Particle filter for underwater terrain navigation

Abstract

In an earlier contribution a particle filter for underwater (UW) navigation is proposed, and applied to an experimental trajectory. This paper focuses on performance improvements and analysis. First, the Cramer Rao lower bound (CRLB) along the experimental trajectory is computed, which is only slightly lower than the particle filter estimate after initial transients. Simple rule of thumbs for how performance depends on the map and sensor quality are derived. Second, a more realistic five state model is proposed, and Rao-Blackwellization is applied to decrease computational complexity. Monte-Carlo simulations on the map demonstrate a performance comparable to the CRLB.