Terrain Aided Underwater Navigation Using Point Mass and Particle Filters

Abstract

Abstract-This paper focuses on obtaining submerged position fixes for underwater vehicles from comparing bathymetric measurements with a bathymetric map. Our algorithms are tested on real data, collected by a HUGIN AUV equipped with a multibeam echo sounder (MBE). Due to our strongly non-linear and non-Gaussian problem, local linearization methods such as the extended Kalman filter (EKF), has proven unsuitable in many terrain types. We therefore focus on two different recursive Bayesian methods, namely the point mass filter (PMF) and a Bayesian bootstrap particle filter (PF). The PMF is a gridbased approximation method, whereas the PF is a sequential Monte Carlo method, based on sampling from the underlying distributions. The two methods are first compared using a 2-dimensional state-space model, only estimating the horizontal position of the vehicle. A problem with this approach is that uncertainty in the estimation of tidal levels may lead to large errors in the depth measurements. To counter this, one may extend the state vector to three dimensions, and this is investigated in the paper. Characteristics such as accuracy, convergence time, terrain dependency, and computational demands for the two methods are compared. In suited terrain both methods yield a horizontal accuracy comparable to the resolution of the map. The results from the PMF are slightly better, but the PMF is also more computationally demanding than the PF. Extending the methods from 2D to 3D makes them significally more robust to depth errors. The computational demands for the PMF increases dramatically in 3D, whereas they are virtually unchanged for the PF. Previous problems of overconfidence in these methods are effectively reduced through sub-sampling of the MBE data.

Sigma Point Kalman Filter for Underwater Terrain-Based Navigation

“…As reported in (Ånonsen and Hallingstad, 2006), problems with overconfidence occur in Bayesian terrain navigation. However, these problems can be countered for to some extent by sub-sampling the MBE beams.…”

“…(Nygren and Jansson, 2004;Jalving et al, 2004;Ånonsen and Hallingstad, 2006). A variety of different terrain-based navigation methods have been proposed in the literature.…”

“…Depth bias estimation was discussed in (Ånonsen and Hallingstad, 2006). The sigma point Kalman filter (SPKF), also known as the unscented Kalman filter (UKF), was first presented in (Julier and Uhlmann, 1997) and further discussed in (Wan and Van Der Merwe, 2000).…”

A survey on Terrain Based Navigation for AUVs

“…Low weight particles are deleted and when the number of particles is bellow a threshold, the mesh resolution is increased duplicating the current set of particles. Anonsen and Hallingstad (2006) compared the PMF against the PF achieving slightly better results for the PMF, but at a higher computational cost. For high dimensional spaces this cost is prohibitive for real time applications therefore, the same author prosposed to use submaps to overcome this problem.…”

Construction Method of the Topographical Features Model for Underwater Terrain Navigation

“…Terrain Aided Navigation (TAN) combined the terrain database with terrain contour matching, which improved the positioning accuracy of inertial navigation of autonomous underwater vehicle (AUV) [1,2]. Terrain Elevation Matching (TEM) is the key technology of TAN.…”