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• W2007901431 abstract "In this paper, a probabilistic approach for estimating time and space-variant parameters of a system, based on sequentially received discrete-time signal values, is presented. The system description is the solution of a linear partial differential equation (PDE). The PDE describes for example the wave propagation of an acoustic wave in a localization system. The solution of the PDE is given by a time-variant and space-variant impulse response. This impulse response is characterized by the time and space-variant parameters in order to track an object, which emits for example an acoustic signal. For estimating the position of the object in an instantaneous way a Bayesian approach has to be used, which considers the dynamic behavior of the parameters in a system model and uncertainties in a stochastic manner by means of probability density functions. Hence, the new approach provides a probabilistic instantaneous model-based signal processing, where the sequentially measured signal values are processed directly and known reference signal sequences are interpreted as part of a time-variant nonlinear measurement equation." @default.
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• W2007901431 date "2009-03-01" @default.
• W2007901431 modified "2023-10-17" @default.
• W2007901431 title "Probabilistic instantaneous model-based signal processing applied to localization and tracking" @default.
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• W2007901431 doi "https://doi.org/10.1016/j.robot.2008.10.013" @default.
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