Wireless solutions for outdoor training polygons

Tomlain, Juraj; Teren, Ondrej; Tomlain, Jan

doi:10.1109/ntsp.2016.7747791

Cited by 1 publication

(1 citation statement)

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“…Last but not least, the temperature of the air, air humidity, wind speed, environment (e.g., foliage density, urban area) and soil characteristics also have an impact on the resulting gunshot pattern. Considering all these phenomena, to effectively detect and identify a gunshot, signal processing, including adaptive filtering and advanced data processing and classification have to be carried out [8] - [11].…”

Section: Introductionmentioning

confidence: 99%

System for an acoustic detection, localisation and classification

2021

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<p class="Abstract">Currently, acoustic detection techniques of gunshots (gunshot detection and its classification) are increasingly being used not only for military applications but also for civilian purposes. Detection, localisation, and classification of a dangerous event such as gunshots employing acoustic detection is a perspective alternative to visual detection, which is commonly used. In some situations, to detect and localise the source of a gunshot, an automatic acoustic detection system, which can classify the caliber, may be preferable. This paper presents a system for acoustic detection, which can detect, localise and classify acoustic events such as gunshots. The system has been tested in open and closed shooting ranges and tested firearms are 9 mm short gun, 6.35 mm short gun, .22 short gun, and .22 rifle gun with various subsonic and supersonic ammunition. As ‘false alarms’, sets of different impulse acoustic events like door slams, breaking glass, etc. have been used. Localisation and classification algorithms are also introduced. To successfully classify the tested acoustic signals, Continuous Wavelet and Mel Frequency Transformation methods have been used for the signal processing, and the fully two-layer connected neural network has been implemented. The results show that the acoustic detector can be used for reliable gunshot detection, localisation, and classification.</p>

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Section: Introductionmentioning

confidence: 99%