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Tensor decomposition-based multitarget tracking in cluttered environments

: Govaers, F.; Demissie, B.; Khan, A.; Ulmke, M.; Koch, W.

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Journal of Advances in Information Fusion : JAIF 14 (2019), No.1, pp.86-97
ISSN: 1557-6418
Journal Article, Electronic Publication
Fraunhofer FKIE ()

Many real-world applications of target tracking and state estimation are nonlinear filtering problems and can therefore not be solved by closed-form analytical solutions. In the recent past, tensor-based approaches have become increasingly popular due to very effective decomposition algorithms, which allow a compressed representation of discretized, high-dimensional data. It has been shown that by means of a Kronecker format of the Fokker–Planck equation, the Bayesian recursion for prediction and filtering can be solved for probability densities in a canonical polyadic decomposition (CPD). In this paper, the application of this approach on tracking multiple targets in a cluttered environment is presented. It is shown that intensity or probability hypothesis density-based filters can well be implemented using the CPD tensor format.