E Valin, P Lockheed Martin Canada Inc Montreal PQ H4P 1K6 Canada

ISBN: (纸本)0819428256

This paper describes an on-going effort to build a Multi-Agent Data fusion Workstation (MADFW) based on a Knowledge-Based System (KBS) BlackBoard (BB) architecture to offer a range of innovative techniques for Data fusion (DP), applicable to various domains. The initial application to be demonstrated is in the area of airborne maritime surveillance where several multi-agent concepts and algorithms have already been studied and demonstrated. The end result will offer the user a flexible and modular environment providing capability for: (a) addition of user defined sensor simulation models and fusion algorithms;(b) integration with existing models and algorithms;and (c) evaluation of performance to derive requirement specifications and help in the design phase towards fielding a real DF system. The workstation is being designed to accommodate modular interchangeable algorithm implementation and performance evaluation of: (1) fusion of positional data from imaging and non-imaging sensors;(2) fusion of attribute information obtained from imaging and non-imaging sensors and other sources such as communication systems, satellites, etc.;and (3) Object Recognition (OR) in imaging dal-a. The design allows algorithms for sensor simulators and measures of performance to reside either on the KBS BE shell or be separate from it, thus facilitating integration with other testbed designs. This architecture also allows the future introduction of fusion management capabilities. The real-time KBS BE shell developed by Lockheed Martin Canada, in collaboration with DREV, is the basis of the MADFW infrastructure. This system is totally generic, and could be used to implement any system comprising of components which can be numeric or AI based. It has been implemented in C++ rather than in a higher-level language (such as LISP, Smalltalk,...) to satisfy the real-time requirement.

关键词： intelligent decision aids data fusion image analysis automatic target recognition knowledge-based system blackboard

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Surface texture measurement by combining signals from two sensing elements of a piezoelectric tactile sensor

Surface texture measurement by combining signals from two se...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Dargahi, J Payandeh, S Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)0819428256

In this paper we report on the design and testing of a prototype surface texture tactile sensor. The sensor can measure both compliance and surface roughness. The design of the sensor is based on the psychophysiological perception of surface texture by the human hand. The sensor essentially consists of a rigid cylinder surrounded by a compliant cylinder. The deformation of the compliant object from rigid to compliant cylinder is used for measuring the compliance of the contact object and variation of the compliant cylinder over a surface profile with reference to the rigid cylinder is used to measurement surface roughness. Two 25 mu m thick polyvinylidene fluride(PVDF) films are used as a tranducer in the tactile sensor system. The sensor in miniaturized form can be used in a laparoscopic grasper for minimally invasive surgery. The theoretical analysis is made and compared with experimental values. The advantages and limitations of the sensor are also discussed.

关键词： piezoelectric robotic tactile sensor laparoscope polyvinylinede fluoride minimally invasive surgery

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Pulse coupled neural network sensor fusion

Pulse coupled neural network sensor fusion

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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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