Multisensor Fusion Estimation for Systems with Uncertain Measurements, Based on Reduced Dimension Hypercomplex Techniques

Abstract

The prediction and smoothing fusion problems in multisensor systems with mixed uncertainties and correlated noises are addressed in the tessarine domain, under Tk-properness conditions. Bernoulli distributed random tessarine processes are introduced to describe one-step randomly delayed and missing measurements. Centralized and distributed fusion methods are applied in a Tk-proper setting, k=1,2, which considerably reduce the dimension of the processes involved. As a consequence, efficient centralized and distributed fusion prediction and smoothing algorithms are devised with a lower computational cost than that derived from a real formalism. The performance of these algorithms is analyzed by using numerical simulations where different uncertainty situations are considered: updated/delayed and missing measurements.

Globally Optimal Distributed Fusion Filter for Descriptor Systems with Time-Correlated Measurement Noises

“…Under some conditions, the above distributed optimal fusion filters [ 28 , 29 , 30 , 31 ] can achieve the globally optimal estimation accuracy in LMV sense. In the recent studies [ 32 , 33 , 34 ], some new improved distributed fusion strategies have been proposed. For nonlinear integrated unmanned aerial vehicle navigation system, a new cubature rule-based distributed fusion strategy has been proposed in [ 32 ].…”

“…In [ 33 ], a novel low-complexity reduced-order fusion filter is designed by fusing a subset of state components rather than all state variables. In [ 34 ], based on reduced dimension hypercomplex technique, the centralized and distributed prediction and smoothing fusion algorithms for system with uncertain measurements are proposed in the tessarine domain. However, to the best of the author’s knowledge, the globally optimal distributed fusion filter for descriptor system with time-correlated measurements has not been reported.…”