Data Fusion Methods for Small Arms Localization Solutions

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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2012
Accession Number
ADA617334

Entities

People

  • David Grasing
  • Sachi Desai

Organizations

  • United States Army Armament Research, Development and Engineering Center

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Angle Of Arrival
  • Bessel Functions
  • Computing-Related Activities
  • Data Fusion
  • Data Science
  • Detectors
  • Errors
  • Information Operations
  • Information Science
  • Maximum Likelihood Estimation
  • Military Research
  • Probability
  • Probability Distributions
  • Small Arms
  • Statistical Sampling
  • Statistics

Fields of Study

  • Engineering
  • Mathematics

Readers

  • Distributed Systems and Data Platform Development
  • Statistical inference.