A Distributed Recursive Maximum Likelihood Implementation for Sensor Registration
Nikolas Kantas, Sumeetpal Sidhu Singh, Arnaud Doucet · 2006
Recursive maximum likelihood (RML) is a popular methodology for estimating unknown static parameters in state-space models. We describe how a completely decentralized version of RML can be implemented in dynamic graphical models through the propagation of suitable messages that are exchanged between neighbouring nodes of the graph. The resulting algorithm can be interpreted as a generalization of the celebrated belief propagation algorithm to compute likelihood gradients. This algorithm is applied to solve the sensor registration and localisation problem for sensor networks. An exact implementation is given for dynamic linear Gaussian models without loop. If loops are present, a loopy version of the algorithm is described. For non-linear non Gaussian scenarios, a sequential Monte Carlo (SMC) or particle filter implementation is sketched