Abstract
The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.
Highlights
The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on Inertial Navigation System (INS)/Global Navigation Satellite System (GNSS) integration [1,2,3,4,5].problems tend to arise during GNSS outages where the navigation solution error grows unboundedly [6,7,8,9]
A slow varying wind is implemented in simulation and the filter estimates the constant component of wind during the flight
The filter is seen to be consistent in the estimation of wind speed as it can be seen from the 1 σ prediction even without an air data system attributed to correctness in the filter setup and the ability of the Unscented Kalman Filter (UKF) to capture higher-order moments
Summary
Problems tend to arise during GNSS outages where the navigation solution error grows unboundedly [6,7,8,9]. This can happen due to intentional or unintentional corruption, even against cryptographically secured GNSS signals [10], rapid dynamics [11], loss of line of sight, and interference [12]. Others have explored advanced integration schemes [2,3,11], and others have investigated advanced error modelling schemes [9,16], saving on weight but introducing additional software complexities
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