Optimal sequential and distributed fusion for state estimation in cross-correlated noise

Yan, Liping; Li, X. Rong; Xia, Yuanqing; Fu, Mengyin

doi:10.1016/j.automatica.2013.09.013

Cited by 112 publications

(69 citation statements)

References 9 publications

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“…The results are displayed in Figure 4, which shows that: (a) the proposed centralized filtering algorithm provides better estimations than the other filtering algorithms since the possibility of different simultaneous uncertainties in the different sensors is considered; (b) the centralized filter [8] outperforms the filter [25] since, even though the latter accommodates the effect of the delays during transmission, it does not take into account the missing measurement phenomenon in the sensors; (c) the filtering algorithm in [4] provides the worst estimations, a fact that was expected since neither the uncertainties in the measured outputs nor the delays during transmission are taken into account. Six-sensor network.…”

mentioning

confidence: 99%

“…So, important advances have been achieved concerning the estimation problem in networked stochastic systems and the design of multisensor fusion techniques [1]. Many of the existing fusion estimation algorithms are related to conventional systems (see e.g., [2][3][4][5], and the references therein), where the sensor measured outputs are affected only by additive noises and each sensor transmits its outputs to the fusion center over perfect connections.…”

Section: Introductionmentioning

confidence: 99%

“…The centralized Kalman-type filter [4] for systems without uncertainties. -The centralized filter [8] for systems with missing measurements.…”

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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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confidence: 99%

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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“…Song et al presents Kalman filtering fusion [11] with 25 feedback and cross-correlated sensor noises for distributed recursive state estimators. The cross-correlation between the measurement noise and process noise are discussed in [12,13,14]. For noise sequences with uncertain variances, the actual filtering error variances [15,16,17] are obtained with a minimal upper bound for all admissible uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Liu

Yang

et al. 2017

Neurocomputing

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This paper investigates the state estimation issue for uncertain networked systems considering data transmission time-delay and cross-correlated noises. A distributed robust Kalman filtering-based perception and centralized fusion method is proposed to improve the estimation accuracy from perturbed measurement; consequently, reduce the amount of redundant information and alleviate the estimation burden. To describe the transmission time-delay and give rise to cross-correlated and state-dependent noises in the exchange measurement among neighbors, a weighted fusion reorganized innovation strategy is proposed to reduce the computational burden and suppress noise effect. Moreover, to obtain the optimal linear estimate, a fusion estimation approach is used for information collaboration by weighting the error cross-covariance matrices. Finally, an illustrative example is presented to demonstrate the effectiveness and robustness of the proposed method.

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“…Traditionally, fusion estimation algorithms were concerned with conventional systems, where the sensors transmit their measured outputs to the fusion center over perfect connections (see, e.g., [1,2] and references therein). However, usually the network characteristics may not be completely reliable and some anomalies (e.g., uncertain observations or missing measurements, random delays, and/or packet dropouts) may arise when the sensor measurements are transmitted to the fusion center.…”

Section: Introductionmentioning

confidence: 99%

Least‐Squares Filtering Algorithm in Sensor Networks with Noise Correlation and Multiple Random Failures in Transmission

Caballero-Águila

Hermoso-Carazo

Linares-Pérez

2017

Mathematical Problems in Engineering

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This paper addresses the least-squares centralized fusion estimation problem of discrete-time random signals from measured outputs, which are perturbed by correlated noises. These measurements are obtained by different sensors, which send their information to a processing center, where the complete set of data is combined to obtain the estimators. Due to random transmission failures, some of the data packets processed for the estimation may either contain only noise (uncertain observations), be delayed (randomly delayed observations), or even be definitely lost (random packet dropouts). These multiple random transmission uncertainties are modelled by sequences of independent Bernoulli random variables with different probabilities for the different sensors. By an innovation approach and using the last observation that successfully arrived when a packet is lost, a recursive algorithm is designed for the filtering estimation problem. The proposed algorithm is easily implemented and does not require knowledge of the signal evolution model, as only the first-and second-order moments of the processes involved are used. A numerical simulation example illustrates the feasibility of the proposed estimators and shows how the probabilities of the multiple random failures influence their performance.

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Optimal sequential and distributed fusion for state estimation in cross-correlated noise

Cited by 112 publications

References 9 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

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Least‐Squares Filtering Algorithm in Sensor Networks with Noise Correlation and Multiple Random Failures in Transmission

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