Recursive estimation for nonlinear stochastic systems with multi-step transmission delays, multiple packet dropouts and correlated noises

Wang, Shaoying; Fang, Huajing; Tian, Xin

doi:10.1016/j.sigpro.2015.03.022

Cited by 61 publications

(51 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A modified fuzzy Kalmantype filtering [5] is presented with finite-step auto-correlated process noises depending on the system state. For the linear discrete time-varying systems with 20 stochastic uncertainties, a realistic mathematical model named the uncertain state-dependent (also called multiplicative) noise [6,7,8,9] is widely applied in a range of scientific and industrial applications. The information fusion steadystate Kalman filtering approach [10] involves different local dynamic models and correlated noises.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the design of the filtering algorithms to reduce the negative influences of the time- 35 delays has received increasing research attention. The augmented state approach [18,19,20] applies the partial differential equation (PDE) and boundary condition equation, and the polynomial approach [21,22] is utilized to solve the multiple time-delay systems. In order to reduce the communication burden, the measurement transformation approach [17, 23,24,25] uses the reorganized 40 measurement sequence, and the delayed system is transformed into the form of the equivalent delay-free counterpart.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Liu

Yang

et al. 2017

Neurocomputing

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…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%

“…For this reason, the fairly conservative assumption that the measurement noises are uncorrelated is commonly weakened in many of the aforementioned research papers on signal estimation. Namely, the optimal Kalman filtering fusion problem in systems with noise cross-correlation at consecutive sampling times is addressed, for example, in [19]; also, under different types of noise correlation, centralized and distributed fusion algorithms for systems with multiplicative noise are obtained in [11,20], and for systems where the measurements might have partial information about the signal in [7].…”

Section: Introductionmentioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

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.

show abstract

“…However, this is not a realistic consideration in many practical situations; for this reason, this conservative assumption is commonly weakened in many papers concerning the signal estimation problem in networked systems, and the presence of correlated noise in the sensor data makes the design of signal estimation algorithms more challenging and interesting. The optimal Kalman filtering fusion problem in systems with cross-correlated noises at consecutive sampling times is addressed, for example, in [14] for systems with multistep transmission delays and multiple packet dropouts, by transforming the system into a stochastic parameterized one. Also, centralized and distributed fusion algorithms are obtained in [15] for uncertain systems with correlated noises and in [5] for systems where the measurements might randomly contain only partial information about the signal.…”

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

View full text Add to dashboard Cite

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.

show abstract

Recursive estimation for nonlinear stochastic systems with multi-step transmission delays, multiple packet dropouts and correlated noises

Cited by 61 publications

References 29 publications

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

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

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

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

Contact Info

Product

Resources

About