Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach

Hiba, Antal; Gáti, Attila; Manecy, Augustin

doi:10.3390/s21062203

Cited by 14 publications

(8 citation statements)

References 46 publications

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“…In a similar context, cameras mounted on unmanned aerial vehicles (UAV) together with object detection can assist sense-and-avoid systems ( 33 , 34 ). Another line of research focuses on vision-based navigation systems for automatic landing approaches for (un)manned aircraft to detect runway and moving objects ( 35 – 36 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

Farhadmanesh

Marković

Rashidi

2022

Transportation Research Record: Journal of the Transportation R

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The vast majority of U.S. airports are not equipped with control towers, which limits their ability to keep records of flight operations. Attempts have been made to use sensor-based technologies to count aircraft operations at non-towered airports; however, they exhibit limited accuracy. To this end, we developed an automated video-based surveillance system capable of detecting general aviation aircraft departure and landing operations, which comprise the vast majority of operations at non-towered airports. The proposed computer vision method is comprised of three modules: aircraft detection, aircraft tracking, and operations count and classification. We explored different camera layouts and state-of-the-art machine learning and deep learning methods to determine the best settings to extract operations trajectory features for operations count and classification. The proposed method was tested at five non-towered airports. Integrating deep-neural-network-based aircraft detectors and image-correlation-based aircraft trackers achieved an accuracy of about 95%, while ensuring processing times that are needed for real-time implementation.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

Farhadmanesh

Marković

Rashidi

2022

Transportation Research Record: Journal of the Transportation R

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“…e landing auto test guides the aircraft along the runway based on the solution of the equations of motion on the glideslope. e glide path is derived simultaneously by comparison of the current aircraft and the runway position (because a 3D runway model is available [19]). ose positions are available based on image data.…”

Section: Design Of the Landing Auto Testmentioning

confidence: 99%

“…Other research results in 2017 show that the pilots did tune on the runway and the runway direction, during approach and landing [18]. Accordingly in the past literature, runway detection methods to find the airport location were used [19,20], but in this study, the airport and runway are simulated and the depth perception algorithm is applied to display the airport's location. Identification of objects on the runway is conducted by an external sensor installed on the aircraft.…”

Section: Introductionmentioning

confidence: 99%

Visual Landing Based on the Human Depth Perception in Limited Visibility and Failure of Avionic Systems

Mobini

Sabzehparvar

2022

Computational Intelligence and Neuroscience

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This paper introduces a novel visual landing system applicable to the accurate landing of commercial aircraft utilizing human depth perception algorithms, named a 3D Model Landing System (3DMLS). The 3DMLS uses a simulation environment for visual landing in the failure of navigation aids/avionics, adverse weather conditions, and limited visibility. To simulate the approach path and surrounding area, the 3DMLS implements both the inertial measurement unit (IMU) and the digital elevation model (DEM). While the aircraft is in the instrument landing system (ILS) range, the 3DMLS simulates more details of the environment in addition to implementing the DOF depth perception algorithm to provide a clear visual landing path. This path is displayed on a multifunction display in the cockpit for pilots. As the pilot’s eye concentrates mostly on the runway location and touch-down point, “the runway” becomes the center of focus in the environment simulation. To display and evaluate the performance of the 3DMLS and depth perception, a landing auto test is also designed and implemented to guide the aircraft along the runway. The flight path is derived simultaneously by comparison of the current aircraft and the runway position. The Unity and MATLAB software are adopted to model the 3DMLS. The accuracy and the quality of the simulated environment in terms of resolution, the field of view, frame per second, and latency are confirmed based on FSTD’s visual requirements. Finally, the saliency map toolbox shows that the depth of field (DOF) implementation increases the pilot’s concentration resulting in safe landing guidance.

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“…Since runway relative navigation during the final approach is a special case where robust and continuous detection of the runway is required, ref. [ 22 ] presented a pure optical approach of a robust threshold marker detection for monocular cameras, which increased sensor redundancy.…”

Section: Related Workmentioning

confidence: 99%

Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing

Weng

Cao

et al. 2022

Sensors

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Improving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs based on monocular vision is proposed in this paper, which allows them to discover landing sites in flight by using equipped optical sensors and avoid a crash or damage during normal and emergency landings. This scheme aims to solve two problems: the requirement of accuracy for runway detection and the requirement of precision for UAV state estimation. First, we design a robust runway detection framework on the basis of YOLOv5 (you only look once, ver. 5) with four modules: a data augmentation layer, a feature extraction layer, a feature aggregation layer and a target prediction layer. Then, the corner prediction method based on geometric features is introduced into the prediction model of the detection framework, which enables the landing field prediction to more precisely fit the runway appearance. In simulation experiments, we developed datasets applied to carrier-based UAV landing simulations based on monocular vision. In addition, our method was implemented with help of the PyTorch deep learning tool, which supports the dynamic and efficient construction of a detection network. Results showed that the proposed method achieved a higher precision and better performance on state estimation during carrier-based UAV landings.

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Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach

Cited by 14 publications

References 46 publications

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

Visual Landing Based on the Human Depth Perception in Limited Visibility and Failure of Avionic Systems

Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing

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