Target Tracking in 3-D Using Estimation Based Nonlinear Control Laws for UAVs

Abstract

This paper presents an estimation based backstepping like control law design for an Unmanned Aerial Vehicle (UAV) to track a moving target in 3-D space. A ground-based sensor or an onboard seeker antenna provides range, azimuth angle, and elevation angle measurements to a chaser UAV that implements an extended Kalman filter (EKF) to estimate the full state of the target. A nonlinear controller then utilizes this estimated target state and the chaser's state to provide speed, flight path, and course/heading angle commands to the chaser UAV. Tracking performance with respect to measurement uncertainty is evaluated for three cases: (1) stationary white noise; (2) stationary colored noise and (3) non-stationary (range correlated) white noise. Furthermore, in an effort to improve tracking performance, the measurement model is made more realistic by taking into consideration range-dependent uncertainties in the measurements, i.e., as the chaser closes in on the target, measurement uncertainties are reduced in the EKF, thus providing the UAV with more accurate control commands. Simulation results for these cases are shown to illustrate target state estimation and trajectory tracking performance.

Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters

“…The quadrature points {X l,k−1/k−1 } m l=1 are given in Equation ( 6). The matrix square root is the lower triangular Cholesky factor provided in Equation (7). The estimate of the predicted state mean and the estimate of the predicted error covariance are given in Equations ( 8) and ( 9), respectively.…”

“…Therefore, the Doppler is not expressed as a frequency in Hz, but as a speed in m/s. In this paper, tracking in Cartesian coordinates is considered, for which the state vector contains at least the position and speed in the x, y, and turn rate [6,7].…”

3D Angle-of-Arrival Positioning Using von Mises-Fisher Distribution

“…The flipping reflects a real-world equipment's behavior to have a unique representation of direction. In Model II the measurements are generated from the VMF model (5).…”

“…An AOA measurement consists of two components: azimuth and elevation. A conventional approach is to model the measurements as noisy versions of the true azimuth and elevation [2][3][4][5][6], and use extended Kalman fiter (EKF) or unscented Kalman filter (UKF) that assume that the measurement noises of azimuth and elevation follow normal distributions. However, this model is problematic in a number of ways: I In this model the solid angle of measurement uncertainty is smaller close to the "pole" directions, i.e.…”

An Improved Extended Kalman Filter for Radar Tracking of Satellite Trajectories

“…Nonlinear state estimation is a desirable and required tool in several engineering applications, especially in aerospace, where it is crucial for tasks such as surveillance, guidance, navigation, attitude control, obstacle avoidance and target tracking [1][2][3][4][5][6]. The problem consists of estimating the state vector (which contains all relevant information to describe the system of the moving target) based on noisy measurements, imperfect models, inaccurate data acquisition systems and environmental perturbations that are unwanted and, in most cases, also unknown [7].…”