Two Stage Particle Filter Based Terrain Referenced Navigation for Computational Efficiency

A novel particle filter (PF) based terrain referenced navigation (TRN) is presented for computational efficiency in this paper. TRN is a navigation method using terrain elevation information, mainly using a Bayesian filter such as a PF.PF based TRN has a disadvantage in that it requires a large number of particles to achieve a good estimation performance, which increases the computationalburden.To solve this disadvantage, two stage particle filter (TSPF) is proposed. TSPF consists of two stages and estimates the nonlinear state variables and the linear state variables separately. In the first stage, the nonlinear state variables are estimated by PF. Then, the linear state variables are estimated by a Kalman filter in the second stage considering a correlation between nonlinear and linear state variables. Since TSPF estimates only nonlinear state variables using PF and estimates linear state variables with only one Kalman filter, the computational efficiency can be greatly improved although the estimation performance is degraded a bit.In order to verify the proposed method, simulationsare performed to compare TSPF with PF andRao Blackwellized PF (RBPF)which has the same purpose as TSPF. The simulation results show similar estimation performance with fewer particles than with all state variables estimated using PF, resulting in less computational burden.