Lidia Forlenza

Flight Test of a Radar-Based Tracking System for UAS Sense and Avoid

Presented 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.

Flight Performance Analysis of an Image Processing Algorithm for Integrated Sense-and-Avoid Systems

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.

Real Time Corner Detection for Miniaturized Electro-Optical Sensors Onboard Small Unmanned Aerial Systems

This 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.

Integrated Obstacle Detection System based on Radar and Optical Sensors

This 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.

Automatic Collision Avoidance System: Design, development and flight tests

This paper presents a fully Automatic Collision Avoidance System (ACAS) for unmanned aerial vehicles. This system has been developed by the Italian Aerospace Research Center (CIRA) in collaboration with the department of Aerospace Engineering of the University of Naples “Federico II”, in the framework of the national funded research project TECVOL (Technologies for the Autonomous Flight). The proposed system is comprised of two subsystems: the Obstacle Detection and Tracking subsystem, which permits to reveal flying intruders in a selected field of regard and to estimate their motion; the Collision Avoidance subsystem, which provides conflict detection and resolution capabilities, addressed in a 3D environment using information about current position and instantaneous speed vectors. The effectiveness of the system has been demonstrated during a flight test campaign, where proper conflict scenarios have been considered. In fact, the proposed ACAS setup was installed onboard a very light aircraft named FLARE (Flight Laboratory for Aeronautical Research), which has been customized with automatic flight capabilities. System architecture and the developed algorithms are described, then some results obtained from the flight test campaign are presented and discussed which demonstrate the reliability and the efficiency of the developed system.

A hardware in the loop facility for testing multisensor sense and avoid systems

The 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.

Data fusion for UAS collision avoidance: results from flight testing

In 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.

Laboratory Test Facility for Simulating a Sense and Avoid Flight System

Within 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-inthe-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.

Real-Time Hardware-in-the-Loop Laboratory Testing for Multisensor Sense and Avoid Systems

This paper focuses on a hardware-in-the-loop facility aimed at real-time testing of architectures and algorithms of multisensor sense and avoid systems. It was developed within a research project aimed at flight demonstration of autonomous non-cooperative collision avoidance for Unmanned Aircraft Systems. In this framework, an optionally piloted Very Light Aircraft was used as experimental platform. The flight system is based on multiple-sensor data integration and it includes a Ka-band radar, four electro-optical sensors, and two dedicated processing units. The laboratory test system was developed with the primary aim of prototype validation before multi-sensor tracking and collision avoidance flight tests. System concept, hardware/software components, and operating modes are described in the paper. The facility has been built with a modular approach including both flight hardware and simulated systems and can work on the basis of experimentally tested or synthetically generated scenarios. Indeed, hybrid operating modes are also foreseen which enable performance assessment also in the case of alternative sensing architectures and flight scenarios that are hardly reproducible during flight tests. Real-time multisensor tracking results based on flight data are reported, which demonstrate reliability of the laboratory simulation while also showing the effectiveness of radar/electro-optical fusion in a non-cooperative collision avoidance architecture.