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The method employs the signal strength as input, and has two steps: an initial estimation of the position of the nodes is obtained centrally by one robot and is based on particle filtering.
This renders the scheme much simpler and more versatile than existing approaches, such as those based on particle filtering.
The tracking architecture is based on particle filtering methods where each particle represents the 3D state of the object, rather than its state in the image, therefore overcoming the nonlinearity caused by the projection model.
The tracking step is based on particle filtering.
Some approaches to deal with non-linearities are based on particle filtering principles [3].
The fusion of audio and vision is based on particle filtering.
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The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load.
Tracking by association is based on particle filter.
In this paper, a structural damage identification method based on particle filters, will therefore be introduced.
Nowadays, around 50 papers dealing with prognostics based on particle filters can be found in the literature.
The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function.
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