Multiphysics-based Sensor Fusion

Period of Performance: 01/01/2015 - 12/31/2015

$693K

Phase 2 STTR

Recipient Firm

Guerci Consulting
2509 N Utah St
Arlington, VA 22207
Principal Investigator

Research Institution

Georgia Institute of Technology
225 North Ave NW
Atlanta, GA 30332
Institution POC

Abstract

ABSTRACT: A new approach to multisensor fusion is proposed that utilizes a knowledge-aided (KA) multi-physics model as the main fusion engine, as opposed to traditional purely statistical methods. The new Multi-Physics Sensor Fusion (MPSF) is enabled byadvances in high performance computing, knowledge-aided (KA) processing, and new techniques in multi-physics modeling. Traditional sensor fusion output data products such as target track, ID, etc., are obtained by queries to the multi-physicsmodel, rather than traditional fusion algorithms that translate sensor measurements to desired output products via usual statiscal methods such as an extended Kalman filter. The MPSF utility will be demonstrated via its application to an extremely challenging target discrimination problem in ballistic missile defense (BMD).; BENEFIT: In addition to better multisensor fusion performance, the MPSF approach also provides a powerful design tool for mutli-sensor Warfighter systems.