Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Serrenho, Felipe G.; Apolinario, J.A.; Ramos, Antônio L. L.; Fernandes, Rigel Procópio

doi:10.3390/s19194271

Cited by 12 publications

(8 citation statements)

References 53 publications

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“…The proposed scheme is evaluated using experimental data from both simulated and recorded gunshot signals. Another application used to estimate the DoA of gunshots-using the same algorithms but with the microphone array embedded in a drone-can be seen in [4,9,21,22].…”

Section: Time-delay Estimation Approachesmentioning

confidence: 99%

“…Encompassing critical domains such as defense, law enforcement, security [1], and surveillance [2], reliable and precise DoA estimation ensures safety, strategic decision-making [3], and operational effectiveness. Applications such as gunshot DoA estimation [4], drone DoA estimation [5], and automotive angle estimation [6] require highly accurate estimates for optimal functionality.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum

Fernandes,

Apolinário,

de Seixas

2024

Sensors

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Accurate direction of arrival (DoA) estimation is paramount in various fields, from surveillance and security to spatial audio processing. This work introduces an innovative approach that refines the DoA estimation process and demonstrates its applicability in diverse and critical domains. We propose a two-stage method that capitalizes on the often-overlooked secondary peaks of the cross-correlation function by introducing a reduced complexity DoA estimation method. In the first stage, a low complexity cost function based on the zero cyclic sum (ZCS) condition is used to allow for an exhaustive search of all combinations of time delays between pairs of microphones, including primary peak and secondary peaks of each cross-correlation. For the second stage, only a subset of the time delay combinations with the lowest ZCS cost function need to be tested using a least-squares (LS) solution, which requires more computational effort. To showcase the versatility and effectiveness of our method, we apply it to the challenging acoustic-based drone DoA estimation scenario using an array of four microphones. Through rigorous experimentation with simulated and actual data, our research underscores the potential of our proposed DoA estimation method as an alternative for handling complex acoustic scenarios. The ZCS method demonstrates an accuracy of 89.4%±2.7%, whereas the ZCS with the LS method exhibits a notably higher accuracy of 94.0%±3.1%, showcasing the superior performance of the latter.

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Section: Time-delay Estimation Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum

Fernandes,

Apolinário,

de Seixas

2024

Sensors

Self Cite

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“…The microphone array based on sound source localization has the advantages of flexible beam control, high spatial resolution, high signal gain and strong interference suppression ability. These advantages make the microphone array system widely used in hearing aids [1] , speech recognition system [2][3] , machining [4] , unmanned aerial vehicles [5][6] and other fields. Conventional beam former (CBF) method adds and fuses the narrowband beam output results at each frequency point without difference, that is, broadband energy detection.…”

Section: Introductionmentioning

confidence: 99%

Harmonic frequency and direction joint estimation for airborne acoustic target localization

He,

Chen,

Luo

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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For the line spectrum detection of airborne sound, sometimes there may be errors in the fundamental frequency of the signal. Moreover, the line spectrum detection only considers the frequency band of the signal fundamental frequency, so the utilization rate of harmonic frequency energy is low. To solve these problems, this paper proposes a joint estimation method of harmonic frequency and direction in airborne acoustic target location based on microphone array. First, using the characteristics of line spectrum signal, minimum variance distortionless response (MVDR) beam scanning is carried out in the frequency band where the signal fundamental frequency is located to avoid fundamental frequency error. Then, according to the frequency multiplication relationship between fundamental frequency and harmonic, the superposition of harmonic frequency energy is used to ensure the peak value of target signal in frequency domain and azimuth. Through Matlab simulation and experimental data analysis, the effectiveness of this method is proved.

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“…Drone audition has been attracting a growing interest because of the increasing availability of agile multi-rotor aerial vehicles [1], [2]. Drone audition techniques enable a drone to localize, enhance, and understand sound sources in the surrounding environment through the analysis of the sound captured by airborne microphones for application in search and rescue [3]- [6], monitoring [7]- [9], recreational filming [10], human-drone interaction and robot autonomy [11], [12]. The performance of acoustic sensing from drones degrades significantly mainly due to two challenges: the strong egonoise from rotating motors and propellers, which leads to extremely low signal-to-noise ratio (SNR), and the movement of the drone and sound sources, which leads to highly dynamic acoustic transmission paths [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Assisted Sound Source Localization From a Flying Drone

Wang

Cavallaro

2022

IEEE Sensors J.

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Sound source localization from a flying drone is a challenging task due to the strong ego-noise from rotating motors and propellers as well as the movement of the drone and the sound sources. To address this challenge, we propose a deep learning-based framework that integrates single-channel noise reduction and multi-channel source localization. In this framework we suppress the ego-noise and estimate a time-frequency soft ratio mask with a single-channel deep neural network (DNN). Then we design two downstream multi-channel source localization algorithms, based on Steered Response Power (SRP-DNN) and Time-Frequency Spatial filtering (TFS-DNN). The main novelty lies in the proposed TFS-DNN approach, which estimates the presence probability of the target sound at individual time-frequency bins by combining the DNN-inferred soft ratio mask and the instantaneous direction of arrival of the sound received by the microphone array. The time-frequency presence probability of the target sound is then used to design a set of spatial filters to construct a spatial likelihood map for source localization. By jointly exploiting spectral and spatial information, TFS-DNN robustly processes signals in short segments (e.g. 0.5 seconds) in dynamic and low signal-noise-ratio scenarios (e.g. SNR -20 dB). Results on real and simulated data in a variety of scenarios (static sources, moving sources and moving drones) indicate the advantage of TFS-DNN over competing methods, including SRP-DNN and the state-of-the-art time-frequency spatial filtering.

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Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Cited by 12 publications

References 53 publications

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum

Harmonic frequency and direction joint estimation for airborne acoustic target localization

Deep-Learning-Assisted Sound Source Localization From a Flying Drone

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