Airborne Radar for sUAS Sense and Avoid

Szatkowski, George N.; Kriz, Andrew; Ticatch, Larry A.; Briggs, Robert C.; Coggin, John; Morris, Christopher M.

doi:10.1109/dasc43569.2019.9081656

Cited by 7 publications

(4 citation statements)

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“…Primary radar is a technology that was widely used within manned aviation to separate aircraft from one another, and it is still sometimes used today as a back-up to the SSR systems, which are dependent upon aircraft transponders, or as a ground radar while aircraft are on the ground [39]. However, primary radar can also be used on board unmanned aircraft as a means of achieving the DAA requirements associated with flight in non-segregated airspace [40]. Unlike SSR and the use of ADS-B In and Out, primary radar does not depend on aircraft having transponders on board in order for them to be detected, and it also detects objects other than aircraft, such as buildings and trees, that would otherwise not be picked up [41].…”

Section: Primary Radarmentioning

confidence: 99%

BVLOS Unmanned Aircraft Operations in Forest Environments

Hartley

Henderson

Jackson³

2022

Drones

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This article presents a review about Beyond Visual Line Of Sight (BVLOS) operations using unmanned aircraft in forest environments. Forest environments present unique challenges for unmanned aircraft operations due to the presence of trees as obstacles, hilly terrain, and remote areas. BVLOS operations help overcome some of these unique challenges; however, these are not widespread due to a number of technical, operational, and regulatory considerations. To help progress the application of BVLOS unmanned aircraft operations in forest environments, this article reviews the latest literature, practices, and regulations, as well as incorporates the practical experience of the authors. The unique characteristics of the operating environment are addressed alongside a clear argument as to how BVLOS operations can help overcome key challenges. The international regulatory environment is appraised with regard to BVLOS operations, highlighting differences between countries, despite commonalities in the considerations that they take into account. After addressing these points, technological, operational, and other considerations are presented and may be taken into account when taking a risk-based approach to BVLOS operations, with gaps for future research to address clearly highlighted. In totality, this article provides a practical understanding of how BVLOS unmanned aircraft operations can be done in forest environments, as well as provides a basis for future research into the topic area.

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Section: Primary Radarmentioning

confidence: 99%

BVLOS Unmanned Aircraft Operations in Forest Environments

Hartley

Henderson

Jackson³

2022

Drones

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“…Most of the V2V-based conflict detection in small UAVs used vision technology due to its light weight and simplicity [16][17][18]. However, other technologies were also used, such as ADS-B, radar, flight alarm (FLARM), DSRC, and telemetry [19][20][21][22][23][24].…”

Section: Vehicle-to-vehicle (V2v) Communicationmentioning

confidence: 99%

“…Two types of flight tests were conducted involving UAS-to-UAS encounters (DJI S1000 octocopter as the host and the Tempest UAV as the intruder) and UAS-to-GA aircraft encounters (the intruder was a Cirrus SR-22) [19]. Other research in collaboration with Virginia Polytechnic Institute and State University and NASA evaluated onboard radar on a DJI Inspire 2 quadcopter to support minimum requirements for sense and avoid (SAA) applications of an sUAS [20]. Furthermore, NASA conducted flight tests to demonstrate the use of onboard autonomy-enabling technologies in scenarios where a non-conforming UAS flies through.…”

Section: Vehicle-to-vehicle (V2v) Communicationmentioning

confidence: 99%

Flight Test Analysis of UTM Conflict Detection Based on a Network Remote ID Using a Random Forest Algorithm

Ruseno

Lin

Guan

2023

Drones

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In an area where unmanned aerial system (UAS) traffic is high, a conflict detection system is one of the important components for the safety of UAS operations. A novel UAS traffic management (UTM) monitoring application was developed, including a conflict detection system using the inverted teardrop area detection based on real-time flight data transmitted from the network remote identification (Remote ID) modules. This research aimed to analyze the performance of the UTM-monitoring application based on flight test data using statistical and machine learning approaches. The flight tests were conducted using several types of small fixed-wing unmanned aerial vehicles (UAVs) controlled by a human pilot using a Taiwan cellular communication network in suburban and rural areas. Two types of scenarios that involved a stationary, on-the-ground intruder and a flying intruder were used to simulate a conflict event. Besides the statistical method calculating the mean and standard deviation, the random forest algorithm, including regressor and classifier modules, was used to analyze the flight parameters and timing parameters of the flight tests. The result indicates that the processing time of the UTM application was the most significant parameter to the conflict warning parameter, besides the relative distance and height between UAVs. In addition, the latency time was higher for the flight in the rural area than the suburban area and also higher for data transmitted from the flying position than the ground position. The findings of our study can be used as a reference for aviation authorities and other stakeholders in the development of future UTM systems.

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“…NASA and the Mid-Atlantic Aviation Partnership conducted the flight test to investigate the applicability and performance of a prototype commercially available sUAS radar to detect and track non-cooperative airborne traffic. The radar selected for this research was a Frequency Modulated Continuous Wave (FMCW) radar with 120-degree azimuth and 80-degree elevation field of view operating at 24.55GHz center frequency with a 200 MHz bandwidth [27]. The ground-based radar can be integrated with a Ground-Based DAA system (GBDAA).…”

Section: ) Active Systemsmentioning

confidence: 99%

Detect and Avoid Considerations for Safe sUAS Operations in Urban Environments

Martinez

Ince

Selvam

et al. 2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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Operations involving small Unmanned Aerial Systems (sUAS) in urban environments are occurring ever more frequently as recognized applications gain acceptance, and new use cases emerge, such as urban air mobility, medical deliveries, and support of emergency services. Higher demands in these operations and the requirement to access urban airspace present new challenges in sUAS operational safety. The presence of Detect and Avoid (DAA) capability of sUAS is one of the major requirements to its safe operation in urban environments according to the current legislation, such as the CAP 722 in the United Kingdom (UK). The platform or its operator proves a full awareness of all potential obstacles within the mission, maintains a safe distance from other airspace users, and, ultimately, performs Collision Avoidance (CA) maneuvers to avoid imminent impacts. Different missions for the defined scenarios are designed and performed within the simulation model in Software Tool Kit (STK) software environment, covering a wide range of practical cases. The acquired data supports assessment of feasibility and requirements to real-time processing. Analysis of the findings and simulation results leads to a holistic approach to implementation of sUAS operations in urban environments, focusing on extracting critical DAA capability for safe mission completion. The proposed approach forms a valuable asset for safe operations validation, enabling better evaluation of risk mitigation for sUAS urban operations and safety-focused design of the sensor payload and algorithms.

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Airborne Radar for sUAS Sense and Avoid

Cited by 7 publications

References 0 publications

BVLOS Unmanned Aircraft Operations in Forest Environments

BVLOS Unmanned Aircraft Operations in Forest Environments

Flight Test Analysis of UTM Conflict Detection Based on a Network Remote ID Using a Random Forest Algorithm

Detect and Avoid Considerations for Safe sUAS Operations in Urban Environments

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