Recursive estimators of signals from measurements with stochastic delays using covariance information

Nakamori, Seiichi; Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa

doi:10.1016/j.amc.2003.12.066

Cited by 60 publications

(33 citation statements)

References 15 publications

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“…We only prove (9) for the case 1 i d−1, while for the cases of i = 0 and i = d, the proofs are similar. (14) yields (9). We easily obtain (10) and (11) from (7), as well as (12) from (5).…”

Section: Optimal Linear Estimators In Finite Horizonmentioning

confidence: 94%

“…In [2], a recursive minimum variance state estimator was presented for linear discrete-time partially observed systems where the observations are transmitted by communication channels with randomly independent delays. Using covariance information, recursive least-squares linear estimators for signals with random delays were studied in [14]. Furthermore, the robust filtering problems with random delays were also investigated in [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal linear estimators for systems with random measurement delays

Sun

Tian

2011

J. Control Theory Appl.

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This paper is concerned with the optimal linear estimation problem for linear discrete-time stochastic systems with random measurement delays. A new model that describes the random delays is constructed where possible the largest delay is bounded. Based on this new model, the optimal linear estimators including filter, predictor and smoother are developed via an innovation analysis approach. The estimators are recursively computed in terms of the solutions of a Riccati difference equation and a Lyapunov difference equation. The steady-state estimators are also investigated. A sufficient condition for the convergence of the optimal linear estimators is given. A simulation example shows the effectiveness of the proposed algorithms.

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Section: Optimal Linear Estimators In Finite Horizonmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Optimal linear estimators for systems with random measurement delays

Sun

Tian

2011

J. Control Theory Appl.

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“…As pointed out in [4,19], the random variable γ k,i accounts for the random varying delay of the i-th sensor and the value β k,i represents the probabilities of delay in the measurements of the i-th sensor. The delayed model in [4] considers the case where the measurements from multiple sensors could have different random delay characteristics.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…Remark 1: Note that the system measurement model (3) was used in [4,17,19]. As pointed out in [4,19], the random variable γ k,i accounts for the random varying delay of the i-th sensor and the value β k,i represents the probabilities of delay in the measurements of the i-th sensor.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Gain-Constrained Recursive Filtering With Stochastic Nonlinearities and Probabilistic Sensor Delays

Wang

Shen

et al. 2013

IEEE Trans. Signal Process.

127

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Abstract-This paper is concerned with the gainconstrained recursive filtering problem for a class of timevarying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

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“…For the same model, a Gaussian approximation filter is derived [19]. Furthermore, many researchers have investigated these problems under different assumptions about the possible delays and different filtering approximations; See [20], [21], [22], [23] and [24], for example. To the best of authors' knowledge, the problem of state estimation in nonlinear systems with random delays has not been fully investigated.…”

Section: Introductionmentioning

confidence: 99%

An approximate minimum variance filter for nonlinear systems with randomly delayed observations

Date¹,

Allahyani

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we extend our earlier results on minimum variance filter for systems with additive multiplicative noise in two different ways. Firstly, we propose a novel characterization of the linearization error in terms of multiplicative noise. Secondly, we also allow for random delay of up to one time step in the measurement. The delay is modelled by Bernoulli random variables. We derive a closed-form expression for the minimum variance filter for the resulting system with a linearized state transition equation, accounting for both the linearization error as well as the random delay. The utility of the proposed filtering algorithm is demonstrated through numerical experiments.

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Recursive estimators of signals from measurements with stochastic delays using covariance information

Cited by 60 publications

References 15 publications

Optimal linear estimators for systems with random measurement delays

Optimal linear estimators for systems with random measurement delays

Gain-Constrained Recursive Filtering With Stochastic Nonlinearities and Probabilistic Sensor Delays

An approximate minimum variance filter for nonlinear systems with randomly delayed observations

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