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Author

B Sindhu

Bio: B Sindhu is an academic researcher from VIT University. The author has contributed to research in topics: Noise measurement & Homotopy. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Proceedings ArticleDOI
30 Nov 2015
TL;DR: The particle flow filter (PFF) is analyzed using the range-azimuth nonlinear filtering problem and the cross-correlation coefficients between the particle states and measurement noise are calculated to show high values of cross-Correlation coefficients.
Abstract: The particle flow filter (PFF) has been claimed to have superior performance in terms of accuracy and computational speed compared with existing nonlinear filters. We carefully examine the PFF algorithm and homotopy used in the PFF. We find three problems in the formulation of the PFF. First, homotopy used in the PFF violates the definition of homotopy. Second, a posterior probability density function (pdf) p(x, λ), is used in the Fokker-Planck equation (FPE) with respect to a parameter λ. A prediction pdf should be used in the FPE, not a posterior pdf. Third, a given measurement is processed a number of times in a loop over the parameter λ and the correlation between a particle state and measurement noise is ignored. We analyze the PFF using the range-azimuth nonlinear filtering problem and calculate the cross-correlation coefficients between the particle states and measurement noise to show high values of cross-correlation coefficients.

3 citations


Cited by
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Proceedings Article
02 Jul 2019
TL;DR: This paper shows that the explicit and incompressible flows, unlike the Gromov flow, are inherently biased and the benefits of implicit stochastic-integration methods are demonstrated and a new adaptive step-size selection heuristic is presented.
Abstract: Particle flow filters are appealing due to their potential resistance to particle collapse. However, common implementations exhibit undesirable biases or particle divergence. This paper shows that the explicit and incompressible flows, unlike the Gromov flow, are inherently biased. Another issue is errors in the numerical integration of the flow. The benefits of implicit stochastic-integration methods are demonstrated and a new adaptive step-size selection heuristic is presented.

8 citations

Journal ArticleDOI
TL;DR: In this paper, a numerical fitting approach is proposed to speed up the particle filter in which the likelihood of particles is analytically inferred/fitted, explicitly or implicitly, based on that of a small number of so-called fulcrums.
Abstract: The likelihood calculation of a vast number of particles forms the computational bottleneck for the particle filter in applications where the observation model is complicated, especially when map or image processing is involved. In this paper, a numerical fitting approach is proposed to speed up the particle filter in which the likelihood of particles is analytically inferred/fitted, explicitly or implicitly, based on that of a small number of so-called fulcrums. It is demonstrated to be of fairly good estimation accuracy when an appropriate fitting function and properly distributed fulcrums are used. The construction of the fitting function and fulcrums are addressed respectively in detail. To avoid intractable multivariate fitting in multi-dimensional models, a nonparametric kernel density estimator such as the nearest neighbori¾?smoother or the uniform kernel average smoother can be employed for implicit likelihood fitting. Simulations based on a benchmark one-dimensional model and multi-dimensional mobile robot localization are provided. Copyright © 2015 John Wiley & Sons, Ltd.

5 citations

Proceedings ArticleDOI
Fred Daum1, Jim Huang1, Arjang Noushin1
TL;DR: This paper explains why all three assertions of Mallick and Sindhu’s particle flow theory for Bayes’ rule are wrong.
Abstract: In a recent paper by Mallick and Sindhu, they assert three “problems” with our particle flow theory for Bayes’ rule. Our paper explains why all three assertions are wrong.

3 citations