A particle filter approach to estimating target location using Brownian bridges

Nunez, Jesse A; Singham, Dashi I.; Atkinson, Michael P.

doi:10.1080/01605682.2019.1570806

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…The basic idea is to use a set of weighted samples randomly drawn from the probability density to approximate the posterior probability density. Since the proposed particle filter algorithm, it has been widely used in the field of nonlinear system parameter estimation, such as target tracking [1], system state detection [2], and simultaneous localization and mapping (SLAM) of robots [3].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] SLAM of Mobile Robot for Wireless Communication Based on Improved Particle Filter

Zhu

Wang

et al. 2021

Wireless Communications and Mobile Computing

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The mobile robot is moved by receiving instructions through wireless communication, and the particle filter is used to simultaneous localization and mapping. Aiming at the problem of the degradation of particle filter weights and loss of particle diversity, which leads to the decrease of filter accuracy, this paper uses the plant cell swarm algorithm to optimize the particle filter. First of all, combining the characteristics of plant cells that affect the growth rate of cells when the auxin content changes due to light stimulation realizes the optimization of the particles after importance sampling, so that they are concentrated in the high-likelihood area, and the problem of particle weight degradation is solved. Secondly, in the process of optimizing particle distribution, the auxin content of each particle is different, which makes the optimization effect on each particle different, so it effectively solves the problem of particle diversity loss. Finally, a simulation experiment is carried out. During the experiment, the robot moves by receiving control commands through wireless communication. The experimental results show that the algorithm effectively solves the problem of particle weight degradation and particle diversity loss and improves the filtering accuracy. The improved algorithm is verified in the simultaneous localization and mapping of the robot, which effectively improves the robot’s performance at the same time positioning accuracy. Compared with the classic algorithm, the robot positioning accuracy is increased by 49.2%. Moreover, the operational stability of the algorithm has also been improved after the improvement.

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Section: Introductionmentioning

confidence: 99%

[Retracted] SLAM of Mobile Robot for Wireless Communication Based on Improved Particle Filter

Zhu

Wang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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Transient dynamics in a nonequilibrium superdiffusive reaction-diffusion process: Nonequilibrium random search as a case study

et al. 2020

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Advertising Cycling to Manage Exclusivity Loss in Fashion Styles

Bruce

Krishnamoorthy

Prasad

2022

Operations Research

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How Should Fashion Brands Use Advertising to Increase Sales while Remaining Exclusive? Fashion consumption signals a consumer’s status to the broader population, so fashion brands and their consumers value exclusivity. Consequently, fashion advertising must balance sales generation with exclusivity loss. In this paper, we develop a model with these features of fashion and estimate it using advertising, price, and sales data for two styles of handbags and sunglasses. Our analysis provides insights for advertising budgeting and scheduling and finds that advertising optimally should decrease as the product increases in popularity and vice versa. This exerts a braking force on sales oscillations so that the fashion cycle decays as does the optimal advertising path. In addition to demonstrating how advertising cycling can impact a fashion firm’s profitability, we show how different styles of a fashion brand can cycle at different rates. By connecting advertising cycles to fashion cycles, we provide prescriptions for how fashion firms should manage different styles of the same brand.

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A particle filter approach to estimating target location using Brownian bridges

Cited by 5 publications

References 32 publications

[Retracted] SLAM of Mobile Robot for Wireless Communication Based on Improved Particle Filter

[Retracted] SLAM of Mobile Robot for Wireless Communication Based on Improved Particle Filter

Transient dynamics in a nonequilibrium superdiffusive reaction-diffusion process: Nonequilibrium random search as a case study

Advertising Cycling to Manage Exclusivity Loss in Fashion Styles

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