Robust Vision-Based Range and Bearing Estimation With Minimal Maneuvering

In this paper, we present a novel range-estimation technique that uses bearing and pixel-size measurements from a monocular camera to estimate both the distance and the physical size of an observed aircraft. The proposed method employs inverse distance and modified polar coordinates to construct a state representation suitable for an Extended Kalman Filter (EKF). This approach is particularly beneficial for small unmanned aerial vehicles (UAVs), which often have limited sensing capabilities. Unlike methods that rely on stereo vision or lidar, our technique estimates range solely from bearing and image measurements. A key challenge in bearing-only tracking is that, without executing deliberate maneuvers, it is inherently difficult to quickly obtain accurate range estimates due to the lack of observability in the early stages. Our method addresses this challenge by exploiting pixel size variations to enhance range observability. Simulation results validate the effectiveness of the proposed approach under the assumption that both aircraft are flying at the same altitude. The simplicity and low cost of this vision-based system make it well-suited for real-time onboard implementation in resource-constrained UAV platforms.