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

共 2091 条记录，以下是161-170 订阅

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fusion of Disparate Spectra for Chemical Identification

Fusion of Disparate Spectra for Chemical Identification

引用

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

作者： Minor, Christian P. Brooke, Heather Johnson, Kevin J. Nova Res Inc Alexandria VA 22308 USA US Navy Res Lab Chem Div Washington DC 20375 USA

ISBN: (纸本)9780819486387

Currently there is no systematic framework for characterizing fused, multisensory systems, and therefore the comparison of multiple independent systems is difficult without extensive field-testing. Development of a framework would allow for theoretical comparisons and enable more rapid prototyping of fused sensor systems, guidance for design from existing sensor components, and more effective engineering of new sensors optimized for use in fused sensor systems. Recent research at NRL has focused on characterizing Fourier transform infrared spectroscopy (FTIR) and mass spectrometry data for fused, multisensor applications to enhance chemical detection and discrimination in the presence of complex interfering backgrounds. An information theoretic approach has been used to elucidate the information content available from spectral data, quantify the ability of these sensing techniques to distinguish chemicals, and determine their susceptibility to noise and resolution limitations. The approach has also been applied to feature extraction and data fusion techniques on these data. Results characterizing the effectiveness of a fused multisensor system combining FTIR and mass spectrometry are presented.

关键词： multimodal sensing data fusion mass spectrometry infrared spectroscopy

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Adaptive multimodal biometric fusion algorithm using particle swarm

Adaptive multimodal biometric fusion algorithm using particl...

引用

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

作者： Veeramachaneni, K Osadciw, LA Varshney, PK Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819449598

This paper introduces a new algorithm called Adaptive Multimodal Biometric fusion Algorithm" (AMEBF), which is a combination of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received from multiple biometric sensors. The system's accuracy improves for a subset of decision fusion rules. The optimal rule is-a function of the error cost and a priori probability of an intruder This Bayesian framework formalizes the design of a system that can adoptively increase or reduce the security level. Particle swarm optimization searches the decision and sensor operating points (i.e. thresholds) space to achieve the desired security level: The optimization function aims to minimize the cost in a Bayesian decision fusion. The particle swarm optimization algorithm results in the fusion rule and the operating points of sensors at which the system can work This algorithm is important to systems designed with varying security needs and user access requirements. The adaptive algorithm is found to achieve desired security level and switch between different rules and sensor operating points for varying needs.

关键词： Bayesian decision fusion particle swarm optimization multimodal biometrics

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Collaborative Wireless sensor Networks: architectures, algorithms and applications

引用

INFORMATION fusion 2015年 22卷 1-2页

作者： Fortino, Giancarlo Bal, Mert Li, Wenfeng Shen, Weiming Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy Miami Univ Dept Engn Technol Hamilton OH 45011 USA Wuhan Univ Technol Sch Logist Engn Wuhan 430063 Peoples R China Natl Res Council Canada London ON N6G 4X8 Canada

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DSP architectures of Covariance Intersection fusion Algorithm for Automotive sensor fusion 14

DSP Architectures of Covariance Intersection Fusion Algorith...

引用

14th International conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Praharshita, D.S.L. Achala, G. Srihari, Pathipati Acharya, U. Shripathi Pardhasaradhi, Bethi India National Institute of Technology Karnataka ECE Dept. India Continental Autonomous Mobility India Pvt Ltd Adas Dept. India

ISBN: (纸本)9798350335095

The data fusion from sensors within the automotive vehicle is vital for improved accuracy and safety. The centralized and information matrix fusion (IMF) algorithms are famous for providing an optimal fusion estimate. However, the IMF is not viable in automotive sensor fusion applications due to the limited bandwidth and low hardware resources. Hence, distributed fusion technology is widely adopted in the automotive sensor applications to achieve high-speed and low-area realizations. This paper proposes three digital signal processing (DSP) architectures for covariance intersection (CI) fusion algorithm: Pipelined-traditional CI, adder-ladder CI, and pipelined adder-ladder CI. The proposed DSP architectures are evaluated with hardware resource consumption (multipliers, adders, and delays), maximum achievable frequency, and latency of the architecture. In addition, proposed CI algorithms for Digital Signal Processing (DSP) architectures are compared with IMF DSP architectures. The hardware resources and optimal pipeline stages required for CI with respect to N number of sensors are provided. The traditional pipeline algorithm requires N number of stages where as the proposed pipelined version of adder-ladder CI requires a N-1 pipeline stage with additional 7N-1 and 7N-3 delay elements for even and odd number of sensors to achieve the overall system operating frequency to an operation of multiplier. The proposed DSP architectures are suitable for automotive sensor fusion due to their high operating frequency and low hardware resources. © 2023 IEEE.

关键词： Digital signal processing

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fusion of imaging and non-imaging sensor information for airborne surveillance

Fusion of imaging and non-imaging sensor information for air...

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

作者： Valin, P Jouan, A Bossé, E Lockheed Martin Canada Montreal PQ H4P 1K6 Canada

ISBN: (纸本)0819431931

This paper presents results from an Adaptable Data fusion Testbed (ADFT) which has been constructed to analyze simulated or real data with the help of modular algorithms for each of the main fusion functions and image interpretation algorithms. The results obtained from data fusion of information coming from an imaging Synthetic Aperture Radar (SAR) and non-imaging sensors (ESM, IFF, 2-D radar) on-board an airborne maritime surveillance platform are presented for two typical scenarios of Maritime Air Area Operations and Direct Fleet Support. An extensive set of realistic databases has been created that contains over 140 platforms, carrying over 170 emitters and representing targets from 24 countries. A truncated Dempster-Shafer evidential reasoning scheme is used that proves robust under countermeasures and deals efficiently with uncertain, incomplete or poor quality information. The evidential reasoning scheme can yield both single ID with an associated confidence level and more generic propositions of interest to the Commanding Officer. For nearly electromagnetically silent platforms, the Spot Adaptive mode of the SAR, which is appropriate for naval targets, is shown to be invaluable in providing long range features that are treated by a 4-step classifier to yield ship category, type and class. Our approach of reasoning over attributes provided by the imagery will allow the ADFT to process in the next phase (currently under way) both FLIR imagery and SAR imagery in different modes (RDP for naval targets, Strip Map and Spotlight Non-Adaptive for land targets).

关键词： imaging non-imaging Dempster-Shafer testbed evidential reasoning maritime surveillance

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Robust speech separation using visually constructed speech signals

Robust speech separation using visually constructed speech s...

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

作者： Aarabi, P Khameneh, NH Univ Toronto Dept Elect & Comp Engn Toronto ON M5M 3G4 Canada

ISBN: (纸本)0819444812

A technique to virtually recreate speech signals entirely from the visual lip motions of a speaker is proposed. By using six geometric parameters of the lips obtained from the Tulips1 database, a virtual speech signal is recreated by using a 3.6s audiovisual training segment as a basis for the recreation. It is shown that the virtual speech signal has an envelope that is directly related to the envelope of the original acoustic signal. This visual signal envelope reconstruction is then used as a basis for robust speech separation where all the visual parameters of the different speakers are available. It is shown that, unlike previous signal separation techniques, which required an ideal mixture of independent signals, the mixture coefficients can be very accurately estimated using the proposed technique in even non-ideal situations.

关键词： audiovisual information fusion blind speech separation independent component analysis audiovisual signal separation robust speech recognition

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Revisions to the JDL data fusion model

Revisions to the JDL data fusion model

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

作者： Steinberg, AN Bowman, CL White, FE ERIM Int Inc Arlington VA 22209 USA

ISBN: (纸本)0819431931

The Data fusion Model maintained by the Joint Directors of Laboratories (JDL) Data fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise and expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi-sensor/multi-source systems. Data fusion involves combining information - in the broadest sense - to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people (or any other sentient beings), it can be useful to include consideration of informational and perceptual stares in addition to the physical state. Developing cost-effective multi-source information systems requires a method for specifying data fusion processing and control functions, interfaces, and associated databases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology: current developments do nor lend themselves to objective evaluation, comparison or re-use. This paper reports on proposed revisions and expansions of the JDL Data fusion model to remedy some of these deficiencies. This involves broadening the functional model and related taxonomy beyond the original military focus, and integrating the Data fusion Tree Architecture model for system description, design and development.

关键词： Data fusion Data modeling Systems modeling Standards development Data processing sensors Algorithm development Data analysis Systems engineering Signal detection

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SCENE CLASSIFICATION AND SEGMENTATION USING MULTISPECTRAL sensor fusion IMPLEMENTED WITH NEURAL NETWORKS 4

SCENE CLASSIFICATION AND SEGMENTATION USING MULTISPECTRAL SE...

引用

4th International conference on applications of Artificial Neural Networks

作者： LAZOFSON, LE KUZMA, TJ BATTELLE MEM INST COLUMBUSOH 43201

ISBN: (纸本)0819412015

Near-simultaneous, multispectral, coregistered imagery of ground target and background signatures were collected over a full diurnal cycle in the MWIR, LWIR, near-infrared, blue, green, and red wavebands using Battelle's portable sensor suite. The imagery data were processed with classical statistical algorithms and artificial neural networks to discriminate target signatures from background clutter and investigate automatic target detection and recognition schemes.

关键词： Neural networks Image processing Image segmentation Artificial neural networks Data processing Image fusion Detection and tracking algorithms Network architectures sensor fusion sensors

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The application of Dempster-Shafer theory for landmine detection

The application of Dempster-Shafer theory for landmine detec...

引用

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

作者： Mudigonda, NR Kacelenga, R Erickson, D Gen Dynam Canada Ltd Calgary AB T2E 8P2 Canada

ISBN: (纸本)0819449598

This paper explores the concepts of multisensor data fusion based on the Dempster-Shafer (DS) evidential theory in order to achieve a mine versus false-alarm (FA) classification of landmine targets. Initially, a decision-level DS algorithm is proposed to combine the evidence from multiple sensors of the landmine detection system developed by the General Dynamics Canada Limited (GD Canada). Subsequently, a feature-level DS fusion algorithm is employed to operate on a set of features reported by the ground penetrating radar (GPR) sensor of the system. The data used in the present study was acquired from the Aberdeen Proving Grounds (APG) test site in USA as part of the Ground Standoff Mine Detection System (GSTAMIDS) trials. The proposed decision-level DS algorithm yielded a probability of detection (Pd) of 92.53% at a false-alarm rate (FAR) value of 0.0697 FAs/m(2). The Pd and the FAR performance results achieved by using the decision-level DS algorithm are comparable with the results obtained using three other decision-level fusion algorithms that were previously developed by GD, Canada based on heuristic, Bayesian inference, and Voting fusion concepts. On the other hand, the feature-level DS fusion, when tested with the information presented by the GPR sensor only, resulted in a higher Pd value of 78.54% as compared to the corresponding result of 61.43% obtained by using the heuristic algorithm. The GPR sensor is one of the three scanning sensors present on the system.

关键词： landmine detection multisensor data fusion Dempster-Shafer theory Bayesian theory voting fusion

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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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