Boomerang mobile counter shooter detection system

Mazurek, Jeffrey A.; Barger, James E.; Brinn, Marshall; Mullen, Richard; Price, D.; Ritter, Scott E.; Schmitt, Dave

doi:10.1117/12.607616

Cited by 28 publications

(12 citation statements)

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“…Currently, there are many commercial gunfire-sound analysis systems. Boomerang [3] is one of these systems. It was developed by DARPA and BNN Technology company in order to build a remote gunfire sound detection system using many large microphones in capturing the sound.…”

Section: Introductionmentioning

confidence: 99%

Effect of Sampling Rate Reduction and Signal Filtering for Gunfire Sound Classification with Spectral Vector using ANN

Tangkawanit

Kanprachar

2020

ECTI-EEC

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To identify the type of the shooting gun, the frequency components of the gunfire sound has been utilized as the input to an Artificial Neural Network (ANN) model [1-2] with a promising recognition accuracy. With this method, the required computational resource is large if the sampling frequency is too high. In this research, the sampling frequency for transforming the input gunfire sound into a digital signal is varied from 44.1 kHz down to 4.41 kHz. 6 different types of gunfire sound are considered. Additionally, the effect of applying different types of signal filtering is studied. It is found that only reducing the sampling frequency on the input gunfire sound signal does not deliver a good performance on gunfire sound classification. To obtain a good classification accuracy, signal filtering has to also be applied. With Chebyshev Type II filter and 4.41 kHz sampling frequency, the classification accuracy is all 100% for the practical range of SNR. This impressive classification accuracy comes with a 10-times reduction on the computational resource; that is, from the sampling frequency of 44.1 kHz to 4.41 kHz. The findings from this work can be applied to the gunfire sound classification system with limited computational resource.

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Section: Introductionmentioning

confidence: 99%

Effect of Sampling Rate Reduction and Signal Filtering for Gunfire Sound Classification with Spectral Vector using ANN

Tangkawanit

Kanprachar

2020

ECTI-EEC

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“…[4] Acoustic sensors have been developed and deployed for the detection and rough location of shooters by the US Military. [5] …”

Section: Acoustic Sensorsmentioning

confidence: 97%

Application of novel quasi-electrostatic sensor arrays for time based data collection and processing of supersonic, subsonic, and transonic revolving projectiles

Benfield

Noras

2014

SPIE Proceedings

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Sensors capable of measuring the quasi-electrostatic field of traveling projectiles have been developed to detect the passage of a bullet in flight. These sensors provide an alternative to existing optical chronograph technologies, which are sensitive to variations in environmental lighting, and magnetic chronographs, which require close proximity to the bullet's path. In contrast, electric field sensors are insensitive to lighting changes and prior testing has demonstrated the ability to reliably detect bullets at distances of at least three meters. A linear array of these sensors has been used to measure the time of flight between the sensors, which with the known distance between the sensors can be used to calculate the projectile's velocity. These velocity measurements are compared to established chronograph technology as a measurement validation. By extending this array of sensors along the projected path of the projectile, a profile of the projectile's position and velocity through flight can be calculated. This expected utility of this data is in refining the calculations that are performed to determine a ballistic solution, particularly in long range engagements, where there has been limited availability of accurate projectile velocity measurements. This robust sensor array that can easily be deployed represents an inexpensive way to experimentally investigate numerous phenomena related to ballistics modeling.

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“…This seven-microphone setup was inspired by the system described in Ref. 12. The sound speed was 350 m/s throughout the simulations.…”

Section: Data Creationmentioning

confidence: 99%

Robust direction-of-arrival by matched-lags, applied to gunshots

Freire

2014

The Journal of the Acoustical Society of America

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This work investigates the direction-of-arrival problem. A time-delay-estimate (TDE) obtained from a peak of a correlation function is subject to two types of error: type I, approximation errors, and type II, errors due to spurious signals. The iterative least-squares algorithm tentatively selects spatially coherent subsets of TDEs containing no type II errors and minor contributions of type I errors ("matched-lags"). Simulations use a seven-microphone array and a gunshot signal. The evaluation methodology is rigorous, comparing empirical distribution functions of estimation error of algorithms through two-sample, one-sided Kolmogorov-Smirnov tests, and quantifying differences with Cohen's D. The direction-of-arrival estimate is improved, specifically at low signal-to-noise ratios.

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Boomerang mobile counter shooter detection system

Cited by 28 publications

References 0 publications

Effect of Sampling Rate Reduction and Signal Filtering for Gunfire Sound Classification with Spectral Vector using ANN

Effect of Sampling Rate Reduction and Signal Filtering for Gunfire Sound Classification with Spectral Vector using ANN

Application of novel quasi-electrostatic sensor arrays for time based data collection and processing of supersonic, subsonic, and transonic revolving projectiles

Robust direction-of-arrival by matched-lags, applied to gunshots

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