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Proceedings of SPIE - The International Society for Optical Engineering

Proceedings of SPIE - The International Society for Optical ...

引用

sensor fusion: architectures, algorithms and applications V

The proceedings contains 33 papers from SPIE 2001 conference on application of sensor fusion: architectures, algorithms, and applications V. The topics discussed includes;feature and decision level fusion;bayesian, neural networks, and genetic algorithms;image data fusion and its applications;image data fusion and its applications;military applications;fusion concepts and architectures;miscellaneous applications and fuzzy logic approaches.

关键词： sensor data fusion

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Simple real time data fusion

Simple real time data fusion

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

作者： Polvani, DG Northrop Grumman Corp Elect Sensors & Syst Sector Annapolis MD 21404 USA

ISBN: (纸本)0819440809

We are developing new simple algorithms to provide automatic methods for fusing data from multiple passive sensors and multiple targets in real time. Initially, we have developed algorithms to fuse data from multiple passive collocated sensors measuring the same quantities (bearing and bearing rate) from multiple targets. MATLAB results with simulated data have been very encouraging. We present results for the probability of correct data association (PA) acid the probability of false data association (PFA) as a function of target angular separation, random noise, and the number of data updates used in the sequential probability ratio test (SPRT).

关键词： hypothesis fusion data association sequential probability ratio test multiple passive sensors multiple targets

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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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fusion under the closed-world assumption

Fusion under the closed-world assumption

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

作者： Grégoire, E Univ Sci & Tech Lille Flandres Artois CRIL F-62307 Lens France

ISBN: (纸本)0819440809

In this paper, new operators for fusing logical knowledge-bases (KBs) are proposed. They are defined in such a way that they can handle KBs that must be interpreted under forms of the closed-world assumption. Such assumptions implicitly augment the KBs with some additional information that could not be deduced using the standard logical deductive apparatus. More precisely, we extend previous recent works about the logical fusion of knowledge to handle such KBs. We focus on the model-theoretic definition of fusion operators to show their limits. In particular, the basic logical concept of model appears too coarse-grained. We solve this problem and propose new operators that cover a whole family of fusion approaches in the presence of variants of the closed-world assumption.

关键词： data fusion knowledge base fusion CWA nonmonotonic logics

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Combination of fuzzy sets and Dempster-Shafer theories in forest map updating using multispectral data

Combination of fuzzy sets and Dempster-Shafer theories in fo...

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

作者： Leduc, F Solaiman, B Cavayas, F ENST Bretagne Dept ITI F-29285 Brest France

ISBN: (纸本)0819440809

This paper explains a new approach to change detection and interpretation in a context of forest map updating. In this temporal change analysis we use a data set composed of map at time To and a satellite image at time T-1 and we refer to this as a mixed fusion approach. The analysis of remotely sensed data always necessitates the use of approximate reasoning. For this purpose, we use fuzzy logic to evaluate the objects' membership values to the considered classes and the Dempster-Shafer theory to analyse the confusion between classes and to find the more evident class to which an object belong.

关键词： map updating forestry change detection fuzzy logic Dempster-Shafer theory data fusion

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The application of spatial likelihood functions to multi-camera object localization

The application of spatial likelihood functions to multi-cam...

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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

The benefits and problems of a multiple-camera object localization system utilizing Spatial Likelihood Functions (SLF) are explored. This method utilizes the angular extent of objects perceived by different cameras in order to find the region in which they intersect. This region will ideally correspond to the original location of the objects. It is shown that as long as the number of cameras is greater than the number of objects, an efficient camera fusion algorithm utilizing SLFs can be successfully employed to localize the objects. In certain situations, especially with a greater number of objects than cameras, false objects will appear among the correctly localized objects. Several different techniques to identify and remove the false objects are proposed, including a heuristic-based ray tracing approach and other multi-modal techniques. The effectiveness of the camera fusion and false object removal approaches are illustrated in the context of several examples.

关键词： visual object localization multi-camera image fusion

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Biologically-based sensor fusion for medical imaging

Biologically-based sensor fusion for medical imaging

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

作者： Aguilar, M Garrett, AL Jacksonville State Univ Knowledge Syst Lab Jacksonville AL 36265 USA

ISBN: (纸本)0819440809

We present an architecture for the fusion of multiple medical image modalities that enhances the original imagery and combines the complementary information of the various modalities. The design principles follow the organization of the color vision system in humans and primates. Mainly, the design of within-modality enhancement and between-modality combination for fusion is based on the neural connectivity of retina and visual cortex. The architecture is based on a system developed and deployed for night vision applications while the first author was at MIT Lincoln Laboratory [1, 2]. Results of fusing various modalities are presented, including: a) fusion of T1-weighted and T2-weighted MRI images, b) fusion of PD, T1-weighted, and T2-weighted, and c) fusion of SPECT and MRI/CT. The results will demonstrate the ability to fuse such disparate imaging modalities with regards to information content and complementarities. These results will show how both brightness and color contrast are used in the resulting color fused images to convey information to the user. In addition, we will demonstrate the ability to preserve the high spatial resolution of modalities such as MRI even when combined with poor resolution images such as from SPECT scans. We conclude by motivating the use of the fusion method to derive more powerful image features to be used in segmentation and pattern recognition.

关键词： image fusion sensor fusion medical image processing automated diagnosis diagnostic aids

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Population bias control for bagging k-NN experts

Population bias control for bagging <i>k</i>-<i>NN</i> exper...

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

作者： Alkoot, FM Kittler, J Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

ISBN: (纸本)0819440809

We investigate bagging of k - NN classifiers under varying set sizes. For certain set sizes bagging often under-performs due to population bias. We propose a modification to the standard bagging method designed to avoid population bias. The modification leads to substantial performance gains, especially under very small sample size conditions. The choice of the modification method used depends on whether prior knowledge exists or not. If no prior knowledge exists then insuring that all classes exist in the bootstrap set yields the best results.

关键词： bagging k-nearest neighbour modified product fusion bootstrap population bias

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sensor array calibration via tracking with the extended Kalman filter

Sensor array calibration via tracking with the extended Kalm...

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

作者： Cevher, V McClellan, JH Georgia Tech Sch ECE CSIP Atlanta GA 30332 USA

ISBN: (纸本)0819440809

Starting with a randomly distributed sensor array with unknown sensor orientations, array calibration is needed before target localization and tracking can be performed using classical triangulation methods. In this paper, we assume that the sensors are only capable of accurate direction of arrival (DOA) estimation. The calibration problem cannot be completely solved given the DOA estimates alone, since the problem is not only rotationally symmetric but also includes a range ambiguity. Our approach to calibration is based on tracking a single target moving at a constant velocity. In this case, the sensor array can be calibrated from target tracks generated by an extended Kalman filter (EKF) at each sensor. A simple algorithm based on geometrical matching of similar triangles will align the seperate tracks and determine the sensor positions and orientations relative to a reference sensor. Computer simulations show that the algorithm performs well even with noisy DOA estimates at the sensors.

关键词： tracking array calibration extended Kalman filter DOA

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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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