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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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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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Genetic algorithms for multi-agent fusion-system learning

Genetic algorithms for multi-agent fusion-system learning

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

作者： Pigeon, L Inglada, J Solaiman, B Def Res Estab Valcartier Informat Syst Technol Sect Val Belair PQ G3J 1X5 Canada

ISBN: (纸本)0819440809

The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be represented as the system openness, the system evolution capabilities and the system performance. Multi-agent architecture can be designed in order to respect the first two efficiency constraints. As for third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first The need for such system and its parameters is explained. A brief review of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. After a brief introduction to genetic algorithms, these are adapted to the particularity of this study. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.

关键词： information fusion multi-agent genetic algorithms intelligent system learning interaction oriented-programming

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Optimising multiresolution pixel-level image fusion

Optimising multiresolution pixel-level image fusion

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

作者： Petrovic, V Xydeas, C Denaro Telecommun Technol YU-11080 Zemun Yugoslavia

ISBN: (纸本)0819440809

A number of pixel level image fusion schemes have been proposed in the past which combine registered input sensor images into a single fused output image. The two general objectives that underpin the operations of these schemes are a) the transfer of all visually important information from input images into a fused image and b) the minimisation of undesirable distortions and artefacts which may be generated in the fused image. fusion is usually achieved by i) the "decomposition" of input images into representations of their spectral bands and ii) a "selection" process which transfers information from input bands to yield the required representation of a single fused output image. Furthermore, decomposition is often based on multiresolution pyramidal representations and the selection process operates on corresponding input image pyramidal levels using "selection templates" which focus on local spectral characteristics. The performance of such a multiresolution pixel level image fusion system depends primarily on the actual decomposition and selection algorithms used. Thus for a given decomposition/selection arrangement, fusion performance is dependent on the pyramid size (i.e. number of level) and template size. Pyramid and template sizes on the other hand greatly influence the system's computational complexity. This paper is concerned with the performance optimisation/characterisation of several multiresolution image fusion schemes, in general and with performance/complexity tradeoffs in particular. Performance is measured using a subjectively meaningful, objective fusion metric which has been proposed recently by the authors and which is based on the preservation of image edge information. Thus fusion systems based on derivatives of Gaussian low-pass pyramid and the Discrete Wavelet transform are examined and their performances versus decomposition/selection parameters are defined and compared. The performance/algorithmic complexity results presented for these mult

关键词： pixel-level image fusion multiresolution fusion fusion performance evaluation fusion complexity

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Odor source identification by grounding linguistic descriptions in an artificial nose

Odor source identification by grounding linguistic descripti...

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

作者： Loutfi, A Coradeschi, S Duckett, T Wide, P Univ Orebro Dept Technol Ctr Appl Autonomous Sensor Syst S-70182 Orebro Sweden

ISBN: (纸本)0819440809

This paper addresses the problem of enabling autonomous agents (e.g., robots) to carry out human oriented tasks using an electronic nose. The nose consists of a combination of passive gas sensors with different selectivity, the outputs of which are fused together with an artificial neural network in order to recognize various human-determined odors. The basic idea is to ground human-provided linguistic descriptions of these odors in the actual sensory perceptions of the nose through a process of supervised learning. Analogous to the human nose, the paper explains a method by which an electronic nose can be used for substance identification. First, the receptors of the nose are exposed to a substance by means of inhalation with an electric pump. Then a chemical reaction takes place in the gas sensors over a period of time and an artificial neural network processes the resulting sensor patterns. This network was trained to recognize a basic set of "pure" substances such as "vanilla", "lavender" and "yogurt" under controlled laboratory conditions. The complete system was then validated through a series of experiments on various combinations of the basic substances. First, we showed that the nose was able to consistently recognize unseen samples of the same substances on which it had been trained. In addition, we present some first results where the nose was tested on novel combinations of substances on which it had not been trained by combining the learned descriptions - for example, it could distinguish "lavender yogurt" as a combination of "lavender" and "yogurt".

关键词： sensors Nose Principal component analysis Artificial neural networks Gas sensors Neural networks Feature extraction Toxicity Machine learning Robots

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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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