Accession Number:

ADA539485

Title:

Combined Statistical, Biological and Categorical Models for Sensor Fusion

Descriptive Note:

Final rept.

Corporate Author:

ARMY NIGHT VISION LAB FORT BELVOIR VA

Personal Author(s):

Report Date:

2010-08-01

Pagination or Media Count:

14.0

Abstract:

The USA RDECOM CERDEC Night Vision and Electronic Sensors Directorates Science and Technology Division investigated sensor fusion along three avenues statistical, biological and categorical. The first two approaches are analyzed simultaneously to provide a precise and rigorous sensor fusion methodology. The statistical model currently enhances Bayesian methods for tracking, and suggests further application to target identification and fusion-involving both low level feature extraction and higher level sensor output combination. The biological model is also applied to multiple levels of the fusion problem. On the lowest level, it utilizes biologically-inspired results for improved feature extraction. On the higher levels, it develops biologically-inspired agency algorithms for sensor output combination and sensor network analysis. Ultimately, we model the entire fusion process with category theory. Category theory allows for the application of advanced mathematical theory to fusion analysis. In addition to using category theory as a modeling tool, in this paper we adapt categorical logic via topos theory to provide an advanced framework for decision fusion-initially using the topos of graphs.

Subject Categories:

  • Statistics and Probability
  • Chemical, Biological and Radiological Warfare

Distribution Statement:

APPROVED FOR PUBLIC RELEASE