Notice of Retraction Adaptive weight particle filter for nor-linear noisy signals

Kong, Xiangsheng; Sun, Jing

doi:10.1109/icnc.2011.6022285

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…As another approach, the issue of particle impoverishment can also be dealt with using the Fission Bootstrap Particle Filter [19][20][21]. In this filter, particles are broken into smaller samples and a new weight is attributed to each particle .…”

Section: -Introductionmentioning

confidence: 99%

Classifying the weights of particle filters in nonlinear systems

Sharifian

Rahimi

Pariz

2016

Communications in Nonlinear Science and Numerical Simulation

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Section: -Introductionmentioning

confidence: 99%

Classifying the weights of particle filters in nonlinear systems

Sharifian

Rahimi

Pariz

2016

Communications in Nonlinear Science and Numerical Simulation

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“…The fusion value of the measurement of a sensor is obtained by finding the corresponding weights. In this process, the AWF method is used [18][19][20][21][22].…”

Section: Adaptive Weighted Fusion Algorithmmentioning

confidence: 99%

Region-Dot Conversion Fusion Algorithm and Application

Fu¹,

Wang

Du³

et al. 2014

Mathematical Problems in Engineering

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The interior decoration materials and the new furniture using formaldehyde, ammonia, and other poisonous substances are known as the main sources of indoor air pollutions. However, it is still a big challenge to estimate accurately the overall air quality by using the current measuring tools. Accordingly, the region-dot fusion (RDF) algorithm is proposed to evaluate the air quality in this paper. For the conversion from a region to a dot, the region-dot function is firstly defined as the summation of the belief function and the weighted width of the belief interval. In the RDF algorithm, the belief intervals of the two sensors with the basic probability functions are calculated based on the measurements of formaldehyde sensor and ammonia sensor. Then, the belief intervals are converted to the specific values. After the computation of collision degree and combination, the pollution level represented by a belief interval with the maximum probability is selected as the outcome of fusion decision. Compared with the weighted fusion algorithm and D-S evidence reasoning method, it is experimentally proved that the RDF algorithm can improve the separability of the belief intervals of the belief functions. Also, the evidence collision degree is decreased dramatically.

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RP Conversion and Air Quality Evaluation

Wang

et al. 2014

2014 International Symposium on Computer, Consumer and Control

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One of the most important issues is indoor air quality in our environment-related. Formaldehyde, ammonia and other toxic substances in interior decoration materials and new furniture is known as a major source of indoor air pollution, indoor air gas into its volatile state. However, it is still using the current measurement tools to accurately estimate a challenge overall air quality. Therefore, the conversion from a region to a point (RP) is developed to evaluate the air quality in this article. Conversion from one area is defined as the first point value. It is the difference between the center and the belief that the threshold interval. In the RP conversion confident interval of the two sensors basic probability function is calculated based on the measurement value sensor of formaldehyde and ammonia sensor. Then, the interval is converted to a particular belief values. After the collision the extent and computing portfolio, the degree of contamination by the maximum probability and belief interval was chosen to represent the results of the integration decision. Compared with the Dempster-Shafer evidential reasoning conversion and weighted fusion method, the experiment proved that the conversion can improve the RP interval faith belief functions separability. In addition, the degree of conflict of evidence greatly reduced, so that the evidential reasoning to proceed.

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Notice of Retraction Adaptive weight particle filter for nor-linear noisy signals

Cited by 3 publications

References 4 publications

Classifying the weights of particle filters in nonlinear systems

Classifying the weights of particle filters in nonlinear systems

Region-Dot Conversion Fusion Algorithm and Application

RP Conversion and Air Quality Evaluation

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