Prototype Development of Cross-Shaped Microphone Array System for Drone Localization Based on Delay-and-Sum Beamforming in GNSS-Denied Areas

Madokoro, Hirokazu; Yamamoto, Satoshi; Watanabe, Kanji; Nishiguchi, Masayuki; Nix, Stephanie; Woo, Hanwool; Sato, Kazuhito

doi:10.3390/drones5040123

Cited by 7 publications

(5 citation statements)

References 48 publications

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“…As for AIM, it does not install any extra equipment on the drone. Other efforts [31], [32] only regard the drone as a mobile sound source and deploy 3D or large microphone arrays to estimate its location. Compared with these techniques, we explore the theoretical connection between the drone's sound and its motions, deduce the drone's dynamic parameters, such as velocity and acceleration, from its sound and track the drone by using only a small 2D microphone array.…”

Section: Drone Localization and Trackingmentioning

confidence: 99%

Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

Sun,

Wang,

Mottola

et al. 2024

IEEE Trans. on Mobile Comput.

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We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m×10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

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Section: Drone Localization and Trackingmentioning

confidence: 99%

Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

Sun,

Wang,

Mottola

et al. 2024

IEEE Trans. on Mobile Comput.

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“…We have utilized various types of drones for sensing the vertical distributions of CO 2 [58], horizontal distributions of particulate matter [59], and crops in rice paddy fields [60]. For this study, aerial images were obtained using the DJI Mavic 2 Pro, which is a compact drone manufactured by DJI.…”

Section: Aerial Imagesmentioning

confidence: 99%

OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications

Madokoro,

Sato,

Nix

et al. 2023

Sensors

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The rapid advancement of climate change and global warming have widespread impacts on society, including ecosystems, water security, food production, health, and infrastructure. To achieve significant global emission reductions, approximately 74% is expected to come from cutting carbon dioxide (CO2) emissions in energy supply and demand. Carbon Capture and Storage (CCS) has attained global recognition as a preeminent approach for the mitigation of atmospheric carbon dioxide levels, primarily by means of capturing and storing CO2 emissions originating from fossil fuel systems. Currently, geological models for storage location determination in CCS rely on limited sampling data from borehole surveys, which poses accuracy challenges. To tackle this challenge, our research project focuses on analyzing exposed rock formations, known as outcrops, with the goal of identifying the most effective backbone networks for classifying various strata types in outcrop images. We leverage deep learning-based outcrop semantic segmentation techniques using hybrid backbone networks, named OutcropHyBNet, to achieve accurate and efficient lithological classification, while considering texture features and without compromising computational efficiency. We conducted accuracy comparisons using publicly available benchmark datasets, as well as an original dataset expanded through random sampling of 13 outcrop images obtained using a stationary camera, installed on the ground. Additionally, we evaluated the efficacy of data augmentation through image synthesis using Only Adversarial Supervision for Semantic Image Synthesis (OASIS). Evaluation experiments on two public benchmark datasets revealed insights into the classification characteristics of different classes. The results demonstrate the superiority of Convolutional Neural Networks (CNNs), specifically DeepLabv3, and Vision Transformers (ViTs), particularly SegFormer, under specific conditions. These findings contribute to advancing accurate lithological classification in geological studies using deep learning methodologies. In the evaluation experiments conducted on ground-level images obtained using a stationary camera and aerial images captured using a drone, we successfully demonstrated the superior performance of SegFormer across all categories.

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“…Based on the underlying method used in drone detection [96] and our originally developed single-sensor platform [97], this paper presents a novel sensing system and shows experimentally obtained results obtained from outdoor experiments. Assuming θ 1 and θ 2 as the angles between the lines perpendicular to p x and p y and the straight line to (x, y), and letting L 1 and L 2 represent the straight distances between the respective microphone array systems and (x, y), then using trigonometric functions, p x and p y are given as presented below [98].…”

Section: Related Studiesmentioning

confidence: 99%

“…Based on our previous study [96], we considered DAS beamforming in the temporal domain. Assuming that single plane waves exist and letting s m (t) be a set of acoustic signals, then delay τ m as expressed for the formula below occurs for incident waves observed for the m-th sensor.…”

Section: Das Beamforming Algorithmmentioning

confidence: 99%

Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming

et al. 2021

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This paper presents an estimation method for a sound source of pre-recorded mallard calls from acoustic information using two microphone arrays combined with delay-and-sum beamforming. Rice farming using mallards saves labor because mallards work instead of farmers. Nevertheless, the number of mallards declines when they are preyed upon by natural enemies such as crows, kites, and weasels. We consider that efficient management can be achieved by locating and identifying the locations of mallards and their natural enemies using acoustic information that can be widely sensed in a paddy field. For this study, we developed a prototype system that comprises two sets of microphone arrays. We used 64 microphones in all installed on our originally designed and assembled sensor mounts. We obtained three acoustic datasets in an outdoor environment for our benchmark evaluation. The experimentally obtained results demonstrated that the proposed system provides adequate accuracy for application to rice–duck farming.

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Prototype Development of Cross-Shaped Microphone Array System for Drone Localization Based on Delay-and-Sum Beamforming in GNSS-Denied Areas

Cited by 7 publications

References 48 publications

Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications

Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming

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