Evaluation of Acoustic Gunshot Localization Methods on Helicopters with Environmental Sound Simulations

Date

2022-07-01

Author

Yılmaz, Murat
Günel Kılıç, Banu

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Although there are ground based localization solutions for Small Arms Fire (SAF) attacks, there are only a few implementations of onboard applications. A simple Acoustic Source Localization (ASL) application employs a microphone array for collecting audio signals, so as to locate sound sources using the measurements from the sound field. The aim of this study is to investigate the possibility of Acoustic Gunshot Localization on a helicopter so as to detect and localize SAF attacks, by simulating the effects of both wave propagation and signal processing parameters for different localization algorithms. Three widely known source localization algorithms, Beamforming, Generalized Cross Correlation and Multiple Signal Classification, were deployed, while proposing pre-processing and post-processing methods, considering domain specific issues, such as the effects of helicopter noise, environmental factors, gunshot signal characteristics and real-life mission requirements. Moreover, a simulation environment was developed, which is capable of manipulating both the algorithm related parameters and the outdoor wave propagation related factors. It was observed that by applying the environmental distortions with the sound field simulator of this study, the localization performances were reduced, resulting in more realistic conclusions and comparisons. This way, both the performance of the proposed processing methods and the effects of parameters that affect gunshot localization on helicopters were reported.

URI

https://link.springer.com/chapter/10.1007/978-3-031-09073-8_32
https://hdl.handle.net/11511/99050

Relation

Cybernetics Perspectives in Systems

Collections

Graduate School of Informatics, Book / Book chapter

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Citation Formats

M. Yılmaz and B. Günel Kılıç, Evaluation of Acoustic Gunshot Localization Methods on Helicopters with Environmental Sound Simulations. 2022.