Widely Linear Estimation for Multisensor Quaternion Systems with Mixed Uncertainties in the Observations
The optimal widely linear state estimation problem for quaternion systems with multiple sensors and mixed uncertainties in the observations is solved in a unified framework. For that, we devise a unified model to describe the mixed uncertainties of sensor delays, packet dropouts and uncertain observ...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión borrador |
| Fecha de publicación: | 2019 |
| País: | España |
| Recursos: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/4156 |
| Acesso em linha: | https://hdl.handle.net/10953/4156 |
| Access Level: | acceso abierto |
| Palavra-chave: | Multisensor system packet dropouts quaternion state-space modeling sensor delays uncertain observations widely linear state estimation |
| Resumo: | The optimal widely linear state estimation problem for quaternion systems with multiple sensors and mixed uncertainties in the observations is solved in a unified framework. For that, we devise a unified model to describe the mixed uncertainties of sensor delays, packet dropouts and uncertain observations by using three Bernoulli distributed quaternion random processes. The proposed model is valid for linear discrete-time quaternion stochastic systems measured by multiple sensors and it allows us to provide filtering, prediction and smoothing algorithms for estimating the quaternion state through a widely linear processing. Simulation results are employed to show the superior performance of such algorithms in comparison to standard widely linear methods when mixed uncertainties are present in the observations. |
|---|