Particle Filter for Underwater Terrain Navigation

In an earlier contribution we proposed a particle filter for under water (UW) navigation, and applied it to an experimental underwater mental trajectory. Here we focus 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 simiulations on the map demonstrate a performance comparable to the CRLB