Multiphysics-based Sensor Fusion

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 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.