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首页> 外文会议>Conference on sensors, and command, control, communications, and intelligence (C3I) technologies for homeland security and homeland defense VIII; 20090415-17; Orlando, FL(US) >Bayesian Detection of Acoustic Muzzle Blasts
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Bayesian Detection of Acoustic Muzzle Blasts

机译：声波炮口爆炸的贝叶斯检测

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摘要

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.
机译：枪声的声学检测具有许多安全和军事应用。大多数枪声会产生声音的枪口爆炸信号以及高频冲击波。但是，有些枪支不能以引起冲击波所需的速度推进子弹，因此，使用消音器可以大大减少枪口爆炸的能量。因此，尽管大多数现有的商业和军事枪击检测系统都是基于Shockwave检测的，但要在广泛的应用中进行可靠的检测，就需要开发结合了枪口爆炸和Shockwave现象学的技术。由于存在非平稳背景信号，通常很难检测到枪口爆炸。枪口爆炸检测的先前方法已经应用了模式识别技术，而没有特别考虑背景信号的非平稳性质，因此这些技术在实际操作条件下可能表现不佳。这项研究的重点是使用贝叶斯自回归模型对非平稳背景进行时域建模。贝叶斯参数估计可以为非平稳建模提供一种有原则的方法，同时还消除了与标准自适应过程相关的稳定性问题。我们的方法在合成数据集上进行了测试，该合成数据集来自实际背景信号的记录和孤立的炮火数据库。在室内和室外条件下，在几种信噪比下，将检测结果与标准自适应方法，最小均方（LMS）算法进行比较。

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