Widely linear estimation for multisensor quaternion systems with mixed uncertainties in the observations

Navarro-Moreno, J.; Fernández-Alcalá, Rosa M.; Jiménez‐López, J. D.; Ruiz-Molina, Juan Carlos

doi:10.1016/j.jfranklin.2018.08.031

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…Alternatively, 4D hypercomplex-based signal processing has been satisfactorily applied as a dimension reduction approach in multi-sensor fusion estimation problems with uncertainties [28][29][30][31][32][33][34][35]. Effectively, the benefit of using hypercomplex algebras is twofold: first, they may provide a compact representation of multidimensional signals and a better insight into the structure of the problem than that provided by a traditional or real formalism, and second, the characterization of certain properness properties related to the vanishing of some correlation or pseudo correlation functions means the dimension of the processes involved may be reduced.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts

Fernández-Alcalá

Jiménez‐López

Bihan

et al. 2023

Sensors

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This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.

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Section: Introductionmentioning

confidence: 99%

An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts

Fernández-Alcalá

Jiménez‐López

Bihan

et al. 2023

Sensors

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“…Alternatively, the multisensor fusion estimation problem has also been analyzed by using 4D hypercomplex algebras [16][17][18][19][20][21][22][23]. These algebras appear to be a natural extension of complex algebras comprising a real part and three imaginary parts, which gives rise to ideal structures for describing phenomena in the real physical world.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…In the literature, the robust smoothing problems mainly consider two situations. In the first case, the noise distribution is known but it is not necessarily Gaussian [29,30,31,32,33,34,35,36,37,38], for instance the noise process is assumed to have a non-Gaussian distribution in order to model outliers, temporary model uncertainties, missing observations or sensor delays. Some of these robust paradigms are adaptive because the parameters of the noises characterizing the state space model are inferred from the collected data.…”

Section: Introductionmentioning

confidence: 99%

Robust fixed-lag smoothing under model perturbations

Su¹,

Zorzi²

2022

Preprint

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A robust fixed-lag smoothing approach is proposed in the case there is a mismatch between the nominal model and the actual model. The resulting robust smoother is characterized by a dynamic game between two players: one player selects the least favorable model in a prescribed ambiguity set, while the other player selects the fixed-lag smoother minimizing the smoothing error with respect to least favorable model. We propose an efficient implementation of the proposed smoother. Moreover, we characterize the corresponding least favorable model over a finite time horizon. Finally, we test the robust fixed-lag smoother in two examples. The first one regards a target tracking problem, while the second one regards a parameter estimation problem.

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Widely linear estimation for multisensor quaternion systems with mixed uncertainties in the observations

Cited by 11 publications

References 25 publications

An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts

An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts

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

Robust fixed-lag smoothing under model perturbations

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