Distributed Point-Mass Filter with Reduced Data Transfer Using Copula Theory

This paper deals with distributed Bayesian state estimation of generally nonlinear stochastic dynamic systems. In particular, distributed point-mass filter algorithm is developed. It is comprised of a basic part that is accurate but data intense and optional step employing advanced copula theory. The optional step significantly reduces data transfer for the price of a small accuracy decrease. In the end, the developed algorithm is numerically compared to the usually employed distributed extended Kalman filter.