As existing hydrocarbon reservoirs are developed and exploited, the need to improve techniques for identifying new reservoirs and describing existing reservoirs grows. Indeed, it has been reported that unless a step c...
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As existing hydrocarbon reservoirs are developed and exploited, the need to improve techniques for identifying new reservoirs and describing existing reservoirs grows. Indeed, it has been reported that unless a step change in resolution is achieved, production from the North Sea fields will start to decline mid decade. This paper explores the potential for tracking techniques to achieve that step change.
The tradeoff between performance and scalability is a fundamental issue in distributed sensor networks. In this paper, we propose a novel scheme to efficiently organize and utilize network resources for tar-et localiz...
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The tradeoff between performance and scalability is a fundamental issue in distributed sensor networks. In this paper, we propose a novel scheme to efficiently organize and utilize network resources for tar-et localization. Motivated by the essential role of geographic proximity in sensing, sensors are organized into geographically local collaborative groups. In a targettracking context, we present a dynamic Group management method to initiate and maintain multiple tracks in a distributed manner. Collaborative groups are formed, each responsible for tracking a single target. The sensor nodes within a group coordinate their behavior using geographically-limited message passing. Mechanisms such as these for managing local collaborations are essential building blocks for scalable sensor network applications.
The multi sensors tracking function developed by AMS for the combat management system (CMS) on the new anti-air-warfare (AAW) Horizon frigates is presented. AMS has already implemented this function on the Italian fas...
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The multi sensors tracking function developed by AMS for the combat management system (CMS) on the new anti-air-warfare (AAW) Horizon frigates is presented. AMS has already implemented this function on the Italian fast patrol boats and it is also implementing it on the new Andrea Doria aircraft carrier. The multi sensor tracking function provides on-board above water sensor data to contribute for the compilation of the tactical picture (TP), a representation of the tactical situation, together with other surveillance information. The TP is to be accurate and stable, as well as enriched with data received from tactical links.
Multi-sensor systems for wide area surveillance and tracking are an area of increasing interest for civil and military applications. In order to design these systems to reliably achieve a specification, performance mo...
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Multi-sensor systems for wide area surveillance and tracking are an area of increasing interest for civil and military applications. In order to design these systems to reliably achieve a specification, performance models are required for each of the system components, to allow synthesis of the overall system performance. The work presented in this paper addresses the problem of a performance model for the target detection and recognition performance of a SAR sensor component in a surveillance system. A Monte Carlo simulation based approach is described which is used to predict the receiver operating characteristic (ROC) as a function of the target, the clutter background and the sensor specification for a template matching recognition algorithm. Some initial results from an experimental validation trial are presented and it is concluded that there is sufficient similarity between the current model's predictions of performance and the actual performance achieved with representative real data to encourage further development work.
Data association is one of the essential parts of a multiple-target- tracking system. The paper introduces a report-track association-evaluation technique based on the well known Markov-chain Monte-Carlo (MCMC) method...
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Data association is one of the essential parts of a multiple-target- tracking system. The paper introduces a report-track association-evaluation technique based on the well known Markov-chain Monte-Carlo (MCMC) method, which estimates the statistics of a random variable by way of efficiently sampling the data space. An important feature of this new association-evaluation algorithm is that it can approximate the marginal association probability with scalable accuracy as a function of computational resource available. The algorithm is tested within the framework of a joint probabilistic data association (JPDA). The result is compared with JPDA tracking with Fitzgerald's simple JPDA data-association algorithm. As expected, the performance of the new MCMC-based algorithm is superior to that of the old algorithm. In general, the new approach can also be applied to other trackingalgorithms as well as other fields where association of evidence is involved.
A particle filter approach is suggested for trackingtargets in the presence of spurious measurements that are exhibit an unknown bias relative to the true target location. The filter is demonstrated for the tracking ...
A particle filter approach is suggested for trackingtargets in the presence of spurious measurements that are exhibit an unknown bias relative to the true target location. The filter is demonstrated for the tracking in the presence of possible wake corruption - i.e. sensor measurements may be "captured" by a wake behind the target.
The paper examines the accuracy of the Bar-Shalom formula for computing the fused estimate from two filters, tracking a single target with the exact minimum mean square estimator. It is shown that the errors are small...
The paper examines the accuracy of the Bar-Shalom formula for computing the fused estimate from two filters, tracking a single target with the exact minimum mean square estimator. It is shown that the errors are small and that the simplicity of the Bar-Shalom formula makes it appropriate to use. Simulation results on its use for fusing estimates from two and three filters are presented.
As existing hydrocarbon reservoirs are developed and exploited, the need to improve techniques for identifying new reservoirs and describing existing reservoirs grows. Indeed, it has been reported that unless a step c...
As existing hydrocarbon reservoirs are developed and exploited, the need to improve techniques for identifying new reservoirs and describing existing reservoirs grows. Indeed, it has been reported that unless a step change in resolution is achieved, production from the North Sea fields will start to decline mid decade. This paper explores the potential for tracking techniques to achieve that step change.
This paper discusses the advantages, disadvantages and methodology of maximum likelihood estimators (MLEs) applied to tracking problems. The paper goes on to explain how a criterion derived by Akaike can, in conjuncti...
This paper discusses the advantages, disadvantages and methodology of maximum likelihood estimators (MLEs) applied to tracking problems. The paper goes on to explain how a criterion derived by Akaike can, in conjunction with the maximum likelihood fit, be used to help optimise the size of the vector of unknown parameters representing the target kinematics. Some of the concepts discussed are illustrated with numerical results relating to a simple bearings-only passive tracking problem.
The paper addresses the multi-targettracking problem for maneuvering targets in cluttered environments. The multiple scan joint probabilistic data association (MJPDA) algorithm is used for the sake of overcoming the ...
The paper addresses the multi-targettracking problem for maneuvering targets in cluttered environments. The multiple scan joint probabilistic data association (MJPDA) algorithm is used for the sake of overcoming the problem of clutter points and targets which have joint observation. A comparison between different filtering methods through the sliding window of scans is presented. The problem of maneuvering targets is addressed and a new tracking algorithm which uses the multiple scan JPDA and interacting multiple model (IMM) together is formulated.
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