Soft resampling for improved information retention in particle filtering

Choppala, Praveen B.; Teal, Paul D.; Frean, Marcus

doi:10.1109/icassp.2013.6638417

Cited by 6 publications

(11 citation statements)

References 13 publications

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“…Residual-systematic resampling for fixed duration of the new randomness and partial resampling for reducing the burden of traffic through hardware networks has been proposed [28] . The development of the weight distribution of the abandoned particles for higher tracking efficiency [29] , and adaptive fission particle filter (AF-PF) to increase the efficiently of parameter tracking by creating a better set of particles [30] , [31] has been reported in literature for enhancing the resampling process. The above works performed resampling satisfactory by providing excellent tracking results, but those algorithms are still complicated and take substantial amount of processing time, preventing the realtime applications to be possible.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

Aunsri

Pipatphol

Thikeaw

et al. 2021

Heliyon

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This paper presents a new algorithm for adaptive resampling, called percentile-based resampling (PBR) in a sequential Bayesian filtering, i.e., particle filter (PF) in particular, to improve tracking quality of the frequency trajectories under noisy environments. Since the conventional resampling scheme used in the PF suffers from computational burden, resulting in less efficiency in terms of computation time and complexity as well as the real time applications of the PF. The strategy to remedy this issue is proposed in this work. After state updating, important high particle weights are used to formulate the pre-set percentile in each sequential iteration to create a new set of high quality particles for the next filtering stage. The number of particles after PBR remains the same as the original. To verify the effectiveness of the proposed method, we first evaluated the performance of the method via numerical examples to a complex and highly nonlinear benchmark system. Then, the proposed method was implemented for frequency estimation for two time-varying signals. From the experimental results, via three measurement metrics, our approach delivered better performance than the others. Frequency estimates obtained by our method were excellent as compared to the conventional resampling method when number of particles were identical. In addition, the computation time of the proposed work was faster than those recent adaptive resampling schemes in literature, emphasizing the superior performance to the existing ones.

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Section: Introductionmentioning

confidence: 99%

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

Aunsri

Pipatphol

Thikeaw

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…The particles with lower weights are replaced by those with higher weights so that the final particle cloud represents the posterior more accurately. This replacement would reweight the replaced particles so that the variance among the weights is reduced [22]. Traditional and traditional variation resampling are two types of resampling.…”

mentioning

confidence: 99%

Neural source localization using particle filter with optimal proportional set resampling

Veeramalla

Talari

2020

ETRI Journal

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To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.

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“…From 2012 to the present, some other advanced resampling strategies have been proposed to modify the onlyweight-based resampling. By means of roughening or jittering in [11,20] to combat the weakness of the particle filter, some ideas are investigated in [21][22][23][24] to fight the degeneracy and impoverishment problem of PF.…”

Section: Resampling Schemesmentioning

confidence: 99%