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With the increasing interest in the utilization of urban airspace for unmanned aerial system (UAS) operations, be it for cargo delivery or passenger transportation, the future city sky could become quite a crowded place if all those visions become reality. In such case, the question of how close two operations could be conducted while sharing an airspace must be answered to ensure the safe and efficient use of the urban airspace. The lateral separation needed to prevent inadvertent intrusion by neighboring tracks is especially important when designing airspace corridors constrained by the urban landscape. This paper documents a series of flight tests conducted in open fields with the goal of assessing the along-track (longitudinal), cross-track (lateral),and altitude (height) deviation under two different flight conditions: operator guided operation within Visual Line of Sight (VLoS), and the waypoint-guided mission analogous to operating Beyond Visual Line of Sight (BVLoS). The flight test statistics were also compared to the Monte-Carlo based path prediction model that was used in earlier studies for collision prediction. The goal is to determine the Flight Technical Error (FTE) that forms a part of the Total System Error (TSE) for Performance Based Navigation (PBN) of UAS to support the establishment of separation requirements in urban airspace. Civil Aviation Authority of Singapore (CAAS) Nanyang Technological University Accepted version This project is supported by the Civil Aviation Authority of Singapore and the Nanyang Technological University, Singapore under their collaboration in the Air Traffic Management Research Institute.

In this paper we propose a novel approach aimed at recovering the 3D position of an UUV from UAV imagery in shallow-water environments. Through combination of UAV and UUV measurements, we show that our method can be utilized as an accurate and cost-effective alternative when compared to acoustic sensing methods, typically required to obtain ground truth information in underwater localization problems. Furthermore, our approach allows for a seamless conversion to geo- referenced coordinates which can be utilized for navigation purposes. To validate our method, we present the results with data collected through a simulation environment and field experiments, demonstrating the ability to successfully recover the UUV position with sub-meter accuracy.

International audience; Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is under development to support both lateral and vertical guidance. A key component of ARAIM is the Integrity Support Message (ISM) which contains validated input parameters including the probabilities of satellite and constellation failure. A service commitment for the former is provided in the Global Positioning System (GPS) performance standards whilst further commitments from GLONASS, Galileo and Beidou are expected to follow. However, Air Navigation Service Providers (ANSPs) may demand some flexibility in setting the ISM. This paper presents a procedure for setting ISM parameters employing a means to determine the a priori probabilities using Bayesian Inference of a Poisson process model given in the literature. This method and a novel way to accumulate the service history are used to analyze the effects of satellite block and age. Results show that whilst the type and age of satellite are clearly drivers in the likelihood of fault, such effects may be accounted for without significant impact on the broadcast values. The paper also addresses the probability of unscheduled satellite outages using the new method. Finally, the paper gives a high-level scheme of how the validity of the ISM parameters may be assured.

Automatic dependent surveillance-broadcast (ADS-B) is an emerging air traffic surveillance technology that overcomes the limitations of today's radar technology and enhances the air traffic control. To provide authenticity, integrity in ADS-B and to improve the computation, communications efficiency, in this paper, we propose a new, efficient, and secure pairing-free ADS-B authentication scheme with Batch Verification in ID-based framework. The proposed scheme is proven secure and is more efficient than the existing schemes.

This paper presents an innovative method of locating airplanes, which uses only voice communication between an air traffic controller and the pilot of an aircraft. The proposed method is described in detail along with its practical implementation in the form of a technology demonstrator (proof of concept), included in the voice communication system (VCS). A complete analysis of the performance of the developed method is presented, including the results of simulation and measurement tests in real conditions. The obtained results are very optimistic and indicate that the proposed solution may constitute an alternative method of locating aircraft in emergency conditions, i.e., a backup solution in the case of failure of other positioning systems.

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a &ldquo, cyber-pilot&rdquo, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a &ldquo, virtual sensor&rdquo, which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

We need a coordinated control method to continuously track a moving target using a group of UAVs. In this paper, we study the predicted reference point guidance method, where the target is considered to move with a constant velocity in a very short time window and the trajectories of the UAVs are designed as several tiny arcs around the target. The control law of the UAV is divided into roll angle control and velocity control. Simulations are used to verify that the proposed control laws have smaller standoff distances and phase angle control errors than the Lyapunov vector field guidance and model-based predictive control, and the wind is considered in the simulation, too. Therefore, we show that our proposed method has higher steady state accuracy among existing techniques.

Trajectories optimisation is a major research topic in air transport and air traffic management, due to its profound impact both on passengers, airlines and the environment in general, and consequently on the perceived value and cost of air transportation. While the challenges associated to the optimisation of the en-route part of a flight are well understood, relative less attention has been devoted to the last part, i.e. the approach and landing. Here we show how open large-scale data sets of aircraft trajectories can be used to characterise the efficiency of flights landing at an airport, measured through the time and distance flown below 10,000 feet. The yielded picture is highly heterogeneous, with the time spent at low altitude varying from an average of 10 minutes for Zurich, up to 16 minutes for London Heathrow. Flights arriving at the same airport also experience highly different times, e.g. from 12 to 20 minutes for London Heathrow, depending on factors like traffic volumes, time of the year and of the day, and on interactions with other traffic patterns and airports. From a more general perspective, this contribution illustrates how the availability of large data sets can be used to improve our understanding of the real behaviour of the system, and especially its deviation from what planned.

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases. European Union (FP7/2007-2013) - 318275 (GET Service)

ICAO Annex 10 — Attachment H provides a guidance for a rationalization of conventional radio navigation aids to support Performance-Based navigation. An optimization of terrestrial navigation infrastructure, which includes a rationalization effort and coordinated evolution, is necessary to maintain a sufficient level of safety and operations in case of GNSS outage. This is an opportunity to introduce new signals with better ranging performance. The hybridization of new systems with legacy DME enables a fully backup navigation system. The hybrid systems make possible to decommission older radio navigation solutions. In this paper we present our assessment tool: marginal benefit. Marginal benefit is the ratio between potential DME to be decommissioned and the number of new signals deployed. The marginal benefit plot can be considered a parameter for air navigation service providers (ANSPs) to have a robust backup navigation infrastructure.

Monitoring of potentially dangerous concentrations of gasses enclosed areas can be a mundane, demanding, and dangerous task and requires a large amount of, both manned hours as well as multiple sensors. Unmanned Aerial Vehicles can be modified to suit a range of different applications. This report gives an overview of the design, implementation, and testing process of a UAV that can perform autonomous flight, target detection, and air quality sensing in an indoor environment. The UAV is required to autonomously navigate through an indoor environment using waypoint navigation and A-Star pathfinding, and locate and confirm the locations of three separate visual targets using a custom coded computer vision solution that uses a camera on board. Once each target is located, the UAV hovers at each target location and performs air quality analysis for 10 seconds with two on board sensors. During testing, the UAV was able to autonomously build its own search pattern once it was made aware of any obstacles in the flight area. All three targets were autonomously confirmed and air quality analysis was successful. All of the important flight data was wirelessly transmitted to a ground control station which included a web page with a graphical user interface. The web page displayed a real time image stream, alongside dynamic positional data in an interactive map of the test area and air quality results in an easy to read format.

In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Marker with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicability in the field of precision agriculture such as, crop health monitoring, pest plant detection which causes detrimental financial damage to crop yields if not noticed at an early stage.

Monitoring of environmental gases is a demanding task that can require long periods of observation and large numbers of sensors. Unmanned Aerial Vehicles (UAVs) presently represent a suitable alternative to monitor large, remote, and difficult to access areas. Due to the wide range and diversity of shape and size, UAVs now possess the capability of carrying specialized sensor modules that can accurately monitor gas-concentrations. This paper describes the design and flight testing of a UAV which carries an on-board camera and a carbon dioxide gas sensor and is capable of autonomous gas sensing while simultaneously visually detecting predefined targets placed at locations inside a room. The detection system autonomously navigates around the flight area using a waypoint navigation system and hovers for 10 seconds once the target has been visually identified taking Air Quality (AQ) samples as it does so. Laboratory, bench and field test results demonstrate the capability of the UAV to detect targets placed in a room and analyse the air quality of samples taken during flight above the target. The data collected during the flight is transmitted, in real time, back to a Ground Control Station (GCS) for visualisation and analysis, where 3D mapping of the target location and gas concentration is presented via a web interface. Test cases verify the capability of the subsystem's integration and the operation of the UAV system as a whole. The image processing algorithm used for the target detection, based on a cascade method, proved to be dependent on the frame transmission rates, which can make the software considerably slower. The developed system provides an effective monitoring system and can be used in a wide range of applications such as gas leaks, fires, and mining applications and if converted for use in an outdoor environment, applications such as agriculture biomass burning emissions and chemical and biological agent detection studies. The system could be used in conjunction with ground sensors and integrated into an extensive gas monitoring system.

This dissertation presents two contributions to the development of autonomous aerial delivery systems (ADSs), both of which advance the prospect of enabling an ADS to land on a moving platform, such as the deck of a ship at sea. The first contribution addresses the problem of estimating the target’s position and velocity. A novel, dual-rate estimation algorithm based on Unscented Kalman filtering allows the ADS to use visual measurements from a fixed monocular sensor to estimate the target’s motion even when the ADS’s swinging motion in flight causes the target to be out of view. The second contribution addresses the problem of planning a landing trajectory considering winds in the vertical air mass between the target’s height and the ADS’s altitude. A wind model that assumes a logarithmic relationship between horizontal wind velocity and height in the air mass enables the ADS’s guidance algorithm to plan a valid landing trajectory in the presence of these winds. This dissertation contains simulation results for the visual estimation algorithm that show that estimation errors are minimal after estimator convergence. Flight test results indicate that the wind modeling algorithm was useful for computing landing trajectories. http://archive.org/details/posendwindestima1094543926 Commander, United States Navy Approved for public release; distribution is unlimited.

International audience; Autopilots for micro aerial vehicles (MAVs) with a maximum permissible avionic payload of only a few grams need lightweight, low-power sensors to be able to navigate safely when flying through unknown environments. To meet these demanding specifications, we developed a simple functional model for an Elementary Motion Detector (EMD) circuit based on the common housefly's visual system. During the last two decades, several insect-based visual motion sensors have been designed and implemented on various robots, and considerable improvements have been made in terms of their mass, size and power consumption. The new lightweight visual motion sensor presented here generates 5 simultaneous neighboring measurements of the 1-D angular speed of a natural scene within a measurement range of more than one decade [25 °/s; 350°/s]. Using a new sensory fusion method consisting in computing the median value of the 5 local motion units, we ended up with a more robust, more accurate and more frequently refreshed measurement of the 1-D angular speed.

The wide range of applications and configurations of UAVs raises the need for different types of navigation methodologies compared to the conventional INS/GPS system. For instance, the limitation in cost, size and weight of indoor UAVs makes conventional navigation system unsuitable for these vehicles. In addition, the INS/GPS navigation system is impractical for indoor applications because the GPS signal is not reliable in closed territories. This paper proposes a new, cost-effective and simple indoor navigation system using three laser beams fixed to the body of the UAV and shooting to the ground. Then, a camera and computer vision algorithm are used to capture the laser dots on the ground and determine their coordinates. The position of laser dots is used to obtain full information about the position and orientation of the UAV. The proposed navigation system can be classified as a vision based navigation system, yet, it does not depend highly on the quality of the video shots taken from the vision camera and does not require a heavy image processing algorithm. An illustrative simulation study is conducted to demonstrate the validity of the proposed navigation system.

Approaches with vertical guidance (APV) can provide greater safety and cost savings to general aviation through accurate GPS horizontal and vertical navigation. However, GPS needs augmentation to achieve APV fault detection (FD) requirements. Aircraft-based augmentation systems (ABAS) fuse GPS with additional sensors at the aircraft. Typical ABAS designs assume high-quality inertial sensors with Kalman filters but these are too expensive for general aviation. Instead of using high-quality (and expensive) sensors, the purpose of this paper is to investigate augmenting GPS with a low-quality micro electro-mechanical system (MEMS) inertial measurement unit (IMU) and aircraft dynamic model (ADM). The IMU and ADM are fused together using a multiple model fusion strategy in a bank of extended Kalman filters (EKF) with the normalized solution separation (NSS) FD scheme. A tightly-coupled configuration with GPS is used and frequent GPS updates are applied to the IMU and ADM to compensate for their errors. Based upon a simulated APV approach, the performance of this architecture in detecting a GPS ramp fault is investigated, showing a performance improvement over a GPS-only "snapshot" implementation of the NSS method. The effect of fusing the IMU with the ADM is evaluated by comparing a GPS-IMU-ADM EKF with a GPS-IMU EKF, where a small improvement in protection levels is shown.

Two Portuguese navigators crossed the South Atlantic in the beginning of the 20th century. They developed and used for the first time scientific methods of astronomic navigation when flying out of sight of land. A new model of sextant that could be used to measure the altitude of a star without the need of the sea horizon was developed. Another instrument called "path corrector" was introduced to calculate graphically the drift of an airplane and the direction of flight, taking into account the intensity and the direction of the winds. For the very first time in the history of the aviation the crossing of the South Atlantic had been achieved, and using an instrument that enabled an airplane's position to be precisely determined by astronomic navigation when flying out of sight of land., Fundação para a Ciência e a Tecnologia

Date of Conference: 24-26 Sept. 2007 Conference name: 19th International Conference on Applied Electromagnetics and Communications, 2007 The work carried out within Work Package 2.4-3 of the EU network "Antenna Centre of Excellence" (ACE) is presented in this paper. This work package is concerned with structuring research on conformal antennas. In more details, the work is focused on the problems associated with full benchmarking of conformal antennas, on development of hybrid programs for analyzing different classes of conformal antennas, and on investigation of properties of algorithms for optimizing beam synthesis and beam-steering for conformal arrays.

The operating environment of aircraft air-data sensors is extremely aggressive with a wide temperature and humidity variation, suspension particles, icing and etc. The Pitot tube is widely used as air-data sensor on aircrafts, which has had many operational issues reported in the past decades due to ice-crystal obstruction of its aperture. The Microelectromechanical Systems (MEMS) technology associated with Thermal Mass Flow Sensors (TMFS) can establish a reformulation on air-data system instrumentation, since it is possible to create micro heaters and thermal sensors on the same substrate without moving parts, simplifying the operational requirements compliant to the aeronautics specifications. Exploring the simplicity and robustness of MEMS technology, a TMFS is herein designed according to criteria that meet aeronautic performance requirements, selecting adequate dimensions of the various structures in the sensor and materials compatible with semiconductor micromachining processes. This paper also presents the numerical results of the interaction of a thermal mass flowing through the integrated heater element of the sensor.