Abstract
The integration of time-of-arrival positioning techniques with terrain-referenced navigation is described within the framework of Bayesian estimation theory. In this approach, two banks of nonlinear filters are used in a multiple-model adaptive configuration. One filter bank represents different terrain elevation bias values, whereas the other bank represents noise parameters used to model non-line-of-sight bias for time-of-arrival positioning. Information is only periodically exchanged between filter banks to limit the overall computational complexity while allowing estimation of time-varying parameters. Monte Carlo simulations of an integrated time-of-arrival and terrain-navigation positioning problem demonstrate the advantages of the dual filter bank under conditions of significant non-line-of-sight bias.
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