This paper is focused on the development and the flight performance analysis of an image-processing technique aimed at detecting flying obstacles in airborne panchromatic images. It was developed within the framework of a research project which aims at realizing a prototypical obstacle detection and identification System, characterized by a hierarchical multisensor configuration. This configuration comprises a radar, that is, the main sensor, and four electro-optical cameras. Cameras are used as auxiliary sensors to the radar, in order to increase intruder aircraft position measurement, in terms of accuracy and data rate. The paper thoroughly describes the selection and customization of the developed image-processing techniques in order to guarantee the best results in terms of detection range, missed detection rate, and false-alarm rate. Performance is evaluated on the basis of a large amount of images gathered during flight tests with an intruder aircraft. The improvement in terms of accuracy and data rate, compared with radar-only tracking, is quantitatively demonstrated. 1. Introduction Following the hmost important guidelines about unmanned aerial systems (UASs) integration into civil airspace, the onboard avionics of these aircraft must include a sense-and-avoid (S&A) system that is capable to replace the human pilot in performing visual collision threat detection and avoidance. In general, the architectures of detect-sense-and-avoid (DS&A) systems, reported in literature [1–5], are comprised of two subsystems, such as the obstacle detection and tracking one and the collision avoidance one. Several solutions have been proposed to provide the “sense” function. They range from standalone electro-optical (EO) sensors to integrated architectures that comprise airborne radars and collaborative systems based on broadcasted information, such as TCAS (traffic collision avoidance system) and ADS-B (automatic-dependent surveillance-broadcast) [6–12]. A multiple sensor configuration allows compensating single sensor shortcomings as documented in detail in [7, 12]. Thus, in the framework of a research project carried out by the Italian Aerospace Research Center (CIRA) and the University of Naples “Federico II,” aimed at demonstrating a fully autonomous collision avoidance system in flight, an integrated radar/EO configuration was selected. Moreover, in order to satisfy required levels of accuracy, data rate, false alarm rate, and missed detection rate, a hierarchical logical architecture has been considered where the radar is the main sensor and the EO system
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