Tracking nonstationary targets using a dynamical system with Markov-modulated parameters
Tracking moving targets from partial measurements is an important problem with applications in control theory and pattern recognition. This letter presents a statistical framework for modeling target motion as the output of a linear dynamical system driven by a random step function representing sequences of idealized target positions. Target trajectories are observed through a noisy nonlinear measurement function, whereas system parameters are modulated by a semi-Markov chain representing changes in target regime. An algorithm is presented for maximum-likelihood parameter estimation from a corpus of observations, and potential applications to articulatory speech recognition are discussed.<<ETX>>