Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises
Metadatos
Mostrar el registro completo del ítemMateria
Least-squares Estimation Algorithms Filtering Fixed-point Smoothing Covariance Information Random Parameter Matrices Time-correlated Noise
Fecha
2019Referencia bibliográfica
Caballero-Águila, R., Hermoso-Carazo, A., & Linares-Pérez, J. (2019). Signal Estimation with Random Parameter Matrices and Time-correlated Measurement Noises. In ICINCO (1) (pp. 481-487). [DOI: 10.5220/0007807804810487]
Patrocinador
Ministerio de Economía, Industria y Competitividad; Agencia Estatal de Investigación; European Union (EU) MTM201784199-PResumen
This paper is concerned with the least-squares linear estimation problem for a class of discrete-time networked
systems whose measurements are perturbed by random parameter matrices and time-correlated additive noise,
without requiring a full knowledge of the state-space model generating the signal process, but only information
about its mean and covariance functions. Assuming that the measurement additive noise is the output of a
known linear systemdriven by white noise, the time-differencing method is used to remove this time-correlated
noise and recursive algorithms for the linear filtering and fixed-point smoothing estimators are obtained by an
innovation approach. These estimators are optimal in the least-squares sense and, consequently, their accuracy
is evaluated by the estimation error covariance matrices, for which recursive formulas are also deduced. The
proposed algorithms are easily implementable, as it is shown in the computer simulation example, where they
are applied to estimate a signal from measured outputs which, besides including time-correlated additive noise,
are affected by the missing measurement phenomenon and multiplicative noise (random uncertainties that can
be covered by the current model with random parameter matrices). The computer simulations also illustrate
the behaviour of the filtering estimators for different values of the missing measurement probability.