# Novel approach to nonlinear/non-Gaussian Bayesian state estimation

##### Citations

10,977 citations

### Cites methods or result from "Novel approach to nonlinear/non-Gau..."

...[14]. This sequential MC (SMC) approach is known variously as bootstrap filtering [ 17 ], the condensation algorithm [29], particle filtering [6], interacting particle approximations [10], [11], and survival of the fittest [24]....

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...1) Sampling Importance Resampling Filter: The SIR filter proposed in [ 17 ] is an MC method that can be applied to recursive Bayesian filtering problems....

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...We use and . This example has been analyzed before in many publications [5], [ 17 ], [25]....

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10,141 citations

7,045 citations

### Cites background from "Novel approach to nonlinear/non-Gau..."

...(The original bootstrap filter (Gordon 1993) resampled at every step, but this is suboptimal....

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6,458 citations

### Cites background or methods from "Novel approach to nonlinear/non-Gau..."

...This includes the multinomial sampling originally proposed in (Gordon et al. 1993), residual resampling (Higuchi 1997, Liu and Chen 1998) and minimum variance sampling (Carpenter, Clifford and Fearnhead 1999a, Crisan and Lyons 1999, Kitagawa 1996)....

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...In (Del Moral and Guionnet 1998b), the algorithm under study is the standard bootstrap filter (Gordon et al. 1993), which is based on an exploration of the space according to the natural dynamics of the system...

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...In particular, we show that the auxiliary particle filtering methods of (Pitt and Shephard: this volume) fall into the same general class of algorithms as the standard bootstrap filter of (Gordon et al. 1993)....

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...The introduction of this key step in (Gordon et al. 1993) led to the first operational SMC method....

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...It is possible to carry out sample regeneration using a mixture approximation (Gordon et al. 1993), see also Liu and West (2001: this volume)....

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5,749 citations

### Cites background from "Novel approach to nonlinear/non-Gau..."

...…modelling (Blake et al., 1993, 1995), object dynamics are modelled as a second order process, conveniently represented in discrete timet as a second order linear difference equation: xt − x̄ = A(xt−1 − x̄)+ Bwt (10) wherewt are independent vectors of independent standard normal variables, the…...

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##### References

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