Integration of launch/impact discrimination algorithm with the UTAMS platform

Desai, Sachi; Morcos, Amir; Tenney, S.M.; Mays, Brian

doi:10.1117/12.777938

Cited by 5 publications

(2 citation statements)

References 0 publications

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“…For example, a Soldier would like to be able to discriminate between the launch of direct-fire weapons, such as small arms and rockets, and indirect fire weapons, such as mortars; and between large-and smallcaliber weapons. Acoustic localization systems have been demonstrated in theater; however, fielding a robust acoustic classification system for weapons systems fire is a more difficult problem 4,5,6 . Localization is based on analyzing differential time delays in the signatures, which can be estimated reliably from distortion caused by the atmosphere and terrain.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction from time domain acoustic signatures of weapons systems fire

Yang

Goldman

2014

SPIE Proceedings

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The U.S. Army is interested in developing algorithms to classify weapons systems fire based on their acoustic signatures. To support this effort, an algorithm was developed to extract features from acoustic signatures of weapons systems fire and applied to over 1300 signatures. The algorithm filtered the data using standard techniques then estimated the amplitude and time of the first five peaks and troughs and the location of the zero crossing in the waveform. The results were stored in Excel spreadsheets. The results are being used to develop and test acoustic classifier algorithms.

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Section: Introductionmentioning

confidence: 99%

Feature extraction from time domain acoustic signatures of weapons systems fire

Yang

Goldman

2014

SPIE Proceedings

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“…Acoustic sensors are good at finding the direction and location of loud acoustic sources, and fielded systems in theater currently report this information (1). Requirements for detection and false alarm rates have been developed for several acoustic systems (2,3).…”

Section: Introductionmentioning

confidence: 99%

Performance Metrics for Acoustic Classification of Weapons Fire

Goldman¹,

Holben²,

Williams³

2012

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