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Title: A Distributed Approach to Dynamic Autonomous Agent Placement for Tracking Moving Targets with Application to Monitoring Urban Environments
Keywords: Evasive targets;Target tracking;Urban environments;Sensor placement;Motion models;Surveillance and reconnaissance;Autonomous vehicles
Issue Date: 22-Nov-2004
Publisher: Georgia Institute of Technology
Description: The problem of dynamic autonomous agent placement for tracking moving targets arises in many real-life applications, such as rescue operations, security, surveillance, and reconnaissance. The objective of this thesis is to develop a distributed hierarchical approach to address this problem. After the approach is developed, it is tested on a number of urban surveillance scenarios. The proposed approach views the placement problem as a multi-tiered architecture entailing modules for low-level sensor data preprocessing and fusion, decentralized decision support, knowledge building, and centralized decision support. This thesis focuses upon the modules of decentralized decision support and knowledge building. The decentralized decision support module requires a great deal of coordination among agents to achieve the mission objectives. The module entails two classes of distributed algorithms: non-model-based algorithms and model-based algorithms. The first class is used as a place holder while a model is built to describe agents knowledge about target behaviors. After the model is built and evaluated, agents switch to the model-based algorithms. To apply the approach to urban environments, urban terrain zones are classified, and the problem is mathematically formulated for two different types of urban terrain, namely low-rise, widely spaced and high-rise, closely spaced zones. An instance of each class of algorithms is developed for each of the two types of urban terrain. The algorithms are designed to run in a distributed fashion to address scalability and fault tolerance issues. The class of model-based algorithms includes a distributed model-based algorithm for dealing with evasive targets. The algorithm is designed to improve its performance over time as it learns from past experience how to deal with evasive targets. Apart from the algorithms, a model estimation module is developed to build motion models online from sensor observations. The approach is evaluated through a set of simulation experiments inspired from real-life scenarios. Experimental results reveal the superiority of the developed algorithms over existing ones and the applicability of the online model-building method. Therefore, it is concluded that the overall distributed approach is capable of handling agent placement or surveillance applications in urban environments among other applications.;Ph.D.;Committee Chair: Dr. George Vachtsevanos; Committee Member: Dr. Aaron Lanterman; Committee Member: Dr. Bonnie Heck; Committee Member: Dr. Jay Lee; Committee Member: Dr. Linda Wills
Type Of Material: OTHER
Appears in Collections:College of Engineering (CoE)

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