Relative target estimation using a cascade of extended Kalman filters

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.