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Adaptive data fusion using the expected output membership function

Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 26-33页

作者： Choi, Jongbae Dickerson, Julie A. Iowa State Univ United States

Fuzzy set methods can improve the fusion of uncertain sensor data. The expected output membership function (EOMF) method uses the fuzzified inputs and possible fuzzy outputs to estimate the fused output. The most likely fuzzy output comes from fusability measures which are calculated using the degrees of the intersections of the possible fuzzy outputs with the fuzzified inputs. The support lengths of the fuzzified inputs can be set proportional to the sensor variance in the fixed case. However, individual measurements can deviate widely from the true value even in accurate sensors. The support length of input sets can be varied by estimating the variation of the input. This adaptation helps deal with occasional bad or noisy measurements. The variation is defined as the absolute change rate of the input with respect to previous output estimates. The EOMF can also be too wide or too narrow compared to the fuzzified inputs. Adaptive methods can help select the size of the EOMF. An example from the control of automated vehicles shows the effectiveness of the adaptive EOMF method, compared to the fixed EOMF method and the weighted average method. The EOMF method shows robustness to outlying measurements when the average fusion operator is used.

关键词： sensor data fusion

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Spectral and Spatial Algorithm Architecture for Classification of Hyperspectral and LIDAR

Spectral and Spatial Algorithm Architecture for Classificati...

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

作者： Rand, Robert S. Khuon, Timothy S. Natl Geospatial Intelligence Agcy Off Innovis Bas & Appl Res Springfield VA 22153 USA

ISBN: (纸本)9780819490858

Architecture for neural net multi-sensor data fusion is introduced and analyzed. This architecture consists of a set of independent sensor neural nets, one for each sensor, coupled to a fusion net. The neural net of each sensor is trained (from a representative data set of the particular sensor) to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. To begin the processing, the 3D point cloud LIDAR data is classified based on a multi-dimensional mean-shift segmentation and classification into clustered objects. Similarly, the multi-band HSI data is spectrally classified by the Stochastic Expectation-Maximization (SEM) into a classification map containing pixel classes. For sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The first layer is the sensor level consisting of two neural nets: spatial neural net and spectral neural net. The second level consists of a single neural net, that is the fusion neural net. The success of the system in utilizing sensor synergism for an enhanced classification is clearly demonstrated by applying this architecture for classifying on November 2010 airborne data collection of LIDAR and HSI over the Gulfport, MS, area.

关键词： sensor fusion Architecture Neural Net HSI LIDAR Classification Mean-shift Stochastic Expectation-Maximization Imagery Back-propagation Perceptron Probability of detection and false alarm

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Integrated spatio-temporal multiple sensor fusion system design

Integrated spatio-temporal multiple sensor fusion system des...

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

作者： Chen, HW Olson, T Lockheed Martin Missiles & Fire Control Orlando Orlando FL 32819 USA

ISBN: (纸本)0819444812

In many missile and fire control applications, targets of interest may be acquired and tracked over some finite period of time with one or more sensors. This allows for the collection of sequential segments or frames of temporal information per sensor as well as across various sensors. By appropriately processing this information, target detection and classification performance can be considerably increased. Furthermore, we have developed new and different fusion strategies (additive and MINMAX fusion) in addition to the traditional strategies. Our test and analysis results show that temporal fusion can improve target classification as well as spatial fusion. In this work we have developed an optimal and novel design for an integrated spatio-temporal multi-sensor fusion system that combines inputs from different sensors as well as from the different time frames of each sensor.

关键词： sensors Signal to noise ratio sensor fusion Reliability Infrared sensors Missiles Statistical analysis Target detection Analytical research Fuzzy logic

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Optimal interval estimation fusion based on sensor interval estimates and confidence degrees

Optimal interval estimation fusion based on sensor interval ...

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

作者： Zhu, YM Li, BH Sichuan Univ Dept Math Chengdu 610064 Peoples R China

ISBN: (纸本)0819449598

The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two popular optimization-criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and. their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the-two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion(11) is a special case of our method. We also point out that in some sense, our combination rule is similar to the combination rule in Dempster-Shafer evidence theory. However, the confidence degrees given in this paper is summable, but they (called mass function in Dempster-Shafer evidence theory) are not there;therefore, Dempster-Shafer's combination rule is not applicable to the interval estimation fusion.

关键词： interval estimation confidence degree combination ride optimal interval estimation fusion

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Dynamic Buffer Sizing for Out-of-order Event Compensation for Time-sensitive applications

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ACM TRANSACTIONS ON sensor NETWORKS 2020年第1期17卷 1–23页

作者： Weiss, Wolfgang Jimenez, Victor J. Exposito Zeiner, Herwig JOANNEUM RES Forsch Gesell MbH DIGITAL Inst Informat & Commun Technol Steyrergasse 17 A-8010 Graz Austria VIRTUAL VEHICLE Res GmbH Inffeldgasse 21a A-8010 Graz Austria

Today's sensor network implementations often comprise various types of nodes connected with different types of networks. These and various other aspects influence the delay of transmitting data and therefore of out-of-order data occurrences. This turns into a crucial problem in time-sensitive applications where data must be processed promptly and decisions must be reliable. In this article, we research dynamic buffer sizing algorithms for multiple, distributed, and independent sources, which reorder event streams, thus enabling subsequent time-sensitive applications to work correctly. To be able to evaluate such algorithms, we had to record datasets first. Five novel dynamic buffer sizing algorithms were implemented and compared to state-of-the-art approaches in this domain. The evaluation has shown that the use of a dynamic time-out buffering method is preferable over a static buffer. The higher the variation of the network or other influences in the environment, the more necessary it becomes to use an algorithm that dynamically adapts its buffer size. These algorithms are universally applicable, easy to integrate in existing architectures, and particularly interesting for time-sensitive applications. Dynamic time-out buffering is still a trade-off between reaction time and out-of-order event compensation.

关键词： Out-of-order event compensation time-sensitive applications distributed systems event processing multi-source event data fusion

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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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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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Decision fusion using fuzzy integral method

Decision fusion using fuzzy integral method

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

作者： Liang, JM Yang, WH Cai, XY Xidian Univ Sch Elect Engn Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)0819436771

It is an intuitionistic demand for multisensor decision fusion processes that the degree of importance of each sensor's local decision towards the final decision should reflect adequately its detection, identification or classification ability, especially in time variant environments. This can be seen clearly in distributed detection systems, where the optimal final decision is the weighed sum of the local decisions, and the weights are functions of the local sensor's detection and false alarm probabilities. When voting fusion strategy is used, it supposes in nature that all the sensors are working at the same performance level. When dealing with soft decision fusion problems, the local sensor's reports represent its uncertainty about the observed phenomena. The most commonly used approaches for soft decision fusion are Bayesian probability theory and Dempster-Shafer evidence theory. Both of them update the evidence using the sensors' reports only, the reliability differences between the decision-making sensors are not considered. In this paper, the decision fusion ability of fuzzy integral is investigated from the viewpoints of both hard decisions and soft decisions. When fuzzy integral is used to fuse multisensor decisions, the fuzzy density can be interpreted as the importance of each sensor's local decision rewards the final decision. Fuzzy densities can be subjectively assigned by experts, or be induced form data set. We present a learning algorithm to distinguish the degree of importance of each sensor's local decision. A genetic algorithm-based hybrid learning method is used to determine the fuzzy densities from the training data set. A multi-pattern classification experiment is simulated to validate our proposed method.

关键词： multisensor decision fusion hard decision soft decision fuzzy measure fuzzy integral fuzzy density genetic algorithms

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MathWeb: a concurrent image analysis tool suite for multi-spectral data fusion

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 351-358页

作者： Achalakul, Tiranee Haaland, Peter D. Taylor, Stephen Syracuse Univ Syracuse United States

This paper describes a preliminary approach to the fusion of multi-spectral image data for the analysis of cervical cancer. The long-term goal of this research is to define spectral signatures and automatically detect cancer cell structures. The approach combines a multi-spectral microscope with an image analysis tool suite, MathWeb. The tool suite incorporates a concurrent Principal Component Transform (PCT) that is used to fuse the multi-spectral data. This paper describes the general approach and the concurrent PCT algorithm. The algorithm is evaluated from both the perspective of image quality and performance scalability.

关键词： Image analysis

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Multi-sensor remote sensing information fusion for urban area classification and change detection

Multi-sensor remote sensing information fusion for urban are...

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

作者： Palubinskas, Gintautas Makarau, Aliaksei Reinartz, Peter German Aerosp Ctr DLR Remote Sensing Technol Inst Oberpfaffenhofen Germany

ISBN: (纸本)9780819486387

Information extraction from multi-sensor remote sensing imagery is an important and challenging task for many applications such as urban area mapping and change detection. A special acquisition (orthogonal) geometry is of great importance for optical and radar data fusion. This acquisition geometry allows to minimize displacement effects due inaccuracy of Digital Elevation Model (DEM) used for data ortho-rectification and existence of unknown 3D structures in a scene. Final data spatial alignment is performed by recently proposed co-registration method based on a Mutual Information measure. For a combination of features originating from different sources, which are quite often non-commensurable, we propose an information fusion framework called INFOFUSE consisting of three main processing steps: feature fission (feature extraction aiming at complete description of a scene), unsupervised clustering (complexity reduction and feature representation in a common dictionary) and supervised classification realized by Bayesian or Neural networks. An example of urban area classification is presented for the orthogonal acquisition of space borne very high resolution WorldView-2 and TerraSAR-X Spotlight imagery over Munich city, South Germany. Experimental results confirm our approach and show a great potential also for other applications such as change detection.

关键词： multi-sensor remote sensing optical image SAR information fusion classification change detection

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