State estimation for micro air vehicles

Abstract

Autopilots for small UAVs are generally equipped with low fidelity sensors that make state estimation challenging. In addition, the sensor suite does not include units that measure angle-of-attack and side-slip angles. The achievable flight performance is directly related to the quality of the state estimates. Unfortunately, the computational resources on-board a small UAV are generally limited and preclude large state Kalman filters that estimate all of the states and sensor biases. In this chapter we describe simple models for the sensors typically found on-board small UAVs. We also describes a simple cascaded approach to state estimation that has been extensively flight tested using the Kestrel autopilot produced by Procerus Technologies. Our intention is to provide a tutorial of continuous-discrete Kalman filtering with application to state estimation for small UAVs. © 2007 Springer-Verlag Berlin Heidelberg.