1,720,967 research outputs found
Flight test of a radar-based tracking system for UAS sense and avoid
No full textPresented here is 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. The hardware and software prototypical sensing system was installed onboard an optionally piloted flying laboratory from the very light aircraft (VLA) category. Test flights with a single intruder aircraft of the same class were carried out to demonstrate autonomous noncooperative unmanned aerial system (UAS) collision avoidance capability and to evaluate the level of achievable situational awareness. First, the adopted architecture and the developed tracking algorithm are presented. Subsequently, flight data gathered in various relative flight geometries, covering chasing flights and quasi-frontal encounters, are analyzed in terms of radar performance, including detection range and range and angle measurement accuracies. The analysis describes the impact of ground echoes and navigation uncertainties, system tracking reliability, and achievable accuracy in estimation of relative position and velocity. On the basis of Global Positioning System (GPS) data gathered simultaneously with obstacle detection flight experiments, a detailed error analysis is conducted. Special emphasis is given to the validation of proposed methodology to separate between intruder and ground echoes, which is a critical aspect for light aircraft due to their limited radar cross sections (RCS) and flight altitudes. In conclusion the radar demonstrates its potential to attain adequate situational awareness, however the limits of single sensor tracking are also pointed out. Above all the negative impact of poor angular accuracy on missed detection and false alarm rates is pointed out
Integrated Obstacle Detection System based on Radar and Optical Sensors
No full textThis paper focuses on an airborne multi-sensor system for autonomous detection and tracking of flying obstacles. The hardware/software prototype integrating Detect, Sense, and Avoid capability has been designed and realized by the Italian Aerospace Research Center and the Department of Aerospace Engineering of the University of Naples “Federico II”. The sensing subsystem is comprised of a Ka-band airborne pulsed radar, a visible panchromatic high-resolution camera, a visible color high-resolution camera, two thermal infrared cameras, and two processing units for image processing and sensor data fusion. Algorithms for object detection in optical images and real time multi-sensor tracking are described in detail. Then, results from flight tests with an intruder aircraft are presented and analyzed. Obstacle detection performance in terms of detection range, accuracy, and reliability, is discussed both for the radar and the panchromatic camera. Finally, first experimental results about standalone radar and radar/electro-optical tracking are analyzed. They demonstrate the potential of sensor fusion for Unmanned Aerial Systems collision avoidance
A Multi-Sensor Obstacle Detection and Tracking System for Autonomous UAV Sense and Avoid
No full textLaboratory Test Facility for Simulating a Sense and Avoid Flight System
No full textWithin a project funded by the Italian Aerospace Research Centre UAV program, the Department of Aerospace Engineering at the University of Naples is in charge of developing an obstacle detection and tracking system aimed at non-cooperative collision avoidance. In this framework, a flight prototype of an autonomous Detect Sense and Avoid system has been installed onboard a Very Light Aircraft for evaluating performance in flight tests. The system is based on multiple-sensor data integration and it includes several components, such as a Ka-band pulsed radar, four Electro Optical sensors and two processing units. This paper is focused on the description of an indoor facility for hardware-in-the-loop tests which was realized to maximize the outcome of flight tests. It includes processing units dedicated to simulate aircraft and intruder dynamics that are provided as input to sensors. In the developed configuration, the radar is replaced by a simulator while the real visible camera unit is used. Flight images are displayed on a LCD screen. The facility permits to test multiple critical flight configurations and different sensors combinations. Moreover, the availability of a well assessed simulator allows the research team to support several activities such as: i) tuning of the data fusion techniques (i.e. tracking based on Kalman filtering); ii) system performance validation for a wide range of scenarios; iii) evaluation of alternative architectures that are difficult to be reproduced during flights. Some results of hardware-in-the-loop tests based on flight data regarding radar-only tracking and EO detection are presented and analyzed. They are fully compliant with the expected performance
Flight Performance Analysis of an Image Processing Algorithm for Integrated Sense-and-Avoid Systems
No full textThis 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
A Hardware-In-The-Loop Facility for Testing Multisensor Sense and Avoid Systems
No full textThe Italian Aerospace Research Centre and the Department of Aerospace Engineering at University of Naples have been involved in a project for the development of an obstacle detection and tracking suite for autonomous collision avoidance of unmanned aerial systems. In this framework, a flight prototype of an autonomous obstacle detect sense and avoid system has been designed and realized. It is installed onboard a very light aircraft named FLARE. The system is based on multiple-sensor data integration and it includes several components, such as a Ka-band pulsed radar, four electro optical sensors and two processing units. A hierarchical sensor configuration has been chosen in which the radar is the main sensor while EO cameras are the auxiliary ones to increase accuracy and data rate. In order to maximize the outcome of flight tests, an indoor facility for hardware-in-the-loop tests has been developed. The indoor facility includes processing units dedicated to simulate aircraft and intruder dynamics that are provided as input to sensors. The radar is replaced by a simulator, while the real visible camera unit is used. Flight images are displayed on a LCD screen. The facility permits to test multiple critical flight configurations and different sensors combinations. Moreover, the availability of a well assessed simulator allows the research team to support several activities such as: i) tuning of the data fusion techniques (i.e. tracking based on Kalman filtering); ii) system performance validation for a wide range of scenarios; iii) evaluation of alternative architectures that are difficult to be reproduced during flights. Some results of hardware-in-the-loop tracking tests based on flight data are briefly summarized and expected flight performance of the electro-optical system as auxiliary sensor is discussed
Image Processing Algorithm for Integrated Sense And Avoid Systems
No full textTo allow Unmanned Aircraft Systems (UAS) accessing National Airspace System (NAS) “Equivalent levels of safety”
to the ones of human vision must be guaranteed. Therefore, an appropriate “Sense and Avoid” technology must be
developed that is capable of detecting, tracking, and avoiding obstacles. The Department of Aerospace Engineering at
University of Naples has been involved in a project funded by the Italian Aerospace Research Centre (CIRA) for the
realization of a prototypical “Obstacle Detection & Identification” (ODID) System. It is installed onboard a Very Light
Aircraft (VLA) and it is characterized by a hierarchical sensor configuration in which the radar is the main sensor while
EO cameras are the auxiliary ones in order to increase accuracy and data rate so that anti-collision requirements are
fulfilled.
This paper focuses on the Image Processing algorithm for the panchromatic camera. Among the several techniques listed
in literature the edge detection – labeling one resulted as the best compromise in terms of computational load, detection
range, false alarm rate, miss detection rate and adaptability at different background luminosity conditions. Moreover it
has been customized in order to allow for reliable operation in a wide range of flight and luminance configurations and it
has been tested and run on a sequence of real images taken during flight tests. At the end, a table that summarizes those
results is presented. Indeed, the output tracking measurements accuracy increases by an order of magnitude with respect
to standalone radar one
Data Fusion for UAS Collision Avoidance: Results from Flight Testing
No full textIn the framework of a research project carried out by the Italian Aerospace Research Center (CIRA) and the Department of Aerospace Engineering of the university of Naples “Federico II”, an integrated radar/electro-optical (EO) system configuration was adopted to demonstrate in flight autonomous non-cooperative UAS collision avoidance. Proper image processing and data fusion algorithms were developed to gain full advantage from these heterogeneous sources. The hardware/software prototypical sensing system was installed onboard an optionally piloted flying laboratory of Very Light Aircraft category, and an extensive flight test campaign with a single intruder aircraft was carried out to evaluate the capability of the tracking system to support autonomous collision avoidance. This paper focuses on data fusion results from flight tests. Potential of radar/EO tracking is pointed out in terms of achievable accuracy in estimating intruder position and velocity. Analysis of estimated distance at closest point of approach shows how the increase in angular accuracy and data rate provided by the EO sensors improves system reliability in collision risk estimation
Real Time Corner Detection for Miniaturized Electro-Optical Sensors Onboard Small Unmanned Aerial Systems
No full textThis paper describes the target detection algorithm for the image processor of a vision-based system that is installed onboard an unmanned helicopter. It has been developed in the framework of a project of the French national aerospace research center Office National d’Etudes et de Recherches Aérospatiales (ONERA) which aims at developing an air-to-ground target tracking mission in an unknown urban environment. In particular, the image processor must detect targets and estimate ground motion in proximity of the detected target position. Concerning the target detection function, the analysis has dealt with realizing a corner detection algorithm and selecting the best choices in terms of edge detection methods, filtering size and type and the more suitable criterion of detection of the points of interest in order to obtain a very fast algorithm which fulfills the computation load requirements. The compared criteria are the Harris-Stephen and the Shi-Tomasi, ones, which are the most widely used in literature among those based on intensity. Experimental results which illustrate the performance of the developed algorithm and demonstrate that the detection time is fully compliant with the requirements of the real-time system are discussed
Flight Demonstration of Radar-based Autonomous Non-cooperative UAS Collision Avoidance
No full textAbstract - In the framework of a research project carried out by the Italian Aerospace Research Center and the Department of Aerospace Engineering of the University of Naples “Federico II”, an integrated radar/electro-optical system configuration was adopted to demonstrate in flight autonomous non-cooperative UAS collision avoidance. Real time data fusion and decision making algorithms were developed to estimate intruders motion and generate proper escape trajectories. The prototypical hardware/software system was installed onboard an optionally piloted flying laboratory of Very Light Aircraft category, and an extensive flight test campaign with a single intruder aircraft was carried out. This paper focuses on the results from radar-based autonomous collision avoidance flight tests. System performance is analyzed in terms of accuracy and reliability in estimating intruder position and velocity, and effectiveness in performing safe avoidance maneuvers while minimizing the deviation from nominal trajectory
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