Energy state prediction methods for airplane state awareness

Duan, Pengfei; Haag, Maarten Uijt de; Etherington, Timothy J.; Smith-Velazquez, Laura

doi:10.1109/dasc.2016.7778085

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…During the execution of a flight-the focus of this contribution-TP is (or will be) present in advanced on-board trajectory planning and guidance algorithms (for ownship trajectories); in applications to enable, for instance, selfseparation or conformance monitoring (i.e., predicting intruder trajectories) [3]; and in a plethora of ground-based air traffic control (ATC) decision support tools, such as for separation management or aircraft sequencing and merging purposes in terminal airspace [4]. Furthermore, advanced TP capabilities are also a key enabler for safety nets, advisory or warning tools, either for on-board or ground-based collision warning and avoidance systems.…”

Section: S Ingle European Sky Air Traffic Management (Atm) Research (...mentioning

confidence: 99%

Interacting Multiple Model Filtering for Aircraft Guidance Modes Identification from Surveillance Data

Khaledian

Sáez

Vilà‐Valls

et al. 2023

Journal of Guidance, Control, and Dynamics

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Accurate and reliable trajectory prediction (TP) is required in several air traffic management systems. Estimating the aircraft trajectory in a vertical plane typically requires the knowledge of a pair of operational instructions. A sequence of operational instructions specifies the aircraft intent, information that is seldom available, besides for the ownship trajectory planning system. In the execution of the trajectory, the aircraft is directed by the (auto)pilot through a series of sequential guidance modes that might override some of the planning instructions of the aircraft intent. Therefore, having reliable guidance mode information is fundamental for the next generation of air- or ground-based TP. The main goal of this contribution is to develop a methodology able to identify in real-time the active guidance modes for both vertical climb and descent profiles, using only Automatic Dependent Surveillance-Broadcast and Enhanced Mode-S Surveillance data. The proposed solution is based on an interacting multiple model (IMM) filtering approach, which uses a bank of filters, each one matched to a possible guidance mode. The guidance mode identification performance of the IMM-based solution is validated with i) a set of simulated representative trajectories and ii) real flight data obtained from flight data recorders.

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Section: S Ingle European Sky Air Traffic Management (Atm) Research (...mentioning

confidence: 99%

Interacting Multiple Model Filtering for Aircraft Guidance Modes Identification from Surveillance Data

Khaledian

Sáez

Vilà‐Valls

et al. 2023

Journal of Guidance, Control, and Dynamics

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“…In general, an accurate knowledge of aircraft performance data and flight-intent is available for ownship TP algorithms. However, on-board applications for intruder trajectory prediction, to enable for instance self-separation or conformance monitoring applications [4], rely on simplified aircraft performance data and have a very limited (or non-existent) knowledge of the intruder's flight-intent. A similar limitation is found for ground-based TPs, which typically use the Airline Procedure Model (ARPM), embedded in the Base of Aircraft Data (BADA) [5].…”

Section: Introductionmentioning

confidence: 99%

Real-time Identification of Guidance Modes in Aircraft Descents Using Surveillace Data

Codina

Batlle

Menéndez

2018

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

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Trajectory predictors require information on the flight-intent in order to estimate the future state of the aircraft. At present, however, such information is not available or it is very limited and coarse (unless predicting the ownship trajectory). In this paper, an interacting multiple-model (IMM) algorithm is proposed to improve the accuracy of short-term trajectory predictions. The active guidance mode of an aircraft is estimated in real-time observing flight data collected only from automatic dependent surveillance-Broadcast (ADS-B) and transponder selective mode (Mode S) emissions. The algorithm is set up with different models corresponding to the most typical guidance modes, and provides the model that better fits the observations. The proposed algorithm is validated by means of two simulated trajectories whose guidance modes were known beforehand. Finally, the performance of the algorithm with real flight data is demonstrated through a detailed example. Promising results are obtained, showing that the active guidance mode can be unequivocally identified with a negligible delay.

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Analysis of pilot feedback regarding the use of state awareness technologies during complex situations

Evans

Young

Daniels

et al. 2016

2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

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A flight simulation study was conducted at NASA Langley Research Center to evaluate flight deck systems that (1) predict aircraft energy state and/or autoflight configuration, (2) present the current state and expected future state of automated systems, and/or (3) show the state of flight-critical data systems in use by automated systems and primary flight instruments. Four new technology concepts were evaluated vis-à-vis current state-of-the-art flight deck systems and indicators. This human-in-the-loop study was conducted using commercial airline crews. Scenarios spanned a range of complex conditions and several emulated causal factors and complexity in recent accidents involving loss of state awareness by pilots (e.g. energy state, automation state, and/or system state). Data were collected via questionnaires administered after each flight, audio/video recordings, physiological data, head and eye tracking data, pilot control inputs, and researcher observations. This paper focuses specifically on findings derived from the questionnaire responses. It includes analysis of pilot subjective measures of complexity, decision making, workload, situation awareness, usability, and acceptability.

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Energy state prediction methods for airplane state awareness

Cited by 4 publications

References 10 publications

Interacting Multiple Model Filtering for Aircraft Guidance Modes Identification from Surveillance Data

Interacting Multiple Model Filtering for Aircraft Guidance Modes Identification from Surveillance Data

Real-time Identification of Guidance Modes in Aircraft Descents Using Surveillace Data

Analysis of pilot feedback regarding the use of state awareness technologies during complex situations

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