Partitioning continuous segmented signals

Abstract

An off-line segmentation of a continuous-time signal is proposed, which changes at unknown transition times and where each segment is modelled as a polynomial with known order but unknown parameters. A model order method based on the maximum likelihood principle is suggested, by imposing the constraint that the complete signal is continuous, for jointly determining the number of segments, the transition times and the parameters of each polynomial. Simulation results show that the proposed approach outperforms the unconstrained segmentation.