Multiple sparse-grid Gauss–Hermite filtering

Abstract

A new method for nonlinear estimation, based on sparse-grid Gauss–Hermite filter (SGHF) and state-space partitioning, termed as Multiple sparse-grid Gauss–Hermite filter (MSGHF) is proposed in this work. Gauss–Hermite filter is a widely acclaimed filtering technique for its high accuracy. But the computational load associated with it is so high, that it becomes difficult to apply it on-board for higher dimensional problems. SGHF showcased comparable performance with the GHF, with less computational burden. The proposed technique, MSGHF, further reduces the computational burden considerably, with the filter accuracy remaining almost the same. Simulation results illustrate the performance of the proposed filter with respect to GHF and SGHF.