Asynchronous data-driven classification of weapon systems

Xin Jin, Kushal Mukherjee, Shalabh Gupta, Asok Ray, Shashi Phoha, Thyagaraju Damarla
2009 Measurement science and technology  
This paper addresses real-time weapon classification by analysis of asynchronous acoustic data, collected from microphones on a sensor network. The weapon classification algorithm consists of two parts: (i) feature extraction from time-series data using Symbolic Dynamic Filtering (SDF), and (ii) pattern classification based on the extracted features using Language Measure (LM) and Support Vector Machine (SVM). The proposed algorithm has been tested on field data, generated by firing of two
more » ... of rifles. The results of analysis demonstrate high accuracy and fast execution of the pattern classification algorithm with low memory requirements. Potential applications include simultaneous shooter localization and weapon classification with soldier-wearable networked sensors. Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.
doi:10.1088/0957-0233/20/12/123001 fatcat:xgqhi6pcvbgbjizkn62aen7awe