Desensitised Kalman filtering
Christopher D. Karlgaard, Haijun Shen · IET Radar Sonar & Navigation · 2013
This study discusses the development of a desensitised optimal filtering technique for systems subject to plant and measurement model parameter uncertainties. Desensitised state estimates are obtained by minimising a cost function consisting of the posterior covariance matrix trace penalised by a weighted norm of the state estimate error sensitivities. The resulting filter is non‐minimum variance but exhibits reduced sensitivity to deviations in the assumed plant model parameters. Solutions are obtained for discrete, continuous and mixed continuous‐discrete non‐linear systems using an extended Kalman filter formulation. An example problem involving orbit determination with parameter uncertainty is provided to illustrate the effectiveness of the proposed filtering technique.