Desensitised Kalman filtering

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.