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检索条件"任意字段=Conference on Sensor Fusion: Architectures, Algorithms, and Applications IV"

共 329 条记录，以下是91-100 订阅

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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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Bayesian approach to sensor fusion in a multi-sensor land mine detection system

Bayesian approach to sensor fusion in a multi-sensor land mi...

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

作者： Erickson, D Kacelenga, R Palmer, D Gen Dynam Canada Ltd Calgary AB T2E 8P2 Canada

ISBN: (纸本)0819444812

In the present study, the investigation by General Dynamics Canada, formerly Computing Devices Canada, into Bayesian Inference shows improved sensor fusion of multiple scanning sensors in the detection of buried anti-tank (AT) mines. This algorithm uses statistical data taken from trials and constructs conditional probabilities for individual sensors in order to better discern landmines.

关键词： landmine detection Baye's rule Bayesian inference conditional probability minimum metal detector ground penetrating radar forward-looking infrared multi-sensor fusion

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Better-than-the-Best fusion Algorithm with Application in Human Activity Recognition

Better-than-the-Best Fusion Algorithm with Application in Hu...

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

作者： Najjar, Nayeff Gupta, Shalabh Univ Connecticut Storrs CT 06269 USA

ISBN: (纸本)9781628416145

This paper introduces the Better-than-the-Best fusion (BB-Fus) algorithm. The BB-Fus algorithm is a simple and effective information fusion algorithm that combines the information from different sources (be it sensors, features or classifiers) to improve the Correct Classification Rate (CCR). It can be observed that in most classification problems, different sensors or features might have different classification accuracies in separating different classes. Therefore, this paper constructs an optimal decision tree that isolates one class at a time with the best sensor to separate that particular class. The paper shows that the decision tree improves the overall CCR as compared to the use of any single sensor or feature for any 3-class classification problem. The efficiency of the BB-Fus algorithm is validated on the Opportunity data set to solve the human activity recognition problem where a set of 56 sensors (including a localization system, accelerometers, inertial measurement units and magnetic sensors mounted on various body parts;besides, accelerometers and gyroscopes mounted on different objects) are used. The CCR resulting from the BB-Fus algorithm is 96% while the best sensor achieved 94% CCR.

关键词： sensor fusion Information fusion Human Activity Recognition BB-Fus Decision Trees

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Quantisation for probability-level fusion on a bandwidth budget

Quantisation for probability-level fusion on a bandwidth bud...

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

作者： Black, JV Bedworth, MD Def Evaluat & Res Agcy Malvern Malvern WR14 3PS Worcs England

ISBN: (纸本)0819428256

Results are established for a simulated data fusion architecture featuring a synthetic two-class Gaussian problem, with Bayesian recognisers. The recognisers output posterior probabilities for each class. The probabilities from two or more recognisers of identical error rate are quantised using the nearest-neighbour coding rule. The coded values are decoded at a fusion centre and fused. A decision is made from the fused probabilities. The performance of the architecture is examined experimentally using code values that are uniformly distributed and code values that are produced using the Linde-Buzo-Grey (LEG) algorithm. Results are produced for two to six sensors and two to 32 code values. These results are compared to fusing probabilities represented using 32 bit floating-point numbers. Using 32 uniform or LEG-produced code values, produces results that are at most only 1% worse than fusing the uncoded probabilities.

关键词： data fusion quantisation architecture

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Intelligent processing techniques for sensor fusion

Intelligent processing techniques for sensor fusion

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

作者： Byrd, K Smith, B Allen, D Morris, N Bjork, C Deal-Giblin, K Nichols Res Corp Huntsville AL 35815 USA

ISBN: (纸本)0819428256

Intelligent processing techniques which can effectively combine sensor data from disparate sensors by selecting and using only the most beneficial individual sensor data is a critical element of exoatmospheric interceptor systems. A major goal of these algorithms is to provide robust discrimination against stressing threats in poor a priori conditions, and to incorporate adaptive approaches in off-nominal conditions. This paper summarizes the intelligent processing algorithms being developed, implemented and tested to intelligently fuse data from passive infrared and active LADAR sensors at the measurement, feature and decision level. These intelligent algorithms employ dynamic selection of individual sensor features and the weighting of multiple classifier decisions to optimize performance in good a priori conditions and robustness in poor a priori conditions. Features can be dynamically selected based on an estimate of the feature confidence which is determined from feature quality and weighting terms derived from the quality of sensor data and expected phenomenology. Multiple classifiers are employed which use both fuzzy logic and knowledge based approaches to fuse the sensor data and to provide a target lethality estimate. Target designation decisions can be made by fusing weighted individual classifier decisions whose output contains an estimate of the confidence of the data and the discrimination decisions. The confidence in the data and decisions can be used in real time to dynamically select different sensor feature data or to request additional sensor data on specific objects that have not been confidently identified as being lethal or non-lethal, The algorithms are implemented in C within a graphic user interface framework. Dynamic memory allocation and the sequential implementation of the feature algorithms are employed. The baseline set of fused sensor discrimination algorithms with intelligent processing are described in this paper. Example results from

关键词： sensor fusion intelligent processing discrimination feature extraction infrared laser radar fuzzy logic

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fusion of imaging and non-imaging data for surveillance aircraft

Fusion of imaging and non-imaging data for surveillance airc...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Shahbazian, E Gagnon, L Duquet, JR Macieszczak, M Valin, P Lockheed Martin Canada (Canada)

ISBN: (纸本)081942482X

This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for Airborne Surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an Identification Friend or Foe (IFF) system, an Electronic Support Measures (ESM) system, a Spotlight Synthetic Aperture Radar (SSAR), a Forward Looking Infra-Red (FLIR) sensor and a Link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (a) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (b) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (c) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as Multi-sensor Data fusion (MSDF), Situation and Threat Assessment (STA) and Resource Management (RM).

关键词： intelligent decision aids data fusion image analysis automatic target recognition real-time adaptive planning situation and threat assessment resource management

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Morphological filters and wavelet based image fusion for concealed weapons detection

Morphological filters and wavelet based image fusion for con...

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

作者： Ramac, LC Uner, MK Varshney, PK Alford, M Ferris, D Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819428256

When viewing a scene for an object recognition task, one imaging sensor may not provide all the information needed for recognition. One way to obtain more information is to use multiple sensors. These sensors should provide images that contain complementary information about the same scene. After preprocessing the source images, we use image fusion to combine the information from the different sensors. The images to be fused may have some details such as shadows, wrinkles, imaging artifacts, etc., that are not needed in the final fused image. One application of morphological filters is to remove objects of a given size range from the image. Therefore, we use morphological filters in conjunction with wavelets to improve the recognition performance after fusion. After morphological filtering, wavelets are used to construct multiresolution representations of the source images. Once the source images are decomposed, the details are combined to form a composite decomposed image. This method allows details at different levels to be combined independently so that important information is maintained in the final composite image. We are developing image fusion algorithms for concealed weapon detection (CWD) applications. fusion is useful in situations where the sensor types have different properties, e.g., IR and MMW sensors. Fusing these types of images results in composite images which contain more complete information for CWD applications such as detection of concealed weapons on a person, In this paper we present our most recent results in this area.

关键词： image fusion mathematical morphology wavelets

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Hardware device for data fusion and novelty detection in condition monitoring

Hardware device for data fusion and novelty detection in con...

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sensor fusion: architectures, algorithms, and applications iv

作者： Taylor, Odin MacIntyre, John Univ of Sunderland Sunderland United Kingdom

A project aimed at developing a modular hardware data fusion unit for condition monitoring was described by the authors two years ago in sensor fusion: architectures, algorithms and applications II. Over the past two years the project has evolved from a research idea into a real-world condition-monitoring device. The hardware device is known as a Local fusion System (LFS) and is part of a larger modular condition monitoring solution. The LFS unit will be discussed in detail in this paper, describing how the design has evolved into a real hardware based condition-monitoring device that will be taken to market by one of the project partners. The device is responsible for learning the normal operating state of a machine component and identifying when it changes, in a process called novelty detection. To learn the normal operating state of a machine, the device learns a representation of the sensors that are connected to the unit (which could be of varying types and number) by using a novel neural network based fusion centre that will be discussed in detail in this paper. The paper will also look pre- and post-processing issues in a limited hardware environment along with some example development data that if from a real-world machine.

关键词： sensor 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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The minimum sample SPRT for sensor or operating point selection in a single or multisensor environment

The minimum sample SPRT for sensor or operating point select...

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

作者： Pawlak, RJ USN Ctr Surface Warfare Dahlgren Div Self Def Sensors Branch Dahlgren VA 22448 USA

ISBN: (纸本)0819428256

This paper describes a technique for employing the sequential probability ratio test (SPRT) in a single or multisensor environment. The technique minimizes the number of sensor decisions required to declare the null or alternative hypothesis when there is a choice of different sensors or sensor operating points. Thus the technique will be dubbed the Minimum-Sample SPRT (MS-SPRT). The first step of the MS-SPRT requires an off-line optimization of the choice of sensors across all possible values of the alternative hypothesis probability. The second step of the technique involves the application of two Kalman filters to estimate the probability of the alternative hypothesis and to optimize a set of sensor probabilities. The sensor probabilities determine the optimal sensor choice that minimizes the expected number of samples before a decision is made. Three examples are given using simulated data. In the first example, it is shown that the MS-SPRT is not necessary. The second example shows the usefulness of the MS-SPRT when there is a step discontinuity in the null/alternative hypothesis probabilities. In the third example, the MS-SPRT facilitates the use of the proper sensor for a probabilistic variation in the hypothesis probabilities.

关键词： SPRT sensor fusion MS-SPRT Kalman

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