Detection of Aircraft Touchdown Using Longitudinal Acceleration and Continuous Wavelet Transformation

Bakunowicz, Jerzy; Rzucidło, Paweł

doi:10.3390/s20247231

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…The estimation results are presented in the form of time charts (Figure 15A and Figure 16A). The conducted wavelet analysis [31] confirmed the assumptions that the estimation based on Equation (1) would be represented by a slow-varying (LF) signal (Figure 15B), while applying the dependencies of Equation ( 12) would make it possible to obtain an estimate containing components with much higher frequencies (Figure 16B).…”

Section: Frequency Analysis Of Signals Selected For Estimationmentioning

confidence: 55%

Estimation of Atmospheric Gusts Using Integrated On-Board Systems of a Jet Transport Airplane—Flight Simulations

Szwed

Rzucidło²,

Rogalski³

2022

Applied Sciences

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Currently, quite accurate measurements of atmospheric gusts are carried out by airport systems only in the vicinity of the runways. There is a still open issue of availability of information about real wind gusts at cruising altitudes and during approach at a considerable distance from the airfield. Standard on-board systems of a jet transport airplane provide some information which is desirable to have knowledge of how flight parameters reflect real gust parameters and their impact on the aircraft dynamics. The paper proposes an algorithm for headwind gust magnitude estimation in relation to aircraft response. The analysed estimation algorithms assume the use of data available from the existing on-board systems only without the employment of any extra sensors or ground and satellite systems. In this way, many problems caused by different structures, configurations, and ways of installation of additional sensors and structural changes are rejected. The algorithms use the classical method for estimation of wind parameters as well as a linear longitudinal model of aircraft dynamics, taking into account the influence of wind gusts. Data fusion was realised with the use of three filtration methods. Results were evaluated to select the most accurate method of the estimation. Test data were obtained from advanced flight simulation. The experimental scenario considered a flight of a passenger twin-engine jet airplane through a layer of programmed gusts. The results of the flight simulations allowed us to determine the accuracy of the proposed gust estimation algorithms in reference to the ideal wind-speed data analysis obtained directly from the simulation environment (with the accuracy of the simulation process). The use of the proposed gust estimation algorithms may provide more accurate signal for integrated on-board systems, especially for wind shear detection and sped-up response time of flight control systems, protecting aircrafts against the adverse impact of encountered wind shear or gusts, e.g., auto-thrust or auto-throttle systems. The dedicated algorithm presented in the paper may increase the safety level of take-off and approach phases in gusty conditions and also during significant changes in wind speed at cruising altitudes in the case of crossing the area of jet stream occurrence.

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Section: Frequency Analysis Of Signals Selected For Estimationmentioning

confidence: 55%

Estimation of Atmospheric Gusts Using Integrated On-Board Systems of a Jet Transport Airplane—Flight Simulations

Szwed

Rzucidło²,

Rogalski³

2022

Applied Sciences

Self Cite

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“…Results show that although the smartphone-based version of the IMUMETER recognizes airplane motion’s phases correctly, the accuracy of distance measurement was insufficient, mainly due to weak accelerometers and GNSS sensor capabilities (acquisition time 1s). It was concluded that a DGPS-based hardware platform with accelerometers of high-frequency response, such as for example used in the cited works [ 27 , 28 , 29 ], would be used in an improved version of the IMUMETER, which is being developed as the next step of this project. Additionally, one practical conclusion is that the use of a smartphone as a hardware platform for airplane motion recognition is rather impossible, as a result of too-slow data acquisition related to airplane motion velocity.…”

Section: Discussionmentioning

confidence: 99%

IMUMETER—A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance

Pytka

Budzyński

Tomiło

et al. 2021

Sensors

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The paper presents the development of the IMUMETER sensor, designed to study the dynamics of aircraft movement, in particular, to measure the ground performance of the aircraft. A motivation of this study was to develop a sensor capable of airplane motion measurement, especially for airfield performance, takeoff and landing. The IMUMETER sensor was designed on the basis of the method of artificial neural networks. The use of a neural network is justified by the fact that the automation of the measurement of the airplane’s ground distance during landing based on acceleration data is possible thanks to the recognition of the touchdown and stopping points, using artificial intelligence. The hardware is based on a single-board computer that works with the inertial navigation platform and a satellite navigation sensor. In the development of the IMUMETER device, original software solutions were developed and tested. The paper describes the development of the Convolution Neural Network, including the learning process based on the measurement results during flight tests of the PZL 104 Wilga 35A aircraft. The ground distance of the test airplane during landing on a grass runway was calculated using the developed neural network model. Additionally included are exemplary measurements of the landing distance of the test airplane during landing on a grass runway. The results obtained in this study can be useful in the development of artificial intelligence-based sensors, especially those for the measurement and analysis of aircraft flight dynamics.

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“…These fields are also propagated from external sources, such as radio navigation systems or communication systems. The issue is of pivotal importance, given the interactions between these fields [5].…”

Section: Introductionmentioning

confidence: 99%

Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases

Michalowska

2023

Energies

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Tests on the content of the electrical component of the electromagnetic field (EMF) were carried out with an NHT3DL broadband meter by Microrad using a 01E (100 kHz ÷ 6.5 GHz) measuring probe. Measurements were made during training flights (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T aircrafts). A neural network was used, the task of which was to learn to predict the successive values of average (ERMS) and instantaneous (EPEAK) electromagnetic fields used here. Such a solution would make it possible to determine the most favorable routes for all aircrafts. This article presents a model of an artificial neural network which aims to predict the intensity of the electrical component of the electromagnetic field. In order to create the developed model, that is, to create a training sequence for the model, a series of measurements was carried out on four types of aircraft (Cessna C172, Cessna C152, Aero AT3, and Technam P2006T). The model was based on long short-term memory (LSTM) layers. The tests carried out showed that the accuracy of the model was higher than that of the reference method. The developed model was able to estimate the electrical component for the vicinity of the routes on which it was trained in order to optimize the exposure of the aircraft to the electrical component of the electromagnetic field. In addition, it allowed for data analysis of the same training flight routes. The reference point for the obtained electric energy results were the normative limits of the electromagnetic field that may affect the crew and passengers during a flight. Monitoring and measuring the electromagnetic field generated by devices is important from an environmental point of view, as well as for the purposes of human body protection and electromagnetic compatibility. In order to improve reliability in general aviation and to adapt to the proposed requirements, aviation training centers are obliged to introduce systems for supervising and analyzing flight parameters.

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Detection of Aircraft Touchdown Using Longitudinal Acceleration and Continuous Wavelet Transformation

Cited by 8 publications

References 22 publications

Estimation of Atmospheric Gusts Using Integrated On-Board Systems of a Jet Transport Airplane—Flight Simulations

Estimation of Atmospheric Gusts Using Integrated On-Board Systems of a Jet Transport Airplane—Flight Simulations

IMUMETER—A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance

Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases

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