Adaptive Air-Data Estimation in Wind Disturbance Based on Flight Data

The instantaneous wind field and air data, including true airspeed, angle of attack, angle of sideslip, cannot be measured and recorded accurately in wind disturbance. A new air data and wind field estimation method is proposed based on flight data in this study. Since the wind field is the horizontal prevailing wind added by turbulence, the slowly time-varying prevailing wind and small-scale turbulence are described by the exponentially correlated stochastic wind model and von Karman turbulence model, respectively. The system update equation of air data is built based on inertial measurements instead of the complex aerodynamic and aero-engine model of aircraft. Benefitted by the post-analysis characteristics of flight data, a forward–backward filtering algorithm was designed to improve the estimation accuracy. Simulation results indicate that the forward–backward filter integrated with the von Karman turbulence model can reduce the estimation error and ensure filtering stability. A further test with actual flight data shows that the forward–backward filter is not only able to track the wide-range change in prevailing wind but also reduce the adverse effects of uncertain disturbance on estimation accuracy.

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A hybrid approach for UAV flight data estimation and prediction based on flight mode recognition

Microelectronics Reliability ◽

10.1016/j.microrel.2018.03.032 ◽

2018 ◽

Vol 84 ◽

pp. 253-262 ◽

Cited By ~ 6

Author(s):

Benkuan Wang ◽

Datong Liu ◽

Wenjuan Wang ◽

Xiyuan Peng

Keyword(s):

Hybrid Approach ◽

Flight Data ◽

Estimation And Prediction ◽

Mode Recognition ◽

Flight Mode ◽

Data Estimation

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An innovative analytic redundancy approach to air data sensor fault detection

The Aeronautical Journal ◽

10.1017/aer.2019.143 ◽

2019 ◽

Vol 124 (1273) ◽

pp. 346-367

Author(s):

S. Prabhu ◽

G. Anitha

Keyword(s):

Fault Detection ◽

Measurement Unit ◽

Sensor Fault ◽

Flight Data ◽

Sensor Performance ◽

Sensor Fault Detection ◽

System Data ◽

Stable Estimation ◽

And Performance ◽

Data Estimation

ABSTRACTThis article presents a potential analytic redundancy approach to detect faults in the air data sensor of an aircraft. In modern aircraft, fault detection of air data sensors is performed using a complex voting mechanism, which requires the availability of redundant air data sensor in all situations. However, to continuously monitor operation and performance of these sensors, the analytic redundancy-based air data estimation and fault detection is highly preferred than estimation with air data probe measurements. The proposed algorithm uses the kinematics of aircraft to estimate air data and detect air data sensor fault. In this paper, a simple mathematical model is developed, which does not consider the forces and moments acting on aircraft and uses measurements only from the Inertial Measurement Unit (IMU) and Navigation System Data (NSD). In order to implement this approach, the Iterated Optimal Extended Kalman Filter (IOEKF) is developed to estimate air data, which provides an accurate and stable estimation. With the estimated states, the physical air data sensor measurements are compared and the residual is calculated to track each sensor performance and to detect the occurrence of a fault. The key advantage of this approach is that it does not require complex dynamic equations and is free from system uncertainties. The proposed algorithm is simulated in MATLAB software using flight simulator flight data and validated using the real-time flight data of Cessna Citation II transport aircraft.

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A Field Demonstration of the Air Traffic Control Tower Flight Data Manager Prototype

PsycEXTRA Dataset ◽

10.1037/e578902012-013 ◽

2011 ◽

Author(s):

Hayley J. Davison Reynolds ◽

Maria Picardi Kuffner ◽

Sarah K. Yenson

Keyword(s):

Traffic Control ◽

Air Traffic Control ◽

Air Traffic ◽

Flight Data ◽

Data Manager

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Dose Tracker Application for Collecting Medication Use Data from International Space Station Crew

Aerospace Medicine and Human Performance ◽

10.3357/amhp.5392.2020 ◽

2020 ◽

Vol 91 (1) ◽

pp. 41-45 ◽

Cited By ~ 1

Author(s):

Virginia. E. Wotring ◽

LaRona K. Smith

Keyword(s):

Data Collection ◽

International Space Station ◽

Sleep Disturbances ◽

Space Station ◽

Medication Use ◽

International Space ◽

Flight Data ◽

Open Questions ◽

Share Data ◽

Usability Feedback

INTRODUCTION: There are knowledge gaps in spaceflight pharmacology with insufficient in-flight data to inform future planning. This effort directly addressed in-mission medication use and also informed open questions regarding spaceflight-associated changes in pharmacokinetics (PK) and/or pharmacodynamics (PD).METHODS: An iOS application was designed to collect medication use information relevant for research from volunteer astronaut crewmembers: medication name, dose, dosing frequency, indication, perceived efficacy, and side effects. Leveraging the limited medication choices aboard allowed a streamlined questionnaire. There were 24 subjects approved for participation.RESULTS: Six crewmembers completed flight data collection and five completed ground data collection before NASA’s early study discontinuation. There were 5766 medication use entries, averaging 20.6 ± 8.4 entries per subject per flight week. Types of medications and their indications were similar to previous reports, with sleep disturbances and muscle/joint pain as primary drivers. Two subjects treated prolonged skin problems. Subjects also used the application in unanticipated ways: to note drug tolerance testing or medication holiday per research protocols, and to share data with flight surgeons. Subjects also provided usability feedback on application design and implementation.DISCUSSION: The volume of data collected (20.6 ± 8.4 entries per subject per flight week) is much greater than was collected previously (<12 per person per entire mission), despite user criticisms regarding app usability. It seems likely that improvements in a software-based questionnaire application could result in a robust data collection tool that astronauts find more acceptable, while simultaneously providing researchers and clinicians with useful data.Wotring VE, Smith LK. Dose tracker application for collecting medication use data from International Space Station crew. Aerosp Med Hum Perform. 2020; 91(1):41–45.

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