This paper proposes an image processing algorithm for 'sense-and-avoid' of aerial vehicles in short-range at low altitude and shows flight experiment results. Since it can suppress the negative effects cause c...
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This paper proposes an image processing algorithm for 'sense-and-avoid' of aerial vehicles in short-range at low altitude and shows flight experiment results. Since it can suppress the negative effects cause cluttered environment in image sequence such as the ground seen or sensitivity of threshold value during low-altitude flight, proposed algorithm has better performance of collision avoidance. Furthermore, proposed algorithm can perform better than simple color-baseddetection and tracking methods because it takes the characteristics of vehicle dynamics into account in image plane. The performance of proposed algorithm is validated by post image processing using video clip taken from flight test and actual flight test with simple avoidance maneuver.
A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two ...
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A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two main steps. Specifically, the first processing step is relevant to obstacle detection and tentative tracking for track confirmation and is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. An extensive experimental analysis is presented which is based on a large set of flight data gathered in realistic near collision scenarios, in different operating conditions in terms of weather and illumination, and adopting different navigation units onboard the ownship. In particular, the focus is set on flight segments at a range between 3 km and 1.3 km, since the major interest is in understanding algorithm potential for relatively large time to collision. System performance is evaluated in terms of declaration range, probability of correct declaration, average number of false positives, tracking accuracy (angles and angular rates in a stabilized North-East-Down reference frame) and robustness with respect to track loss phenomena. Promising results are achieved regarding the trade-off between declaration range and false alarm probability, while the onboard navigation unit is found to heavily impact tracking accuracy.
Improper usage of UAV or drone allows people to conduct strikes on vital properties, such as a military base. The critical capabilities of drone are its movement flexibility and its relatively small size. These abilit...
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ISBN:
(纸本)9781728137964
Improper usage of UAV or drone allows people to conduct strikes on vital properties, such as a military base. The critical capabilities of drone are its movement flexibility and its relatively small size. These abilities make standard radar systems hard to detect drone position. Thus, the counter-attack maneuver towards drone attacks becomes difficult. We propose a framework to establish a radar-like information system that can track the location of any intruder drone attacking a prohibited area. It works by using a UAV tracker equipped with a calibrated 2D camera and GPS sensor. This tracker uses a camera to detect the existence of an intruder, then follow the intruder while sending the intruder's coordinate to a specific server by estimating the intruders' location using both sensors. We describe the mathematical model to estimate the relative position of an intruder drone from our UAV tracker by using a 2D camera, then combine the obtained information with GPS sensor data to pinpoint the coordinate of any intruder. We adopt the Area Expansion Principle as the mathematical model. As a result, we reduce the cost of intruder drone detector system that is useful for defending an area.
The paper focuses on research results relevant to non-cooperative sense and avoid based on different sensing architectures and algorithms. In particular, first a radar/electrooptical system is presented where real tim...
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ISBN:
(纸本)9781479920693
The paper focuses on research results relevant to non-cooperative sense and avoid based on different sensing architectures and algorithms. In particular, first a radar/electrooptical system is presented where real time data fusion is based on a central level scheme and an Extended Kalman Filter. It was validated in flight tests carried out in the framework of TECVOL project carried out by the Italian Aerospace Research Centre (CIRA) and the University of Naples "Federico II". In order to evaluate the impact of innovative technologies on system performance, solutions based on Particle Filtering are then introduced which have been developed and implemented in radar-only framework for off-line simulations, taking advantage from sensor data gathered in flight. Finally, algorithms relevant to vision-based sense and avoid are briefly presented, and preliminary results based on flight images in near collision scenarios are discussed.
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