Optimal segmentation by random process fusion

We introduce in this article an optimal segmentation method of nonstationary random processes. Segmentation of a non stationary process consists in assuming piecewise stationarity and in detecting the instants of change. We consider here that all the data from all the sensors are available in a same rime and perform a global segmentation. The bayesian fusion method we propose for the segmentation is based on the introduction of a joint prior model for the simultaneously segmentation and estimation of data coming from a set of sensors. We build a change process and define its prior distribution for the data fusion. That allows us to propose the MAP estimate as well as some minimum contrast estimate as a solution. We define, in the parametric processes distribution case, the expression and signification of all the segmentation's parameters. We compare the performance of our detection method in the case of two or three sensor. Application to the fusion of wind data velocity and direction is proposed.