A Sequential Lp-norm Filter for Robust Estimation

A novel robust sequential L-p filter is developed in this paper by leveraging the maximum a posteriori estimation theory and using generalised normal distributions to represent both state prediction errors and measurement residuals. The formulation leads to the flexibility of choosing the parameter p for two different types of aforementioned error sources. Numerical simulations are given for a nonlinear ground tracking scenario, with measurements corrupted with outliers. Results indicate the new L-p-norm filter presents robustness to filter initialisation errors and measurement outliers and outperforms a standard unscented Kalman filters and the Huber unscented Kalman filter in terms of error statistics.