Track Estimation with Binary Derivative Observations

We focus in the work presented here on in the estimation of a target trajectory defined by whether a time constant parameter is a simple stochastic process or a random walk with binary observations. The binary observations come from binary derivative sensors, that is, the target is getting closer or moving away. Such a binary observation has a time property that will be used to ensure the quality of the velocity estimation, through single index model or classification for the constant velocity movement. In the second part of this work we present a new algorithm for target tracking within a binary sensor network when the target trajectory is assumed to be modelled by a random walk. For a given target this algorithm provides an estimation of its velocity and its position. The greatest improvements are made through a position correction and velocity analysis.