Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises

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

“…Multiplicative noise uncertainties and missing measurements are some of the random phenomena that usually arise in the sensor measured outputs and motivate the design of new estimation algorithms (see e.g., [7][8][9][10][11], and references therein).…”

“…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].…”

“…As in [25], correlated random delays in the transmission are assumed to exist, with different delay rates at each sensor; however, the proposed observation model is more general than that considered in [25] since, besides the random delays in the transmission, multiplicative noises and missing phenomena in the measured outputs are considered; also cross-correlation between the different sensor additive noises is taken into account. Unlike [7][8][9][10][11] where multiplicative noise uncertainties and/or missing measurements are considered in the sensor measured outputs, in this paper random delays in the transmission are also assumed to exist. Hence, a unified framework is provided for dealing simultaneously with missing measurements and uncertainties caused by multiplicative noises, along with random delays in the transmission and, hence, the proposed fusion estimators have wide applicability.…”

Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

“…For systems with norm-bounded uncertain parameters, the improved robust H 2 and H ∞ filters were presented by the linear matrix inequality (LMI) approach, 17 and further, for systems with mixed uncertainties including norm-bounded uncertain parameters, the robust estimations were presented by the LMI approach. 18,19 In addition, the innovation analysis approach or the projection approach 20,21 and the linear minimum-variance optimal estimation rule 22 were used to design optimal estimators for system with multiplicative noises. However, the multiplicative noises in the works of Yang et al 16 and Wang et al 18 only occur in the state matrix, whereas in the works of Liu et al, 20,22 the multiplicative noises only occur in the measurement matrix.…”

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

“…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. Also in [16] for systems with multiplicative noise and two-step random transmission delays, the centralized and distributed fusion estimation problems are addressed by using the state augmentation approach and, even though white noises are considered in the original model, the observation noises of the augmented system are correlated.…”