A Model-based Tightly Coupled Architecture for Low-Cost Unmanned Aerial Vehicles for Real-Time Applications

This paper investigates the navigation performance of a vehicle dynamic model-based (VDM-based) tightly coupled architecture for a fixed-wing Unmanned Aerial Vehicle (UAV) during a global navigation satellite system (GNSS) outage for real-time applications. Unlike an Inertial Navigation System (INS) which uses inertial sensor measurements to propagate the navigation solution, the VDM uses control inputs from either the autopilot system or direct pilot commands to propagate the navigation states. The proposed architecture is tested using both raw GNSS observables (Pseudorange and Doppler frequency) and Micro-Electro-Mechanical Systems-grade (MEMS) Inertial Measurement Unit (IMU) measurements fused using an extended Kalman filter (EKF) to aid the navigation solution. Other than the navigation states, the state vector also includes IMU errors, wind velocity, VDM parameters, and receiver clock bias and drift. Simulation results revealed significant performance improvements with a decreasing number of satellites in view during 140 seconds of a GNSS outage. With two satellites visible during the GNSS outage, the position error improved by one order of magnitude as opposed to a tightly coupled INS/GNSS scheme. Real flight tests on a small fixed-wing UAV show the benefits of the approach with position error being an order of magnitude better as opposed to a tightly coupled INS/GNSS scheme with two satellites in view during 100 seconds of a GNSS outage

Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles

The 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