LIFT OFF: LoRaWAN Installation and Fiducial Tracking Operations for the
Flightline of the Future
- URL: http://arxiv.org/abs/2311.15912v1
- Date: Mon, 27 Nov 2023 15:22:17 GMT
- Title: LIFT OFF: LoRaWAN Installation and Fiducial Tracking Operations for the
Flightline of the Future
- Authors: Ari Goodman, Ryan O'Shea
- Abstract summary: LIFT OFF successfully provided a real-time updating map of all tracked assets using GPS sensors for people and support equipment and with visual fiducials for aircraft.
Future follow-on work is anticipated to apply the technology to other environments including carriers and amphibious assault ships.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Real-time situational awareness for the location of assets is critical to
ensure missions are completed efficiently and requirements are satisfied. In
many commercial settings, the application of global positioning system (GPS)
sensors is appropriate to achieve timely knowledge of the position of people
and equipment. However, GPS sensors are not appropriate for all situations due
to flight clearance and operations security concerns. LIFT OFF: LoRaWAN
Installation and Fiducial Tracking Operations for the Flightline of the Future
proposes a hybrid framework solution to achieve real-time situational awareness
for people, support equipment, and aircraft positions regardless of the
environment. This framework included a machine-vision component, which involved
setting up cameras to detect AprilTag decals that were installed on the sides
of aircraft. The framework included a geolocation sensor component, which
involved installing GPS sensors on support equipment and helmets. The framework
also included creating a long-range wide area network (LoRaWAN) to transfer
data and developing a user interface to display the data. The framework was
tested at Naval Air Station Oceana Flightline, the United States Naval Test
Pilot School, and at Naval Air Warfare Center Aircraft Division Lakehurst. LIFT
OFF successfully provided a real-time updating map of all tracked assets using
GPS sensors for people and support equipment and with visual fiducials for
aircraft. The trajectories of the assets were recorded for logistical analysis
and playback. Future follow-on work is anticipated to apply the technology to
other environments including carriers and amphibious assault ships in addition
to the flightline.
Related papers
- Long-Range Vision-Based UAV-assisted Localization for Unmanned Surface Vehicles [7.384309568198598]
Global positioning system (GPS) has become an indispensable navigation method for field operations with unmanned surface vehicles (USVs) in marine environments.
GPS may not always be available outdoors because it is vulnerable to natural interference and malicious jamming attacks.
We present a novel method that utilizes an Unmanned Aerial Vehicle (UAV) to assist in localizing USVs in restricted marine environments.
arXiv Detail & Related papers (2024-08-21T08:37:37Z) - Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied
Scenarios [66.05091704671503]
We present a novel angle navigation paradigm to deal with flight deviation in point-to-point navigation tasks.
We also propose a model that includes the Adaptive Feature Enhance Module, Cross-knowledge Attention-guided Module and Robust Task-oriented Head Module.
arXiv Detail & Related papers (2024-02-04T08:41:20Z) - Pose Estimation and Tracking for ASIST [0.0]
Aircraft Ship Integrated Secure and Traverse (ASIST) is a system designed to arrest helicopters safely and efficiently on ships.
PETA (Pose Estimation and Tracking for ASIST) is a research effort to create a helicopter tracking system prototype without hardware installation requirements for ASIST system operators.
PETA demonstrated the potential for state-of-the-art computer vision algorithms Faster R-CNN and HRNet to be used to estimate the pose of helicopters in real-time.
arXiv Detail & Related papers (2023-11-30T16:15:29Z) - Computer Vision for Carriers: PATRIOT [0.0]
PATRIOT is a prototype system which takes existing camera feeds, calculates aircraft poses, and updates a virtual Ouija board interface with the current status of the assets.
Software was tested with synthetic and real-world data and was able to accurately extract the pose of assets.
arXiv Detail & Related papers (2023-11-27T15:23:25Z) - Precise Payload Delivery via Unmanned Aerial Vehicles: An Approach Using
Object Detection Algorithms [0.0]
We describe the development of a micro-class UAV and propose a novel navigation method.
It incorporates a deep-learning-based computer vision approach to identify and precisely align the UAV with a target marked at the payload delivery position.
This proposed method achieves a 500% increase in average horizontal precision over conventional GPS-based approaches.
arXiv Detail & Related papers (2023-10-10T05:54:04Z) - MSight: An Edge-Cloud Infrastructure-based Perception System for
Connected Automated Vehicles [58.461077944514564]
This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles.
MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction.
Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
arXiv Detail & Related papers (2023-10-08T21:32:30Z) - VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale
Outdoor Environments [49.82314641876602]
We present a new dataset named VPAIR.
The dataset was recorded on board a light aircraft flying at an altitude of more than 300 meters above ground.
The dataset covers a more than one hundred kilometers long trajectory over various types of challenging landscapes.
arXiv Detail & Related papers (2022-05-23T18:50:08Z) - ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and
Response with AI [55.41644538483948]
Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response operations.
We have developed the free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS.
We demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide timely predictions of catastrophic flooding events.
arXiv Detail & Related papers (2022-01-25T14:51:19Z) - Deep Learning Aided Routing for Space-Air-Ground Integrated Networks
Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks.
With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links.
We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
arXiv Detail & Related papers (2021-10-28T14:12:10Z) - A Multi-UAV System for Exploration and Target Finding in Cluttered and
GPS-Denied Environments [68.31522961125589]
We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles.
The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map.
Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
arXiv Detail & Related papers (2021-07-19T12:54:04Z) - Demo Abstract: Indoor Positioning System in Visually-Degraded
Environments with Millimetre-Wave Radar and Inertial Sensors [44.58134907168034]
We present a real-time indoor positioning system which fuses millimetre-wave (mmWave) radar and Inertial Measurement Units (IMU) data via deep sensor fusion.
Good accuracy and resilience were exhibited even in poorly illuminated scenes.
arXiv Detail & Related papers (2020-10-26T17:41:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.