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This thesis by Karl Granström explores advanced methodologies in the realm of autonomous robotics, specifically focusing on Simultaneous Localization and Mapping (SLAM) and Multiple Target Tracking (MTT). The work addresses the challenges robots face in mapping environments and tracking moving objects using Probability Hypothesis Density (PHD) filters. Granström presents innovative approaches for detecting loop closures in SLAM with laser range sensors and extends target tracking by modeling targets with Gaussian mixtures and Gaussian inverse Wishart mixtures. This research is particularly relevant for applications in urban environments, where accurate tracking of pedestrians, bicycles, and vehicles is essential. The intended audience includes researchers and professionals in robotics and electrical engineering.
Robotics. --- Target acquisition. --- Robotics --- Target acquisition
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Backscattering --- Target acquisition --- Research
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Describes a prototype version of the tactical air target recommender (TATR), an "expert system" developed at RAND to help tactical air targeteers select and prioritize airfields and target elements on those air fields. Under the interactive direction of a user, TATR can preferentially order enemy airfields, determine targets to attack on those airfields, and identify weapon systems that are effective against those targets. Its primary functions are to provide an attack plan and to project the results of implementing that plan. It applies predetermined heuristics to generate an initial plan, which can then be modified by the user. By projecting plan results over a series of days, TATR can assist the user in deciding on the best plan to implement. The program also interactively maintains and updates its databases and provides detailed information on plans, friendly force capability, and enemy force posture and status. TATR is programmed in the ROSIE programming language. Earlier versions of TATR were reported in N-1645 and N-1976.
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