USING N-MODULAR REDUNDANCYWITH KALMAN FILTERS FORUNDERWATER VEHICLE POSITIONESTIMATION

Abstract: Underwater navigation faces many problems with accurately estimating the absolute positionof an underwater vehicle. Neither Global Positioning system (GPS) nor Long Baseline (LBL) orShort Baseline (SBL) are possible to use for a military vehicle acting under stealth, since thesetechniques require the vehicle to be in the vicinity of a nearby ship or to surface and raise its antenna. It will therefore have to rely on sensors such as Doppler Velocity Log (DVL) and a compassto estimate its absolute position using dead reckoning or an Inertial Navigation System (INS). Thisthesis presents an alternative Multiple model Kalman Filter (KF) to the existing Multiple ModelAdaptive Estimator (MMAE) algorithm using n-Modular Redundancy (NMR), in order to gaina more accurate result than with a single KF. By analyzing how different amounts of filters andvoter types affect the accuracy and precision of the velocity and heading estimations, the potentialbenefits and drawbacks can be drawn for each solution. Such benefits and drawbacks were alsovisually evaluated in a Matlab script which was used to calculate the coordinates using the velocityand heading from the speed sensors and compass, without the need for running the filtered states onthe vehicle’s navigation system. The results present the potential of using a multiple model KF inthe form of an NMR, which was demonstrated by both the amount of reduced noise in the velocitystates and how the filters were used in a virtual navigation system created in Matlab.