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

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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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Image fusion techniques for robust inspection of specular surfaces

Image fusion techniques for robust inspection of specular su...

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Multisensor. Multisource Information fusion: architectures, algorithms, and applications 2003

作者： León, Fernando Puente Kammel, Sören Lehrstuhl Messsystem- Sensotechnik Technische Universitat Munchen D-80290 München Germany Postfach 6980 D-76128 Karlsruhe Germany

This contribution presents a new fusion strategy to inspect specular surfaces. To cope with illumination problems, several images are recorded with different lighting. Typically, the information of interest is extracted from each image separately and is then combined at a decision level. However, in our approach all images are processed simultaneously by means of a centralized fusion - no matter whether the desired results are images, features or symbols. Since the information fused is closer to the source, a better exploitation of the raw data is achieved. The sensors are virtual in the sense that a single camera is employed to record all images with different illumination patterns. The fusion problem is formulated by means of an energy function. Its minimization yields the desired fusion results, which describe surface defects. The performance of the proposed methodology is illustrated by means of two case studies: the analysis of machined surfaces, and the inspection of painted free-form surfaces. The programmable light sources utilized are a DMD, and an LED based illumination device, respectively. In both cases, the results demonstrate that by generating complementary imaging situations and using fusion techniques, a reliable yet cost-efficient inspection is attained matching the needs of industry.

关键词： sensor data fusion

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Analyzing and Modeling Encryption Overhead for sensor Network Nodes

Analyzing and Modeling Encryption Overhead for Sensor Networ...

引用

Proceedings of the Second ACM International Workshop on Wireless sensor Networks and applications, WSNA 2003

作者： Ganesan, Prasanth Venugopalan, Ramnath Peddabachagari, Pushkin Dean, Alexander Mueller, Frank Sichitiu, Mihail Center for Embedded Systems Research Department of Electrical Engineering North Carolina State University Raleigh NC 27695 United States

ISBN: (纸本)1581137648

Recent research in sensor networks has raised security issues for small embedded devices. Security concerns are motivated by the deployment of a large number of sensory devices in the field. Limitations in processing power, battery life, communication bandwidth and memory constrain the applicability of existing cryptography standards tor small embedded devices. A mismatch between wide arithmetic for security (32 bit word operations) and embedded data bus widths (often only 8 or 16 bits) combined with lack of certain operations (e.g., multiply) in the ISA present other challenges. This paper offers two contributions. First, a survey investigating the computational requirements for a number of popular cryptographic algorithms and embedded architectures is presented. The objective of this work is to cover a wide class of commonly used encryption algorithms and to determine the impact of embedded architectures on their performance. This will help designers predict a system's performance for cryptographic tasks. Second, methods to derive the computational overhead of embedded architectures in general for encryption algorithms are developed. This allows one to project computational limitations and determine the threshold of feasible encryption schemes under a set of the constraints for an embedded architecture. Experimental measurements indicate uniform cryptographic cost for each encryption class and each architecture class and negligible impact of caches. RC4 is shown to outperform RC5 for the Motes Atmega platform contrary to the choice of RC5 for the Motes project, a choice driven in large by memory constraints. The analytical model allows to assess the impact of arbitrary embedded architectures as a multi-variant function for each encryption scheme. Overall, our results are not only valuable to assess the feasibility of encryption schemes for existing embedded architectures, they also extend to assess the feasibility of encryption methods for new algorithms and archi

关键词： sensor data fusion

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Improvement of sensor fusion with misalignment by MHT tracking

Improvement of sensor fusion with misalignment by MHT tracki...

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conference on Signal and Data Processing of Small Targets

作者： Baltes, R FGAN FKIE D-53343 Wachtberg Germany

ISBN: (纸本)0819450774

Also in fusing measurements from multiple sensors with misalignment. the specific characteristics of MHT achieve a superior tracking performance if compared to less complex algorithms. The parameters of the sensors, the scenario and the tracking process define the extent and type of degradation by misalignment. Their influence is analyzed here. In addition. the effect of misalignment on MHT tracking is compared with the NN (nearest neighbor) and PDAF tracking algorithms. Due to the details of the sensor modeling and a sensor data processing, which includes track initiation and cancellation, the results widely comply with real world applications. The study covers centralized and decentralized tracking (track correlation with measurement fusion) for collocated and distributed fusion. The detection and correction of misalignment itself is not included here.

关键词： misalignment MHT track extraction centralized decentralized fusion collocated distributed sensors

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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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Optimal centralized state fusion estimation for multi-sensor system with correlated measurement noise

Optimal centralized state fusion estimation for multi-sensor...

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Proceedings of 2003 IEEE conference on Control applications

作者： Jin, Xue-Bo Sun, You-Xian Coll. of Informatics and Electronics Zhejiang Inst. of Sci. and Technol. Hangzhou Zhejiang 310033 China Institute of Modern Control Eng. Zhejiang University Hangzhou Zhejiang 310027 China

ISBN: (纸本)078037729X

Many centralized fusion estimation algorithms assumed that measurement noises among sensors are uncorrelated, but it is not true when there exist the same unmodeled measurement noise source. In this paper, based on matrix similarity transform, covariance matrix of correlated measurement noise is successfully parallel decomposed and the linear measurement models are transformed. Optimal centralized fusion estimation algorithm is presented. When measurement noises are uncorrelated, the results here are reduced to the standard optimal centralized fusion estimation algorithms.

关键词： Estimation

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sensor bias correction in simultaneous localization and mapping

Sensor bias correction in simultaneous localization and mapp...

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6th International conference on Information fusion

作者： Perera, LDL Wijesoma, WS Challa, S Adams, MD Nanyang Technol Univ Div Control & Instrumentat Singapore 2263 Singapore

ISBN: (纸本)0972184449

The imprecision of sensor measurements due to systematic and nonsystematic errors can give rise to severe problems in several autonomous navigation tasks. In particular, the errors due to sensor bias can render existing Simultaneous Localization and Map Building (SLAM) algorithms useless as such biases cause the estimators to diverge. This paper describes a method to estimate and compensate these ever present and particularly cumbersome sensor and input biases in real time in the context of SLAM applications without an a priori map of the environment. The validity of the proposed methodology is verified via simulations for the case of an autonomous land vehicle navigating in a completely unknown 2D terrain. It is assumed that biased and noisy range and bearing measurements to point landmarks are obtainable in real-time, using a sensor such as a laser scanner.

关键词： SLAM tracking filtering estimation map building localization robotics

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Combining belief functions and fuzzy membership functions

Combining belief functions and fuzzy membership functions

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Florea, MC Jousselme, AL Grenier, D Bossé, T Univ Laval Quebec City PQ G1K 7P4 Canada

ISBN: (纸本)0819449598

In several practical applications of data fusion and more precisely in object identification problems, we need to combine imperfect information coming from different sources (sensors, humans, etc.), the resulting uncertainty being naturally of different kinds. In particular, one information could naturally been expressed by a membership function while the other could best be represented by a belief function. Usually, information modeled in the fuzzy sets formalism (by a membership function) concerns attributes like speed, length, or Radar Cross Section whose domains of definition are continuous. However, the object identification problem refers to a discrete and finite framework (the number of objects in the data base is finite and known). This implies thus a natural but unavoidable change of domain. To be able to respect the intrinsic characteristic of uncertainty arising from the different sources and fuse it in order to identify an object among a list of possible ones in the data base, we need (1) to use a unified framework where both fuzzy sets and belief functions can be expressed, (2) to respect the natural discretization of the membership function through the change of domain (from attribute domain to frame of discernment). In this paper, we propose to represent both fuzzy sets and belief function by random sets. While the link between belief functions and random sets, is direct, transforming fuzzy sets into random sets involves the use of a-cuts for the construction of the focal elements. This transformation usually generates a large number of focal elements often unmanageable in a fusion process. We propose a way to reduce the number of focal elements based on some parameters like the desired number of focal elements, the acceptable distance from the approximated random set to the original discrete one, or the acceptable loss of information.

关键词： random sets fuzzy sets belief functions object identification information fusion

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CB detection and early warning - Fusing disparate sensors into the detection process - Program overview

CB detection and early warning - Fusing disparate sensors in...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Birenzvige, A Sickenberger, D Reyes, F Underwood, W Reif, C Gonski, D Fargues, M Nelson, B USA SBCCOM Edgewood Chem & Biol Ctr Aberdeen Proving Ground MD 21010 USA

ISBN: (纸本)0819449598

Improved situational awareness is a primary goal for the Objective Force. Knowing where the enemy is and what are the threats to his troops provides the commander with the information he needs to plan his mission and provide his forces with maximum protection from the variety of threats that are present on the battlefield. sensors play an important role in providing critical information to enhance situational awareness. The sensors that are used on the battlefield include, among others, seismic, acoustic, and cameras in different spectral ranges of the electromagnetic spectrum. These sensors help track enemy movement and serve as part of an intrusion detection system. Characteristically these sensors are relatively cheap and easy to deploy. Chemical and biological agent detection is currently relegated to sensors that are specifically designed to detect these agents. Many of these sensors are collocated with the troops. By the time alarm is sounded the troops have already been exposed to the agent. In addition, battlefield contaminants frequently interfere with the performance of these sensors and result in false alarms. Since operating in a contaminated environment requires the troops to don protective garments that interfere with their performance we need to reduce false alarms to an absolute minimum. The Edgewood Chemical and Biological Center (ECBC) is currently conducting a. study to examine the possibility of detecting chemical and biological weapons as soon as they are deployed. For that purpose we conducted a field test in which the acoustic, seismic and electro-magnetic signatures of conventional and simulated chemical / biological 155mm artillery shells were recorded by an array of corresponding sensors. Initial examination of the data shows a distinct differences in the signatures of these weapons. In this paper we will provide detailed descriptions of the test procedures. We will describe the various sensors used and describe the differences in the signa

关键词： battlefield sensors weapons discrimination sensor and data fusion

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Probabilistic and fuzzy information fusion applied to radar system ranking

Probabilistic and fuzzy information fusion applied to radar ...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Lecornu, L Debon, R Komorniczak, W Solaiman, B ENST Bretagne ITI Dpt F-29285 Brest France

ISBN: (纸本)0819449598

The decision making systems make use of heterogeneous information to identify an object class or a target, which are affected by various kinds of imperfection. First, information issued from measures (radar measures, images) of an observation is represented by X variables. Generally, on these X variables, each class can be described through a probability distribution function. These decision systems also integrate expert prior knowledge to assist the decision. Such information is defined by Y variables and is represented by fuzzy membership functions. The question is how to combine appropriately these two kinds of variables in order to improve the efficiency of the decision process. In this paper, we present a decision model combining probabilistic and fuzzy variables. The decision is defined using a fuzzy Bayesian approach, which takes into account these two imperfections. Only two classes are considered using one X variable and one Y variable. Then an extension is proposed to suit more complicated cases. To validate the interest of this approach, we compare it with the standard Bayesian classification and fuzzy classification, applied separately to synthetic data. In addition, we will see how our approach can be applied to the problem of radar system ranking, on which system resources are limited and as a consequence, decisions about priorities must be taken. Using the system information sources (i.e. probabilistic: radar measurements, fuzzy: prior expert knowledge, evidential), a comparison between Bayesian classification, fuzzy classification, system decision and the proposed approach is presented.

关键词： multifunction radar tracking fusion fuzzy Bayesian approach fuzzy classification system decision theoretical framework fusion

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