In the present study, the investigation by General Dynamics Canada, formerly Computing Devices Canada, into Bayesian Inference shows improved sensorfusion of multiple scanning sensors in the detection of buried anti-...
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ISBN:
(纸本)0819444812
In the present study, the investigation by General Dynamics Canada, formerly Computing Devices Canada, into Bayesian Inference shows improved sensorfusion 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.
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 Surveillan...
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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).
The Multi-sensorfusion Management (MSFM) algorithm positions multiple, detection-only, passive sensors in a two-dimensional plane to optimise the fused probability of detection using a simple decision fusion method, ...
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ISBN:
(纸本)0819431931
The Multi-sensorfusion Management (MSFM) algorithm positions multiple, detection-only, passive sensors in a two-dimensional plane to optimise the fused probability of detection using a simple decision fusion method, previously the MSFM algorithm was evaluated on two synthetic problem domains comprising of both static and moving targets(1). In the original formulation the probability distribution of the target location was modelled using a non-parametric approach. The logarithm of the fused detection probability was used as a criterion function for the optimisation of the sensor positions. This optimisation used a straightforward gradient ascent approach, which occasionally found local optima. Following the placement optimisation the sensors were deployed and the individual sensor detections combined using a logical OR fusion rule. The target location distribution could then be updated using the method of sampling, importance re-sampling (SIR). In the current work the algorithm is extended to admit a richer variety of behaviour. More realistic sensor characteristic models are used which include detection-plus-bearing sensors and false alarm probabilities commensurate with actual sonar sensor systems. In this paper the performance of the updated MSFM algorithm is illustrated on a realistic anti-submarine warfare (ASW) application(2) in which the placement of the sensors is carried out incrementally, allowing for the optimisation of both the location and the number of sensors to be deployed.
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 ...
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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.
Most of the studies reported in the open literature on sensorfusion for target detection in multisensor environments have proposed fusion strategies that are essentially independent of the identity of the object as o...
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ISBN:
(纸本)081942482X
Most of the studies reported in the open literature on sensorfusion for target detection in multisensor environments have proposed fusion strategies that are essentially independent of the identity of the object as observed by the individual sensors. This independence makes the fusion strategies symmetric relative to the identity of the objects in terms of their target or non-target (decoys, clutter etc.) status. In this study, new ground is broken in terms of fusion strategies which, by being dependent on the identity of the objects as perceived by the individual sensors, can be asymmetric relative to the identity of the object under observation. The study analyzes the scope for and benefits of deployment of these asymmetric fusion strategies as compared to the conventional Boolean logic based symmetric fusion strategies studied previously. Under these conventional fusion strategies, while use of the Boolean AND logic for fusion tends to minimize the false alarm rate, use of OR logic maximizes the detection probability. Under the asymmetric fusion strategies conceived here, it is possible to drive the decision process towards maximizing the probability of detection of lethal objects (targets) while simultaneously minimizing the false alarm rates. The performance of these asymmetric fusion strategies, when embedded in a recursive structure that permits multiple observation and temporal fusion along the time line, are analyzed relative to that of the conventional symmetric fusion strategies to parametrically determine their domains of beneficial deployment.
This plenary presentation offers a panoramic overview of the field of multi-sensor, and/or multi-source information fusion from three complementary perspectives, namely, architectures, algorithms, and applications. Th...
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Most research and prototype development of automated methods for situational estimation in the data fusion community have applied heuristic approaches coupled to techniques for uncertainty management. Reasoning theori...
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ISBN:
(纸本)081942482X
Most research and prototype development of automated methods for situational estimation in the data fusion community have applied heuristic approaches coupled to techniques for uncertainty management. Reasoning theorists would label these methods as those of the parametric reasoning class. Such methods are reasonable when the so-called ''closed world'' assumption can be confidently applied (ability to full prespecify expected conditions) which might have been reasonable in the ''Soviet Era'' but would appear fragile/brittle for current-day application. Motivated in part by these considerations and by the need to consider much more cost-effective knowledge-based-system development in an era of declining budgets, this paper offers some discussion on the applicability of more formal methods of reasoning for KBS. It is concluded that strictly formal methods for real-world applications require yet further theoretical development but that movement toward formalization is possible.
Traditional surface reconstruction techniques have focused exclusively on contour sections in one anatomical direction. However, in certain medical situations, such as in presurgical planning and radiation treatment, ...
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ISBN:
(纸本)081942482X
Traditional surface reconstruction techniques have focused exclusively on contour sections in one anatomical direction. However, in certain medical situations, such as in presurgical planning and radiation treatment, medical scans are taking of the patient in three orthogonal directions to better localize pathologies. fusion techniques must be used to register this data with respect to a surface fitting method. We explore the issues involved in fusing data from ellipsoid anatomy, such as the brain, heart, and major organs. The output of the fusion process is a set of data points which are correlated to one another to represent the surface of a single object. This data network is then used as input to a surface fitting algorithm which depends on two sampling metrics which we derive. The solution to this problem is important in presurgical planning, radiation treatment, and telemedical systems.
Polaris sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent o...
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ISBN:
(纸本)9781510601130
Polaris sensor Technologies reports on the development of Pedestrian Automated System for Enforcement and Safety (PASES), a radar and video based system used to monitor vehicle and pedestrian traffic with the intent of improving pedestrian safety. Data is fused from a system of multiple sensors and multiple sensor modalities to identify vehicular violations of pedestrian right of way. A focus was placed on the selection of low cost COTS sensors to make the system more widely available to state and local DOTs with limited budgets. applications include automated enforcement, adaptive traffic control, and improved intersection and crosswalk design based on high quality data available for traffic engineering. We discuss early results with high fidelity sensors, and the performance trades made in order to make the system affordable. A discussion of the system processing architecture is included which highlights the treatment of each sensor data type, and the means of combining the processed data products into state information related to traffic incidents involving vehicles and pedestrians.
Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks. To improve track quality, we extend previous tracking approaches by investigating human cognitive-level ...
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Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks. To improve track quality, we extend previous tracking approaches by investigating human cognitive-level fusion for constraining the set of plausible targets where the number of targets is not known a priori. The target track algorithm predicts a belief in the position and pose for a set of targets and an automatic target recognition algorithm uses the pose estimate to calculate an accumulated target-belief classification confidence measure. The human integrates the target track information and classification confidence measures to determine the number and identification of targets. This paper implements the cognitive belief filtering approach for sensorfusion and resolves target identity through a set-theory approach by determining a plausible set of targets being tracked.
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