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Selection of fusion operations using rank-score diversity for robot mapping and localization

Selection of fusion operations using rank-score diversity fo...

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conference on Multisensor, Multisource Information fusion

作者： Lyons, Damian M. Hsu, D. Frank Ma, Qiang Wang, Liang Fordham Univ Comp Vis & Robot Lab Dept Comp & Informat Sci Bronx NY 10458 USA

ISBN: (纸本)9780819466938

In this paper, we evaluate the use of a rank-score diversity measure for selecting sensory fission operations for a robot localization and mapping application. Our current application involves robot mapping and navigation in art outdoor urban search and rescue situation in which we have many similar and mutually occluding landmarks. The robot is a 4-wheel direct drive platform equipped with visual, stereo depth and ultrasound sensors. In such an application it's difficult to make useful and realistic assumptions about the sensor or environment statistics. Combinatorial fusion Analysis(CFA) is used to develop an approach to fission with unknown sensor and environment statistics. A metric is proposed that will indicate when fission from a set of fission alternatives will produce a more accurate estimation of depth than either sonar or stereo alone and when not. Experimental results are reported to illustrate that two CFA criteria are viable predictors to distinguish between positive fission cases (the combined system performs better than or equal to the individual systems) and negative cases.

关键词： sensors Environmental sensing Associative arrays Ultrasonography sensor fusion Imaging systems Calibration Statistical analysis Stereoscopic cameras Visualization

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Real-time target tracking simulations in large disparate sensor networks

Real-time target tracking simulations in large disparate sen...

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conference on Multisensor, Multisource Information fusion

作者： Lin, Hong Rushing, John Graves, Sara Criswell, Evans Tanner, Steve Univ Alabama Huntsville AL 35799 USA

ISBN: (纸本)9780819466938

Real-time target tracking in large disparate sensor networks has been simulated with a parallelized search based data fusion algorithm using a simulated annealing approach. The networks are composed of large numbers of low fidelity binary and bearing-only sensors, and small numbers of high fidelity position sensors over a large region. The primitive sensors provide limited information, not sufficient to locate targets;the position sensors can report both range and direction of the targets. Target positions are determined through fusing information from all types of sensors. A score function, which takes into account the fidelity of sensors of different types, is defined and used as the evaluation function for the optimization search. The fusion algorithm is parallelized using spatial decomposition so that the fusion process can finish before the arrival of the next set of sensor data. A series of target tracking simulations are performed on a Linux cluster with communication between nodes facilitated by the Message Passing Interface (MPI). The probability of detection (POD), false alarm rate (FAR), and average deviation (AVD) are used to evaluate the network performance. The input target information used for all the simulations is a set of target track data created from a theater level air combat simulation.

关键词： disparate sensor network parallel data fusion simulated annealing real-time target tracking

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Noise reduction method for the multi-sensor system measuring DC current

Noise reduction method for the multi-Sensor system measuring...

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conference on Multisensor, Multisource Information fusion

作者： Wang, Jing Geng, Yingsan Song, Zhengxiang Wang, Jianhua Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Peoples R China

ISBN: (纸本)0819462985

Multi-sensor system has been proposed to measure electric current in a non-contact way. According to the Ampere's Law, the value of the current flowing in a conductor can be obtained through processing the outputs of the magnetic sensors around the conductor. As the discrete form of the Ampere's Law is applied, measurement noises are introduced when there exists the interference magnetic field induced by nearby current flowing conductors. In this paper, the measurement noises of the multi-sensor system measuring DC current are examined to reveal the impact of the interference magnetic field and the number of the magnetic sensors on the measurement accuracy. A noise reduction method based on Kalman filtering is presented. Computer simulation and experiment results show that the method greatly improves the accuracy without seriously increasing the computation load in comparison with other approaches.

关键词： multi-sensor noises DC current

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ID Box: A multisource attribute data fusion function for target identification

ID Box: A multisource attribute data fusion function for tar...

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sensor fusion: architectures, algorithms, and applications iv

作者： Simard, Marc-Alain Valin, Pierre Lesage, Frederic Lockheed Martin Canada Montreal Canada

The R&D group at Lockheed-Martin Canada has developed a target identifier function called ID Box. This computer program performs five main functions: first it transforms the sensor attribute input into a few contact ID declarations, second, it evaluates the association score between the contact declarations and the ID propositions of a current target track, third it performs attribute contact to track fusion using a modification of the Dempster-Shafer evidential theory, fourth the ID Box, using a platform library, produces a translator that unifies the information within track identity and the attribute input, and fifth, it manages the distribution of results to a system human computer interface. Our exhaustive platform library enables the ID Box to fuse attribute data from almost all kinds of sensor or information sources that may be found on large warships or patrol aircraft. These attributes are the radar cross section and the moving parts from surveillance radars, allegiance from interrogator systems, emitter composition from electronics support measure systems, spoken language from communication intercept systems, acoustical signature from sonar systems, propulsion type from IR detectors, dimensional data from imaging systems and other classification attributes from various systems or operators including dynamical parameters from positional trackers. This paper presents and describes the ID Box.

关键词： sensor data fusion

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Target identification with Bayesian networks

Target identification with Bayesian networks

引用

sensor fusion: architectures, algorithms, and applications iv

作者： Hautaniemi, Sampsa K. Korpisaari, Petri T. Saarinen, Jukka P.P. Tampere Univ of Technology Tampere Finland

Tracking algorithms can tell fairly reliable where target is heading. That is enough in civilian aviation, but in defence applications it might be not. Target's type and its hostility are at least as important. Normally, identification of type or friend or foe (IFF) cannot be determined from target's kinematic information. To identify a target we also need other information. Every plane type has its own specialities e.g. we know that certain type has two engines which affects directly to heat of exhaust fumes. This kind of speciality is generally referred as an attribute information. Because attribute information is type depended, it must be modelled by an expert, who has beforehand knowledge of the target's causality relations. One of the best theories to get expert's knowledge into a tracking system is Bayesian networks. Bayesian networks is a model that describes relationships between attributes. In this paper we concentrate to identification problem. Question is how comprehension of the target's type changes with time when observations are corrupted by noise. We illustrate theory of Bayesian networks and explain its place in tracking system. Finally we analyze performance of Bayesian networks in case where the problem is to identify targets from noisy data set.

关键词： sensor data fusion

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Model fusion for road extractions from multi-source satellite images

Model fusion for road extractions from multi-source satellit...

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

作者： Pigeon, L Lecornu, L Solaiman, B Brunet, G DREV Informat Syst Technol Sect Val Belair PQ G3J 1X5 Canada

ISBN: (纸本)0819440809

In the field of pattern recognition from satellite images, the existing road extraction methods have been either too specialized or too time-consuming. The challenge then has been to develop a general and close to real-time road extraction method. This study falls in this perspective and aims at developing a close to real-time semi-automatic system able to extract linear planimetric features (including roads). The major concern of the study is to combine the most efficient tools to deal with the road primitive extraction process in order to handle multi-resolution and multi-type raw images. Hence, this study brought along a new model fusion characterized by the combination of operator input points (in 2D or 3D), fuzzy image filtering, cubic natural splines and the A* algorithm First, a cubic natural splines interpolation of the operator points is used to parameterize the A* algorithm cost function with the consequence to restrict the mining research area. Second, the heuristic function of the same algorithm is combined with the fuzzy filtering which proves to be a fast and efficient tool for the selection of the primitive most promising points. The combination of the cost function and the heuristic function leads to a limited number of hypothetical paths, hence decreasing the computation time. Moreover, the combination of the A* algorithm and the splines leads to a new way to solve the perceptual grouping problems. Results related to the problem of feature discontinuity suggest new research perspectives in relation to noisy area (urban) as well as noisy data (radar images).

关键词： fusion road extraction remote-sensing hypothetical reasoning intelligent system cubic splines interpolation fuzzy filtering A* algorithm perceptual grouping

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Soft body sensor networks: architectures and applications

Soft body sensor networks: Architectures and Applications

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2020 IEEE International conference on Networking, Sensing and Control, ICNSC 2020

作者： Dong, Wentao Cheng, Xiao Yao, Daojin School of Electrical and Automation Engineering East China Jiaotong University Nanchang China Rail Transportation Technology Innovation Center East China Jiaotong University Nanchang China

ISBN: (纸本)9781728168531

Body sensor networks (BSNs) are designed to collect, process, and transmit the biological data from different parts of human body from wearable electronics with limited resources. Conventional BSN node is also designed and fabricated by the rigid sensors, printed circuit board (PCB), and integrated chip (IC), which are not compatible with the soft human skin. The concepts of soft body sensor networks (SBSNs) are given in the manuscript based on wireless sensor networks and flexible electronics. SBSN node is composed of soft sensors, flexible data modulation and process unit and flexible wireless communication unit, and all the components of SBSN node are designed to be bended and be compatible with soft skin surface by flexible electronic. The architecture of SBSNs are built with several layers: soft sensors, data collection, wireless communication, body could, data fusion algorithm and monitoring interface for further online healthy monitoring. Multisensory decision fusion algorithm has been presented with biometric and medical monitoring applications which is subsequently analyzed for medical diagnosis. Emotion recognitions are realized to predict healthy state based on SBSNs using the intelligent data fusion algorithms. © 2020 IEEE.

关键词： sensor nodes

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CB round discrimination fusing visible and infrared camera data

CB round discrimination fusing visible and infrared camera d...

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

作者： Nelson, BN Birenzvige, A Underwood, W Sickenberger, D GEO CENTRES INC Newton Ctr MA 02459 USA

ISBN: (纸本)0819449598

In support of the Disparate sensor Integration (DSI) Program a number of imaging sensors were fielded to determine the feasibility of using information from these systems to discriminate between chemical simulant and high explosives munitions. The imaging systems recorded video from 160 training and 100 blind munitions detonation events. Two types of munitions were used;155 mm high explosives rounds and 155 mm chemical simulant rounds. In addition two different modes of detonation were used with these two classes of munitions;detonation on impact (point detonation) and detonation prior to impact (airblasts). The imaging sensors fielded included two visible wavelength cameras, a near infrared camera, a mid wavelength infrared camera system and a long wavelength infrared camera system. Our work to date has concentrated on using the data from one of the visible wavelength camera systems and the long wavelength infrared camera system. The results provided in this paper clearly show the potential for discriminating between the two types of munitions and the two detonation modes using these camera data. It is expected that improved classification robustness will be achieved when the camera data described in this paper is combined with results and discriminating features generated from some of the other camera systems as well as the acoustic and seismic sensors also fielded in support of the DSI Program. The paper will provide a brief description of the camera systems and provide still imagery that show the four classes of explosives events at the same point in the munitions detonation sequence in both the visible and long wavelength infrared camera data. Next the methods used to identify frames of interest from the overall video sequence will be described in detail. This will be followed by descriptions of the features that are extracted from the frames of interest. A description of the system that is currently used for performing classification with the extracted feature

关键词： feature level fusion explosives event classification spectral band fusion image processing

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Classifiers and distance-based evidential fusion for road travel time estimation

Classifiers and distance-based evidential fusion for road tr...

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conference on Multisensor, Multisource Information fusion

作者： El Faouzi, N. -E. Lefevre, E. ENTPE INRETS Lab Ingn Circulat Transport 25 Ave Francois Mitterand Case 24 F-69675 Bron France Univ Artois Fac Sci Appl Lab Informat & Automat Artois LGI2A EA 3926 F-62400 Bethune France

ISBN: (纸本)0819462985

This paper addresses the road travel time estimation on an urban axis by classification method based on evidence theory. The travel time (IT) indicator can be used either for traffic management or for drivers' information. The information used to estimate the travel time (induction loop sensor, cameras, probe vehicle,...) is complementary and redundant. It is then necessary to implement strategies of multi-sensors data fusion. The selected framework is the evidence theory. This theory takes more into account the imprecision and uncertainty of multisource information. Two strategies were implemented. The first one is classifier fusion where each information source, was considered as a classifier. The second approach is a distance-based classification for belief functions modelling. Results of these approaches, on data collected on an urban axis in the South of France, show the outperformance of fusion strategies within this application.

关键词： evidence theory classifier combination travel time estimation

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FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous sensors 34

FieldHAR: A Fully Integrated End-to-end RTL Framework for Hu...

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34th IEEE International conference on Application-Specific Systems, architectures and Processors (ASAP)

作者： Liu, Mengxi Zhou, Bo Zhao, Zimin Hong, Hyeonseok Kim, Hyun Suh, Sungho Rey, Vitor Fortes Lukowicz, Paul German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany RPTU Kaiserslautern Landau Dept Comp Sci Kaiserslautern Germany Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul South Korea

ISBN: (纸本)9798350346855

In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration. FieldHAR aims to address the lack of apparatus to transform complex HAR methodologies often limited to offline evaluation to efficient run-time edge applications. The framework uses parallel sensor interfaces and integer-based multi-branch convolutional neural networks (CNNs) to support flexible modality extensions with synchronous sampling at the maximum rate of each sensor. To validate the framework, we used a sensor-rich kitchen scenario HAR application which was demonstrated in a previous offline study. Through resource-aware optimizations, with FieldHAR the entire RTL solution was created from data acquisition to ANN inference taking as low as 25% logic elements and 2% memory bits of a low-end Cyclone iv FPGA and less than 1% accuracy loss from the original FP32 precision offline study. The RTL implementation also shows advantages over MCU-based solutions, including superior data acquisition performance and virtually eliminating ANN inference bottleneck.

关键词： FPGA sensor fusion Human Activity Recognition Neural Networks

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