Abstract:
Target tracking in usually performed assuming the target under consideration is adequately modelled as a point target with no reference to constraints on its motion. Howe...Show MoreMetadata
Abstract:
Target tracking in usually performed assuming the target under consideration is adequately modelled as a point target with no reference to constraints on its motion. However, in reality, target motion is constrained by achievable maximum speeds and accelerations. This is non-standard information and its incorporation into conventional tracking, while promising significant benefits, poses a significant challenge in constructing the posterior probability density function (PDF) of target state. In this paper, the optimal Bayes' recursion for this posterior PDP is derived and an algorithm for implementing this recursion based on stochastic sampling techniques is presented. An illustrative example demonstrating the benefits of incorporating this information is also provided.
Date of Conference: 10-13 July 2000
Date Added to IEEE Xplore: 06 August 2002
Print ISBN:2-7257-0000-0