Efficient adaptation of design parameters of derivative-free filters

The paper deals with state estimation of nonlinear discrete time stochastic dynamic systems with a focus on derivative-free filters. Design parameters of the filters are treated and an efficient way for their adaptation is proposed. The efficiency is based on observing a degree of nonlinearity of the nonlinear state and measurement functions at the working point by means of a non-Gaussianity measure. The adaptation is executed only if the nonlinearity is severe and the design parameter adaptation may bring a significant improvement of the estimate quality. Otherwise the adaptation is switched off to keep computational complexity of the filter low. The developed algorithm is illustrated using a numerical example of bearings-only target tracking.