Aircraft Go-Arounds Associated to Vessel Traffic: Hamburg Finkenwerder Case Study

Arts, Emy; Kamtsiuris, Alexander; Raddatz, Florian; Wende, Gerko

doi:10.3390/engproc2022028005

Cited by 1 publication

(4 citation statements)

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“…Some machine learning methods are compared in [8]. Combined K-means clustering and LSTM neural networks have been combined in [19]. Gaussian Mixture Models have been used in [20].…”

Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

“…For instance, the engine fan speed is used in [20]. In any case, many steps seem necessary in the machine learning literature: selection of the parameters, implementation of a decision tree classifier and clustering of the results in [18], transformation of trajectory data into fixed length sequential data before using an LSTM neural network in [19]. The difficulty of obtaining a reliable training dataset leads some authors to use simulated data [19].…”

Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

“…• The traditional metrics for classification problems such as the error rate, precision and recall [13,14,18,19,20] • Metrics that focus on the total duration of each phase [7] • Metrics that examine the transitions that are incorrectly predicted between phases as well as the total number of transitions [9] In all contributions, the results are, of course, initially visualized. Because it is easy to find a degenerate segmentation that would provide an exact value for the duration of each phase while alternating the flight phases very randomly, it seems reasonable to consider that at least two metrics should be used.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Synthetic data have been used in [7] to validate the model. Data from an aircraft simulator are employed in [18] and [19]. Flight Data Recorder (FDR) data are encountered in [20].…”

Section: Datamentioning

confidence: 99%

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Hidden Markov Models and Flight Phase Identification

Perrichon,

Gendre,

Klein

2024

JOAS

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The use of Hidden Markov Models (HMMs) in segmenting flight phases is a compelling approach with significant implications for aviation and aerospace research. It leverages the temporal sequences of flight data to delineate various phases of an aircraft’s journey, making it a valuable tool for enhancing the anal- ysis of flight performance and safety. In this work, we implement a multivariate HMM to identify 6 flight phases: taxi, takeoff, climb, cruise, approach and rollout. We reach a median global accuracy of about 97% over a sample of several thousand flights with a very low number of decoded unlikely transitions. Regarding several performance metrics, our method is competitive with existing methods in the litera- ture, such as fuzzy logic. Additionally, it provides, for each point of the flight, a probability of belonging to each phase. Even in situations where there are missing values in the data, HMMs remain effective, ensuring that no critical information is lost during the segmentation process. We show that HMMs work seamlessly with the fine granularity of Flight Data Recorder (FDR) data. HMMs offer remarkable flex- ibility and adaptability, proving particularly effective when the number or order of phases is unknown or not predetermined, as is often the case with complex flight scenarios such as helicopter flights. This adaptability is crucial for handling the diverse range of flight operations that differ from one aircraft to another. An example is given with the segmentation of an Automatic Dependent Surveillance–Broadcast (ADS-B) helicopter flight operated by the Swedish National Police.

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“…Some machine learning methods are compared in [8]. Combined K-means clustering and LSTM neural networks have been combined in [19]. Gaussian Mixture Models have been used in [20].…”

Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

Section: Brief Review Of Existing Approaches 21 the Two Main Approachesmentioning

confidence: 99%

Section: Performance Metricsmentioning

confidence: 99%

“…Synthetic data have been used in [7] to validate the model. Data from an aircraft simulator are employed in [18] and [19]. Flight Data Recorder (FDR) data are encountered in [20].…”

Section: Datamentioning

confidence: 99%

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