A Cooperative Bearing-Rate Approach for Observability-Enhanced Target Motion Estimation

TL;DR

This paper introduces a cooperative bearing-rate estimator that enhances observability in vision-based target motion estimation, improving accuracy and maneuverability tracking in robotic applications.

Contribution

The paper proposes the STT-R estimator, integrating bearing rate information within a distributed recursive least squares framework, to improve target tracking performance.

Findings

Enhanced estimation accuracy demonstrated in simulations

Reduced lag in velocity estimation shown in experiments

Effective tracking of highly maneuverable targets achieved

Abstract

Vision-based target motion estimation is a fundamental problem in many robotic tasks. The existing methods have the limitation of low observability and, hence, face challenges in tracking highly maneuverable targets. Motivated by the aerial target pursuit task where a target may maneuver in 3D space, this paper studies how to further enhance observability by incorporating the \emph{bearing rate} information that has not been well explored in the literature. The main contribution of this paper is to propose a new cooperative estimator called STT-R (Spatial-Temporal Triangulation with bearing Rate), which is designed under the framework of distributed recursive least squares. This theoretical result is further verified by numerical simulation and real-world experiments. It is shown that the proposed STT-R algorithm can effectively generate more accurate estimations and effectively reduce the lag in velocity estimation, enabling tracking of more maneuverable targets.