Page 181 - Innovations in Intelligent Machines

State Estimation for Micro Air Vehicles

Randal W. Beard

Department of Electrical and Computer Engineering
Brigham Young University, Provo, Utah
beard@ee.byu.edu

Abstract. Autopilots for small UAVs are generally equipped with low fidelity sen-
sors 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 pre-
clude 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.

High fidelity estimates of the position, velocity, attitude, and angular rates
are critical for successful guidance and control of intelligent UAVs. The achiev-
able fidelity of the state estimates depends upon the quality of the sensors
on-board the UAV. Unfortunately, high quality sensors are usually heavy and
expensive. This is particularly true for sensors that directly measure the atti-
tude of the UAV. In this chapter we focus on the problem of state estimation
using light weight, inexpensive, low quality sensors. In doing so, our target
platforms are small and micro air vehicles with limited payload capacity.
In recent years, several autopilots for small UAVs have appeared on the
commercial market. These include the Procerus Kestrel [4], the Cloudcap
Piccolo [2], and the Micropilot MP2028 [3]. Each of these autopilots use the
following sensors:

• rate gyros,
• accelerometers,
• pressure sensors, and
• GPS.
We will assume throughout this chapter that these are the only sensors that
are available for state estimation.

R.W. Beard: State Estimation for Micro Air Vehicles, Studies in Computational Intelligence
(SCI) 70, 173–199 (2007)
www.springerlink.com c Springer-Verlag Berlin Heidelberg 2007