A Deep Water Bathymetric Particle Filter for Position Estimation in GNSS-Denied Environments
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In this talk, the authors assess the viability of a particle filter to provide long term operationally relevant navigation aiding to an inertial navigation system via bathymetric map matching. Previous literature has demonstrated the ability to accurately estimate position using bathymetric depth measurements on small scale autonomous platforms. However, these tests were typically in shallow coastal waters for short duration missions. This paper demonstrates a particle filter implementation that provides useful position estimates for long duration missions in open ocean. This implementation can provide position estimates that recover a position estimate from a de-localized inertial navigation system at maximum drift error and at times is capable of accurately estimating position below the map-pixel resolution. The authors conclude that this algorithm can provide a suitable GNSS-denied position estimation for inertial navigation.