Variational Bayesian Adaptive Cubature Information Filter Based on Wishart Distribution

Dong, Peng; Jing, Zhongliang; Leung, Henry; Shen, Kai

doi:10.1109/tac.2017.2704442

Cited by 98 publications

(50 citation statements)

References 21 publications

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“…Thus the Student-t distribution with heavy-tailed is used to model the uncertainties exhibiting frequent occurrence of the outliers [20]- [24]. A linear distributed consensus filter with CI strategy to handle measurement outliers is proposed in [25], where the measurement noise of each sensor node is modeled by the multivariate Student-t distribution and variational Bayesian (VB) method [26], [27] is used to approximate the joint posterior density. Then it is extended to nonlinear cases in [28] where hybrid consensus strategy [9], [11] is used.…”

Section: Measurement Vectormentioning

confidence: 99%

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

Wang¹,

Dong

Shen

et al. 2020

IEEE Access

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In the estimation of distributed sensor networks, process noise and measurement noise may have outliers which have heavy-tailed characteristics. To solve this problem, this paper proposes a distributed consensus estimating method for sensor networks based on Student-t distribution. In the state space model, both process noise and measurement noise are modeled as Student-t distributions with heavytailed characteristics. First, for the assumption that the process noise and measurement noise have the same degree of freedom parameters, an exact distributed consensus Student-t filtering algorithm is derived. In practical applications, this assumption is often not true, and due to the increasing degrees of freedom, the method will quickly converge to the traditional distributed consensus Kalman filter. Therefore, it is necessary to relax the assumption of the same degree of freedom and keep the degree of freedom unchanged within a certain range. Based on this, an approximate distributed consensus Student-t filter algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.

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Section: Measurement Vectormentioning

confidence: 99%

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

Wang¹,

Dong

Shen

et al. 2020

IEEE Access

Self Cite

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show abstract

“…Since the inverse gamma distribution is a special case of the inverse wishart distribution in onedimensional space, the discussed model in this paper is much more general and thus more information can be utilized for filter design. Interested readers are referred to [30] and [31] for a detailed introduction.…”

Section: Remarkmentioning

confidence: 99%

Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

Chen

Gao

2020

EURASIP J. Adv. Signal Process.

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The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties. The Rényi information divergence, as a criterion, is to choose the best match model that has the minimum divergence between candidate models and true mode. Subsequently, the model-conditioned estimation based on variational Bayesian approximation is proposed to estimate system state and measurement noise variances. To deal with the coupled noise intractability, the moments matching technique is used to obtain the mixed statistics of measurement noise at the fusion stage. The proposed algorithm is compared with the interacting multiple models (IMM) algorithm and the variational Bayesian-interacting multiple models (IMM-VB) algorithm via two scenarios for maneuvering target tracking, and simulation results show that the MSA-VB has improved estimation and tracking performance.

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“…Then the optimal natural parameter of q(x k ) is given by η * 2 = η 1 , where η 1 is the natural parameter of p(x k |y k , N k ). Proof: Substituting (24) and (25)…”

Section: A Vb-mmentioning

confidence: 99%

“…Different from the general description of the collaborative estimation problem [27], [28], the VB based method supplies an information-theoretic perspective and provides an analytical approximation to the posterior probability of the unknown parameters [25], which has benefits of low computational cost and analytic tractable [26]. For example, VB filters were employed to approximate the JPD of the state and unknown noise parameters, where the noises were modeled by Inverse-Gamma distribution [23], Gaussian mixture distribution [24] and Wishart distribution [25], respectively. However, unlike the independence of noise and state, the guidance parameter and state are tightly coupled, which makes the above method ineffective for our problem.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Estimation of State and Guidance Parameter for Interceptor Based on Variational Bayesian Technique

Lin

Zhao

Li³

et al. 2020

IEEE Access

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In this paper, we study the state estimation problem with an unknown guidance parameter of the interceptor, where the guidance parameter is modeled by normal-gamma distribution. To solve the problem, we propose a variational Bayesian (VB) based collaborative estimation algorithm for state and parameters, where the joint posterior distribution (JPD) of state and guidance parameter is approximated by a free-form distribution. The proposed algorithm can be divided into two-stage iterative steps: in variational Bayesian expectation (VB-E) step, with guidance parameter fixed, the cubature Kalman filter (CKF) is employed to realize state estimation. In variational Bayesian maximum (VB-M) step, the statistical characteristics of the guidance parameter are then deduced with state fixed. The state and the guidance parameter can be effectively estimated by performing VB-E and VB-M steps recursively. Finally, we illustrate the effectiveness of the proposed algorithm by a collaborative estimation problem in the twodimensional aerial engagement scenario.

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Variational Bayesian Adaptive Cubature Information Filter Based on Wishart Distribution

Cited by 98 publications

References 21 publications

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

Distributed Consensus Student-t Filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

Collaborative Estimation of State and Guidance Parameter for Interceptor Based on Variational Bayesian Technique

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