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Fault detection and multi-classifier fusion for unmanned aerial vehicles (UAVs)

Fault detection and multi-classifier fusion for unmanned aer...

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

作者： Yan, WZ GE Corp R&D Niskayuna NY 12309 USA

ISBN: (纸本)0819440809

UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a piloted aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naive Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers') show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.

关键词： fault detection classifier fusion classifier combination UAVs

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Short memory fuzzy fusion image recognition schema employing spatial and Fourier descriptors

Short memory fuzzy fusion image recognition schema employing...

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

作者： Raptis, SN Tzafestas, SG Natl Tech Univ Athens Dept Elect & Comp Engn Intelligent Robot & Automat Lab GR-15773 Athens Greece

ISBN: (纸本)0819440809

Single images quite often do not bear enough information for precise interpretation due to a variety of reasons. Multiple image fusion and adequate integration recently became the state of the art in the pattern recognition field. In the paper presented here an enhanced multiple observation schema is discussed investigating improvements to the baseline fuzzy-probabilistic image fusion methodology. The first innovation introduced consists in considering only a limited but seemingly more effective part of the uncertainty information obtained by a certain time restricting older uncertainty dependencies and alleviating computational burden that is now needed for a short sequence (stored into memory) of samples. The second innovation essentially establishes a new variant to the method applying conditional uncertainty reasoning separately for each single feature instead of equally grouping them into feature-blind object hypotheses. Experiment settings include a sequence of independent views obtained by a camera being moved around the investigated object.

关键词： fuzzy sets feature fusion probabilistic fusion Fourier-texture descriptors

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Performance limits for multi-platform scene-referenced navigation systems.

Performance limits for multi-platform scene-referenced navig...

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

作者： Ralph, JF Januarius, EM Smith, MI Edwards, KL Bernhardt, M Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)0819440809

The accuracy of aircraft/weapon navigation systems has improved dramatically since the introduction of global positioning systems and terrain-referenced navigation systems into integrated navigation suites. Future improvements, in terms of reliability and accuracy, could arise from the inclusion of navigation systems based on the correlation of known ground features with imagery from a visual band or infrared sensor, often called scene matching and area correlation or scene-referenced navigation. This paper considers the use of multi-platform fusion techniques to improve on the performance of individual scene-referenced navigation systems. Consideration is also given to the potential benefits of multi-platform fusion for scene-referenced object localisation algorithms that could be used in association with infrared targeting aids.

关键词： visual navigation sensor fusion distributed sensor systems object localization

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Robust multi-source sound localization using temporal power fusion

Robust multi-source sound localization using temporal power ...

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

作者： Aarabi, P Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)0819440809

In the past several years, many different algorithms have attempted to address the problem of robust multi-source time difference of arrival (TDOA) estimation, which is necessary for sound localization. Different approaches, including general cross correlation, multiple signal classification (MUSIC), and the maximum likelihood (ML) approach, have made different trade-offs between robustness and efficiency. A new approach presented here offers a much more efficient yet robust mechanism for TDOA estimation. This approach iteratively uses small sound signal segments to compute a local crosscorrelation based TDOA estimate. All of the different local estimates are combined to form the probability density function of the TDOA. Because the power of the secondary sources will be greater than the others for a certain set of the local signal segments, the TDOA corresponding to these sources will be associated with a peak in the TDOA probability density function. This way, the TDOAs of several different sources, along with their signal strength can be estimated. A real-time implementation of the proposed approach is used to show its improved accuracy and robustness. The system was consistently able to correctly localize sound sources with SNRs as low as 3 dB.

关键词： multi-source sound localization direction of arrival estimation beamforming temporal information fusion

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Multi-sensor feature fusion methods and results

Multi-sensor feature fusion methods and results

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

作者： Heagy, D Barnes, R Bechhoefer, E Mitre Corp MS W967 Mclean VA 22101 USA

ISBN: (纸本)0819440809

Analysts responsible for supporting time dominated threat decisions are faced with a growing volume of sensor data. Most efforts to increase discrimination among targets using multiple types of sensors encounter the same problems: sensor data are received in large volumes. sensor data are highly variable. Signature features are represented by many dimensions. Feature values are intercorrelated, random or not related to target differences. Decision rules for classifying new target data are difficult to define. This paper describes a new methodology for solving several problems: selecting signature features, reducing variability, increasing discrimination accuracy, and developing decision rules for classifying new target signatures. The results from using a combination of exploratory and multivariate statistical techniques show potential improvements over the traditional Dempster-Shafer approach. This project uses data from operational prototype sensors and vehicles of interest for threat analysis. Acoustic and seismic sensor data came from an unattended ground sensor and three military vehicles. Although the resulting algorithms are specific to the data set, the data screening and fusion methods tested in this project may be useful with other types of sensor and target data.

关键词： feature fusion multisensor classification data screening signal processing pattern recognition feature templates multivariate statistics Dempster-Shafer threat analysis

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Development of a sensor integration strategy for robotic application based on geometric optimization

Development of a sensor integration strategy for robotic app...

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

作者： Nandi, GC Mitra, D Indian Sch Mines Dept Engn & Min Machinery Dhanbad 826004 Bihar India

ISBN: (纸本)0819440809

sensor fusion is an important technology, which is growing exponentially due to its tremendous application potential. Appropriate fusion technology is needed to be developed specially when a system requires redundant sensors to be used. More the redundancy in sensors, more is the computational complexity for controlling the system and more is its intelligence level. This research presents a strategy developed for multiple sensor fusion, based on geometric optimization. Each sensor's uncertainty model has been developed. Using Lagrangian optimization techniques the individual sensor's uncertainty has been fused to reduce the overall uncertainty to generate a consensus among the sensors regarding their acceptable values. Using fission-fusion architecture, the precision level has further been improved. Subsequently, using feed back from the fused sensory information, the net error has further been minimized to any pre assigned value by developing a fusion technique in the differential domain (FDD). The techniques have been illustrated using synthesized data from two types of sensors (optical encoder and a single camera vision sensor). The application experience of the same fusion strategy in improving the precision of correctness of stereo matching using multiple baselines has also been discussed.

关键词： uncertainty ellipsoid sensor fusion fission-fusion architecture fusion in the differential domain stereo matching multiple baselines

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Metric sensitivity of the multi-sensor information fusion process under instance-based learning

Metric sensitivity of the multi-sensor information fusion pr...

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

作者： Dasarathy, BV Dynet Inc Huntsville AL 35814 USA

ISBN: (纸本)0819440809

The study investigates the sensitivity of the instance-based-learning (IBL) driven multi-source information fusion process to the underlying distance metric. An audio-visual system for recognition of spoken French vowels is used as an example for this investigation. Three different distance measures, namely, Euclidean, city block and chess board metrics, are employed for this initial foray into metric sensitivity analysis. In this example, the test phase encompasses a broader range of noise environments of the audio signal as compared to the training phase. The system is thus exercised in both trained and untrained noise regimes. Under the untrained regime, interpolation as well as extrapolation or off-nominal scenarios are considered. In the former, the signal to noise ratio in the test phase is within the range used in training phase but does not specifically include it. In the latter, the signal to noise ratio in the test phase is outside the range used in the training phase. It is observed that while both of the single-sensor based decision systems individually are not very sensitive to the choice of the metric, the fused decision system is indeed significantly more sensitive to this choice. The city block metric offers better performance as compared to the other two in the case of the fused audio-visual system across most of the spectrum of noise environments, except for the extreme off-nominal conditions wherein the Euclidean offers slightly better performance. The chess board metric offers the lowest performance across the entire test range. The lack of training in the interpolation scenario has a noticeably strong effect on audio performance under the chess board metric.

关键词： Metric sensitivity Instance-based learning information fusion audio-visual system

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Taking advantage of misclassifications to boost classification rate in decision fusion

Taking advantage of misclassifications to boost classificati...

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

作者： Goebel, K Mysore, SP GE Corp Res & Dev Niskayuna NY 12309 USA

ISBN: (纸本)0819440809

This paper presents methods to boost the classification rate in decision fusion with partially redundant information. This is accomplished by utilizing the information of known misclassifications of certain classes to systematically modify class output. For example, if it is known beforehand that tool A misclassifies class 1 often as class 2, then it appears to be prudent to integrate that information into the reasoning process if class 1 is indicated by tool B and class 2 is observed by tool A. Particularly this preferred misclassification information is contained in the asymmetric (cross-correlation) entries of the confusion matrix. An operation we call "cross-correlation" is performed where this information is explicitly used to modify class output before the first fused estimate is calculated. We investigate several methods for cross-correlation and discuss the advantages and disadvantages of each. We then apply the concepts introduced to the diagnostic realm where we aggregate the output of several different diagnostic tools. We show how the proposed approach fits into an information fusion architecture and finally present results motivated from diagnosing on-board faults in aircraft engines.

关键词： classification diagnostics information fusion decision fusion correlation cross-correlation

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Assessment of data and knowledge fusion strategies for prognostics and health management

Assessment of data and knowledge fusion strategies for progn...

引用

IEEE Aerospace conference

作者： Roemer, MJ Kacprzynski, GJ Orsagh, RF Impact Technol LLC Rochester NY 14623 USA

ISBN: (纸本)0780365992

Various data, feature and knowledge fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of anomaly, diagnostic and prognostic technologies. fusion of relevant sensor data, maintenance database information, and outputs from various diagnostic and prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. The data fusion strategies discussed in this paper are principally probabilistic in nature and are used to aid in directly identifying confidence bounds associated with specific component fault identifications and predictions. Dempster-Shafer fusion, Bayesian inference, fuzzy-logic inference, neural network fusion and simple weighting/voting are the algorithmic approaches that are discussed in this paper. Data fusion architectures such as centralized fusion, autonomous fusion, and hybrid fusion are described in terms of their applicability to fault diagnosis and prognosis. The final goal is to find the optimal combination of measured system data. data fusion algorithms, and associated architectures for obtaining the highest overall prediction/detection confidence levels associated with a specific application. To achieve this goal, a set of metrics has been developed for gauging the performance and effectiveness of a fusion strategy. Specifically, this paper will demonstrate how various metrics are used for assessing individual and fused vibration-based diagnostic algorithms. Evaluation of the diagnostic strategies was performed using gearbox seeded-fault and accelerated failure data taken with the MDTB (Mechanical Diagnostic Test Bed) at the ARL Lab at Penn State University.

关键词： sensor data fusion

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Visual localization using Bayesian decision fusion on omnidirectional sensing

Visual localization using Bayesian decision fusion on omnidi...

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

作者： Paletta, L Frintrop, S Hertzberg, J Inst Digital Image Proc Joanneum Res A-8010 Graz Austria

ISBN: (纸本)0819440809

Omnidirectional visual sensors have been successfully introduced recently to robot navigation, providing improved localization performances and a more stable path following behavior. As a consequence of the sensor characteristics, occlusion of the entire panoramic visual field becomes very unlikely. The presented work exploits these characteristics providing a Bayesian framework to gain even partial evidence about a current location by applying decision fusion on the multi-directional visual context. The panoramic image is first partitioned into a fixed number of overlapping unidirectional camera views, i,e., appearance sectors. For each sector image one learns then a posterior distribution over potential locations within a predefined environment. The ambiguity in a local sector interpretation is then resolved by Bayesian reasoning over the spatial context of the current position, discriminating occlusions which do not fit to the appearance model of subsequent sector views. The results from navigation experiments in an office using a robot equipped with an omnidirectional camera demonstrate that the Bayesian reasoning allows highly occlusion tolerant localization to enable visual navigation of autonomous robots even at crowded places such as offices, factories, and urban environments.

关键词： Omnidirectional vision Bayesian decision fusion robot navigation occlusion tolerant localization

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