This paper presents a new ground-based visual approach for guidance and safe landing of an unmanned aerial vehicle (UAV) in Global Navigation Satellite System(GNSS)-denied environments. In our previous work, the old system consists of one pan-tilt unit(PTU) with two cameras, whose detection range is limited by the baseline. To achieve longrange detection and cover wide field of regard, we mounted two separate sets of PTU integrated with visible light camera on both sides of the runway instead of our previous assembled stereo vision system. Then, the well-known AdaBoost method was evaluated with regard to detecting and tracking the target. To achieve the relative position between the UAV and landing area, we used triangulation to calculate the 3D coordinates of the UAV. By combining the estimated position in the closed loop control, we obtain the autonomous landing strategy. Finally, we present several real flights in outdoor environments, and compare its accuracy with ground truth provided by GNSS. The results support the validity and accuracy of the presented system.

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This paper presents a framework for tracking a mobile ground target (MGT) using a fixed-wing unmanned aerial vehicle (UAV). Challenges from pure theories to practical applications, including varying illumination, computational limits and a lack of clarity are considered. The procedure consists of four steps, namely: target detection, target localization, states estimation and UAV guidance. Firstly, the MGT in the wild is separated from the background using a Laplacian operator-based method. Next, the MGT is located by performing coordinate transformations with the assumption that the altitude of the ground is invariant and known. Afterwards, a Kalman filter is used to estimate the location and velocity of the MGT. Finally, a modified guidance law is developed to guide the UAV to circle and track the MGT. The performance of our framework is validated by simulations and a number of actual flight tests. The results indicate that the framework is effective and of low computational complexity, and in particular our modified guidance law can reduce the error of the tracking distance by about 75% in specified situations. With the proposed framework, such challenges caused by the actual system can be tackled effectively, and the fixed-wing UAV can track the MGT stably.

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