Improved distributed particle filters for tracking in a wireless sensor network

Kang, Kai; Maroulas, Vasileios; Schizas, Ioannis D.; Bao, Feng

doi:10.1016/j.csda.2017.07.009

Cited by 24 publications

(7 citation statements)

References 36 publications

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“…as training data. Use the optimization procedure (24) to obtain the kernel learned filtering density p n+1 introduced in (23); -Carry out the resampling procedure to generate new samples {x n+1 i } N i=1 that follow the kernel learned filtering density p n+1 ; end while (EnKF) [15], which are both state-of-the-art Bayes filter methods. In the third example, we solve a Lorenz-96 tracking problem.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…This makes the effective particle-size decrease dramatically. To address the degeneracy of particles, a resampling procedure is introduced to re-generate particles in high probability regions [30,12,1,35,26,3,24]. But when solving highly nonlinear or high dimensional problems, the existing resampling techniques are either less effective or very difficulty to implement [32].…”

Section: Introductionmentioning

confidence: 99%

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A Kernel Learning Method for Backward SDE Filter

Archibald¹,

Bao²

2022

Preprint

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In this paper, we develop a kernel learning backward SDE filter method to estimate the state of a stochastic dynamical system based on its partial noisy observations. A system of forward backward stochastic differential equations is used to propagate the state of the target dynamical model, and Bayesian inference is applied to incorporate the observational information. To characterize the dynamical model in the entire state space, we introduce a kernel learning method to learn a continuous global approximation for the conditional probability density function of the target state by using discrete approximated density values as training data. Numerical experiments demonstrate that the kernel learning backward SDE is highly effective and highly efficient.

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Section: Numerical Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Kernel Learning Method for Backward SDE Filter

Archibald¹,

Bao²

2022

Preprint

Self Cite

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“…In addition, the problems of the detection and tracking of targets have attracted some attentions and many results of the distributed fusion algorithm via the PF have been proposed in Zhang and Ji (2012), Ghirmai (2016), Kang et al (2018), Papa et al (2018). For example, in Zhang and Ji (2012), the problem of the bearings-only tracking has been addressed by using the distributed fusion PF approach for nonlinear systems over sensor networks.…”

Section: Distributed Fusion Pfmentioning

confidence: 99%

“…For example, in Zhang and Ji (2012), the problem of the bearings-only tracking has been addressed by using the distributed fusion PF approach for nonlinear systems over sensor networks. In Ghirmai (2016), Kang et al (2018), the distributed PF algorithms have been developed for nonlinear systems over the wireless sensor networks. In Papa et al (2018), the distributed multi-sensor filtering algorithm based on particle filter has been proposed for nonlinear systems by using likelihood consensus approach.…”

Section: Distributed Fusion Pfmentioning

confidence: 99%

A survey on distributed filtering, estimation and fusion for nonlinear systems with communication constraints: new advances and prospects

Yang

2020

Systems Science & Control Engineering

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In this paper, some recent results on the distributed filtering, estimation and fusion algorithms for nonlinear systems with communication constraints are reviewed. First, some network-induced phenomena and communication protocols in the networked environment are recalled. Second, the recent advances of distributed fusion algorithms for nonlinear systems are discussed, which include distributed fusion extended Kalman filtering, distributed fusion unscented Kalman filtering, distributed fusion cubature Kalman filtering, distributed fusion particle filtering. Subsequently, some new distributed filtering, estimation and fusion algorithms for nonlinear systems subject to communication constraints are summarized and discussed. Finally, conclusion is given and several possible research topics on distributed filtering, estimation and fusion for nonlinear networked systems are pointed out.

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“…In this paper, we develop a drift homotopy implicit particle filter method that combines the drift homotopy particle filter [24,27] and the implicit particle filter [14]. The central concept of the drift homotopy particle filter is to construct a sequence of intermediate systems called drift homotopy dynamics, and then transport particles by using the Markov Chain Monte Carlo (MCMC) sampling method, which is driven by those intermediate homotopy systems, to high density regions of the desired state distribution.…”

mentioning

confidence: 99%

A drift homotopy implicit particle filter method for nonlinear filtering problems

Li¹,

Bao²,

Gallivan³

2022

DCDS-S

Self Cite

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<p style='text-indent:20px;'>In this paper, we develop a drift homotopy implicit particle filter method. The methodology of our approach is to adopt the concept of drift homotopy in the resampling procedure of the particle filter method for solving the nonlinear filtering problem, and we introduce an implicit particle filter method to improve the efficiency of the drift homotopy resampling procedure. Numerical experiments are carried out to demonstrate the effectiveness and efficiency of our drift homotopy implicit particle filter.</p>

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Improved distributed particle filters for tracking in a wireless sensor network

Cited by 24 publications

References 36 publications

A Kernel Learning Method for Backward SDE Filter

A Kernel Learning Method for Backward SDE Filter

A survey on distributed filtering, estimation and fusion for nonlinear systems with communication constraints: new advances and prospects

A drift homotopy implicit particle filter method for nonlinear filtering problems

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