• Dr. William Whitacre
  • Northrop Grumman
  • 113 McBryde Hall
  • 4:00 p.m.
  • Faculty Host: Dr. Craig Woolsey

Geolocation is the process of using sensor data to develop statistical estimates of a point of interest (POI) on the ground. When using uninhabited aerial vehicles (UAVs) with gimballing cameras, each UAV, based on its position and orientation, points the camera (through a gimballing payload mount inside the UAV) at the POI on the ground. While the aircraft is moving, and the POI is potentially moving, the camera gimbals must adjust their angles such that the POI always remains within the field of view of the camera. The objective of geolocation is then to estimate the position (2D or 3D) of the POI. Complicating this problem are uncertainties in the aircraft position and orientation, gimbal angles, camera specifications and measurements, and disturbances such as turbulence and engine vibrations.

This presentation will cover four important topics for cooperative geolocation:
1.  Decentralized estimation algorithm - A decentralized filtering approach is shown for geolocation that provides tracking accuracy and is further scalable with the number of UAVs

2.  Decentralized bias estimation - In practice, there are non zero mean errors (biases), which degrade geolocation accuracy. Therefore, a decentralized approach is developed to simultaneously estimate the unknown location of the POI as well as the biases of each UAV.

3.  Methods for communication loss and delay - Communication is an important part of a cooperative geolocation mission.  However, in practice, communication losses and delays are inevitable. Therefore, a new method for cooperative geolocation in the presence of communication loss, termed the predicted information method, is developed.

4.  Orbit optimization - The optimization of periodic orbits for tracking both stationary and moving targets with two uninhabited aerial vehicles is considered. A natural metric that takes into account the fact that the orbits are periodic, is used and three key results are obtained.

Flight tests with the ScanEagle UAV made by Insitu Inc. will be shown to validate each of the algorithms presented.