Search NASA⌕ Search

SEARCH · Search NASA

Results for “AIRCRAFT DETECTION”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Augmented Reality in a Simulated Tower Environment: Effect of Field of View on Aircraft Detection

An optical see-through, augmented reality display was used to study subjects' ability to detect aircraft maneuvering and landing at the Dallas Ft. Worth International airport in an ATC Tower simulation. Subjects monitored the traffic patterns as if from the airport's western control tower. Three binocular fields of view (14 deg, 28 deg and 47 deg) were studied in an independent groups' design to measure the degradation in detection performance associated with the visual field restrictions. In a second experiment the 14 deg and 28 deg fields were presented either with 46% binocular overlap or 100% overlap for separate groups. The near asymptotic results of the first experiment suggest that binocular fields of view much greater than 47% are unlikely to dramatically improve performance; and those of the second experiment suggest that partial binocular overlap is feasible for augmented reality displays such as may be used for ATC tower applications.

Ellis, Stephen R.↗
NASA NTRS1990

Development of an adaptive failure detection and identification system for detecting aircraft control element failures

A methodology for designing a failure detection and identification (FDI) system to detect and isolate control element failures in aircraft control systems is reviewed. An FDI system design for a modified B-737 aircraft resulting from this methodology is also reviewed, and the results of evaluating this system via simulation are presented. The FDI system performed well in a no-turbulence environment, but it experienced an unacceptable number of false alarms in atmospheric turbulence. An adaptive FDI system, which adjusts thresholds and other system parameters based on the estimated turbulence level, was developed and evaluated. The adaptive system performed well over all turbulence levels simulated, reliably detecting all but the smallest magnitude partially-missing-surface failures.

Bundick, W. Thomas↗
NASA NTRS1993

Feasibility of detecting aircraft wake vortices using passive microwave radiometers

The feasibility of detecting the cold core of the wake vortex from the wingtips of an aircraft using a passive microwave radiometer was investigated. It was determined that there is a possibility that a cold core whose physical temperature drop is 10 C or greater and which has a diameter of 5 m or greater can be detected by a microwave radiometer. The radiometer would be a noise injection balanced Dicke radiometer operating at a center frequency of 60 GHz. It would require a noise figure of 5 dB, a predetection bandwidth of 6 GHz, and an integration time of 2 seconds resulting in a radiometric sensitivity of 0.018 K. However, three additional studies are required. The first would determine what are the fluctuations in the radiometric antenna temperature due to short-term fluctuations in atmospheric pressure, temperature, and relative humidity. Second, what is the effect of the pressure and temperature drop within the cold core of the wake vortex on its opacity. The third area concerns the possibility of developing a 60 GHz radiometer with a radio metric sensitivity an order of magnitude improvement over the existing state of the art.

Harrington, Richard F.↗
NASA NTRSDate not supplied

Ground to Air Testing of a Fused Optical-Radar Aircraft Detection and Tracking System

Onboard detection and tracking capability are integral to the sensing component in collision avoidance systems needed to safely operate autonomous urban air taxis and small Unmanned Aircraft Systems. Ground-based validation of detection and tracking systems is an important milestone towards the end goal of real-time collision avoidance using onboard sensors and algorithms. In this work, we evaluate three Extended Kalman Filter(EKF)based fusion trackers with radar and vision detection inputs, and compare them with baseline trackers for each sensor type. Performance is assessed using field collected data of ground to air test flights with the sensors co-located on a stable platform with an instrumented multirotor acting as the intruder performing a waypoint pattern at a distance of1.1km to 0.3km to simulate a head-on collision geometry. Fusing an image-based morphological detector with a radar detector using an EKF covered 74% of the ground truth position updates logged by the flight controller on the multirotor within 50 meters after accounting for alignment offsets while covering 15% more ground truth updates relative to radar only. Removing timestamps when the intruder aircraft is occluded by trees and only considering timestamps where the radar has an update, the EKF image-based morphological detector combined with the radar detector covered 90% of the of the ground truth position updates within 50metersand 97% within 100 meters.

Chester V Dolph↗
NASA NTRS2004

Aircraft Detection System Ensures Free-Space Laser Safety

As scientists continue to explore our solar system, there are increasing demands to return greater volumes of data from smaller deep-space probes. Accordingly, NASA is studying advanced strategies based on free-space laser transmissions, which offer secure, high-bandwidth communications using smaller subsystems of much lower power and mass than existing ones. These approaches, however, can pose a danger to pilots in the beam path because the lasers may illuminate aircraft and blind them. Researchers thus are investigating systems that will monitor the surrounding airspace for aircraft that could be affected. This paper presents current methods for safe free space laser propagation.

Smithgall, Brian↗
NASA NTRS2021

Detection and Tracking of Aircraft in the Far-Field from Small Unmanned Aerial Systems

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small Unmanned Aerial System (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with FAA well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of Linear Kalman-filtering and Extended Kalman-filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS vs. fixed-wing sUAS and multirotor sUAS vs. general aviation(GA)plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.

Unmanned Aerial System↗
NASA NTRS2017

Ensuring Interoperability Between Unmanned Aircraft Detect-and-Avoid and Manned Aircraft Collision Avoidance

The Unmanned Aircraft Systems (UAS) community in the United States has identified the need for a collision avoidance region in which UAS Detect-and-Avoid (DAA) vertical guidance is restricted to preclude interoperability issues with manned aircraft collision avoidance system vertical resolution advisories (RAs). This paper documents the process by which the collision avoidance region was defined. Three candidate definitions were evaluated on 1.3 million simulated pairwise encounters between UAS and manned aircraft covering a wide range of horizontal and vertical closure rates, angles, and miss distances. Each definition was evaluated with regard to UAS DAA interoperability with manned aircraft collision avoidance in terms of how well it achieved: 1) the primary objective of restricting DAA vertical guidance prior to RAs when the aircraft are close, and 2) the secondary objective of avoiding unnecessary restrictions of DAA vertical guidance at DAA alerts when the aircraft are further apart. The collision avoidance region definition that fully achieves the primary objective and best achieves the secondary objective was recommended to and accepted by the UAS community in the United States. By this definition, UAS and manned aircraft are in the collision avoidance region where DAA vertical guidance is restricted when the time to closest point of approach (CPA) is less than 50 seconds and either the time to co-altitude is less than 50 seconds or the current vertical separation is less than 800 feet.

unmanned aircraft systems; collision avoidance; in↗
NASA NTRS1992

Aircraft IR/acoustic detection evaluation. Volume 2: Development of a ground-based acoustic sensor system for the detection of subsonic jet-powered aircraft

The design and performance of a ground-based acoustic sensor system for the detection of subsonic jet-powered aircraft is described and specified. The acoustic detection system performance criteria will subsequently be used to determine target detection ranges for the subject contract. Although the defined system has never been built and demonstrated in the field, the design parameters were chosen on the basis of achievable technology and overall system practicality. Areas where additional information is needed to substantiate the design are identified.

Kraft, Robert E.↗
NASA NTRS2003

Maneuver Classification for Aircraft Fault Detection

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, identifying all possible faulty and proper operating modes is clearly impossible. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

Oza, Nikunj C.↗
NASA NTRS2012

Tunable architecture for aircraft fault detection

A method for detecting faults in an aircraft is disclosed. The method involves predicting at least one state of the aircraft and tuning at least one threshold value to tightly upper bound the size of a mismatch between the at least one predicted state and a corresponding actual state of the non-faulted aircraft. If the mismatch between the at least one predicted state and the corresponding actual state is greater than or equal to the at least one threshold value, the method indicates that at least one fault has been detected.

Ganguli, Subhabrata↗
NASA NTRS2019

An Improved Far-Field Small Unmanned Aerial System Optical Detection Algorithm

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation for autonomous small Unmanned Aerial System (sUAS) operations. This work presents an aircraft detect and track pipeline that fuses image differencing and morphological filtering detections for inputs to a Kalman-based object tracking pipeline. The pipeline is evaluated using two types of flight encounters: 1) motorcopter sUAS vs. fixed-wing sUAS 2) motorcopter sUAS vs general aviation plane.

Chester Dolph↗
NASA NTRS2000

Obstacle Detection Algorithms for Aircraft Navigation: Performance Characterization of Obstacle Detection Algorithms for Aircraft Navigation

The research reported here is a part of NASA's Synthetic Vision System (SVS) project for the development of a High Speed Civil Transport Aircraft (HSCT). One of the components of the SVS is a module for detection of potential obstacles in the aircraft's flight path by analyzing the images captured by an on-board camera in real-time. Design of such a module includes the selection and characterization of robust, reliable, and fast techniques and their implementation for execution in real-time. This report describes the results of our research in realizing such a design. It is organized into three parts. Part I. Data modeling and camera characterization; Part II. Algorithms for detecting airborne obstacles; and Part III. Real time implementation of obstacle detection algorithms on the Datacube MaxPCI architecture. A list of publications resulting from this grant as well as a list of relevant publications resulting from prior NASA grants on this topic are presented.

Kasturi, Rangachar↗
NASA NTRS2004

Negative Selection Algorithm for Aircraft Fault Detection

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

Dasgupta, D.↗
NASA NTRS2004

Immunity-Based Aircraft Fault Detection System

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

Dasgupta, D.↗
NASA NTRS2005

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

Wong, Derek↗
NASA NTRS1998

A New Approach to Overcoming Spatial Aliasing in Structural Damage Detection

Aircraft, reusable launch vehicles,unmanned aircraft, and other advanced structures are being built using lightweight composite materials/metals with design safety factors as low as 1.25. These advanced structures operate in uncertain and severe environments and are susceptible to damage such as delamination, fiber/matrix damage, hydrothermal strain in composite materials, and fatigue and cracking in metals. To ensure human safety and load-bearing integrity these structures must be inspected to detect and locate often invisible damage and faults before they become catastrophic. Conventional methods of non-destructive evaluation sometimes miss significant damage and are time consuming and expensive to perform. In contrast, vibrometry or vibration signature techniques are a global method of structural integrity monitoring that potentially can efficiently detect damage on large structures, including damage that is away from sensor locations, and in the interior of structures. However, a barrier problem in damage detection using vibration measurements is the need to measure the vibration response at a large number of points on the structure. Typically, model reduction or expansion procedures such as Guyan reduction or dynamic expansion are attempted to overcome the problem of insufficient measurements or spatial aliasing. These approaches depend on using information from the healthy model, and thus put error into the reduction used to represent the damaged structure. In this paper, a Frequency Response Function technique is used to detect damage to a fixed-free beam. The technique uses measured frequency response functions from the healthy structure as reference data, and then monitors vibration measurements during the life of the structure to detect damage. In an analytical simulation using a finite-element model of a beam, damage was located using only sparse measurements because the technique uses both rotation and translation measurements from the damaged structure.

Schulz, Mark J.↗
NASA NTRS1985

The application of the detection filter to aircraft control surface and actuator failure detection and isolation

The performance of the detection filter in detecting and isolating aircraft control surface and actuator failures is evaluated. The basic detection filter theory assumption of no direct input-output coupling is violated in this application due to the use of acceleration measurements for detecting and isolating failures. With this coupling, residuals produced by control surface failures may only be constrained to a known plane rather than to a single direction. A detection filter design with such planar failure signatures is presented, with the design issues briefly addressed. In addition, a modification to constrain the residual to a single known direction even with direct input-output coupling is also presented. Both the detection filter and the modification are tested using a nonlinear aircraft simulation. While no thresholds were selected, both filters demonstrated an ability to detect control surface and actuator failures. Failure isolation may be a problem if there are several control surfaces which produce similar effects on the aircraft. In addition, the detection filter was sensitive to wind turbulence and modeling errors.

Bonnice, W. F.↗