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.