Information Matrix Fusion for Nonlinear, Asynchronous and Heterogeneous Systems

The Information Matrix Fusion (IMF) algorithm is extended from linear, homogeneous and synchronous systems to nonlinear, asynchronous (with arbitrary local tracker sampling times for full rate as well as reduced rate communication) and heterogeneous systems. In this case, the heterogeneous estimates from local trackers (LT) are in different state spaces with various dimensions and are related by a nonlinear relationship with no inverse transformation. The main application of these results is the fusion of tracks from radar and IR/EO (infrared/electrooptical) sensors, which estimate target states that are nonlinearly related and of different dimensions with nonlinear filters. Different from Track-to-track Fusion, the IMF does not require the cross-covariance between the estimation errors. The performance of the proposed algorithm is shown via simulation based on Monte-Carlo runs.