SBIR-STTR Award

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 by advances 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-physics model, rather than traditional fusion algorithms that translate sensor measurements to desired output products via usual statiscal methods such as an extended Kalman filter.

Benefit:
In addition to better multisensor fusion performance, the MPSF approach also provides a powerful design tool for mutli-sensor systems.

Keywords:
Multisensor Fusion, Multi-Physics, Knowledge-Aided Processing, Kasssper, Darpa, Physics Based Signal Processing

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.