Artillery/mortar round type classification to increase system situational awareness

Sachi Desai; David Grasing; Amir Morcos; Myron Hohil

doi:10.1117/12.777935

16 April 2008 Artillery/mortar round type classification to increase system situational awareness

Sachi Desai, David Grasing, Amir Morcos, Myron Hohil

Proceedings Volume 6943, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VII; 694319 (2008) https://doi.org/10.1117/12.777935
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feedforward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

Citation Download Citation

Sachi Desai, David Grasing, Amir Morcos, and Myron Hohil "Artillery/mortar round type classification to increase system situational awareness", Proc. SPIE 6943, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VII, 694319 (16 April 2008); https://doi.org/10.1117/12.777935

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KEYWORDS

Acoustics

Sensors

Algorithm development

Artillery

Situational awareness sensors

Classification systems

Feature extraction

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