Abstract
AbstractThe paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an extended Kalman filter (EKF) which uses the VDM as the main process model. Control inputs from the autopilot system are used to drive the navigation solution. Using a predefined trajectory with segments of both high and low dynamics and a variable wind profile, Monte Carlo simulations reveal a degrading performance in varying periods of GNSS outage lasting 10 s, 20 s, 30 s, 60 s and 90 s, respectively. These are followed by periods of re-acquisition where the navigation solution recovers. With a GNSS outage lasting less than 60 s, the position error gradually grows to a maximum of 8⋅4 m while attitude errors in roll and pitch remain bounded, as opposed to an inertial navigation system (INS)/GNSS approach in which the navigation solution degrades rapidly. The model-based approach shows improved navigation performance even with parameter uncertainties over a conventional INS/GNSS integration approach.
Highlights
Over the last few years, low-cost, mass-market unmanned aerial vehicles (UAVs) have seen increasing usage in ‘dull, dangerous and dirty’ (D-D-D) fields
The most common navigation architecture in these platforms is based on an inertial navigation system (INS) integrated with a global navigation satellite system (GNSS) (Kim and Sukkarieh, 2003; George and Sukkarieh, 2005)
The approach used a standard INS/GNSS integration architecture using an unscented Kalman filter that would still be disabled in case of inertial measurement unit (IMU) failure
Summary
Over the last few years, low-cost, mass-market unmanned aerial vehicles (UAVs) have seen increasing usage in ‘dull, dangerous and dirty’ (D-D-D) fields. With an INS/GNSS architecture, problems tend to arise during GNSS outages where the navigation solution drifts unboundedly (Hide, 2003; Quinchia et al, 2013; Mwenegoha et al, 2019). To improve the navigation solution during GNSS outages, El-Diasty and Pagiatakis (2009) explored advanced inertial measurement unit (IMU) error modelling schemes. The architecture relied on conventional INS/GNSS integration, which would disable the navigation solution in case of IMU failure. Experimental tests (Mueller et al, 2016) using a quadrotor showed significant improvement in position estimation during GNSS outages; the navigation solution could still be disabled in case of IMU failure. This paper presents findings of the error characteristics of a model-based approach in different GNSS outage lengths and varying roll rates, especially during turns. Comparisons with a conventional INS/GNSS approach are made
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