Fusion estimation using measured outputs with random parameter matrices subject to random delays and packet dropouts

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

doi:10.1016/j.sigpro.2016.02.014

Cited by 55 publications

(38 citation statements)

References 28 publications

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“…The n x -dimensional signal process x k k≥1 has zero mean and its autocovariance function is expressed in a separable form, s,0 ) T . Furthermore, Hypothesis 1 covers even situations where the system matrix in the state-space model is singular, although a different factorization must be used in those cases (see e.g., [21]). Hence, Hypothesis 1 on the signal autocovariance function covers both stationary and non-stationary signals, providing a unified context to deal with a large number of different situations and avoiding the derivation of specific algorithms for each of them.…”

Section: Signal Processmentioning

confidence: 99%

“…Furthermore, when the sensors send their measurements to the processing center via a communication network some additional network-induced phenomena, such as random delays or measurement losses, inevitably arise during this transmission process, which can spoil the fusion estimators performance and motivate the design of fusion estimation algorithms for systems with one (or even several) of the aforementioned uncertainties (see e.g., [12][13][14][15][16][17][18][19][20][21][22][23][24], and references therein). All the above cited papers on signal estimation with random transmission delays assume independent random delays at each sensor and mutually independent delays between the different sensors; in [25] this restriction was weakened and random delays featuring correlation at consecutive sampling times were considered, thus allowing to deal with some common practical situations (e.g., those in which two consecutive observations cannot be delayed).…”

Section: Introductionmentioning

confidence: 99%

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Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

2017

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Abstract:In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.

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Section: Signal Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

2017

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“…A great advantage of assumption (A1) is that it covers situations in which the signal evolution model is known, for both stationary and non-stationary signals (see, e.g., [23]). In addition, in uncertain systems with state-dependent multiplicative noise, as those considered in [6,32], the signal covariance function is factorizable, as it is shown in Section 5.…”

Section: Observation Model and Preliminariesmentioning

confidence: 99%

Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission

Caballero-Águila

Hermoso-Carazo

Linares-Pérez

2017

Sensors

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This paper is concerned with the optimal fusion estimation problem in networked stochastic systems with bounded random delays and packet dropouts, which unavoidably occur during the data transmission in the network. The measured outputs from each sensor are perturbed by random parameter matrices and white additive noises, which are cross-correlated between the different sensors. Least-squares fusion linear estimators including filter, predictor and fixed-point smoother, as well as the corresponding estimation error covariance matrices are designed via the innovation analysis approach. The proposed recursive algorithms depend on the delay probabilities at each sampling time, but do not to need to know if a particular measurement is delayed or not. Moreover, the knowledge of the signal evolution model is not required, as the algorithms need only the first and second order moments of the processes involved. Some of the practical situations covered by the proposed system model with random parameter matrices are analyzed and the influence of the delays in the estimation accuracy are examined in a numerical example.

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“…NCSs have received significant attention for their successful applications in space exploration, target tracking, remote surgery, unmanned aerial vehicles, industrial monitoring, and other areas in recent years [1][2][3][4][5][6][7][8][9][10][11][12]. As is well known, network-induced phenomena, such as communication delays, fading measurements or packet dropouts, quantization effects, and sensor saturations, are unavoidable in data transmission of practical networked systems due mainly to the sudden environment changes, intermittent transmission congestions, random failures, and repairs of components [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Linear Estimators for Time-Delay Systems with Fading Measurements and Correlated Noises

Wang

2018

Journal of Electrical and Computer Engineering

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The optimal linear estimation problems are investigated in this paper for a class of discrete linear systems with fading measurements and correlated noises. Firstly, the fading measurements occur in a random way where the fading probabilities are regulated by probability mass functions in a given interval. Furthermore, time-delay exists in the system state and observation simultaneously. Additionally, the multiplicative noises are considered to describe the uncertainty of the state. Based on the projection theory, the linear minimum variance optimal linear estimators, including filter, predictor, and smoother are presented in the paper. Compared with conventional state augmentation, the new algorithm is finite-dimensionally computable and does not increase computational and storage load when the delay is large. A numerical example is provided to illustrate the effectiveness of the proposed algorithms.

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Fusion estimation using measured outputs with random parameter matrices subject to random delays and packet dropouts

Cited by 55 publications

References 28 publications

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission

Optimal Linear Estimators for Time-Delay Systems with Fading Measurements and Correlated Noises

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