The Use of Brain-Computer Interface to Control Unmanned Aerial Vehicle

Paszkiel, Szczepan; Sikora, Mariusz

doi:10.1007/978-3-030-13273-6_54

Cited by 22 publications

(5 citation statements)

References 13 publications

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“…The target trajectory translates into a range variation, which in turn determines the long-term frequency variation in the IF signal according to (4), which is normally represented by a 2D spectrogram [25].…”

Section: Fmcw Radar and Micro-doppler Generalitymentioning

confidence: 99%

“…Over the past decade, the use of unmanned aerial vehicles (UAVs) has grown dramatically in a variety of applications such as surveillance [1], air traffic management [2], and civil and commercial demands [3], because working with them provides new opportunities and enables applications not possible with conventional aircraft. Because of their flexibility, UAVs are applied in biomedical applications, for example, in conjunction with brain-controlled interfaces [4]. UAVs can overcome the need for supervising personnel in extended scenarios like massive farming, monitoring of critical infrastructure, and logistic services in remote areas [3,5], helping to meet the need for reliable surveillance in crowded or dangerous sites.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

et al. 2021

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With the increase in small unmanned aerial vehicle (UAV) applications in several technology areas, detection and small UAVs classification have become of interest. To cope with small radar cross-sections (RCSs), slow-flying speeds, and low flying altitudes, the micro-Doppler signature provides some of the most distinctive information to identify and classify targets in many radar systems. In this paper, we introduce an effective model for the micro-Doppler effect that is suitable for frequency-modulated continuous-wave (FMCW) radar applications, and exploit it to investigate UAV signatures. The latter depends on the number of UAV motors, which are considered vibrational sources, and their rotation speed. To demonstrate the reliability of the proposed model, it is used to build simulated FMCW radar images, which are compared with experimental data acquired by a 77 GHz FMCW multiple-input multiple-output (MIMO) cost-effective automotive radar platform. The experimental results confirm the model’s ability to estimate the class of the UAV, namely its number of motors, in different operative scenarios. In addition, the experimental results show that the motors rotation speed does not imprint a significant signature on the classification of the UAV; thus, the estimation of the number of motors represents the only viable parameter for small UAV classification using the micro-Doppler effect.

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Section: Fmcw Radar and Micro-doppler Generalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

et al. 2021

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“…It expresses the trajectory of UCAV in the real 3D battlefield more vividly. Even more, in [14], Paszkiel et al discussed the use of brain-computer interface to control unmanned aerial vehicles. A novel method is proposed to control the UCAV with a microcomputer connected with the human brain.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Zhu

Wang

et al. 2021

Mathematics

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Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs.

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“…The Unmanned Aerial Vehicle (UAV), as a typical complex electromechanical system, has been used in military and commercial fields widely, but is also accompanied with a high fault rate. Improving the competence of fault diagnosis and ground maintenance, so as to improve the functionality and reliability of UAV, which is as important as exploring the modern UAV control methods such as using a brain-computer interface [1], has currently become the subject of further research emphasis [2][3][4]. With the continuous development of Prognostics Health Management (PHM) technology, a great quantity of sensors are employed in the new generation of large fixed-wing UAV, bringing the explosive growth of flight data scale [5].…”

Section: Introductionmentioning

confidence: 99%

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

et al. 2021

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In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process.

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The Use of Brain-Computer Interface to Control Unmanned Aerial Vehicle

Cited by 22 publications

References 13 publications

Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar

A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

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