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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architecture, Algorithms, and Applications 2005"

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Real time implementation of image alignment and fusion

Real time implementation of image alignment and fusion

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Conference on multisensor, multisource information fusion

作者： Dwyer, D Smith, M Dale, J Heather, J Octec Ltd Western Ctr Bracknell RG12 1RW Berks England

ISBN: (纸本)0819457981

Many modern imaging and surveillance systems contain more than one sensor. For example, most modern airborne imaging pods contain at least visible and infrared sensors. Often these systems have a single display that is only capable of showing data from either camera, and thereby fail to exploit the benefit of having simultaneous multi-spectral data available to the user. It can be advantageous to capture all spectral features within each image and to display a fused result rather than single band imagery. This paper discusses the key processes necessary for an image fusion system and then describes how they were implemented in a real-time, rugged hardware system. The problems of temporal and spatial misalignment of the sensors and the process of electronic image warping must be solved before the image data is fused. The techniques used to align the two inputs to the fusion system are described and a summary is given of our research into automatic alignment techniques. The benefits of different image fusion schemes are discussed and those that were implemented are described. The paper concludes with a summary of the real-time implementation of image alignment and image fusion by Octec and Waterfall Solutions and the problems that have been encountered and overcome.

关键词： image alignment image fusion real time image processing

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A generic model of tactical plan recognition for threat assessment

A generic model of tactical plan recognition for threat asse...

引用

Conference on multisensor, multisource information fusion

作者： Suzic, R Swedish Def Res Agcy FOI SE-17290 Stockholm Sweden

ISBN: (纸本)0819457981

Plan recognition has to be performed in a statistically robust manner concerning a possibly infinite number of tactical situations and different types of units. We need a generic model for tactical plan recognition where we combine observations and a priori knowledge in a flexible manner by using suitable methodologies and by having a large hypothesis space taken into account. Threat and therefore observed agent's plans should be put into a context. Here, we propose Multi-Entity Bayesian Networks (MEBN), introduced in [2], which enable the composition of Bayesian Networks from the network pieces, as the key methodology when designing flexible plan recognition models. However, Bayesian network pieces (fragments) must be compatible and therefore we propose ontology for generic plan recognition using Bayesian network fragments. Additionally, we claim that by using multi-entity network fragments we expand the hypothesis space and using this approach various multi-agents structures can be expressed. Our final contribution is that we incorporate the use of explicit utilities in our plan recognition model.

关键词： Fuzzy logic Sensors Network architectures Data modeling Defense and security Defense technologies Environmental sensing Stochastic processes information fusion Statistical modeling

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Sensor fusion for coastal waters surveillance

Sensor fusion for coastal waters surveillance

引用

Conference on multisensor, multisource information fusion

作者： Doshi, B Benmohamed, L Chimento, P Wang, IJ Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (纸本)0819457981

We describe a large sensor field whose mission is to protect coastal waters by detecting objects like submarines. The system is buoy-based and distributed over a littoral area. The opportunities for detection are short and intermittent and the signal to noise ratio is low. The topology of the field changes with time due to currents, wind, tides and storms. The field has a number of gateway nodes that have the capability to transmit off-field through a satellite, a ship or a plane. We propose an approach to fusion that includes on-buoy processing, cooperative processing with nearest neighbors and the potential for off-field processing. Each stage of processing tries both to minimize false positive events and to maximize the probability of detection when an object is present. It also tries to minimize power used in order to prolong the life of the field. We analyze the optimal placement of gateway nodes in the field to minimize power consumption and maximize reliability and probability of successful off-field transmission. We analyze the duty cycles of the sensor and gateway nodes to optimize lifetime. We also analyze the traffic that the field will be expected to handle in order to support network control and coordination, distributed fusion, off-field communication (including queries and responses, and reporting of detection events), forwarding of traffic through individual sensor nodes toward gateways or for fusion.

关键词： distributed sensor field sensor networks coastal surveillance

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Hybrid framework for fusion 2+in multi-multi air engagement

Hybrid framework for fusion 2+in multi-multi air engagement

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Conference on multisensor, multisource information fusion

作者： Lee, KD Llinas, J Adleson, R Decis Analyt Corp Arlington VA 22202 USA

ISBN: (纸本)0819457981

fusion 2+ of air-to-air engagement involves pressing, real-time constraints and very large amounts of imperfect data. Real-time data acquired during an air-to-air engagement will have different types of imperfection;two representative classes of imperfection are vagueness and ambiguity in the data. However, the current approaches of managing fusion 2+ are limited to utilize either vague data or ambiguous data. The most popular fusion technique for vague data is Fuzzy Logic, and for ambiguous data, the Bayesian Network. The challenge addressed in this proposal is to explore the framework of a hybrid processing fusion 2+ model that can formally process both vague (fuzzy) and ambiguous (probabilistic) data types. There are two major issues for building this fusion 2+ model. The first issue is to mathematically integrate the heterogeneous models, which have different domains, probability and possibility. The second issue is to programmatically integrate two different S/Ws. For solving the first issue, this research explores and adopts two novel transformation methods between probability and possibility and compares the sensitivity between methods. Also this research provides an Object Oriented Tool for building a hybrid model by adopting an Application Programming Interface, so that we can model the complex (multi-to-multi) fusion 2+ model of an air-to-air engagement.

关键词： fusion 2+ situation awareness Bayesian networks fuzzy logic Application Programming Interface (API)

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A group tracking algorithm for ground vehicle convoys

A group tracking algorithm for ground vehicle convoys

引用

Conference on multisensor, multisource information fusion

作者： Chin, WC Lee, HB Xiao, XH Ng, GW How, KY DSO Natl Labs Singapore 118230 Singapore

ISBN: (纸本)0819457981

Tracking multiple ground targets under clutter and in real time poses several likely challenges: vehicles often get masked by foliage or line-of-sight (LOS) problems, manifesting in misdetections and false alarms. Further complications also arise when groups of vehicles merge or split. This paper presents an attempt to address these issues using a group tracking approach. Group tracking is a way to ameliorate, or at least soften the impact of such issues from the hope that at least partial information will be received from each target group even when the probability of detection, P-D of each individual member is low. A Strongest Neighbour Association (SNA) method of measurement-to-track association based on space-time reasoning and track-measurement similarity measures has been derived. We combine the association strengths of the space-time dynamics, the degree-of-overlap and the historical affinity metrics to relate measurements and tracks. The state estimation is based on standard Kalman filter. Lastly, a Pairwise Historical Affinity Ratios (PHAR) is proposed for the detection of a split scenario. This method has been tested to work well on a simulated convoy-splitting scenario. Monte Carlo experiment runs of six different error rates with five different compositions of errors have been conducted to assess the performance of the tracker. Results indicated that the group tracker remains robust (> 80% precision) even in the presence of high individual source track error rates of up to 30%.

关键词： group tracking strongest neighbour association Kalman filtering data fusion

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Prior knowledge integration of road dependant ground target behaviour for improving tracking reliability

Prior knowledge integration of road dependant ground target ...

引用

Conference on multisensor, multisource information fusion

作者： Gattein, S Vannoorenberghe, P Contat, M EADS S&DE Voie Future F-27106 Val De Reuil France

ISBN: (纸本)0819457981

This paper describes an algorithm for tracking ground targets, mainly based on measurements from a MTI radar. We examine the use of prior knowledge on target type for automatically improving the performance of a purely kinematic tracker. This tracker is an adaptation from a Variable Structure Interacting Multiple Model (VS-IMM) estimator acting within an S-Dimensional Assignment method. The algorithm can thus track several targets with false alarms, and the variable structure allows to use only the relevant dynamic models relative to roads in the vicinity of the target's prediction. However the sets of possible on-road and off-road models do not interact as in the usual IMM mechanism. Instead the on-road behaviour of a target across S-I radar scans is optimally managed in a hypothesis tree, since we do not mix incompatible road models. However, both global on-road and off-road behaviours are handled by mode transition probabilities, making it possible to track targets that change their behaviour regarding the road network with the same algorithm. Next an automatic measured type data integration scheme is examined, that can be connected to the above kinematic framework. The incoming measured type data is modelled by a belief function to reflect uncertainty, as well as the sensor's reliability. Simulation results show the operation of the kinematic tracker for road targets, illustrating the relevance of the variable structure dynamic model set.

关键词： data fusion target tracking VS-MM S-D assignment belief function

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Combination of multi-source information to ground target group tracking

Combination of multi-source information to ground target gro...

引用

multisensor, multisource information fusion: architecture, algorithms, and applications 2005

作者： Xiao, Xuhong Lee, Hian Beng Chin, Wei Chuen Ng, Gee Wah How, Khee Yin DSO National Laboratories 20 Science Park Drive Singapore 118230 Singapore

Ground targets can be detected by multiple classes of sources in military surveillance. There are two main challenges for the acquisition of ground situation picture from data collected by multiple sources. First, different sources provide different information that describes military entities at different granularities and accuracies. This makes processing of data in one unified tracker difficult. Secondly, the data update rates of these sources vary, some update rates could be very low (such as hours), leading to greater difficulty for data association. This paper presents our attempt in multi-source ground target tracking, taking the above two issues into consideration. Targets are tracked in groups, and multiple trackers are designed so that data of different granularities are processed by the respective trackers. Tracks from these trackers are then correlated to form the common picture. Two strategies are proposed to handle the problem of varying data update rate. The first strategy is to exploit different approaches to calculate the beliefs of data association according to update rates. When update rate is high, the belief is calculated by a distance function based on estimated kinematical states. When update rate is low, the belief of data association is computed using Bayesian network. Bayesian network infers the beliefs based on observed information and domain knowledge. The second strategy is to exploit the complementary information in different trackers to improve data association. The first step is to find the correlation among tracks from different trackers. This track-track correlation information is fed back to modify the beliefs of data associations in the tracks. Experiments demonstrated that such combination of multi-source information not only produces more complete ground picture, but also helps to improve the data association accuracy in the respective trackers.

关键词： Sensor data fusion

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Real time sensor validation and fusion for distributed autonomous sensors

Real time sensor validation and fusion for distributed auton...

引用

Conference on multisensor, multisource information fusion

作者： Yuan, XJ Li, XS Buckles, B Tulane Univ EECS Dept New Orleans LA 70118 USA

ISBN: (纸本)0819453579

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

关键词： real time sensor validation and fusion distributed sensor system design dynamic validation curve file based knowledge system confidence weighted averaging robust diagnostic maps

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Impact of expanding the multiplicity of information sources on fusion system architecture and fusion logic options

Impact of expanding the multiplicity of information sources ...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Dasarathy, BV

ISBN: (纸本)0819449598

Various fusion system architectures postulated and studied previously for environments with two and three data sources are further explored in this study to bring out the expanding scope for delineating the architecture options for multiple data source environments. A spectrum of single and multi-stage fusion architecture options are defined. The potential for such expansion of choices is illustrated using the scenario with four data sources as an example. Potential problem environments corresponding to this range of two to four data sources are identified. Various fusion logic strategies that can be brought to bear for the analysis of these fusion architecture options, when these fusion architectures are employed for Decisions In - Decision Out (DEI-DEO) fusion, are also discussed.

关键词： sensor fusion decision fusion fusion strategies multi-sensor suites

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Improved image segmentation using an inference fusion architecture

Improved image segmentation using an inference fusion archit...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Brucks, T Riddle, J Van Maasdam, P Raytheon Missile Syst Tucson AZ 85734 USA

ISBN: (纸本)0819449598

Image segmentation, a key component in many Automatic Target Recognition (ATR) systems, has received considerable attention in the research community in recent years. A variety of segmentation approaches exist, and attempts have been made to combine various approaches in order to find more robust solutions. In this paper, the authors describe an inference fusion architecture for combining individual segmentation concepts which results in improved performance over the individual algorithms. We consider segmentation algorithms with several disparate cost functions as experts with a narrowly defined set of goals. The information obtained from each expert is combined and weighted with available evidence using an agent based inference system, resulting in an adaptive, robust and highly flexible image segmentation. Results obtained by applying this approach will be presented.

关键词： ATR image segmentation inference systems information fusion

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