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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007"

共 282 条记录，以下是261-270 订阅

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Derivation of ontological relations using formal methods in a situation awareness scenario

Derivation of ontological relations using formal methods in ...

引用

Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Matheus, CJ Baclawski, KP Kokar, MM Versatile Informat Syst Inc Framingham MA 01701 USA

ISBN: (纸本)0819449598

This paper describes a case study of relation derivation within the context of situation awareness. First we present a scenario in which inputs are supplied by a simulated Level 1 system. The inputs are events annotated with terms from an ontology for situation awareness. This ontology contains concepts used to represent and reason about situations. The ontology and the annotations of events are represented in DAML and Rule-NIL and then systematically translated to a formal method language called MetaSlang. Having all information expressed in a formal method language allows us to use a theorem prover, SNARK, to prove that a given relationship among the Level 1 objects holds (or that it does not hold). The paper shows a proof of concept that relation derivation in situation awareness can be done within a formal framework. It also identifies bottlenecks associated with this approach, such as the issue of the large number of potential relations that may have to be considered by the theorem prover. The paper discusses ways of resolving this as well as other problems identified in this study.

关键词： situation awareness ontology formal method information fusion relation derivation events

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Recursive algorithm of the optimal estimation fusion

Recursive algorithm of the optimal estimation fusion

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multisensor. multisource information fusion: architectures, algorithms, and applications 2003

作者： Zhou, Jie Zhu, Yunmin College of Mathematics Sichuan University Chengdu Sichuan 610064 China

In a distributed estimation or tracking system, the global estimate are generated by the local estimates. Under the assumption of independence cross sensor noises, Bar-shalom proposed a two sensor tracking fusion formula, which used only the inverses of covariances of single sensor estimation errors. In this paper, we present more general multi-sensor estimation fusion formula, where the assumption of independence cross sensor noises and the direct computation of the generalized inverse of joint covariance of multiple sensor estimation errors are not necessary. Instead, as the number of sensors increases, a recursive algorithm is presented, in which only the inverses or the generalized inverses of the matrices having the same dimension as that of the covariance matrices of single sensor estimate errors are required.

关键词： Sensor data fusion

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Multi-channel nonlinear phase analysis for time frequency data fusion

Multi-channel nonlinear phase analysis for time frequency da...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Mavandadi, S Aarabi, P Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

ISBN: (纸本)0819449598

A general method for time delay of arrival (TDOA) estimation for time-frequency information fusion is analyzed. This technique, for which the generalized cross correlation method and histogram methods are special cases, results in a low TDOA estimation error And high efficiency in computation. The proposed method relies on a non-linear phase-error selector function, which acts as a reward and punish method for the phase error at each frequency. Three different selector function candidates, consisting of cosine, rectangular, and triangular functions are analyzed using simulations. In the presence of Gaussian noise, the rectangular selector function performs better than the cosine at signal-to-noise ratios (SNRs) higher than 10dB while for lower SNRs the cosine function performs better. With speech noise, the cosine function, which corresponds to the generalized cross correlation technique, has higher anomaly percentages and higher root-mean-square errors than the rectangular function. This suggests, that in general, the rectangular selector function, which can be computed more easily than the cosine selector function, is superior technique to the generalized cross correlation method for. real-time applications.

关键词： delay-of-arrival estimation time-frequency data fusion microphone arrays speech processing

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Structural analysis of multisensor arrays for speech separation applications

Structural analysis of multisensor arrays for speech separat...

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multisensor. multisource information fusion: architectures, algorithms, and applications 2003

作者： Modir Shanechi, Maryam Aarabi, Parham Dept. of Electrical and Comp. Eng. University of Toronto 10 King's College Road Toronto Ont. M5S 3G4 Canada

This paper analyzes the beamforming separation effectiveness of several different microphone array configurations. These configurations consist of 4, 8, and 16 microphones placed in a plane for two-dimensional speech separation, with inter-microphone distances varying from 10cm to 160cm in logarithmic step sizes. It is discovered that the linear and bilinear arrays result in the largest signal-to-noise ratio (SNR) gain after delay-and-sum beamforming in the case of speech signal and speech noise. Also, simulations show that larger inter-microphone distances result in a higher SNR gain, although the practicality of the higher inter-microphone distances is limited for certain applications where the array size is constrained.

关键词： Acoustic arrays

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Optimal interval estimation fusion based on sensor interval estimates and confidence degrees

Optimal interval estimation fusion based on sensor interval ...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Zhu, YM Li, BH Sichuan Univ Dept Math Chengdu 610064 Peoples R China

ISBN: (纸本)0819449598

The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two popular optimization-criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and. their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the-two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion(11) is a special case of our method. We also point out that in some sense, our combination rule is similar to the combination rule in Dempster-Shafer evidence theory. However, the confidence degrees given in this paper is summable, but they (called mass function in Dempster-Shafer evidence theory) are not there;therefore, Dempster-Shafer's combination rule is not applicable to the interval estimation fusion.

关键词： interval estimation confidence degree combination ride optimal interval estimation fusion

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Object aggregation using Neyman Pearson analysis

Object aggregation using Neyman Pearson analysis

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multisensor. multisource information fusion: architectures, algorithms, and applications 2003

作者： Bai, Li Hinman, Michael Computer Fusion Laboratory Electrical and Engineering Dept. Temple University Temple PA 19122 United States IFE AFRL Rome Research Sites Rome NY 13441 United States

This paper presents a novel approach to: 1) distinguish military vehicle groups, and 2) identify names of military vehicle convoys in the level-2 fusion process. The data is generated from a generic Ground Moving Target Indication (GMTI) simulator that utilizes Matlab and Microsoft Access. This data is processed to identify the convoys and number of vehicles in the convoy, using the minimum timed distance variance (MTDV) measurement. Once the vehicle groups are formed, convoy association is done using hypothesis techniques based upon Neyman Pearson (NP) criterion. One characteristic of NP is the low error probability when a-priori information is unknown. The NP approach was demonstrated with this advantage over a Bayesian technique.

关键词： Sensor data fusion

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Multiple algorithm fusion in a corrosion detection algorithm used with shipboard tank and void inspection systems

Multiple algorithm fusion in a corrosion detection algorithm...

引用

Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Nelson, BN Slebodnick, P Lemieux, EJ CEO CENTERS Inc Newton Ctr MA 02459 USA

ISBN: (纸本)0819449598

Over the last several years the Naval Research Laboratory has developed video based systems for inspecting tanks (ballast, potable water, fuel, etc.) and other voids on ships. Over this past year, we have extensively utilized the Insertable Stalk Inspection System (ISIS) to perform inspections of shipboard tanks and voids. This system-collects between 15 and 30 images of the tank or void being inspected as well as a video archive of the complete inspection process. A corrosion detection algorithm analyzes the collected imagery. The corrosion detection algorithm output is the percent coatings damage in the tank being inspected. The corrosion detection algorithm consists of four independent algorithms that each separately assesses the coatings damage in each of the images that are analyzed. The algorithm results are fused to attain a single coatings damage value for each of the analyzed images. The damage values for each of the images are next aggregated in order to develop a single coatings damage value for the tank being inspected. This paper concentrates on the methods used to fuse the results from the four independent algorithms that assess corrosion damage at the individual image level as well as the methods used to aggregate the results from multiple images to attain a single coatings damage level. Results from both calibration tests and double blind testing are provided in the paper to demonstrate the advantages of the video inspection systems and the corrosion detection algorithm.

关键词： information fusion image processing automatic corrosion detection

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Integration of language and sensor information

Integration of language and sensor information

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Perlovsky, LI Weijers, B Air Force Res Lab Bedford MA 01731 USA

ISBN: (纸本)0819449598

The talk describes the development of basic technologies of intelligent systems fusing data from multiple domains and leading to automated computational techniques for understanding data contents. Understanding involves inferring appropriate decisions and recommending proper actions, which in turn requires fusion of data and knowledge about objects, situations, and actions. Data might include sensory data, verbal reports, intelligence intercepts, or public records, whereas knowledge ought to encompass the whole range of objects, situations, people and their behavior, and knowledge of languages. In the past, a fundamental difficulty in combining knowledge with data was the combinatorial complexity of computations, too many combinations of data and knowledge pieces had to be evaluated. Recent progress in understanding of natural intelligent systems, including the human mind, leads to the development of neurophysiologically motivated architectures for solving these challenging problems, in particular the role of emotional neural signals in overcoming combinatorial complexity of old logic-based approaches. Whereas past approaches based on logic tended to identify logic with language and thinking, recent studies in cognitive linguistics have led to appreciation of more complicated nature of linguistic models. Little is known about the details of the brain mechanisms integrating language and thinking. Understanding and fusion of linguistic information with sensory data represent a novel challenging aspect of the development of integrated fusion systems. The presentation will describe a non-combinatorial approach to this problem and outline techniques that can be used for fusing diverse and uncertain knowledge with sensory and linguistic data.

关键词： information fusion language sensors intelligent systems understanding linguistic models knowledge non-combinatorial cognitive neural

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Combat air identification fusion algorithm (CAIFA)

Combat air identification fusion algorithm (CAIFA)

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Butler, CA Baker, JE Crowe, JA Kierstead, DP Mauro, CA Daniel H Wagner Associates Inc Hampton VA 23669 USA

ISBN: (纸本)0819449598

The Combat Air Identification fusion Algorithm (CAIFA), developed by Daniel H. Wagner, Associates, is a prototype, inferential reasoning algorithm for air combat identification. Bayesian reasoning and updating techniques are used in CAIFA to fuse multi-source identification evidence to provide identity estimates-allegiance, nationality, platform type, and intent-of detected airborne objects in the air battle space, enabling positive and rapid Combat Identification (CID) decisions. CAIFA was developed for the Composite Combat Identification (CCID) project under the Office of Naval Research (ONR) Missile Defense (MD) Future Naval Capability (FNC) program. CAIFA processes identification (ID) attribute evidence generated by surveillance sensors and other information sources over time by, updating the identity estimate for each target using Bayesian inference. CAIFA exploits the conditional interdependencies of attribute variables by constructing a context-dependent Bayesian Network (BN). This formulation offers a well-established, consistent approach for evidential reasoning, renders manageable the potentially large CID state space, and provides a flexible and extensible representation to accommodate requirements for model reconfiguration/restructuring. CAIFA enables reasoning across and at different levels of the Air Space Taxonomy.

关键词： combat identification Bayesian networks multi-source integration

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Use of multi sensor fusion technology to meet the challenges of emerging EO & RF threats to a combat aircraft

Use of multi sensor fusion technology to meet the challenges...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Shukla, AK Parthasarathy, T Rao, PNAP Govt India Minist Def Aeronaut Dev Agcy Bangalore 560017 Karnataka India

ISBN: (纸本)0819449598

The pilot on-board a combat aircraft encounters during any mission a dynamically varying threat environment of diverse EO and RF threats. Different sensors are carried on-board the aircraft to combat these threats. However, these sensors have their own limitations and no single sensor is able to perform in all kinds of situations. In addition, the technological advances in the threat scenario - in terms of higher speeds, small signatures and multimode guidance - and increased complex threats in the battlefield leading to generation of large amount of data input to the pilot make his decision making task very difficult due to increased workload. These challenges can be efficiently handled by deployment of a system on-board the aircraft with a comprehensive goal of autonomous target detection and tracking, situation and threat assessment and decision making based on multi-sensor data fusion techniques. In this paper, major emerging EO and RF threats for a combat aircraft and some important EO and RF sensors on-board the aircraft have been discussed. A design approach for the development of a multi-sensor data fusion system for a combat aircraft to provide better threat assessment than that provided by any single stand alone sensor has also been presented.

关键词： Multi-Sensor Data fusion Electro-Optical (EO) Radio Frequency (RF) Moving Target Image (MTTM) Multiple Hypothesis Tracking (MHT) Probabilistic Data Association (PDA)

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