Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Optimal segmentation of random processes
IEEE Transactions on Signal Processing
Two algorithms to segment white Gaussian data with piecewise constant variances
IEEE Transactions on Signal Processing
Joint segmentation of wind speed and direction using a hierarchical model
Computational Statistics & Data Analysis
A recursive fusion filter for angular data
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Energy-aware sparse approximation technique (EAST) for rechargeable wireless sensor networks
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
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We present in this article a Bayesian estimation method for the joint segmentation of a set of piecewise stationary processes. The estimate we propose is based on the maximization of the posterior distribution of the change instants conditionally to the process parameter estimation. It is defined as a penalized contrast function with a first term related to the fit to the observation and a second term of penalty. The expression of the contrast function is deduced from the log-likelihood of the parametric distribution that models the statistic evolution of processes in the stationary segments. In the case of joint segmentation the penalty term is deduced from the prior law of change instants. It is composed of parameters that guide the number and the position of changes and of parameters that will bring prior information on the joint behavior of processes. This work is applied to the estimation of wind statistics parameters. We use data available from a cup anemometer and a wind vane, supposed to be piecewise stationary. The contrast function is deduced from the circular Von Mises distribution for the wind direction and from the log-normal distribution for the speed. The feasibility and the contribution of our method are shown on synthetic and real data.