Optimal errors-in-variables filtering

Guidorzi, Roberto; Diversi, Roberto; Soverini, Umberto

doi:10.1016/s0005-1098(02)00200-5

Cited by 29 publications

(25 citation statements)

References 14 publications

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“…(19) and (20), we have an explicit solution of the errors-in-variables filtering problem of Guidorzi et al (2003) as a (modified) Kalman filter. Note 6 (Misfit/latency): More general estimation problems occur when the signals u, y are generated by the stochastic model (3) with a noise covariance matrix V v := cov(col (v 1 , v 2 )), and the signals u d , y d , available for estimation, are generated by the measurement error model (2).…”

Section: Theorem 4 the Solution Of The Noisy Filtering Problem Ismentioning

confidence: 99%

“…The noisy input/output (I/O) estimation problem is first put forward in Guidorzi, Diversi, and Soverini (2003), where it is called errors-in-variables filtering. In Guidorzi et al (2003), Diversi, Guidorzi, and Soverini (2003a), a transfer functions approach is used and recursive algorithms that solve the filtering problem are derived.…”

Section: Introductionmentioning

confidence: 99%

“…In Guidorzi et al (2003), Diversi, Guidorzi, and Soverini (2003a), a transfer functions approach is used and recursive algorithms that solve the filtering problem are derived. The treatment, however, is limited to the SISO case and the obtained solution is not linked to the classical Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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Linear dynamic filtering with noisy input and output

Markovsky

Moor

2005

Automatica

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State estimation problems for linear time-invariant systems with noisy inputs and outputs are considered. An efficient recursive algorithm for the smoothing problem is presented. The equivalence between the optimal filter and an appropriately modified Kalman filter is established. The optimal estimate of the input signal is derived from the optimal state estimate. The result shows that the noisy input/output filtering problem is not fundamentally different from the classical Kalman filtering problem.

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Section: Theorem 4 the Solution Of The Noisy Filtering Problem Ismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linear dynamic filtering with noisy input and output

Markovsky

Moor

2005

Automatica

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“…The EIV filtering problem, i.e. the optimal (minimal variance) estimation of the true system input and output on the basis of their noisy observations and of the knowledge of the process and noise models, has been, in fact, formulated and solved only recently (Guidorzi et al, 2002;Guidorzi et al, 2003) making reference to polynomial and to state-space models. The computational aspects of EIV filtering have then been analyzed in (Diversi et al, 2003a) with the goal of deriving a fast and robust formulation suitable for real-time implementations.…”

Section: Introductionmentioning

confidence: 99%

“…Errorsin-variables models assume, on the contrary, the presence of unknown additive noise also on the inputs; the associated filtering procedures concern thus the optimal (minimal variance) estimation not only of the system state and output but also of the input. Optimal EIV filtering has been formulated and solved only recently (Guidorzi et al, 2003) making reference to SISO models; this paper extends the efficient algorithm proposed in (Diversi et al, 2003a), based on the Cholesky factorization, to the more general multivariable case. …”

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confidence: 99%

Optimal Errors–in–variables Filtering in the Mimo Case

Diversi

Guidorzi

Soverini

2005

IFAC Proceedings Volumes

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The Errors-in-Variables (EIV) stochastic environment constitutes a superset of most common stochastic environments considered, for instance, in Kalman filtering or in equation-error identification where the process input is assumed as noise-free. Errorsin-variables models assume, on the contrary, the presence of unknown additive noise also on the inputs; the associated filtering procedures concern thus the optimal (minimal variance) estimation not only of the system state and output but also of the input. Optimal EIV filtering has been formulated and solved only recently (Guidorzi et al., 2003) making reference to SISO models; this paper extends the efficient algorithm proposed in (Diversi et al., 2003a), based on the Cholesky factorization, to the more general multivariable case.

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Fault diagnosis of a simulated industrial gas turbine via identification approach

Simani

2006

Adaptive Control & Signal

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In this paper, a model-based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors-in-variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single-shaft industrial gas turbine MATLAB/ SIMULINK 1 simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte-Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI.

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Optimal errors-in-variables filtering

Cited by 29 publications

References 14 publications

Linear dynamic filtering with noisy input and output

Linear dynamic filtering with noisy input and output

Optimal Errors–in–variables Filtering in the Mimo Case

Fault diagnosis of a simulated industrial gas turbine via identification approach

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