Distributed fusion cubature Kalman filters for nonlinear systems

Hao, Gang; Sun, Shuli

doi:10.1002/rnc.4709

Cited by 34 publications

(10 citation statements)

References 35 publications

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“…Then,L N can be easily obtained by substituting (29) into (25). Furthermore, usingx k|Y N obtained in (21), the second layer of the TLNF filter is designed to enhance the robustness against unexpected measurement noise.…”

Section: Volume 8 2020mentioning

confidence: 99%

“…Hao et al [28] proposed a weighted measurement fusion (WMF) algorithm that showed asymptotic optimality using the UKF for a nonlinear system with multiple sensors. Hao et al [29] proposed a distributed fusion CKF weighted by matrices (MW-CKF) based on the KF framework and spherical cubature rule for multisensory nonlinear systems. Sun et al [30] reviewed various distributed fusion estimation (DFE) algorithms for multisensor networked systems comparing performance.…”

Section: Introductionmentioning

confidence: 99%

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Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization

2020

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In this paper, we propose a new state estimator called the two-layer nonlinear finite impulse response (TLNF) filter and adopt this new filter and unscented Kalman filter (UKF) as subfilters to create the fusion TLNF/UK filter. The TLNF filter is constructed with measurements that are redefined by weighting the estimated states acquired through minimizing the cost function based on the Frobenius norm. The efficient iterative form of the TLNF filter is also developed in this paper. Using the fact that the UKF and the TLNF filter each takes a different type of memory structure, the fusion TLNF/UK filter is designed as a robust nonlinear state estimator taking both advantages of each filter. To obtain the best fusion estimates, probabilistic weights are computed based on Bayes' rule and the likelihood of each filter. Both simulation and experimental results for mobile robot indoor localization have shown that the fusion TLNF/UK filter achieves a higher level of accuracy and robustness under practical situations. INDEX TERMS State estimation, fusion algorithm, finite impulse response (FIR) filter, unscented Kalman filter (UKF).

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Section: Volume 8 2020mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization

2020

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“…The estimation performance is accumulated mean square error (AMSE) in position at time k [41]- [43]:…”

Section: Simulation Examplementioning

confidence: 99%

The Research on Distributed Fusion Estimation Based on Machine Learning

Peng

Hao

2020

IEEE Access

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Multi-sensor distributed fusion estimation algorithms based on machine learning are proposed in this paper. Firstly, using local estimations as inputs and estimations of three classic distributed fusion (weighted by matrices, by diagonal matrices and by scalars) as the training sets, three distributed fusion algorithms based on BP network (BP net-based fusion weighted by matrices, by diagonal matrices and by scalar) are proposed and the selection basis of the number of nodes in hidden layer is given. Furthermore, by using local estimations as inputs and centralized fusion estimation as training set, another recurrent netbased distributed fusion algorithm is proposed, in the case that neither true states nor cross-covariance matrices is available. This method is not limited to the linear minimum variance (LMV) criterion, so its accuracy is higher than the classical three distributed fusion algorithms. A radar tracking simulation verifies the effectiveness of the proposed fusion networks. INDEX TERMS Distributed fusion, machine learning, recurrent networks, BP network.

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“…As for distributed state estimation problems, many researchers have made a great deal of work with abundant results. [7][8][9][10][11][12] Just name a few for examples. A consensus-based distributed algorithm, which executed some consensus steps about measurements and innovation covariances, was proposed to approximate the centralized Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Distributed Kalman filtering for uncertain dynamic systems with state constraints

Duan

2020

Intl J Robust & Nonlinear

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This article addresses the distributed state estimation problem for uncertain time-varying dynamic systems with state constraints over a sensor network. By using a null space method, the distributed state estimation problem for uncertain dynamic systems with state constraints can be cast into a new unconstrained distributed state estimation problem for reduced uncertain dynamic systems. A constrained distributed Kalman filter is proposed, and it is shown that the full state estimates can be recovered at any time and satisfy the constraints. An optimized upper bound of the estimation error covariance of each sensor is obtained, and the corresponding gains are designed. The application conditions of the proposed algorithm are mild, and they can be off-line checked. Furthermore, the computational requirements in this article are also reduced compared with the existing results. Finally, the performance of the proposed filter algorithm is demonstrated through numerical simulations.

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Distributed fusion cubature Kalman filters for nonlinear systems

Cited by 34 publications

References 35 publications

Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization

Two-Layer Nonlinear FIR Filter and Unscented Kalman Filter Fusion With Application to Mobile Robot Localization

The Research on Distributed Fusion Estimation Based on Machine Learning

Distributed Kalman filtering for uncertain dynamic systems with state constraints

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