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

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

Current Safety Nets Within the U.S. National Airspace System

There are over 70,000 flights managed per day in the National Airspace System, with approximately 7,000 aircraft in the air over the United States at any given time. Operators of each of these flights would prefer to fly a user-defined 4D trajectory (4DT), which includes arrival and departure times; preferred gates and runways at the airport; efficient, wind-optimal routes for departure, cruise and arrival phase of flight; and fuel efficient altitude profiles. To demonstrate the magnitude of this achievement a single flight from Los Angeles to Baltimore, accesses over 35 shared or constrained resources that are managed by roughly 30 air traffic controllers (at towers, approach control and en route sectors); along with traffic managers at 12 facilities, using over 22 different, independent automation system (including TBFM, ERAM, STARS, ASDE-X, FSM, TSD, GPWS, TCAS, etc.). In addition, dispatchers, ramp controllers and others utilize even more systems to manage each flights access to operator-managed resources. Flying an ideal 4DT requires successful coordination of all flight constraints among all flights, facilities, operators, pilots and controllers. Additionally, when conditions in the NAS change, the trajectories of one or more aircraft may need to be revised to avoid loss of flight efficiency, predictability, separation or system throughput. The Aviation Safety Network has released the 2016 airliner accident statistics showing a very low total of 19 fatal airliner accidents, resulting in 325 fatalities1. Despite several high profile accidents, the year 2016 turned out to be a very safe year for commercial aviation, Aviation Safety Network data show. Over the year 2016 the Aviation Safety Network recorded a total of 19 fatal airliner accidents [1], resulting in 325 fatalities. This makes 2016 the second safest year ever, both by number of fatal accidents as well as in terms of fatalities. In 2015 ASN recorded 16 accidents while in 2013 a total of 265 lives were lost. How can we keep it that way and not upset the apple cart by premature insertion of innovative technologies, functions, and procedures? In aviation, safety nets function as the last system defense against incidents and accidents. Current ground-based and airborne safety nets are well established and development to make them more efficient and reliable continues. Additionally, future air traffic control safety nets may emerge from new operational concepts

Risk-based supervisory guidance for detect and avoid involving small unmanned aircraft systems

A formidable barrier for small Unmanned Aircraft Systems (UAS) to be integrated into civil airspace is that small UAS currently lack the ability to Detect and Avoid (DAA) other aircraft during ight operations; however, this ability is an essential part of regulations governing the general operation of aircraft in civil airspace. In this way, the research described is focused on achieving an equivalent level of safety for small UAS as manned aircraft in civil airspace. A small UAS DAA system was proposed to guide small UAS to detect nearby traffic, identify hazards, assess collision risks, perform mitigation analyses, and choose appropriate maneuvers to avoid potential collisions in mid-air encounters. To facilitate system development and performance evaluation, the proposed DAA system was designed and implemented on a fast-time simulation-based analysis platform, on which a set of quantifiable analysis metrics were designed for small UAS to improve situation awareness in hazard identification and collision risk assessment; and a learning-based Smart Decision Tree Method (SDTM) was developed to provide real-time supervisory DAA guidance for small UAS to avoid potential collisions in mitigation analysis. The theoretical research achieved was also integrated into an effort to implement an Automatic Collision Avoidance System (ACAS) to verify the short range DAA performance for small UAS in the visual-line-of-sight ight tests performed at the RAVEN test site in Argentia, NL

Performance Analysis Of Automatic Dependent Surveillance-Broadcast (ADS-B) And Breakdown Of Anomalies

This thesis work analyzes the performance of Automatic Dependent Surveillance-Broadcast (ADS-B) data received from Grand Forks International Airport, detects anomalies in the data and quantifies the associated potential risk. This work also assesses severity associated anomalous data in Detect and Avoid (DAA) for Unmanned Aircraft System (UAS). The received data were raw and archived in GDL-90 format. A python module is developed to parse the raw data into readable data in a .csv file. The anomaly detection algorithm is based on Federal Aviation Administration\u27s (FAA) ADS-B performance assessment report. An extensive study is carried out on two main types of anomalies, namely dropouts and altitude deviations. A dropout is considered when the update rate exceeds three seconds. Dropouts are of different durations and have a different level of risk depending on how much time ADS-B is unavailable as the surveillance system. Altitude deviation refers to the deviation between barometric and geometric altitude. Deviation ranges from 25 feet to 600 feet have been observed. As of now, barometric altitude has been used for separation and surveillance while geometric altitude can be used in cases where barometric altitude is not available. Many UAS might not have both sensors installed on board due to size and weight constrains. There might be a chance of misinterpretation of vertical separation specially while flying in National Airspace (NAS) if the ownship UAS and intruder manned aircraft use two different altitude sources for separation standard. The characteristics and agreement between two different altitudes is investigated with a regression based approach. Multiple risk matrices are established based on the severity of the DAA well clear. ADS-B is called the Backbone of FAA Next Generation Air Transportation System, NextGen. NextGen is the series of inter-linked programs, systems, and policies that implement advanced technologies and capabilities. ADS-B utilizes the Satellite based Global Positioning System (GPS) technology to provide the pilot and the Air Traffic Control (ATC) with more information which enables an efficient navigation of aircraft in increasingly congested airspace. FAA mandated all aircraft, both manned and unmanned, be equipped with ADS-B out by the year 2020 to fly within most controlled airspace. As a fundamental component of NextGen it is crucial to understand the behavior and potential risk with ADS-B Systems

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

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project Subcommittee Final

UAS Integration in the NAS Project overview with details from each of the subprojects. Subprojects include: Communications, Certification, Integrated Test and Evaluation, Human Systems Integration, and Separation Assurance/Sense and Avoid Interoperability

TCL3 UTM (UAS Traffic Management) Flight Tests, Airspace Operations Laboratory (AOL) Report

The Technology Capability Level-3 (TCL3) flight tests were conducted at six different test sites located across the USA from March to May of 2018. The campaign resulted in over 830 data collection flights using 28 different aircraft and involving 20 flight crews. Flights not only varied in duration, but also in the environments and terrains over which they flew. The TCL3 tests highlighted four different types of tests: three tests focused on Communication, Navigation and Surveillance (CNS); six tests focused on Sense and Avoid (SAA) technologies; six tests focused on USS Data and Information Exchange (DAT); and five tests focused on exploring fundamental Concepts of the project (CON). This document presents data collected during the TCL3 tests that informed the operators experiencesthe quality of the unmanned aerial system (UAS) Service Supplier (USS) information that the operator was provided with, the usefulness of this information, and the usability of the automation, both while airborne and on the ground. It is intended to complement the reports written by the test sites and the quantitative reports and presentations of the UAS Traffic Management (UTM) project. With the goal of instructing what the minimum information requirements and/or best practices might be in TCL3 operations, the driving enquiry was: How do you get the information you need, when you need it, to successfully fly a UAS in UTM airspace? This enquiry touches on two requirements for displays, which are to provide adequate situation awareness (SA) and to share information through a USS. The six test sites participating in the TCL3 tests flew a subset of the 20 tests (outlined above), with most sites working on a subset of each of the four types: Communications, Navigation and Surveillance (CNS); DAT; CON; and Sense and Avoid (SAA). The, mainly qualitative, data addressed in this report was collected by the AOL (Airspace Operations Laboratory) both on-site and remotely for each test. The data consists of the contents of end-of-day debriefs, end-of-day surveys, observer notes, and flight test information, all submitted as part of the Data Management Plan (DMP)