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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2019

Alternative Surveillance Fast Time Simulation with Sensor Uncertainties and Mitigation

This presentation describes a simulation plan for the fast time simulation 3 for the modeling and simulation team in the UAS (Unmanned Aircraft Systems) in the NAS (National Airspace System)project. Objectives: Investigate the effect of limited surveillance volume and realistic radar noise on DAA (Detect and Avoid) performance; Evaluate the ability of the sensor uncertainty mitigation (SUM) feature of the DAIDALUS [Dectect and Avoid...] algorithm to improve DAA alerting and guidance [Includes content on SWaP-C (size, weight, power, and cost) of unmanned aircraft; and Near-mid-air-collision (NMAC).]

UAS↗
NASA NTRS2017

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Detect-And-Avoid (DAA) Systems

The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is "generic" in that it makes no assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Accordingly, GRACE is well suited to research applications where alerting and guidance is a central feature and requirements are fluid involving a wide range of aviation technologies. GRACE has been used at NASA in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests.

Unmanned Aircraft↗
NASA NTRS2016

UAS Integration in the NAS Project: Flight Test 3 Data Analysis of JADEM-Autoresolver Detect and Avoid System

The Unmanned Aircraft Systems Integration in the National Airspace System project, or UAS Integration in the NAS, aims to reduce technical barriers related to safety and operational challenges associated with enabling routine UAS access to the NAS. The UAS Integration in the NAS Project conducted a flight test activity, referred to as Flight Test 3 (FT3), involving several Detect-and-Avoid (DAA) research prototype systems between June 15, 2015 and August 12, 2015 at the Armstrong Flight Research Center (AFRC). This report documents the flight testing and analysis results for the NASA Ames-developed JADEM-Autoresolver DAA system, referred to as 'Autoresolver' herein. Four flight test days (June 17, 18, 22, and July 22) were dedicated to Autoresolver testing. The objectives of this test were as follows: 1. Validate CPA prediction accuracy and detect-and-avoid (DAA, formerly known as self-separation) alerting logic in realistic flight conditions. 2. Validate DAA trajectory model including maneuvers. 3. Evaluate TCAS/DAA interoperability. 4. Inform final Minimum Operating Performance Standards (MOPS). Flight test scenarios were designed to collect data to directly address the objectives 1-3. Objective 4, inform final MOPS, was a general objective applicable to the UAS in the NAS project as a whole, of which flight test is a subset. This report presents analysis results completed in support of the UAS in the NAS project FT3 data review conducted on October 20, 2015. Due to time constraints and, to a lesser extent, TCAS data collection issues, objective 3 was not evaluated in this analysis.

flight test↗
NASA NTRS2019

A Detect and Avoid System in the Context of Multiple-Unmanned Aircraft Systems Operations

NASA's Unmanned Aircraft Systems Integration into the National Airspace System (UAS in the NAS) project examines the technical barriers associated with the operation of UAS in civil airspace. For UAS, the removal of the pilot from onboard the aircraft has eliminated the ability of the ground-based pilot in command (PIC) to use out-the-window visual information to make judgements about a potential threat of a loss of well clear with another aircraft. NASA's Phase 1 research supported the development of a Detect and Avoid (DAA) system that supports the ground-based pilot's ability to detect potential traffic conflicts and determine a resolution maneuver, but existing display/alerting requirements did not account for multiple UAS control (1:N). Demands for increased scalability of UAS in the NAS operations are expected to create a need for simultaneous control of UAs, and thus, a new DAA HMI design will likely be necessary. Previous research, however, has found performance degradations as the number of vehicles under operator control has increased. The purpose of the current human-in-the-loop (HITL) simulation was to examine the viability of 1:N operations with the Phase 1 DAA alerting and guidance. Sixteen UAS pilots flew three scenarios with varying number of UAs under their control (1:1, 1:3, 1:5). In addition to their supervisory and sensor mission responsibilities, pilots were to utilize the DAA system to remain DAA well clear (DWC) during scripted conflicts of mixed severity. Measured response times, separation performance, mission task data, and subjective feedback were collected to assess how the multi-UAS control configuration impacted pilots' ability to maintain DAA well clear and perform the mission tasks. Overall, the DAA system proved surprisingly adaptive to multi-UAS control for preventing losses of DAA well clear (LoDWC). The findings suggest that, while multi-UAS operators are able to maintain safe separation (DWC) from other traffic, their ability to efficiently perform missions drastically decreases with their number of controlled vehicles. Pilot feedback indicated that, for this context, the use of automation support tools for completing and managing mission tasks would be appropriate and desired, especially for ensuring efficient use of assets. Finally, human-machine interface (HMI) design considerations for multi-UAS operations are discussed.

Monk, Kevin J.↗
NASA NTRSDate not supplied

Impact of Airborne Radar Uncertainties on Detect-and-Avoid Systems’ Performance

Impact of sensor uncertainty on Detect-and-Avoid (DAA) systems’ performance is investigated. Key metrics analyzed are the loss of DAA well clear ratio, the near-mid-air-collision risk ratio, the alert ratio, and the number of maneuvers per loss of DAA well clear. Sensitivity of these metrics to the magnitude of sensor uncertainty, pilots’ selection of maneuver, and surveillance range is investigated. Benefits of a dynamic buffer around the DAA well clear separation boundary, computed based on track accuracies, are also analyzed. These metrics are computed from open- and closed-loop simulations of a large number of representative encounters between an unmanned aircraft system and a manned aircraft. Results show that sensor uncertainty degrades safety metrics considerably but has only a minor effect on the number of maneuvers per loss of DAA well clear. The number of maneuvers, nonetheless, can be reduced by a 5◦ buffer away from the edge of the range of conflict-resulting heading in pilots’ selection of maneuver.

UAS↗
NASA NTRS2016

NASA UAS Integration into the NAS Project Detect and Avoid Display Evaluations

As part of the Air Force - NASA Bi-Annual Research Council Meeting, slides will be presented on phase 1 Detect and Avoid (DAA) display evaluations. A series of iterative human-in-the-loops (HITL) experiments were conducted with different display configurations to objectively measure pilot performance on maintaining well clear. To date, four simulations and two mini-HITLs have been conducted. Data from these experiments have been incorporated into a revised alerting structure and included in the RTCA SC 228 Phase 1 Minimum Operational Performance Standards (MOPS) proposal. Plans for phase 2 are briefly discussed.

Unmanned Aircraft Systems (UAS)↗
NASA NTRSDate not supplied

Alert Timing Assessment for Unmanned Aircraft System Terminal Area Operations

A Detect and Avoid (DAA) system is an essential enabler for integrating Unmanned Aircraft Systems (UAS) into the U.S. National Airspace System. A DAA system uses a suite of sensors, trackers and alerting and guidance algorithms to assist a remote pilot in maintaining separation from airborne traffic. The initial minimum performance standards focused on en route operations; in order to facilitate operations in and around the airport environment, this paper presents the derivation of the early alert threshold for UAS terminal operations.

Unmanned Aircraft Systems↗
NASA NTRS2020

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project: Low SWaP HITL 2, SC-228 Outbrief, EO/IR & ATAR Breakout

This presentation provides an overview of a recently completed human-in-the-loop simulation, conducted as part of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project. This study explored the differential effects of four low size, weight, and power radar declaration ranges on pilot and system performance. Pilots were to avoid DAA well clear against cooperative and non-cooperative aircraft under 1.5, 2, 2.5, and 3 nautical mile non-cooperative detection ranges. Results revealed that pilot performance was primarily influenced by warning-level alert duration, with sufficient alerting time being most prevalent at 2.5 nautical miles and above.

Monk, Kevin J.↗
NASA NTRS2020

Detect-and-Avoid: Flight Test 6 Scripted Encounters Data Analysis

The Unmanned Aircraft System (UAS) in the National Airspace System (NAS) project conducted Flight Test 6 (FT6) in 2019. The ultimate goal of this flight test was to produce data to inform RTCA SC-228's Phase II Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) and Low Size, Weight, and Power Sensors. This report documents the analysis of scripted encounters' data. Scripted encounters own were analyzed and categorized based on the outcome of alert, maneuver guidance, and effectiveness of pilots' maneuver in resolving conflicts. Results indicate that UAS pilots' decisions as well as intruder maneuvers are leading factors that contribute to ineffective DAA maneuvers. Results also show that adding buffers to the DAA's suggested minimum turn angle improves effectiveness of the DAA maneuvers.

Wang, Wei-Ching↗
NASA NTRS2021

Detect-and-Avoid Surveillance Range Requirements for Electro-Optical/Infra-Red Sensors

A detect-and-avoid (DAA) system provides surveillance, alerting, and maneuver guidance (referred to as guidance in this report) that are critical to an unmanned aircraft system’s (UAS) ability to maintain separation from manned aircraft and other unmanned aircraft. The last decade has seen significant progress in the development of DAA requirements, spearheaded by RTCA Special Committee 228 (SC228) and subsequently by other standards organizations such as EUROCAE and ASTM. SC-228’s development of DAA requirements assumes the UAS follows instrument flight rules (IFR) and has a remote pilot or operator in the loop. As of the publication of this document, the SC-228’s latest Minimum Operational Performance Standards (MOPS) for DAA, versioned as DO-365B [1], DAA systems use onboard and/or ground surveillance systems to detect traffic. The surveillance systems must detect both cooperative and non-cooperative air traffic. Cooperative traffic are vehicles that have a broadcasting transponder, while non-cooperative traffic do not, and so must be detected via radar or other sensors. A DAA system’s alerting and guidance functions alert the pilot/operator in the loop of potential hazards, such as intruder aircraft, and provide maneuver solutions which help the pilot/operator avoid or mitigate observed hazards. A UAS pilot is expected to coordinate with air traffic control (ATC) before executing a conflict avoidance maneuver if the type of alert is not urgent enough to require an immediate maneuver.

uncrewed aviation systems↗
NASA NTRS2017

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Unmanned Aircraft Detect-And-Avoid Systems

The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is generic since it was designed without any assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Therefore, GRACE was adopted as a core component of the Java Architecture for Detect-And-Avoid (DAA) Extensibility and Modeling, developed by NASA as a research and modeling tool for Unmanned Aerial Systems Integration in the National Airspace System (NAS). GRACE has been used in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests.

Unmanned Aircraft↗
NASA NTRS2018

Sensor Uncertainty Mitigation and Dynamic Well Clear Volumes in DAIDALUS

This paper presents recent updates to DAIDALUS (Detect & Avoid Alerting Logic for Unmanned Systems), a detect and avoid (DAA) software package for the integration of civil UAS into the airspace. DAIDALUS is the reference implementation of detect and avoid for unmanned aircraft systems chosen by RTCA Special Committee 228 (SC-228), and it is included in its corresponding Minimum Operational Performance Standards (MOPS) document, DO-365. This paper reports on the integration into DAIDALUS of two new capabilities, namely dynamic well clear volumes and sensor uncertainty mitigation.'

Narkawicz, Anthony↗
NASA NTRS2017

Validation of Minimum Display Requirements for a UAS Detect and Avoid System

The full integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS), a prerequisite for enabling a broad range of public and commercial UAS operations, presents several technical challenges to UAS developers, operators and regulators. A primary barrier is the inability for UAS pilots (situated at a ground control station, or GCS) to comply with Title 14 Code of Federal Regulations sections 91.111 and 91.113, which require pilots to see and avoid other aircraft in order to maintain well clear. The present study is the final in a series of human-in-the-loop experiments designed to explore and test the various display and alerting requirements being incorporated into the minimum operational performance standards (MOPS) for a UAS-specific detect and avoid system that would replace the see and avoid function required of manned aircraft. Two display configurations were tested - an integrated display and a standalone display - and their impact on pilot response times and ability to maintain DAA well clear were compared. Results indicated that the current draft of the MOPS result in high-level performance and did not meaningfully differ by display configuration.

unmanned aircraft systems↗
NASA NTRS2019

Defining Well Clear Separation for Unmanned Aircraft Systems Operating with Non-Cooperative Aircraft

Detect-and-Avoid (DAA) systems are essential to the safe operations of Unmanned Aircraft Systems, and have the objectives of mitigating collisions with and remaining Well Clear of manned aircraft. This paper analyzes four candidate DAA Well Clear definitions for non-cooperative aircraft using mitigated performance metrics of DAA systems. These DAA Well Clear definitions were proposed in previous work based on their unmitigated collision risk and maneuver initiation range. In this work they are evaluated using safety and operational suitability metrics computed from a large number of representative encounters. Results suggest that although the four candidate DAA Well Clear definitions provide comparable safety, the alerting characteristics give preference for the DAA Well Clear definition without a temporal parameter.

Chen, Christine C.↗
NASA NTRS2019

Defining Well Clear Separation for Unmanned Aircraft Systems Operating with Noncooperative Aircraft

Detect-and-Avoid (DAA) systems are essential to the safe operations of Unmanned Aircraft Systems, and have the objectives of mitigating collisions with and remaining Well Clear of manned aircraft. This paper analyzes four candidate DAA Well Clear definitions for non-cooperative aircraft using mitigated performance metrics of DAA systems. These DAA Well Clear definitions were proposed in previous work based on their unmitigated collision risk and maneuver initiation range. In this work they are evaluated using safety and operational suitability metrics computed from a large number of representative encounters. Results suggest that although the four candidate DAA Well Clear definitions provide comparable safety, the alerting characteristics give preference for the DAA Well Clear definition without a temporal parameter.

Well Clear↗
NASA NTRS2017

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project: Terminal Operations HITL 1: Primary Results

This presentation covers the primary results of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project Terminal Operations Foundational Human-in-the-Loop (HITL) simulation. The study tasked 16 pilots (half with manned piloting experience, and the other half with unmanned piloting experience) with maintaining "well clear" from other traffic while performing three different types of approaches into the Santa Rosa airport. A detect and avoid (DAA) system was provided to pilots to assist their ability to manage separation. The DAA system used in this test conformed to the criteria defined by RTCA Special Committee 228 (SC-228) in their Phase 1 Minimum Operational Performance Standards (MOPS) for UAS intending to operate in the NAS. The Phase 1 system was not designed to account for terminal operations, focusing instead on en route operations. To account for this, three different alerting and guidance configurations were presently tested in order to determine their effect on pilots operating the system in the terminal area. Results indicated that pilots with the alerting and guidance condition that provided the least amount of assistance (fewer alert levels and guidance types) experienced slightly increased pilot response times and rates of losses of separation. Additional data is presented on the effects of approach type and descriptive data on pilot maneuver preferences and ATC interoperability.

terminal environmen↗
NASA NTRS2016

Flight Test 4 Preliminary Results: NASA Ames SSI

Realization of the expected proliferation of Unmanned Aircraft System (UAS) operations in the National Airspace System (NAS) depends on the development and validation of performance standards for UAS Detect and Avoid (DAA) Systems. The RTCA Special Committee 228 is charged with leading the development of draft Minimum Operational Performance Standards (MOPS) for UAS DAA Systems. NASA, as a participating member of RTCA SC-228 is committed to supporting the development and validation of draft requirements as well as the safety substantiation and end-to-end assessment of DAA system performance. The Unmanned Aircraft System (UAS) Integration into the National Airspace System (NAS) Project conducted flight test program, referred to as Flight Test 4, at Armstrong Flight Research Center from April -June 2016. Part of the test flights were dedicated to the NASA Ames-developed Detect and Avoid (DAA) System referred to as JADEM (Java Architecture for DAA Extensibility and Modeling). The encounter scenarios, which involved NASA's Ikhana UAS and a manned intruder aircraft, were designed to collect data on DAA system performance in real-world conditions and uncertainties with four different surveillance sensor systems. Flight test 4 has four objectives: (1) validate DAA requirements in stressing cases that drive MOPS requirements, including: high-speed cooperative intruder, low-speed non-cooperative intruder, high vertical closure rate encounter, and Mode CS-only intruder (i.e. without ADS-B), (2) validate TCASDAA alerting and guidance interoperability concept in the presence of realistic sensor, tracking and navigational errors and in multiple-intruder encounters against both cooperative and non-cooperative intruders, (3) validate Well Clear Recovery guidance in the presence of realistic sensor, tracking and navigational errors, and (4) validate DAA alerting and guidance requirements in the presence of realistic sensor, tracking and navigational errors. The results will be presented at RTCA Special Committee 228 in support of final verification and validation of the DAA MOPS.

Detect-And-Avoid (DAA)↗