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9 April 2007Maximum likelihood ensemble filter applied to multisensor systems
Maximum Likelihood Ensemble Filter (MLEF) is an alternative deterministic ensemble based filter technique
that optimizes a non-linear cost function along with a Maximum Likelihood approach. In addition to the common
use of ensembles for calculating error covariance, the ensembles in MLEF are exploited to efficiently calculate
Hessian preconditioning and the gradient of the cost function.
This study is divided into two segments. The first part presents a one sensor approach, were MLEF is
compared to different filters using Lorenz 63 system. These filters are: Extended Kalman Filter, Ensemble
Kalman Filter. The second part develops a multi-sensor system. Here we study a moving particle on an orbit
obtained from the same Lorenz system. We analyze the information content of MLEF's ensemble subspace for
each sensor and consider the effects of different number of ensembles on the fusion process. In practice, when
using ensemble based filtering techniques, a large ensemble size is required to obtain the best results. In this
study we show that MLEF can obtain similar results using a smaller ensemble size by utilizing an information
matrix, where essential characteristics are captured. This is a vital consideration when working with multi-sensor data fusion systems.