Detect-and-Avoid Well Clear and Low SWaP Operations
This presentation recaptures NASA's work in the low SWaP surveillance area in the UAS in the NAS project.
Wu, Minghong G.↗
Engineering topics
Wu, Minghong G.
Publications and source records attributed to Wu, Minghong G..
DO-366 Appendix-C&D Update for High and Medium UA Speed Ranges
This presentation shows the latest analysis results of the DAA airborne radar's field of view analysis for high and medium speed UAs.
McDonough, Kevin↗
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↗
Evaluation of Non-Cooperative DWC with Open-Loop and Closed Loop Simulations
This joint briefing presents open and closed loop metrics about the performance of a reference Detect-and-Avoid algorithm using encounters built from NASA's UAS mission trajectories and Lincoln Lab's uncorrelated encounter model.
Wu, Minghong G.↗
Detect and Avoid: Efforts from NASA's UAS Integration into the NAS Project
NASA's Unmanned Aerial Systems (UAS) integration into the National Air Space (NAS) project has been working closely with the FAA and RTCA Special Committee 228 to identify and break down barriers to UAS integration. A focus of this work is on detect and avoid (DAA) technologies. A pilot has responsibility to see and avoid other aircraft and to remain "well clear," using their best judgment (Federal Aviation Regulations (FAR) Sec. 91.113). For UAS to perform this function, the see function is replaced by sensors to detect the other aircraft. Secondly, the pilot judgment of well clear has to be replaced by a mathematical expression. For Phase 1 of this effort, a well clear violation was defined if all three of these conditions are true: a) the horizontal clearance is less than 4000 ft., and b) the vertical clearance is less than 450 ft., and c) the time to loss of well clear is less than 35 seconds. This definition was developed with a great deal of community input and testing to ensure interoperability with Air Traffic Control (ATC) and pilots of manned aircraft. Appropriate guidance, alerting and displays were developed to allow UAS, with the appropriate sensors, to effectively maintain well clear. This work contributed to FAA Technical Standard Orders: TSO-C211, Detect and Avoid and TSO-C212, ATAR for Traffic Surveillance. Phase 2 of this work extends the operational environment to include the terminal area and lesser capable aircraft that might not have the payload capability to carry the RADAR defined in Phase 1. This session reports on work from Phase 1 and initial work in Phase 2.
Detect and Avoid (DAA)↗
Comparative Analysis of ACAS-Xu and DAIDALUS Detect-and-Avoid Systems
The Detect and Avoid (DAA) capability of a recent version (Run 3) of the Airborne Collision Avoidance System-Xu (ACAS-Xu) is measured against that of the Detect and AvoID Alerting Logic for Unmanned Systems (DAIDALUS), a reference algorithm for the Phase 1 Minimum Operational Performance Standards (MOPS) for DAA. This comparative analysis of the two systems' alerting and horizontal guidance outcomes is conducted through the lens of the Detect and Avoid mission using flight data of scripted encounters from a recent flight test. Results indicate comparable timelines and outcomes between ACAS-Xu's Remain Well Clear alert and guidance and DAIDALUS's corrective alert and guidance, although ACAS-Xu's guidance appears to be more conservative. ACAS-Xu's Collision Avoidance alert and guidance occurs later than DAIDALUS's warning alert and guidance, and overlaps with DAIDALUS's timeline of maneuver to remain Well Clear. Interesting discrepancies between ACAS-Xu's directive guidance and DAIDALUS's "Regain Well Clear" guidance occur in some scenarios.
Davies, Jason T.↗
An Alternative Time Metric to Modified Tau for Unmanned Aircraft System Detect And Avoid
A new horizontal time metric, Time to Protected Zone, is proposed for use in the Detect and Avoid (DAA) Systems equipped by unmanned aircraft systems (UAS). This time metric has three advantages over the currently adopted time metric, modified tau: it corresponds to a physical event, it is linear with time, and it can be directly used to prioritize intruding aircraft. The protected zone defines an area around the UAS that can be a function of each intruding aircraft's surveillance measurement errors. Even with its advantages, the Time to Protected Zone depends explicitly on encounter geometry and may be more sensitive to surveillance sensor errors than modified tau. To quantify its sensitivity, simulation of 972 encounters using realistic sensor models and a proprietary fusion tracker is performed. Two sensitivity metrics, the probability of time reversal and the average absolute time error, are computed for both the Time to Protected Zone and modified tau. Results show that the sensitivity of the Time to Protected Zone is comparable to that of modified tau if the dimensions of the protected zone are adequately defined.
alert↗
An Alternative Time Metric to Modified Tau for Unmanned Aircraft System Detect And Avoid
A new horizontal time metric, Time to Protected Zone, is proposed for use in the Detect and Avoid (DAA) Systems equipped by unmanned aircraft systems (UAS). This time metric has three advantages over the currently adopted time metric, modified tau: it corresponds to a physical event, it is linear with time, and it can be directly used to prioritize intruding aircraft. The protected zone defines an area around the UAS that can be a function of each intruding aircraft's surveillance measurement errors. Even with its advantages, the Time to Protected Zone depends explicitly on encounter geometry and may be more sensitive to surveillance sensor errors than modified tau. To quantify its sensitivity, simulation of 972 encounters using realistic sensor models and a proprietary fusion tracker is performed. Two sensitivity metrics, the probability of time reversal and the average absolute time error, are computed for both the Time to Protected Zone and modified tau. Results show that the sensitivity of the Time to Protected Zone is comparable to that of modified tau if the dimensions of the protected zone are adequately defined.
UAS↗
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↗
Optimum Strategies for Selecting Descent Flight-Path Angles
An information processing system and method for adaptively selecting an aircraft descent flight path for an aircraft, are provided. The system receives flight adaptation parameters, including aircraft flight descent time period, aircraft flight descent airspace region, and aircraft flight descent flyability constraints. The system queries a plurality of flight data sources and retrieves flight information including any of winds and temperatures aloft data, airspace/navigation constraints, airspace traffic demand, and airspace arrival delay model. The system calculates a set of candidate descent profiles, each defined by at least one of a flight path angle and a descent rate, and each including an aggregated total fuel consumption value for the aircraft following a calculated trajectory, and a flyability constraints metric for the calculated trajectory. The system selects a best candidate descent profile having the least fuel consumption value while the fly ability constraints metric remains within aircraft flight descent flyability constraints.
Wu, Minghong G.↗
SSI-ARC Flight Test 3 Data Review
The "Unmanned Aircraft System (UAS) Integration into the National Airspace System (NAS)" Project conducted flight test program, referred to as Flight Test 3, at Armstrong Flight Research Center from June - August 2015. Four flight test days were dedicated to the NASA Ames-developed Detect and Avoid (DAA) System referred to as Autoresolver. 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. Resulting flight test data and analysis results will be used to evaluate the DAA system performance (e.g., trajectory prediction accuracy, threat detection) and to add fidelity to simulation models used to inform Minimum Operating Performance Standards (MOPS) for integrating UAS into routine NAS operations.
detect and avoid↗
Minimum-Cost Aircraft Descent Trajectories with a Constrained Altitude Profile
An analytical formula for solving the speed profile that accrues minimum cost during an aircraft descent with a constrained altitude profile is derived. The optimal speed profile first reaches a certain speed, called the minimum-cost speed, as quickly as possible using an appropriate extreme value of thrust. The speed profile then stays on the minimum-cost speed as long as possible, before switching to an extreme value of thrust for the rest of the descent. The formula is applied to an actual arrival route and its sensitivity to winds and airlines' business objectives is analyzed.
altitude restrictions↗
Enhancing the Traffic Management Advisor's Schedule by Time Advance
A time advance algorithm associated with the scheduling functionalities of the Traffic Management Advisor (TMA) for arrival flights is presented and evaluated. The algorithm enhances TMA's meter fix schedule by advancing the flights' Scheduled Time of Arrival (STA) by an amount that minimizes their systemic operating cost. The systemic operating cost leverages the inherent trade-off of time and fuel efficiency resident in the cost index of modern flight management systems. The resulting STAs are achievable by speeding up the leading flights from their desired nominal speed profiles. A key advantage of this approach is that it reduces systemic delay to tight groupings of arriving aircraft as well as increases sustained throughput of the operation. A fast-time, Monte Carlo simulation that emulates TMA's scheduling functionalities is performed for arrival flights to the Phoenix Airport to quantify the benefit of the time advance algorithm. Results show consistent time saving benefits, ranging from 3 to 50 minutes for 112 flights with varying levels of traffic congestion.
arrival traffic management↗
Trajectory Prediction Accuracy and Error Sources for Regional Jet Descents: Results of a 2010 Flight Trial at Denver International Airport using SkyWest Revenue Flights - Part II
The Efficient Descent Advisor (EDA) controller automation tool generates trajectory-based speed, path, and altitude-profile advisories to facilitate efficient, continuous descents into congested terminal airspace. While prior field trials have assessed the trajectory prediction accuracy for large jet (i.e., Boeing and Airbus) types, smaller (i.e., regional and business) jet types present unique challenges involving different descent procedures and Flight Management System (FMS) capabilities. This paper quantifies the trajectory prediction accuracy for small jet revenue flight descents based on SkyWest Canadair Regional Jet 200, 700, and 900 aircraft arrivals to Denver in the fall of 2010. Post flight test data analysis and SkyWest pilot interviews uncovered unexpected variation between flight crews due to different interpretations of (1) which fixed flight path angle (FPA) to fly based on the flight trial procedure and (2) how to fly the descent to achieve the target FPA. Pilot reports were used to select a subset of flights where pilots indicated an FPA according to the flight trial procedure to remove the unexpected variation due to (1) to focus on (2). Results for the subset for en-route descents, from prior to top of descent to the meter fix 30 to 130 nmi downstream, indicate that aircraft arrived to the meter fix six seconds early with about a twelve second standard deviation. Large FPA errors up to one degree relative to the EDA flight trial procedure were detected after the flight trial as a characteristic of the unexpected variation. It is recommended that quantitative validation be performed during future flight trials so that experimental procedures can be adjusted if unexpected results are detected.
Field Test Results↗
Trajectory Prediction Accuracy and Error Sources for Regional Jet Descents: Results of a 2010 Flight Trial at Denver International Airport using a Global 5000 Test Aircraft - Part I
The Efficient Descent Advisor (EDA) controller automation tool generates trajectory-based speed, path, and altitude-profile advisories to facilitate efficient, continuous descents into congested terminal airspace. While prior field trials have assessed the trajectory-prediction accuracy for large jet (i.e., Boeing and Airbus) types, smaller (i.e., regional and business) jet types present unique challenges involving different descent procedures and Flight Management System (FMS) capabilities. A small-jet field trial was conducted at Denver in the fall of 2010 with the objective of measuring trajectory prediction accuracy and quantifying the primary sources of error. This paper uses data collected onboard a Bombardier Global 5000 test aircraft to quantify the size and sources of trajectory prediction error. Error sources were quantified for the 44 runs by incrementally replacing predicted data with data collected onboard the aircraft and measuring the effect on time error. Results for en-route descents, from prior to top of descent to the meter fix 60-120 nmi downstream, indicate that the aircraft arrived an average 15 seconds earlier than predicted, with a standard deviation of 10 seconds. Target Mach and CAS deceleration were found to be the two largest error sources. If CAS deceleration error was reduced using a typical, more predictable level flight deceleration then the arrival time prediction error in 2010 would be on par with a 2009 flight trial of Airbus and Boeing revenue flights. Four of the error sources, tracker jumps, CAS deceleration, target Mach, and path distance, lend themselves to significant reductions with modest to no changes to ATC automation andor procedures. Wind error and its impact on arrival time error was significantly reduced in 2010 compared to a 1994 flight test using NASAs Boeing 737 test aircraft.
Trajectory Prediction Error↗
The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability
A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.
trajectory prediction uncertainty↗