Extended Kalman Filter Using Orthogonal Polynomials

This paper reports a new extended Kalman filter where the underlying nonlinear functions are linearized using a Gaussian orthogonal basis of a weighted L2 space. As we are interested in computing the states' mean and covariance with respect to Gaussian measure, it would be better to use a linearization, that is optimal with respect to the same measure. The resulting first-order polynomial coefficients are approximately calculated by evaluating the integrals using (i) third-order Taylor series expansion (ii) cubature rule of integration. Compared to direct integration-based filters, the proposed filter is far less susceptible to the accumulation of round-off errors leading to loss of positive definiteness. The proposed algorithms are applied to four nonlinear state estimation problems. We show that our proposed filter consistently outperforms the traditional extended Kalman filter and achieves a competitive accuracy to an integration-based square root filter, at a significantly reduced computing cost.

Automated summary

This paper introduces a new extended Kalman filter that linearizes nonlinear functions and approximates first-order polynomial coefficients for computing states' mean and covariance. Compared to traditional extended Kalman filters, the proposed filter achieves better performance with significantly reduced computing cost.