Back-end algorithms that enhance the functionality of a biomimetic acoustic gunfire direction finding system

Yirong Pu; Sarah Kelsall; Leah Ziph-Schatzberg; Allyn Hubbard

doi:10.1117/12.818811

5 May 2009 Back-end algorithms that enhance the functionality of a biomimetic acoustic gunfire direction finding system

Yirong Pu, Sarah Kelsall, Leah Ziph-Schatzberg, Allyn Hubbard

Author Affiliations +

Proceedings Volume 7305, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII; 730513 (2009) https://doi.org/10.1117/12.818811
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Increasing battlefield awareness can improve both the effectiveness and timeliness of response in hostile military situations. A system that processes acoustic data is proposed to handle a variety of possible applications. The front-end of the existing biomimetic acoustic direction finding system, a mammalian peripheral auditory system model, provides the back-end system with what amounts to spike trains. The back-end system consists of individual algorithms tailored to extract specific information. The back-end algorithms are transportable to FPGA platforms and other general-purpose computers. The algorithms can be modified for use with both fixed and mobile, existing sensor platforms. Currently, gunfire classification and localization algorithms based on both neural networks and pitch are being developed and tested. The neural network model is trained under supervised learning to differentiate and trace various gunfire acoustic signatures and reduce the effect of different frequency responses of microphones on different hardware platforms. The model is being tested against impact and launch acoustic signals of various mortars, supersonic and muzzle-blast of rifle shots, and other weapons. It outperforms the cross-correlation algorithm with regard to computational efficiency, memory requirements, and noise robustness. The spike-based pitch model uses the times between successive spike events to calculate the periodicity of the signal. Differences in the periodicity signatures and comparisons of the overall spike activity are used to classify mortar size and event type. The localization of the gunfire acoustic signals is further computed based on the classification result and the location of microphones and other parameters of the existing hardware platform implementation.

Citation Download Citation

Yirong Pu, Sarah Kelsall, Leah Ziph-Schatzberg, and Allyn Hubbard "Back-end algorithms that enhance the functionality of a biomimetic acoustic gunfire direction finding system", Proc. SPIE 7305, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII, 730513 (5 May 2009); https://doi.org/10.1117/12.818811

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