Flight Path Measurement of Drones Using Microphone Array and Performance Improvement Method Using Unscented Kalman Filter

Lee, Jiwon; Go, Yeong-Ju; Kim, Seungkeum; Choi, Jong-Soo

doi:10.5139/jksas.2018.46.12.975

Cited by 2 publications

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“…The technology of detecting targets using acoustic signals has been previously studied in a variety of fields [5][6][7], from implementing a robot that detects sounds by mimicking human ears to noise source instrumentation systems for the analysis of mechanical noise. Prior to the development of radar, this technique was used to detect the invasion of enemy aircraft by sound to defend against anti-aircraft.…”

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confidence: 99%

An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

Choi

2021

Drones

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Currently, the detection of targets using drone-mounted imaging equipment is a very useful technique and is being utilized in many areas. In this study, we focus on acoustic signal detection with a drone detecting targets where sounds occur, unlike image-based detection. We implement a system in which a drone detects acoustic sources above the ground by applying a phase difference microphone array technique. Localization methods of acoustic sources are based on beamforming methods. The background and self-induced noise that is generated when a drone flies reduces the signal-to-noise ratio for detecting acoustic signals of interest, making it difficult to analyze signal characteristics. Furthermore, the strongly correlated noise, generated when a propeller rotates, acts as a factor that degrades the noise source direction of arrival estimation performance of the beamforming method. Spectral reduction methods have been effective in reducing noise by adjusting to specific frequencies in acoustically very harsh situations where drones are always exposed to their own noise. Since the direction of arrival of acoustic sources estimated from the beamforming method is based on the drone’s body frame coordinate system, we implement a method to estimate acoustic sources above the ground by fusing flight information output from the drone’s flight navigation system. The proposed method for estimating acoustic sources above the ground is experimentally validated by a drone equipped with a 32-channel time-synchronized MEMS microphone array. Additionally, the verification of the sound source location detection method was limited to the explosion sound generated from the fireworks. We confirm that the acoustic source location can be detected with an error performance of approximately 10 degrees of azimuth and elevation at the ground distance of about 150 m between the drone and the explosion location.

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Section: Introductionmentioning

confidence: 99%

An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

Choi

2021

Drones

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“…Audio: During the flight of the UAV, the motors and propellers will make sound, and the UAV will be detected, classified, and located by a system equipped with acoustic sensors. Lee et al estimated the Direction of Arrival (DOA) of the drone by using the acoustic signal generated when the drone is flying [4]. In the vicinity of the airport, noise from aircraft engines and vehicles is very loud, so it is difficult to accurately distinguish the audio of UAV from the high ambient noise.…”

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confidence: 99%

“…On the other hand, although target detection and tracking based on video images has been well studied, there are still many problems, such as the running rate, which is also a problem in target detection, tracking, and expelling. 4.…”

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confidence: 99%

“…There are some challenges for UAVs when performing autonomous vision-based integrating identification, tracking, and expelling. These problems mainly come from the following aspects: (1) Due to the influence of the UAV structure strength, power configuration, flight altitude, ambient temperature and other factors, the control system design is complicated; (2) the airborne minicomputer has a limited data processing capacity; (3) the low-cost single-chip microcomputer is used to realize high-rate visual control and trigger mechanism control; (4) The flight environment of the UAV is complicated and there are many factors that restrict the efficiency. Motivated by these problems, we have systematically studied one UAV's integrated identification, tracking, and expelling functions based on computer vision.…”

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confidence: 99%

“…Through 3D printing technology, the simulated simple launch device was designed for experimental verification. 4.…”

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See 2 more Smart Citations

Design of Airport Obstacle-Free Zone Monitoring UAV System Based on Computer Vision

Wang

Zhang

et al. 2020

Sensors

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In recent years, a rising number of incidents between Unmanned Aerial Vehicles (UAVs) and planes have been reported at airports and airfields. A design scheme for an airport obstacle-free zone monitoring UAV system based on computer vision is proposed. The system integrates the functions of identification, tracking, and expelling and is mainly used for low-cost control of balloon airborne objects and small aircrafts. First, a quadcopter dynamic model and 2-Degrees of Freedom (2-DOF) Pan/Tilt/Zoom (PTZ) model are analyzed, and an attitude back-stepping controller based on disturbance compensation is designed. Second, a low and slow small-target self-identification and tracking technology is constructed against a complex environment. Based on the You Only Look Once (YOLO) and Kernel Correlation Filter (KCF) algorithms, an autonomous target recognition and high-speed tracking plan with great robustness and high reliability is designed. Third, a PTZ controller and automatic aiming strategy based on Anti-Windup Proportional Integral Derivative (PID) algorithm is designed, and a simplified, automatic-aiming expelling device, the environmentally friendly gel ball blaster, which features high speed and high accuracy, is built. The feasibility and stability of the project can be verified through prototype experiments.

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Flight Path Measurement of Drones Using Microphone Array and Performance Improvement Method Using Unscented Kalman Filter

Cited by 2 publications

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An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

Design of Airport Obstacle-Free Zone Monitoring UAV System Based on Computer Vision

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