Tracking a non-cooperative target is a challenge, because in unfamiliar environments most targets are unknown and unspecified. Stereovision is suited to deal with this issue, because it allows to passively scan large areas and estimate the relative position, velocity and shape of objects. This research is an experimental effort aimed at developing, implementing and evaluating a real-time non-cooperative target tracking methods using stereovision measurements only. A computer-vision feature detection and matching algorithm was developed in order to identify and locate the target in the captured images. Three different filters were designed for estimating the relative position and velocity, and their performance was compared. A line-of-sight control algorithm was used for the purpose of keeping the target within the field-of-view. Extensive analytical and numerical investigations were conducted on the multi-view stereo projection equations and their solutions, which were used to initialize the different filters. This research shows, using an experimental and numerical evaluation, the benefits of using the unscented Kalman filter and the total least squares technique in the stereovision-based tracking problem. These findings offer a general and more accurate method for solving the static and dynamic stereovision triangulation problems and the concomitant line-of-sight control.
Position and velocity estimation using Global Navigation Satellite Systems (GNSS) has been widely studied and implemented. In contrast to existing GNSS, the idea of using low Earth orbit (LEO) satellites for position and velocity determination is relatively new. On one hand, the launch to LEO is more affordable compared to GNSS orbits. On the other hand, LEO satellites provide reduced coverage and suffer from orbit determination uncertainties. In this article, we study position and velocity estimation for an aerial platform using signals from a LEO satellite constellation, designed to produce a relatively long coverage duration, while minimizing the geometric dilution of precision. We determine the receiver’s position by using the trilateration method and the velocity by using Doppler estimation, and improve the accuracy thereof by utilizing an Extended Kalman Filter (EKF). We suggest a solution for the trilateration initialization problem, which arises for LEO navigation satellites, which relies on averaging the Earth projection of all the satellites within sight. We examine two scenarios, one wherein the EKF’s dynamical model matches the reference dynamical model, and another with a model mismatch. When the dynamical model is approximated, the EKF reduces the position and velocity errors considerably. When the dynamical model is known, the position and velocity errors can be reduced by an order of magnitude.
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