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

作者： Johnson, Kevin Minor, Christian USN Res Lab Washington DC 20375 USA Nova Res Inc Alexandria VA 22308 USA

ISBN: (纸本)9780819490858

sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented.

关键词： sensor fusion Bayes decision theory

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Combined force and position Polyvinylidene Fluoride (PVDF) Robotic tactile sensing system

Combined force and position Polyvinylidene Fluoride (PVDF) R...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Dargahi, J Eastwood, AR Kemp, IJ Glasgow Caledonian Univ. (United Kingdom)

ISBN: (纸本)081942482X

This paper reports on a tactile sensing system with only three sensing elements. The magnitude and position of the applied force is obtained by utilising triangulation approach combined with membrane stress, some information about shape of the contact object is obtained. The sensor is designed to overcome the problems of cross-talk between sensing elements, complexity and fragility which is associated with some PVDF tactile sensors arranged in matrix form. The theoretical analysis of the sensor is made and compared with experimental results. The limitation of the sensor is also reported.

关键词： tactile sensor robotics polyvinylidene fluoride piezoelectric cross talk sensing element

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The data fusion system architecture for unattended ground sensors

The data fusion system architecture for unattended ground se...

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

作者： Zhang, JL Zhao, YM 54 Res Inst Elect Ind Shijiazhuang 050081 Hebei Province Peoples R China

ISBN: (纸本)0819428256

In this paper,it develops an artificial intelligence method that uses object-oriented approach to construct the blackboard of data fusion for unattented ground sensors including geophone sensor, acoustic sensor, pressure sensor, infra-red sensor, magnetic sensor, image sensor etc.. It can perform detection, correlation, association and estimation to the sensors' output and obtain the exact recognition of targets, the number of target groups and the estimation for both the states of targets and the situation and threat. The whole blackboard is divided into three regions, including: single sensor fusion region, multisensor fusion region and threat estimation region. The three regions are expressed in classes. Knowledges of each domain in three regions are also expressed by classes and encapsulated in class hierarchy structure. Thus the whole blackboard can be viewed as object forest, the distributed knowledge inference can be realized by object reference. Both statistics and hierarchy inference approaches are used in the blackboard structure so as to efficiently perform fusion and inference. Furthermore,The method is realized in C++ language and demonstrated by the simulation of sensor alarming datum under battlefield environment.

关键词： sensor data fusion data association target recognition state estimation

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Multisensor statistical interval estimation fusion

Multisensor statistical interval estimation fusion

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

作者： Zhu, YM Gan, Y Li, XR Sichuan Univ Dept Math Chengdu 610064 Sichuan Peoples R China

ISBN: (纸本)0819444812

This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter theta. A multisensor convex linear statistic fusion model for optimal interval estimation fusion is established. A Gaussian-Seidel iteration algorithm for searching for the fusion weights is proposed. In particular, we suggest convex combination minimum variance fusion that reduces huge computation of fusion weights and yields near optimal estimate performance generally, and moreover, may achieve exactly optimal performance for some specific distributions of obsevation data. Numerical examples are provided and give additional support to the above results.

关键词： interval estimation coverage probability convex linear fusion least mean square

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STAC: A Comprehensive sensor fusion Model for Scene Characterization

STAC: A Comprehensive Sensor Fusion Model for Scene Characte...

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

作者： Kira, Zsolt Wagner, Alan R. Kennedy, Chris Zutty, Jason Tuell, Grady Georgia Tech Res Inst Atlanta GA 30332 USA

ISBN: (纸本)9781628416145

We are interested in data fusion strategies for Intelligence, Surveillance, and Reconnaissance (ISR) missions. Advances in theory, algorithms, and computational power have made it possible to extract rich semantic information from a wide variety of sensors, but these advances have raised new challenges in fusing the data. For example, in developing fusion algorithms for moving target identification (MTI) applications, what is the best way to combine image data having different temporal frequencies, and how should we introduce contextual information acquired from monitoring cell phones or from human intelligence? In addressing these questions we have found that existing data fusion models do not readily facilitate comparison of fusion algorithms performing such complex information extraction, so we developed a new model that does. Here, we present the Spatial, Temporal, Algorithm, and Cognition (STAC) model. STAC allows for describing the progression of multi-sensor raw data through increasing levels of abstraction, and provides a way to easily compare fusion strategies. It provides for unambiguous description of how multi-sensor data are combined, the computational algorithms being used, and how scene understanding is ultimately achieved. In this paper, we describe and illustrate the STAC model, and compare it to other existing models.

关键词： multisensor fusion data fusion scene characterization scene understanding

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A fuzzy model-based multi-sensor data fusion system

A fuzzy model-based multi-sensor data fusion system

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

作者： Prajitno, P Mort, N Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)0819440809

A fuzzy model-based multi-sensor data fusion system is presented in this paper. The system is capable of accommodating both non-linear sensors of the same type and different (non-commensurate) sensors and to give accurate information about the observed system state by combining readings from them at feature/decision level. The data fusion system consists of process model and knowledge-based sensor model units based on a fuzzy inference system that predicts the future system and sensor states based on the previous states and the inputs. The predicted state is used as a reference datum in the sensor validation process which is conducted through a fuzzy classifier to categorise each sensor reading as a valid or invalid datum. The data fusion unit combines the valid sensor data to generate the feature/decision output. The corrector unit functions as a filtering unit to provide the final decision on the value of the current state based on the current measurement (fused output) and the predicted state. The results of the simulation of this system and other data fusion systems have been compared to justify the capability of the system.

关键词： multisensor data fusion fuzzy inference system function approximation predictive-based data fusion method

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Decision fusion strategies for target detection with a three-sensor suite

Decision fusion strategies for target detection with a three...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Dasarathy, BV Dynetics Inc. (United States)

ISBN: (纸本)081942482X

Boolean logic based decision fusion strategies for target detection with two sensors have been studied in detail in the literature over the years. Increasing the number of sensors to three, offers an aided dimension of flexibility in the design of fusion strategies. One could visualize a single stage fusion wherein the decision outputs of all the three sensor subsystems are fused simultaneously under a variety of strategies such as AND logic, or majority (simple or firm decisions only) logic, or a no-firm contradiction logic. Alternatively, one could explore a two-stage fusion strategy, wherein either an AND or an OR logic is used at the first stage combining the decisions of two of the sensor subsystems, followed by a similar logic choice combining the fused decision from the first level with the decision from the third sensor subsystem. Of these strategies, while some may turn out to be equivalent in a mathematical sense, others remain clearly unique. The study analyzes these strategies to assess their relative benefits. The papers concludes with a brief discussion on possible extensions in terms of temporal fusion strategies that exploit information derived from multiple looks and the potential for application to real-world problems such as mine detection.

关键词： sensor fusion decision fusion fusion strategies three-sensors suite mine detection

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Data fusion multi-agent framework

Data fusion multi-agent framework

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conference on sensor fusion: architectures, algorithms, and applications IV

作者： Gatepaille, S Brunessaux, S Abdulrab, H Matra Syst & Informat F-27106 Val De Reuil France

ISBN: (纸本)0819436771

This paper describes a work currently in progress whose aim is to design, develop and evaluate a Multi-Agent Framework for Data fusion (DFMAF). This is being done with the support of a battlefield surveillance demonstrator application, named TA-10 [3,6]. Through the following chapters, we will describe the benefits of using such a Framework for data fusion problems. Firstly, we will briefly present the multi-agent research domain. Then, we will go into further details to describe DFMAF, the multi-agent framework designed to help solving data fusion problems. The appropriateness of DFMAF to data fusion problems will also be pointed out. Next, the implementation and use of DFMAF in the support application will be detailed as well as the assessment procedure followed. Finally, we will conclude and expose the future work which will be done.

关键词： data fusion battlefield surveillance multi-sensor multiple target tracking multi-agent systems distributed systems

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Efficient fusion network with label generation and branch transformations for visible and infrared images fusion

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Infrared Physics & Technology 2025年 150卷

作者： Dongdong Sun Chuanyun Wang Tian Wang Qian Gao Zhaokui Li College of Artificial Intelligence Shenyang Aerospace University Shenyang 110136 China Institute of Artificial Intelligence Beihang University Beijing 100191 China School of Computer Science Shenyang Aerospace University Shenyang 110136 China

The information obtained from a single sensor is often insufficient for accurate environmental perception. In contrast, multi-sensor fusion significantly enhances perceptual accuracy, with the integration of infrared and visible light sensors being one of the most common approaches in multi-sensor fusion. Infrared and visible image fusion aims to combine the texture details of visible images with the thermal radiation information of infrared images to produce fused images with enhanced visual quality, which find broad applications in night vision, surveillance, and target detection. Existing fusion methods primarily focus on improving the quality of fused images, often at the expense of computational efficiency due to the reliance on complex, multi-layered architectures. This limitation makes them unsuitable for deployment on low-power terminal devices. To address these challenges, this paper proposes a novel image fusion framework, termed TSfusion, which leverages teacher-student learning to achieve both high-quality fusion and high computational efficiency. A teacher network, equipped with sequence-model-based feature extraction blocks and progressive integration modules, generates high-quality fusion labels by overcoming the limited receptive field of conventional convolutional networks. The lightweight student network adopts a reparameterisation-based Fast Inception Module, ensuring robust fitting capabilities while maintaining high-speed operation. Additionally, a novel multi-scale loss function is introduced to guide the student network in learning from the teacher at both semantic and pixel levels. Experimental results on three benchmark datasets demonstrate that the proposed TSfusion framework achieves superior visual and quantitative performance compared to state-of-the-art methods. Furthermore, TSfusion exhibits exceptional inference speed, outperforming all existing fusion algorithms, making it highly suitable for real-time applications. The implementatio

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Multisensor Data fusion with Disparate Data Sources

Multisensor Data Fusion with Disparate Data Sources

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Minor, Christian P. Hammond, Mark H. Johnson, Kevin J. Rose-Pehrsson, Susan L. Nova Res Inc 1900 Elkin St 320 Alexandria VA 22308 USA US Navy Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819481740

In many instances, sensing tasks are best addressed with multiple sensing modalities. However, fusion of the outputs of disparate sensor systems presents a significant challenge to forming a cohesive sensing system. A discussion of strategies for fusion of disparate sensor data is presented and illustrated with examples of real time and retrospective data fusion for multisensor systems. The first example discussed is a real-time system for situational awareness and the detection of damage control events in ship compartments. The second example is a retrospective data fusion framework for a multisensor system for the detection of buried unexploded ordnance at former bomb and target ranges.

关键词： data fusion multisensor system fire detection UXO remediation

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