Bayesian detection of acoustic muzzle blasts

Kenneth D. Morton Jr.; Leslie Collins

doi:10.1117/12.818547

5 May 2009 Bayesian detection of acoustic muzzle blasts

Kenneth D. Morton Jr., Leslie Collins

Proceedings Volume 7305, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII; 730511 (2009) https://doi.org/10.1117/12.818547
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Acoustic detection of gunshots has many security and military applications. Most gunfire produces both an acoustic muzzle-blast signal as well as a high-frequency shockwave. However some guns do not propel bullets with the speed required to cause shockwaves, and the use of a silencer can significantly reduce the energy of muzzle blasts; thus, although most existing commercial and military gunshot detection systems are based on shockwave detection, reliable detection across a wide range of applications requires the development of techniques which incorporate both muzzle-blast and shockwave phenomenologies. The detection of muzzle blasts is often difficult due to the presence of non-stationary background signals. Previous approaches to muzzle blast detection have applied pattern recognition techniques without specifically considering the non-stationary nature of the background signals and thus these techniques may perform poorly under realistic operating conditions. This research focuses on time domain modeling of the non-stationary background using Bayesian auto-regressive models. Bayesian parameter estimation can provide a principled approach to non-stationary modeling while also eliminating the stability concerns associated with standard adaptive procedures. Our proposed approach is tested on a synthetic dataset derived from recordings of actual background signals and a database of isolated gunfire. Detection results are compared to a standard adaptive approach, the least-mean squares (LMS) algorithm, across several signal to background ratios in both indoor and outdoor conditions.

Citation Download Citation

Kenneth D. Morton Jr. and Leslie Collins "Bayesian detection of acoustic muzzle blasts", Proc. SPIE 7305, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII, 730511 (5 May 2009); https://doi.org/10.1117/12.818547

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