Title :
Data fusion methods for small arms localization solutions
Author :
Grasing, D. ; Desai, Shaishav
Author_Institution :
Acoust. & Networked Sensors Div., US Army RDECOM-ARDEC, Picatinny, NJ, USA
Abstract :
In this paper several methods and models for improving small arms localization are investigated. Each acoustic sensor is placed at a disparate location and it is assumed that each system may or may not return an estimated range and/or azimuth shooter. Various simple geometric based data fusion methods are proposed and their performance evaluated. Models of localization errors are also proposed and these models are used herein to develop a maximum likelihood approach to data fusion. The parameters of these statistical distributions are estimated from real world data. Comparing/contrasting the results of both methods side by side, it can be shown that while the maximum likelihood based approach performs the best, decent results can be achieved with the simpler geometric based approach.
Keywords :
acoustic signal processing; maximum likelihood estimation; sensor fusion; shock waves; acoustic sensor; azimuth shooter; geometric based data fusion methods; localization errors; maximum likelihood approach; performance evaluation; small arms localization solutions; statistical distributions; Azimuth; Data models; Fires; Maximum likelihood detection; Maximum likelihood estimation; Sensors; Acoustic Sensors; Data Fusion; Gunfire/Small Arms Locization; Impulsive Events; Maximum Likelihood;
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2