Relative Navigation of Air Vehicles

Abstract

This paper derives the full equations of motion for relative navigation of air vehicles. An extended Kalman filter is used for estimating the relative position and attitude of two air vehicles, designated leader and follower. All leader states are assumed known, while the relative states are estimated using line-of-sight measurements between the vehicles along with acceleration and angular rate measurements of the follower. Noise is present on all measurements, while biases are present only on the latter two. The global attitude is parameterized using a quaternion, while the local attitude error is given by a three-dimensional attitude representation. An application of the new theoretical developments is also given, which involves determining an optimal trajectory to improve the estimation accuracy of the system. A cost function is derived based upon the relative position elements of the estimator covariance. State constraints are appended to the cost function using an exponential term. Results show that minimization of this cost function yields a trajectory that improves accuracy of both the position and attitude state estimates.

UAV path planning for maximum visibility of ground targets in an urban area

“…Extending the current algorithm to a 3-dimensional problem including altitude optimisation would be straightforward. For assigning multiple UAVs on the same circular path, each UAV has to either communicate to the other UAVs in order to receive the position information or estimate it using a vision navigation approach as shown in [1,2].…”

Guidance and Navigation for UAV Airborne Docking

“…Of particularly note is the work by Darling [6] which demonstrated formation flight at 20 m separation, using a vision-only pose estimation algorithm. Various forms of the Kalman filter have been used to fuse onboard sensor data with vision-only pose estimates [4,14] and with raw marker observations [10,24]. Our work has similarities with the latter but differs by estimating the relative state directly, rather than a concatenated individual vehicle states.…”

Combining Stereo Vision and Inertial Navigation for Automated Aerial Refueling

“…The upcoming KC-46 Pegasus refueling tanker already has a stereo camera system in place for the boom operators which could be utilized to perform SMV [24]. Additionally, many of the vision algorithms used rely on markers or beacons to assist the MV systems [6,22,23,25] which may require additional expensive modifications to all receiver aircraft. Combining SMV, GPS, and INS through a KF will allow the system to make the best use of all three sensors and take advantage of the existing KC-46 technology.…”