Noise prediction of multi-rotor UAV by RPM fluctuation correction method

Han, Danxiang; Gwak, Doo Young; Lee, Soogab

doi:10.1007/s12206-020-0305-2

Cited by 19 publications

(11 citation statements)

References 8 publications

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“…36 Finally, BPF modulation aspects are critical for achieving realistic auralizations of rotorcraft noise, 37 and likewise, BPF modulation characteristics will benefit future extensions of noise prediction tools. [38][39][40][41]…”

Section: B Present Contribution and Outlinementioning

confidence: 99%

Quantifying modulation in the acoustic field of a small-scale rotor using bispectral analysis

Baars

Bullard

Mohamed

2021

AIAA Scitech 2021 Forum

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This paper describes a methodology to quantify inter-frequency modulation in the acoustic field of a small-scale rotor. How the blade passing frequency modulates the intensity of the higher-frequency (broadband) noise content is of specific interest, as this modulation is a major factor in the human perception of rotor noise from advanced air mobility vehicles and drones. A proposed modulation-parameter is based on post-processing steps that are applicable to a single acoustic time series. First, an auto-bispectral analysis assesses the dominant nonlinear, quadratic inter-frequency coupling between the blade passing frequency and the higher-frequency noise content. Secondly, the degree of modulation is determined using a robust parameter: a correlation parameter between the (low-frequency) modulating BPF signal and an envelope of the (higher-frequency) carrier signal. Provided that a single parameter is obtained for a given acoustic time series, the directivity pattern of the modulation strength can be inferred from data available from standard acoustic measurement campaigns. For illustration, an 11 inch diameter single-rotor in hover is considered, with acoustic data taken at 420 microphone positions within a plane perpendicular to the rotor disk. It is revealed that modulation is confined to a sector θ ≈ (10 • , −45 • ), where θ = 0 • is the rotor plane and negative angles are in the direction of the rotor-induced flow. The strongest modulation appears around θ ≈ −15 • . This work aids in quantifying the phenomenological description of modulation, namely that it results from the periodic advance and retreat of certain rotor blade's noise sources, relative to a stationary observer.

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Section: B Present Contribution and Outlinementioning

confidence: 99%

Quantifying modulation in the acoustic field of a small-scale rotor using bispectral analysis

Baars

Bullard

Mohamed

2021

AIAA Scitech 2021 Forum

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“…A drone, also known as unmanned aerial vehicle (UAV), is an aircraft without a human pilot on board [ 1 , 2 ]. There has been a rapid development of drones for the past few decades due to the advancement of components such as micro electro-mechanical systems (MEMS) sensors, microprocessors, high energy lithium polymer (LiPo) batteries, as well as more efficient and compact actuators [ 3 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…There has been a rapid development of drones for the past few decades due to the advancement of components such as micro electro-mechanical systems (MEMS) sensors, microprocessors, high energy lithium polymer (LiPo) batteries, as well as more efficient and compact actuators [ 3 – 5 ]. Drones are now present in many daily life activities [ 2 , 6 – 8 ]. They are used in many applications such as inspecting pipelines and power lines, surveillance and mapping, military combat, agriculture, delivery of medicines in remote areas, aerial mapping, and many others [ 2 , 9 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

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A review on drones controlled in real-time

Kangunde

Jamisola

Theophilus

2021

Int. J. Dynam. Control

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This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

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“…The current study proposes the RPM (rotation per minute) speed wave correction method, which corrects the classical noise prediction method of rotorcrafts, considers the frequency modulation effect, and identifies UAVs. Large-scale public activity venues, airports, government agencies, and other places requiring UAV detection have large background noise; thus, the noise emitted by UAVs for identification is difficult to capture [5]. To address this concern, a UAV flight path recognition network based on radar monitoring data is constructed in accordance with the conditions identified by research on UAV flight path identification and the characteristics of radar data information of low-altitude surveillance systems.…”

Section: Introductionmentioning

confidence: 99%

Multitarget Real‐Time Tracking Algorithm for UAV IoT

Hong

Yang

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.

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Noise prediction of multi-rotor UAV by RPM fluctuation correction method

Cited by 19 publications

References 8 publications

Quantifying modulation in the acoustic field of a small-scale rotor using bispectral analysis

Quantifying modulation in the acoustic field of a small-scale rotor using bispectral analysis

A review on drones controlled in real-time

Multitarget Real‐Time Tracking Algorithm for UAV IoT

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