Explosion detection and recognition is a critical capability to provide situational awareness to the war-fighters in battlefield. Acoustic sensors are frequently deployed to detect such events and to trigger more expensive sensing/sensor modalities (i.e. radar, laser spectroscope, IR etc.). Acoustic analysis of explosions has been intensively studied to reliably discriminate mortars, artillery, round variations, and type of blast (i.e. chemical/biological or high-explosive). One of the major challenges is high level of noise, which may include non-coherent noise generated from the environmental background and coherent noise induced by possible mobile acoustic sensor platform. In this work, we introduce a new acoustic scene analysis method to effectively enhance explosion classification reliability and reduce the false alarm rate at low SNR and with high coherent noise. The proposed method is based on acoustic signature modeling using Hidden Markov Models (HMMs). Special frequency domain acoustic features characterizing explosions as well as coherent noise are extracted from each signal segment, which forms an observation vector for HMM training and test. Classification is based on a unique model similarity measure between the HMM estimated from the test observations and the trained HMMs. Experimental tests are based on the acoustic explosion dataset from US ARMY ARDEC, and experimental results have demonstrated the effectiveness of the proposed method.

Date Published: 16 April 2008
PDF: 8 pages
Proc. SPIE 6963, Unattended Ground, Sea, and Air Sensor Technologies and Applications X, 69630G (16 April 2008); doi: 10.1117/12.783445

Published in SPIE Proceedings Vol. 6963:
Unattended Ground, Sea, and Air Sensor Technologies and Applications X
Edward M. Carapezza, Editor(s)