A. Doucet and C. Andrieu. Particle filtering for partially observed Gaussian state space models. Technical Report CUED/FINFENG /TR393, Department of Engineering, University of Cambridge CB2 1PZ Cambridge, September 2000. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
A Sequential Monte Carlo Filter for Joint Linear/Nonlinear State.. - Iltis (Correct)
....conditional Kalman filtering appears in [16] but in the context of unknown time invariant parameters. The method of [16] could be applied to DS CDMA if the code delays were time invariant, but this assumption is not realistic. The SMC KF can also be viewed as a Rao Blackwellized filter following [17], in which Kalman filters are updated condi tioned on samples generated by SIR or SIS. However, the method of [17] is still not directly applicable to the DS CDMA channel estimation problem, since the measurement function is assumed independent of the conditionally linear process. Finally, the ....
....of [16] could be applied to DS CDMA if the code delays were time invariant, but this assumption is not realistic. The SMC KF can also be viewed as a Rao Blackwellized filter following [17] in which Kalman filters are updated condi tioned on samples generated by SIR or SIS. However, the method of [17] is still not directly applicable to the DS CDMA channel estimation problem, since the measurement function is assumed independent of the conditionally linear process. Finally, the sampling method in the SMC KF has similarities to the Gaussian Sum Particle Filter (GSPF) of [18] in which the sam ....
C. Andrieu and A. Doucet, "Particle filtering for partially observed Gaussian state space models." To appear in the Journal of the Royal Statistical Society, Series B, 2002.
Particle Filters for Positioning, Navigation and Tracking - Gustafsson, Gunnarsson, .. (2002) (1 citation) (Correct)
....applying an optimal filter to an approximate model. A well known problem with the particle filter is that its performance degrades quickly when the dimension of the state dimension increases. A key theoretical contribution here is to apply marginalization techniques [36] adopted and extended from [12], leading to that the Kalman filter can be used to estimate (or eliminate) all position derivatives, and the particle filter is applied to the part of the state vector containing only the position. Thus, the particle filter dimension is only 2 or 3, depending on the application, and this is the ....
....The e t and x pf 0 can have arbitrarily given distributions. As the indices indicate, the Kalman filter will be applied to one part and the particle filter for the other part of the state vector. For a derivation of the algorithm, see the Appendix or [36] A similar result is presented in [12] for the general case, where the state space equation is linear and Gaussian, but one observes a z t instead of y t ,wheretherelation p(z t y t ) is known. An algorithmically similar approach is given in [5] as an approximate solution to an altitude o# set in terrain navigation. The result ....
[Article contains additional citation context not shown here]
A. Doucet and C. Andrieu. Particle filtering for partially observed Gaussian state space models. Technical Report CUED/FINFENG /TR393, Department of Engineering, University of Cambridge CB2 1PZ Cambridge, September 2000.
Particle filter for tracking linear Gaussian target with.. - Augustine Ooi Arnaud (2003) Self-citation (Doucet) (Correct)
No context found.
C. Andrieu and A. Doucet, "Particle filtering for partially observed Gaussian state space models" J. Royal Statist. Soc. B (Methodological), vol. 64, no.4, pp. 827-836, 2002
Particle Filters for Positioning, Navigation and Tracking - Gustafsson, Gunnarsson, .. (2002) (1 citation) (Correct)
No context found.
A. Doucet and C. Andrieu. Particle filtering for partially observed Gaussian state space models. Technical Report CUED/FINFENG /TR393, Department of Engineering, University of Cambridge CB2 1PZ Cambridge, September 2000.
Mobility Tracking in Cellular Networks with.. - Mihaylova, Bull.. (2005) (Correct)
No context found.
C. Andrieu and A. Doucet. Particle filtering for partially observed Gaussian state-space models. J. of R. Statist. Soc. B., 64(4):827--836, 2002.
3D Tracking of Morphable Objects Using Conditionally .. - Marks, Hershey.. (2004) (Correct)
No context found.
A. Doucet and C. Andrieu, "Particle filtering for partially observed gaussian state space models," J. R. Statist. Soc. B, vol. 64, pp. 827-- 838, 2002.
Marginalized Particle Filters for Mixed Linear.. - Schön, Gustafsson.. (Correct)
No context found.
C. Andrieu and A. Doucet. Particle filtering for partially observed Gaussian state space models. Journal of the Royal Statistical Society, 64(4):827--836, 2002.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC