Multisensor Distributed Weighted Kalman Filter Fusion With Network Delays, Stochastic Uncertainties, Autocorrelated, and Cross-Correlated Noises

Xing, Zirui; Xia, Yuanqing; Yan, Liping; Lu, Kunfeng; Gong, Qinghai

doi:10.1109/tsmc.2016.2633283

Cited by 54 publications

(26 citation statements)

References 33 publications

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“…Hence, the problems of correlated noises and missing measurements attracted the attention of many scholars and a large number of results were published in [8][9][10][11][12][13]. Recently, in [14], the problem of state estimation was solved for a class of linear discrete-time stochastic systems subject to missing data and correlated noises, where the estimators were unbiased and the estimation error covariances were minimized.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

You

Jiang

Huang

et al. 2018

Discrete Dynamics in Nature and Society

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The locally optimal filter is designed for a class of discrete-time systems subject to stochastic nonlinearity functions, finite-step correlated noises, and missing measurements. The multiplicative noises are employed to describe the random disturbances in the system model. The phenomena of missing measurements occur in a random way and the missing probability is characterized by Bernoulli distributed random variables with known conditional probabilities. Based on the projection theory, a class of Kalman-type locally optimal filter is constructed and the filtering error covariance matrix is minimized in the sense of minimum mean square error principle. Also, by solving the recursive matrix equation, we can obtain the filter gain. Finally, two examples are provided: one is a numerical example to illustrate the feasibility and effectiveness of the proposed filtering scheme; the other is to solve the problem of target estimation for a tracking system considering networked phenomena.

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Section: Introductionmentioning

confidence: 99%

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

You

Jiang

Huang

et al. 2018

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…e fading measurement appears in a random way, and the fading phenomenon for each sensor is described by an individual random variable taking a value in a given interval [20]. Furthermore, some results have been reported on the Kalman filtering problems of systems with uncertain correlated noise [23][24][25][26]. By introducing the fictitious noises to compensate the stochastic uncertainties, the system under consideration can be converted into one with only uncertain noise variances [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…However, in all these papers, the results focus on finding the optimal estimators, under which the state delay and observation delay are not considered simultaneously. Moreover, a few phenomena of imperfect transmission including the fading measurement and the time delay could be easily incorporated, and the optimal estimation problems for linear uncertain systems with single delayed measurement have not taken fading measurements into account [25,26].…”

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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“…KF techniques help in combining different measurements in such a way that individual sensor shortcomings are minimized and, as a result, errors are reduced [8,9,10,11]. The KF has a criterion of minimizing the mean squared error, and was designed for linear systems, however most practical systems are nonlinear [12].…”

Section: Problem Statementmentioning

confidence: 99%

“…11 shows the illustration of the variation of the reference roll plotted against roll from GA in degrees versus time in seconds. From this observation, during the time of 0 to 500 seconds there wasn't much deviation from the reference.…”

mentioning

confidence: 99%

Multi-Sensor Attitude and Heading Reference System Design Using Genetically Optimized Kalman Filter

Gessesse¹

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Attitude and Heading Reference System (AHRS) is a self-contained sensors assembly that can estimate full 3D orientation of an object. 3D orientation estimation is of great importance in many applications such as robot navigation, augmented/virtual reality, and mobile mapping. The AHRS system model involves integration of angular rate measurements from gyroscope which are fused with absolute measurements from magnetometer/accelerometer using Extended Kalman Filter (EKF). EKF accuracy is greatly affected by process noise parameters and measurement noise parameters. Therefore, this thesis developed a systematic method of EKF noise parameters optimization using a hybrid stochastic, Genetic Algorithms (GA)-based approach supported by Design of Experiments (DoE) technique. Noise parameters are partially obtained using stochastic modeling methods such as Gauss-Markov (GM) and Allan Variance (AV). Then, further optimization is performed using GA and DoE methods. The proposed approach has been developed in MATLAB and tested on simulation data and verified on real data collected under different scenarios. Results showed that the proposed approach can provide 40-60% better accuracy compared to conventional methods within few GA iterations. In addition, application of DoE technique reduces GA iterations to convergence by approximately 60%.

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Multisensor Distributed Weighted Kalman Filter Fusion With Network Delays, Stochastic Uncertainties, Autocorrelated, and Cross-Correlated Noises

Cited by 54 publications

References 33 publications

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

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

Multi-Sensor Attitude and Heading Reference System Design Using Genetically Optimized Kalman Filter

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