Domenico Accardo

TL;DR: In order to evaluate the performance of the collision avoidance system, numerical simulations have been performed taking into account the obstacle detection sensors’ accuracy, unmanned aircraft’s and intruder's flight dynamics, navigation system accuracy and latencies, and collision avoidance logic.

TL;DR: In this article, an analytical study of the error introduced by the centroiding algorithm is presented and it is shown that a systematic contribution and a random one exist, which result from image processing assumptions and photometric measure uncertainty, respectively.

TL;DR: This paper presents a visual-based approach that allows an UAV to detect and track a cooperative flying vehicle autonomously using a monocular camera, based on template matching and morphological filtering, which is a key building block of cooperative architectures designed to improve UAV navigation performance either under nominal GNSS coverage or in GNSS-challenging environments.

TL;DR: The polynomial fitting and Neural Network compensation algorithms are compared on selected testing points where the two techniques have the largest difference and it is highlighted that the proposed method has better performance on these points.

TL;DR: An analysis of an extensive flight campaign aimed at characterizing peculiarities, advantages, and limitations of an obstacle detection and tracking system based on a pulse radar demonstrates its potential to attain adequate situational awareness.