Segmentation of Signals Using Piecewise Constant Linear Regression Models
1994 (English)Report (Other academic)
The signal segmentation approach described herein assumes that the signal can be accurately modelled by a linear regression with piece-wise constant parameters. A simultaneous estimate of the change times is considered. The maximum likelihood and maximum a posteriori probability estimates are derived after marginalization of the linear regression parameters and the measurement noise variance, which are considered as nuisance parameters. A well-known problem is that the complexity of segmentation increases exponentially in the number of data. Therefore, two inequalities are derived enabling the exact estimate to be computed with quadratic complexity. A linear in time complexity recursive approximation is proposed as well, based on these inequalities. The method is evaluated on a speech signal previously analyzed in literature, showing that a comparable result is obtained directly without the usual tuning effort. It is also detailed how it successfully has been applied in a car for online segmentation of the driven path for supporting guidance systems.
Place, publisher, year, edition, pages
Linköping: Linköping University , 1994. , 29 p.
LiTH-ISY-R, ISSN 1400-3902 ; 1672
Segmentation, Signal, Linear regression models
IdentifiersURN: urn:nbn:se:liu:diva-55138ISRN: LITH-ISY-R-1672OAI: oai:DiVA.org:liu-55138DiVA: diva2:315707