Title: Relative Target Estimation using a Cascade of Extended Kalman Filters
Author(s): Jerel Nielsen and Randal W. Beard
Published in: Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
Portland, Oregon
Pages: 2273 - 2289
Cite this article: Nielsen, Jerel, Beard, Randal W., "Relative Target Estimation using a Cascade of Extended Kalman Filters," Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 2273-2289.
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Abstract: This paper presents a method of tracking multiple ground targets from an unmanned aerial vehicle (UAV) in a 3D reference frame. The tracking method uses a monocular camera and makes no assumptions on the shape of the terrain or the target motion. The UAV runs two cascaded estimators. The first is an Extended Kalman Filter (EKF), which is responsible for tracking the UAV’s state, such as position and velocity relative to a fixed frame. The second estimator is an EKF that is responsible for estimating a fixed number of landmarks within the camera’s field of view. Landmarks are parameterized by a quaternion associated with bearing from the camera’s optical axis and an inverse distance parameter. The bearing quaternion allows for a minimal representation of each landmark’s direction and distance, a filter with no singularities, and a fast update rate due to few trigonometric functions. Three methods for estimating the ground target positions are demonstrated: the first uses the landmark estimator directly on the targets, the second computes the target depth with a weighted average of converged landmark depths, and the third extends the target’s measured bearing vector to intersect a ground plane approximated from the landmark estimates. Simulation results show that the third target estimation method yields the most accurate results.