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multisensor, multisource information fusion: architectures, algorithms, and applications 2009

作者： Sander, Jennifer Heizmann, Michael Goussev, Igor Beyerera, Jürgen Lehrstuhl für Interaktive Echtzeitsysteme Adenauerring 4 D-76131 Karlsruhe Germany Fraunhofer-Institut für Informations- und Datenverarbeitung IITB Fraunhoferstraße 1 D-76131 Karlsruhe Germany

ISBN: (纸本)9780819476111

Local Bayesian fusion approaches aim to reduce high storage and computational costs of Bayesian fusion which is separated from fixed modeling assumptions. Using the small world formalism, we argue why this proceeding is conform with Bayesian theory. Then, we concentrate on the realization of local Bayesian fusion by focussing the fusion process solely on local regions that are task relevant with a high probability. The resulting local models correspond then to restricted versions of the original one. In a previous publication, we used bounds for the probability of misleading evidence to show the validity of the pre-evaluation of task specific knowledge and prior information which we perform to build local models. In this paper, we prove the validity of this proceeding using information theoretic arguments. For additional efficiency, local Bayesian fusion can be realized in a distributed manner. Here, several local Bayesian fusion tasks are evaluated and unified after the actual fusion process. For the practical realization of distributed local Bayesian fusion, software agents are predestinated. There is a natural analogy between the resulting agent based architecture and criminal investigations in real life. We show how this analogy can be used to improve the efficiency of distributed local Bayesian fusion additionally. Using a landscape model, we present an experimental study of distributed local Bayesian fusion in the field of reconnaissance, which highlights its high potential. © 2009 SPIE.

关键词： Efficiency

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Enhancing situational awareness by means of visualization and information integration of sensor networks

Enhancing situational awareness by means of visualization an...

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

作者： Timonen, J. Vankka, J. Finnish Natl Def Univ Dept Mil Technol POB 7 FI-00861 Helsinki Finland

ISBN: (纸本)9780819495471

This paper presents a solution for information integration and sharing architecture, which is able to receive data simultaneously from multiple different sensor networks. Creating a Common Operational Picture (COP) object along with the base map of the building plays a key role in the research. The object is combined with desired map sources and then shared to the mobile devices worn by soldiers in the field. The sensor networks we used focus on location techniques indoors, and a simple set of symbols is created to present the information, as an addition to NATO APP6B symbols. A core element in this research is the MUSAS (Mobile Urban Situational Awareness System), a demonstration environment that implements central functionalities. information integration of the system is handled by the Internet Connection Engine (Ice) middleware, as well as the server, which hosts COP information and maps. The entire system is closed, such that it does not need any external service, and the information transfer with the mobile devices is organized by a tactical 5 GHz WLAN solution. The demonstration environment is implemented using only commercial off-theshelf (COTS) products. We have presented a field experiment event in which the system was able to integrate and share real time information of a blue force tracking system, received signal strength indicator (RSSI) based intrusion detection system, and a robot using simultaneous location and mapping technology (SLAM), where all the inputs were based on real activities. The event was held in a training area on urban area warfare.

关键词： Common Operational Picture information integration urban warfare wireless sensor platform Mobile Urban Area Situational Awareness System Situational Awareness

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A full-scale prototype multisensor system for fire detection and situational awareness

A full-scale prototype multisensor system for fire detection...

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Conference on multisensor, multisource information fusion

作者： Minor, Christian P. Johnson, Kevin J. Rose-Pehrsson, Susan L. Owrutsky, Jeffrey C. Wales, Stephen C. Steinhurst, Daniel A. Gottuk, Daniel T. Novo Res Inc 1900 Eikin StSuite 230 Alexandria VA 22308 USA US Naval Res Lab Washington DC 20375 USA Hughes Associates Inc Baltimore MD 21227 USA

ISBN: (纸本)9780819466938

A data fusion-based, multisensory detection system, called "Volume Sensor", was developed under the Advanced Damage Countermeasures (ADC) portion of the US Navy's Future Naval Capabilities program (FNC) to meet reduced manning goals. A diverse group of sensing modalities was chosen to provide an automated damage control monitoring capability that could be constructed at a relatively low cost and also easily integrated into existing ship infrastructure. Volume Sensor employs an efficient, scalable, and adaptable design framework that can serve as a template for heterogeneous sensor network integration for situational awareness. In the development of Volume Sensor, a number of challenges were addressed and met with solutions that are applicable to heterogeneous sensor networks of any type. These solutions include: 1) a uniform, but general format for encapsulating sensor data, 2) a communications protocol for the transfer of sensor data and command and control of networked sensor systems, 3) the development of event specific data fusion algorithms, and 4) the design and implementation of modular and scalable system architecture. In full-scale testing on a shipboard environment, two prototype Volume Sensor systems demonstrated the capability to provide highly accurate and timely situational awareness regarding damage control events while simultaneously imparting a negligible footprint on the ship's 100 Mbps Ethernet network and maintaining smooth and reliable operation in a real-time fashion.

关键词： fire detection flood detection multisensor multicriteria data fusion multivariate analysis video sensor audio sensor spectral sensor sensor network

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A field demonstration of multi-INT surveillance and data fusion

A field demonstration of multi-INT surveillance and data fus...

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Conference on multisensor, multisource information fusion

作者： Na, Arthur Havens, Steve Argon ST (United States)

ISBN: (纸本)9780819471659

Collecting data in any domain is often only the first step in creating intelligence. This is the beginning of a process which includes fusing that data with other data sources to create something more than the individual data elements. This has been historically difficult because collection sensors are optimized for their respective conventional domains. We have developed a multi-sensor system combining an electro-optical camera and infi-ared camera, using a revolutionary data handling and manipulating process to fuse multiple types of data to create inteffigence. To address the data fusion needs of such a system, we have developed an internationally recognized, open data fusion language called Transducer Markup Language (TML). TML was used in conjunction with the multi-sensor system to describe data from the EO and IR cameras and support sensors all within a self-contained mobile delivery system during mifitary exercises in July 2007. The TML data was collected and disseminated to multiple users within a Service Oriented architecture (SOA), after which it was Ruther distributed using Cursor-On-Target (COT) messages over a second network. This demonstrated both data fusion of multiple sensor data sources and integration into existing data distribution infrastructures. Key innovations of this demonstration included the use of multiple sensors including EO & IR cameras and a single sensor data exchange language to capture and describe sensor output.

关键词： sensor fusion Transducer Markup Language sensor network SOA sensor actuator data exchange standard common data model common sensor model sensor ontology

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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 ...

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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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A group tracking algorithm for ground vehicle convoys

A group tracking algorithm for ground vehicle convoys

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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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Scalable sensor management for automated fusion and tactical reconnaissance

Scalable sensor management for automated fusion and tactical...

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

作者： Walls, Thomas J. Wilson, Michael L. Partridge, Darin C. Haws, Jonathan R. Jensen, Mark D. Johnson, Troy R. Petersen, Brad D. Sullivan, Stephanie W. Naval Res Lab 4555 Overlook Ave SW Washington DC 20375 USA

ISBN: (纸本)9780819495471

The capabilities of tactical intelligence, surveillance, and reconnaissance (ISR) payloads are expanding from single sensor imagers to integrated systems-of-systems architectures. Increasingly, these systems-of-systems include multiple sensing modalities that can act as force multipliers for the intelligence analyst. Currently, the separate sensing modalities operate largely independent of one another, providing a selection of operating modes but not an integrated intelligence product. We describe here a Sensor Management System (SMS) designed to provide a small, compact processing unit capable of managing multiple collaborative sensor systems on-board an aircraft. Its purpose is to increase sensor cooperation and collaboration to achieve intelligent data collection and exploitation. The SMS architecture is designed to be largely sensor and data agnostic and provide flexible networked access for both data providers and data consumers. It supports pre-planned and ad-hoc missions, with provisions for on-demand tasking and updates from users connected via data links. Management of sensors and user agents takes place over standard network protocols such that any number and combination of sensors and user agents, either on the local network or connected via data link, can register with the SMS at any time during the mission. The SMS provides control over sensor data collection to handle logging and routing of data products to subscribing user agents. It also supports the addition of algorithmic data processing agents for feature/target extraction and provides for subsequent cueing from one sensor to another. The SMS architecture was designed to scale from a small UAV carrying a limited number of payloads to an aircraft carrying a large number of payloads. The SMS system is STANAG 4575 compliant as a removable memory module (RMM) and can act as a vehicle specific module (VSM) to provide STANAG 4586 compliance (level-3 interoperability) to a non-compliant sensor system. Th

关键词： multi-INT data fusion sensing modality sensor management network sensor tasking automation algorithmic processing scalable systems-of-systems

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Combination of multi-source information to ground target group tracking

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

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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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Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rand, Robert S. Khuon, Timothy S. National Geospatial-Intelligence Agency Office of Innovision Basic and Applied Research 7500 GEOINT Drive Springfield VA 22153 United States

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. © 2012 SPIE.

关键词： Sensor data fusion

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Navigation of uncertain terrain by fusion of information from real and synthetic imagery

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Lyons, Damian M. Nirmal, Paramesh Paul Benjamin, D. Fordham University Robotics and Computer Vision Laboratory Bronx NY 10458 United States Pace University Department of Computer Science New York NY 10023 United States

ISBN: (纸本)9780819490858

We consider the scenario where an autonomous platform that is searching or traversing a building may observe unstable masonry or may need to travel over unstable rubble. A purely behaviour-based system may handle these challenges but produce behaviour that works against long-terms goals such as reaching a victim as quickly as possible. We extend our work on ADAPT, a cognitive robotics architecture that incorporates 3D simulation and image fusion, to allow the robot to predict the behaviour of physical phenomena, such as falling masonry, and take actions consonant with long-term goals. We experimentally evaluate a cognitive only and reactive only approach to traversing a building filled with various numbers of challenges and compare their performance. The reactive only approach succeeds only 38% of the time, while the cognitive only approach succeeds 100% of the time. While the cognitive only approach produces very impressive behaviour, our results indicate how much better the combination of cognitive and behaviour-based can be. © 2012 SPIE.

关键词： Robots

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