Low complexity fusion estimation algorithms in multisensor environment

This paper is focused on two fusion estimation algorithms weighted by matrices and scalars. Relationship between them is theoretically established. We present two fast algorithms addressing computation of matrix weights that arise in multidimensional estimation problems. The first algorithm is based on the Cholesky factorization. And since determination of the optimal matrix weights in real-time applications is not practical, we propose the second algorithm based on approximate calculations using special approximation for cross-covariances. Analysis of computational complexity of the both fast fusion algorithms is proposed. Examples demonstrating low-computational complexity of the fast fusion algorithms are given.