Visual Flight Research Articles

50 & 25 Years Ago

Application of Digital Computers to Air Traffic Control in the United States; Howard J. Kirshner (p. 14): “Radar-oriented tracking and alphanumeric display systems using digital computers are just now emerging as the key data processing tool in air traffic control, although the Great Falls Center, using air defense computer and display facilities, has been in operation since 1963.” (p. 15) “Aircraft operations in the United States are conducted under two forms of ATC procedures. The first procedure is called Visual Flight Rules, or VFR, and the second, Instrument Flight Rules, or IFR. … Under Visual Flight Rules, it is the responsibility of the aircraft’s pilot to maintain adequate separation from other aircraft. … The ground-based ATC system is responsible for providing separation among aircraft flying under IFR, although in clear weather the pilot also has a responsibility to see and avoid other traffic, since not all aircraft in most segments of low-altitude airspace will be under control of the ground system.” (p. 16) “At 61 locations having radars and beacons, the computer-based Advanced Radar Terminal System (ARTS) is being deployed.” [Editor’s note: The article describes in detail the use of computers to analyze and display information that is captured by radar and beacon facilities. The impression is that none of these systems is yet (1972) ready for use but will become available soon.]

Robustness in bio‐inspired visually guided multi‐agent flight and the gain modulation hypothesis

AbstractAerial swarm implementations often underperform their feedback design goals due to sensor noise, modeling errors, and external disturbances, each of which can often be mitigated by including robustness in the design approach. At the same time, gain modulation has been measured in numerous insect flight examples involving relative position regulation and tracking. Gains are often lower than what initially appears optimal, which is not fully explained by existing hypotheses. This article analyzes the robustness of a visual feedback control system modeling insect flight feedback paths by using optic flow to regulate an agent's motion relative to its neighbors. This study considers the system's robustness to sensor noise and configuration deviations, which appear as a nonlinearity in the observation equation often neglected in linear feedback. The analysis indicates robust performance can be attained by a static gain controller by appropriate choice of gain. Experimental results using an optic flow swarm implementation in robotic systems are used to quantify the actual system performance for static gain controllers. Performance is compared when the system responds to ideal optic flow (no sensor noise) and to camera‐based optic flow swarm implementation (sensor noise and configuration uncertainty). [Correction added on 10 November 2022, after first online publication: the underline in the word ‘compared’ of the preceding sentence has been removed in this version.] The analysis suggests neural gain modulation in insect feedback paths may support robust group motion stabilization.

Sleep, Workload, and Stress in Aerial Firefighting Crews.

BACKGROUND:The challenges of climate change and increasing frequency of severe weather conditions has demanded innovative approaches to wildfire suppression. Australia's wildfire management includes an expanding aviation program, providing both fixed and rotary wing aerial platforms for reconnaissance, incident management, and quick response aerial fire suppression. These operations have typically been limited to day visual flight rules operations, but recently trials have been undertaken extending the window of operations into the night, with the assistance of night vision systems. Already a demanding job, night aerial firefighting operations have the potential to place even greater physical and mental demands on crewmembers. This study was designed to investigate sleep, fatigue, and performance outcomes in Australian aerial firefighting crews.METHODS:A total of nine subjects undertook a 21-d protocol, completing a sleep and duty diary including ratings of fatigue and workload. Salivary cortisol was collected daily, with additional samples provided before and after each flight, and heart rate variability was monitored during flight. Actigraphy was also used to objectively measure sleep during the data collection period.RESULTS:Descriptive findings suggest that subjects generally obtained >7 h sleep prior to flights, but cortisol levels and self-reported fatigue increased postflight. Furthermore, the greatest reported workload was associated with the domains of 'performance' and 'mental demand' during flights.DISCUSSION:Future research is necessary to understand the impact of active wildfire response on sleep, stress, and workload on aerial firefighting crews.Sprajcer M, Roberts S, Aisbett B, Ferguson S, Demasi D, Shriane A, Thomas MJW. Sleep, workload, and stress in aerial firefighting crews. Aerosp Med Hum Perform. 2022; 93(10):749-754.

Implementation and Real-Time Validation of a European Remain Well Clear Function for Unmanned Vehicles

The full integration of Remotely Piloted unmanned vehicles into civil airspace requires first and foremost the integration of a traffic Detect and Avoid (DAA) system into the vehicle. The DAA system supports remote pilots in performing their task of remaining Well Clear from other aircraft and avoiding collisions. Several studies related to the design of a Remain Well Clear function have been performed that served as input for the development of technical standards applicable to non-European countries. In this paper, a Remain Well Clear implementation is proposed that, using the results of past international projects, fits European airspace needs and specificities and can be acceptable to both remote pilots and air traffic controllers, with only minimal impact on the standard operating procedures used for manned aircraft. The proposed Remain Well Clear software has been successfully validated through real-time simulations with pilots and controllers in the loop considering traffic encounters and mission scenarios typically found in European airspace. The achieved results highlight the appropriate situational awareness provided by the proposed RWC function and its effective support to the remote pilot in making adequate decisions in conflict solving. Real-time simulation tests showed that, in almost all cases, an RWC maneuver is successfully performed, giving the RP sufficient time to assess the conflict, coordinate with the controller, if needed, and execute the maneuver. The fundamental role of the proposed RWC function has been especially evident in uncontrolled airspace classes where the controller does not provide any separation provision. Moreover, its effectiveness has also been tested in encounters with aircraft flying under visual flight rules in controlled airspace, where the controller is not informed or has less information regarding these aircraft. The results from validation tests imply two key potential safety benefits, namely: the mitigation of performing a collision avoidance maneuver and the prevention of potential conflict while not disrupting the traffic flow with possible further consequences of generating other potentially hazardous situations.

Mental Workload Artificial Intelligence Assessment of Pilots’ EEG Based on Multi-Dimensional Data Fusion and LSTM with Attention Mechanism Model

EEG has been proved to be an effective tool for researchers’ cognition and mental workload by detecting the changes of brain activity potential. The mental workload of pilots in aviation flight is closely related to the characteristics of flight tasks. The previous methods have problems such as lack of objectivity, low EEG analysis ability and lack of real-time analysis ability. In order to solve these problems, this paper proposes a multi-dimensional data fusion brain workload calculation method based on flight effect evaluation, which integrates vision, operational behavior and visual gaze, and classifies and analyzes them in combination with EEG data. This method evaluates the mental workload of pilots from three aspects: visual gaze behavior, control behavior and flight effect in the simulated flight experimental environment, and realizes a more objective mental workload analysis. Then, the synchronously collected EEG data are segmented and sampled to form a dataset, and an LSTM neural network model integrating attention mechanism is established, in which the attention mechanism is used to improve the feature processing ability of the network model for the classification of complex EEG data. After machine learning training, the final model can achieve 94% detection accuracy for 2-s EEG data, and has the ability of real-time analysis in the application environment. Compared with the previous similar LSTM model, the accuracy is improved by 6%, which also shows the effectiveness of the model.

Infrared and Visible Image Fusion with Deep Neural Network in Enhanced Flight Vision System

The Enhanced Flight Vision System (EFVS) plays a significant role in the Next-Generation low visibility aircraft landing technology, where the involvement of optical sensing systems increases the visual dimension for pilots. This paper focuses on deploying infrared and visible image fusion systems in civil flight, particularly generating integrated results to contend with registration deviation and adverse weather conditions. The existing enhancement methods push ahead with metrics-driven integration, while the dynamic distortion and the continuous visual scene are overlooked in the landing stage. Hence, the proposed visual enhancement scheme is divided into homography estimation and image fusion based on deep learning. A lightweight framework integrating hardware calibration and homography estimation is designed for image calibration before fusion and reduces the offset between image pairs. The transformer structure adopting the self-attention mechanism in distinguishing composite properties is incorporated into a concise autoencoder to construct the fusion strategy, and the improved weight allocation strategy enhances the feature combination. These things considered, a flight verification platform accessing the performances of different algorithms is built to capture image pairs in the landing stage. Experimental results confirm the equilibrium of the proposed scheme in perception-inspired and feature-based metrics compared to other approaches.

The role of flight simulation in flight training of pilots for crisis management

Every day over 102.000 commercial passenger flights take place on a global scale above our heads. Because of that many questions arise in the areas of safety and security in massive air traffic. If airlines use crisis management, any activity concerning the preparation of the crew and the aircraft for a particular task - flight gets more significance and importance. During the flight, the pilot - the captain of the aircraft, is the key person of any company activity that includes transport of people and goods, and that is the reason why a pilot's psycho-physical abilities and flying performance must be constantly checked, evaluated and improved. In a plane crash near North Macedonia's capital city Skopje, the aviation investigators discovered the number of serious failures in pilot training that led to the fall of a private plane Cessna 340. The investigation revealed that although the pilot was certificated to fly in instrument meteorological conditions, his actions in the air indicate that he was not well trained. European and American aviation authorities have clear legal regulations regarding the training and certification for individual flight categories as well as a categorized simulation technique where pilots exercise before they get one of the flying categories. Flight simulators play an essential role in the training of the future pilots for both visual and instrumental flight conditions

Transformer Network Intelligent Flight Situation Awareness Assessment Based on Pilot Visual Gaze and Operation Behavior Data

Situational awareness is the ability of pilots to master flight status, which is of great significance to aviation flight safety and flight effect. According to the information processing model, the pilot’s main steps of processing information are feeling, perception and execution. There are many problems in situation awareness analysis guided by visual gaze, such as large analysis deviation and high delay due to various influencing factors and complex characteristics. In order to solve this problem, this paper proposes a situation awareness assessment method based on artificial intelligence neural network and integrating visual gaze and flight control. First, this paper carries out simulated flight training experiments for flight cadets, and collects the data of eye movement, line of sight tracking, flight control and flight parameters of pilot cadets. Then, aiming at the flight subjects, a situation awareness analysis method based on events is established, and the situation awareness state in the experiment is evaluated and analyzed through the flight parameter data. Then, the visual gaze and flight control data are sliced in the unit of situational awareness events, and the data set is constructed. Finally, this paper designs a multi-channel sequence data classification and analysis model based on transformer, in which the situation awareness characteristics of visual gaze and operation behavior are analyzed through the attention mechanism. The experimental results show that the accuracy of situation awareness classification of the designed neural network model to the experimental data set is 96%, and can classify and evaluate the pilot’s situation awareness state in 5[Formula: see text]s.

Analysis of Saturated Capacity on the Runway Based on Advisory Circular 150/5060-5 As a Study Material for Airport Management Learning

The demand for air transportation from year to year until 2018 continues to increase. However, in 2019 it decreased as a result of the increase in air ticket fares, as well as in 2020 as a result of the COVID-19 pandemic. Although demand is experiencing a decline, knowing airport capacity is important in airport planning, which is one of the disciplines studied in airport management. Therefore, this research aims to estimate the saturation capacity of the runway at Sultan Mahmud Badaruddin International Airport (SMB) II, using the method developed by the Federal Aviation Administration (FAA) and specified in the Advisory Circular 150/5060-5. The results of this study can be used as study material in learning the field of airport management science. Based on the data obtained and processed, the results show that the saturation capacity of the runway per hour for runway 11 directions is 53 for Visual Flight Rules (VFR) and 49 Instrument Flight Rules (IFR). Meanwhile, the direction of runway 29 is 50 in VFR conditions and 46 in IFR conditions. So that the average saturation capacity for both runway directions is 51 in VFR conditions and 48 in IFR conditions. This runway capacity calculation method cannot accommodate differences in the flight separation rules applied, so that this capacity will be achieved if Airnav Indonesia, Palembang Branch as the manager of flight navigation services at SMB II Airport applies the same rules as the FAA. The currently applied separation rules are relatively larger than those set by the International Civil Aviation Organization (ICAO) and also the FAA. As an ICAO member country, it is still possible to lower the separation rules set by ICAO to increase runway capacity.The conclusions of the research have been obtained from the analysis of the saturated capacity of the runway based on the advisory circular 150/5060-5 which has been used as a study material for learning in the field of airport management science.

Tracking control a twin-rotor helicopter used for building measuring with PID-MPC controller

Abstract In recent years, due to the development of UAV (Unmanned aerial vehicle) technology, UAVs have been used in an increasingly wide range of applications, including the architecture industry. In the field of architecture, the technologies of capturing images of building conditions with UAVs have a huge development, such as architectural planning, remote terrain detection, construction safety supervision and so forth. In general, the applications can be expanded widly, owing to the convenient operation of UAVs for the flyers and that does not require a high degree of precision for the control. However, when it comes to penetration between and within buildings. The lack of flight vision and signal interference often leads to collisions and crashes. Hence, this paper studies how to further optimise the control accuracy of construction measurement UAVs. The aim is to reduce the difficulty of operating the UAV when it faces to interference during architecture measurement. In this paper, a three DOFs two-rotor UAV is selected as the object of study. A mathematical model of the UAV is constructed, which is followed by the designing of an advanced controller based on a PID controller and an MPC (model predictive controller) controller, used for realising offset free reference tracking when changing elevation, pitch and travel angles. The usage of this controller can significantly reduce the cost of control during flight to reduce control bias in the presence of disturbing factors. The application of the controller to UAVs allows for automated and intelligent UAV control.

An Off-Axis Flight Vision Display System Design Using Machine Learning

We propose an off-axis flight vision display system design with a free-form surface using machine learning to simulate the visual distance variation during take-off and landing training for pilots. This design is realized by ray tracing using ZEMAX software, where we build and optimize a series of initial systems that meet the corresponding optical specifications. A deep neural network is used to train the regression model, which is specifically designed to predict the fitted polynomial model for the free-form surface of the system. Our results demonstrate that the design of a flight visual display system can be transformed into a machine learning problem and further optimized by training and learning with abundant data, providing an avenue to design more powerful and complex imaging optical systems.

Pilots’ mental workload variation when taking a risk in a flight scenario: a study based on flight simulator experiments

Pilots’ operation behavior in flight is associated with their mental state variables such as workload, situation awareness, stress, etc. The objective of this study was to investigate the dynamic process of mental workload for pilots who perform a risky flight task in simulated scenarios. Two empirical experiments were conducted to address this issue. In experiment one, 19 trainee pilots divided into high-risk and low-risk groups performed a target-search task in a low-altitude visual flight. The results showed a statistically significant interaction between groups and segments for heart rate variability (HRV). The same pattern of physiological results was replicated among participants in experiment two, in which 19 airline pilots completed an approach with low visibility. These findings highlighted the relationship between mental workload variation and risk-taking behavior, which could be considered in improving pilot selection and training to improve flight safety.

An effective metaheuristic with a differential flight strategy for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

A discrete fruit fly optimisation algorithm based on a differential flight strategy (DFFO_DF) is proposed for solving the distributed permutation flowshop scheduling problem with sequence-dependent setup times. In the olfactory exploration stage, four types of neighbourhood perturbation operators are designed. An olfactory exploration mechanism is proposed to guide fruit flies during the exploration. In the visual flight stage, to avoid the algorithm from falling into a local optimum, we abandon the mode of all fruit flies flying towards the best individual and propose a differential flight strategy for the fruit flies to make better use of the information of different individuals. A local search method based on critical factories and job blocks helps fruit flies to improve their search capabilities. In addition, the lower bound property is applied to some operators contained in the DFFO_DF to reduce the search space. The proposed algorithm is evaluated using a detailed experimental design to determine the appropriate values of the key parameters. Finally, the proposed DFFO_DF is compared with several state-of-the-art algorithms based on different test instances. The experimental results prove that the DFFO_DF is an effective metaheuristic algorithm.

Laser tracking leader-follower automatic cooperative navigation system for UAVs

Currently, small payload and short endurance are the main problems of a single UAV in agricultural applications, especially in large-scale farmland. It is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative navigation. This study proposed a laser tracking leader-follower automatic cooperative navigation system for multi-UAVs. The leader in the cluster fires a laser beam to irradiate the follower, and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking device. Based on the existing kernel correlation filter (KCF) tracking algorithm, an improved KCF real-time spot tracking method was proposed. Compared with the traditional KCF tracking algorithm, the recognition and tracking rate of the optimized algorithm was increased from 70% to 95% in indoor environment, and was increased from 20% to 90% in outdoor environment. The navigation control method was studied from two aspects: the distance coordinate transformation model based on micro-gyroscope and navigation control strategy. The error of spot position was reduced from the maximum (3.12, −3.66) cm to (0.14, 0.12) cm by correcting the deviation distance of the spot at different angles through a coordinate correction algorithm. An image coordinate conversion model was established for a complementary metal-oxide-semiconductor (CMOS) camera and laser receiving device at different mounting distances. The laser receiving device was divided into four regions, S0-S3, and the speed of the four regions is calculated using an uncontrollable discrete Kalman filter. The outdoor flight experiments of two UAVs were carried out outdoors using this system. The experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm, and the coefficient of variation is 0.0181. The average flight error on the Z-axis is 7.3 cm, and the coefficient of variation is 0.0414. This study demonstrated the possibility and adaptability of the developed system to achieve multi-UAVs cooperative navigation. Keywords: two-UAVs cooperative, visual navigation, laser tracking DOI: 10.25165/j.ijabe.20221502.6350 Citation: Ming R, Zhou Z Y, Lyu Z C, Luo X W, Zi L, Song C C, et al. Laser tracking leader-follower automatic cooperative navigation system for UAVs edited. Int J Agric & Biol Eng, 2022; 15(2): 165–176.

GA Pilot Perceptions of Speech Systems to Transcribe and Submit PIREPs

Flying into hazardous weather can be a cause of aviation incidents and accidents. Accidents involving general aviation (GA) pilots who are not instrument rated who fly into instrument meteorological conditions (IMC) are often fatal. Pilot weather reports (PIREPs) can increase the accuracy and timeliness of current and forecasted weather conditions. They are an essential tool used by pilots to avoid flying into hazardous weather as well as meteorologists to develop and update aviation forecasts. Thus, a large number of accurate PIREPs with the best source of current weather coming from pilots and air traffic controllers are needed. Pilots are often unable to make PIREPs because of workload in the cockpit or because it is cumbersome to leave the air traffic control (ATC) frequency to contact flight the flight service station (FSS). Currently, air traffic controllers must solicit and disseminate PIREPs. However, air traffic controllers’ primary obligation is to provide traffic separation and traffic alerts. During poor weather, when PIREPs are needed the most, controllers are often too busy to solicit and disseminate PIREPs (NTSB, 2017a). This study administered a descriptive survey to inquire about how likely pilots would be to use a speech recognition system (SRS) to transcribe and submit PIREPs automatically while flying in three distinct flight regimes: instrument flight rules (IFR), visual flight rules (VFR) with flight following, and VFR without flight following. The survey employed cross-section design and included Likert scale questions. For each flight regime, additional information was obtained through an open-ended follow-up question. The Likert scale responses indicated that pilots were neutral about using a SRS to transcribe and submit PIREPs in each flight regime.Spradley’s (1979) domain analysis was used to identify common themes and patterns from the open-ended responses. Major findings from flying IFR were that pilots found it easier to speak directly to air traffic control, or pilots were too busy to submit PIREPs while flying IFR. Major findings from flying VFR with flight following were that pilots thought it was easier to report PIREPs directly to air traffic control or to a flight service station, and it was more accurate to report PIREPs directly to an aviation professional. However, they were willing to try a SRS. Major findings from flying VFR without flight following were that pilots wanted the opportunity to review a PIREP submission for accuracy and were willing to try the system. Significant differences were determined by making a comparison between the three groups.

Spatial tuning of translational optic flow responses in hawkmoths of varying body size

To safely navigate their environment, flying insects rely on visual cues, such as optic flow. Which cues insects can extract from their environment depends closely on the spatial and temporal response properties of their visual system. These in turn can vary between individuals that differ in body size. How optic flow-based flight control depends on the spatial structure of visual cues, and how this relationship scales with body size, has previously been investigated in insects with apposition compound eyes. Here, we characterised the visual flight control response limits and their relationship to body size in an insect with superposition compound eyes: the hummingbird hawkmoth Macroglossum stellatarum. We used the hawkmoths’ centring response in a flight tunnel as a readout for their reception of translational optic flow stimuli of different spatial frequencies. We show that their responses cut off at different spatial frequencies when translational optic flow was presented on either one, or both tunnel walls. Combined with differences in flight speed, this suggests that their flight control was primarily limited by their temporal rather than spatial resolution. We also observed strong individual differences in flight performance, but no correlation between the spatial response cutoffs and body or eye size.

Improving the tactical scanning of student pilots: A gaze-based training intervention for transition from visual flight into instrument meteorological conditions

Eye tracking has been applied to train novice drivers and clinicians; however, such applications in aviation are limited. This study develops a gaze-based intervention using video-based, expert commentary, and 3M (Mistake, Mitigation, Mastery) training to instruct visual flight rule student pilots on an instrument cross-check to mitigate the risk of losing aircraft control when they inadvertently enter instrument meteorological conditions (IMC). Twenty general aviation student pilots were randomized into control and experimental groups. Dwell time, return time, entropy, Kullback-Leibler divergence, and deviations from flight paths were compared before and after training to straight-and-level-flight (LF) and standard left level turn (LT) scenarios. After the training, the experimental pilots significantly increased dwell time on primary instruments (PIs), reduced randomness in visual search, and fixated on the PIs in shorter times (in the scenario of LT). In terms of piloting, all experimental pilots successfully kept the aircraft control while five control pilots lost control in IMC; significant differences in altitude and rate of climb deviations were observed between groups (in the scenario of LF).

Mechanisms of punctuated vision in fly flight

To guide locomotion, animals control gaze via movements of their eyes, head, and/or body, but how the nervous system controls gaze during complex motor tasks remains elusive. In many animals, shifts in gaze consist of periods of smooth movement punctuated by rapid eye saccades. Notably, eye movements are constrained by anatomical limits, which requires resetting eye position. By studying tethered, flying fruit flies (Drosophila), we show that flies perform stereotyped head saccades to reset gaze, analogous to optokinetic nystagmus in primates. Head-reset saccades interrupted head smooth movement for as little as 50ms-representing less than 5% of the total flight time-thereby enabling punctuated gaze stabilization. By revealing the passive mechanics of the neck joint, we show that head-reset saccades leverage the neck's natural elastic recoil, enabling mechanically assisted redirection of gaze. The consistent head orientation at saccade initiation, the influence of the head's angular position on saccade rate, the decrease in wing saccade frequency in head-fixed flies, and the decrease in head-reset saccade rate in flies with their head range of motion restricted together implicate proprioception as the primary trigger of head-reset saccades. Wing-reset saccades were influenced by head orientation, establishing a causal link between neck sensory signals and the execution of body saccades. Head-reset saccades were abolished when flies switched to a landing state, demonstrating that head movements are gated by behavioral state. We propose a control architecture for active vision systems with limits in sensor range of motion. VIDEO ABSTRACT.

YOLO-Highway: An Improved Highway Center Marking Detection Model for Unmanned Aerial Vehicle Autonomous Flight

Automatic visual navigation flight of an unmanned aerial vehicle (UAV) plays an important role in the highway maintenance field. Automatic highway center marking detection is the most important part of the visual navigation flight of a UAV. In this study, the UAV-viewed highway data are collected from the UAV perspective. This paper proposes a model named the YOLO-Highway that uses an improved form of the You Only Look Once (YOLO) model to enhance the real-time detection of highway marking problems. The proposed model is mainly designed by optimizing the network structure and the loss function of the original YOLOv3 model. The proposed model is verified by the experiments using the highway center marking dataset, and the results show that the average precision (AP) of the trained model is 82.79%, and the frames per second (FPS) is 25.71 f/s. In comparison with the original YOLOv3 model, the detection accuracy of the proposed model is improved by 7.05%, and its speed is improved by 5.29 f/s. Moreover, the proposed model had stronger environmental adaptability and better detection precision and speed than the original model in complex highway scenarios. The experimental results show that the proposed YOLO-Highway model can accurately detect the highway center markings in real-time and has high robustness to changes in different environmental conditions. Therefore, the YOLO-Highway model can meet the real-time requirements of the highway center marking detection.

CFIT PREVENTION WITH COMBINED ENHANCED FLIGHT VISION SYSTEM AND SYNTHETIC VISION SYSTEM

This paper aims to combine two vision systems as a new technology to suggest the improvement in the continuing problem of Controlled Flight into Terrain (CFIT) accidents. A combined enhanced flight vision system and synthetic vision system seems to hold the solution, while it unhesitatingly accommodated to the commercial and General aviation instrument environment if pilots are getting minimal instrument training. This research shows the benefit of combined enhanced flight vision and synthetic vision systems based on analysis of the accident that occurred in 2013 on approach at Birmingham-Shuttlesworth International Airport. It is shown that the use of a combined system, could have prevented this accident. The head-up display is taken to facilitate this technology to the pilot to be able to see and read accurately using sky lenses.