Sensor Fusion Algorithm Using a Model-Based Kalman Filter for the Position and Attitude Estimation of Precision...

Area of Science:

• Aerospace Engineering
• Robotics
• Control Systems

Background:

• Unmanned Aerial Vehicles (UAVs) are increasingly used for payload delivery and navigation.
• Lightweight UAVs, termed Precision Aerial Delivery Systems (PADSs), require accurate yet cost-effective flight dynamics models.
• Existing nonlinear models are often too complex for low-cost applications, necessitating exploration of linear alternatives.

Purpose of the Study:

• To propose and compare two linear flight dynamics models for Precision Aerial Delivery Systems (PADSs).
• To evaluate the performance of these linear models against a nonlinear 6-DOF model using a Kalman filter.
• To determine the suitability of linear models for low-cost UAV applications.

Main Methods:

• Development and comparison of two linear models: a linearized 6-DOF model and a double integrator model.
• Implementation of a sensor fusion algorithm utilizing a Kalman filter for position and attitude estimation.
• Simulation-based performance evaluation against a nonlinear 6-DOF model.

Main Results:

• Both linear models, when integrated with a Kalman filter, can estimate the flight dynamics of PADSs during smooth flight.
• The double integrator model is suitable for small acceleration changes.
• The linearized 6-DOF model effectively captures nonlinear characteristics, even during moderately steep turns.

Conclusions:

• Linear models, particularly the linearized 6-DOF model, offer a viable and accurate approach for modeling lightweight UAV flight dynamics in cost-sensitive applications.
• Kalman filter-based sensor fusion enhances the performance of linear models for PADS navigation.
• The choice of linear model depends on the expected flight maneuvers and required fidelity.