The continuous-discrete time feedback particle filter

In this paper, the feedback particle filter (FPF) algorithm is introduced for the continuous-discrete time nonlinear filtering problem. As with the continuous-time FPF, the continuous-discrete time algorithm i) admits an innovation error-based feedback control structure, and ii) requires a solution of an Euler-Lagrange boundary value problem (E-L BVP). These solutions are described in closed-form for the linear Gaussian filtering problem. For the general nonlinear non-Gaussian case, an algorithm is described to obtain an approximate solution of the E-L BVP. Comparisons are also made to the particle flow filter algorithm introduced by Daum and Huang.

[1]  Rui Ma,et al.  Generalizing the Posterior Matching Scheme to higher dimensions via optimal transportation , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[2]  Fred Daum,et al.  Particle flow for nonlinear filters with log-homotopy , 2008, SPIE Defense + Commercial Sensing.

[3]  Florian Nadel,et al.  Stochastic Processes And Filtering Theory , 2016 .

[4]  Fred Daum,et al.  Nonlinear filters with particle flow induced by log-homotopy , 2009, Defense + Commercial Sensing.

[5]  Sean P. Meyn,et al.  Multivariable feedback particle filter , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[6]  D. Crisan,et al.  Fundamentals of Stochastic Filtering , 2008 .

[7]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[8]  S. Reich A dynamical systems framework for intermittent data assimilation , 2011 .

[9]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[10]  Sean P. Meyn,et al.  A mean-field control-oriented approach to particle filtering , 2011, Proceedings of the 2011 American Control Conference.

[11]  Sean P. Meyn,et al.  Feedback particle filter with mean-field coupling , 2011, IEEE Conference on Decision and Control and European Control Conference.

[12]  Sanjoy K. Mitter,et al.  A Variational Approach to Nonlinear Estimation , 2003, SIAM J. Control. Optim..

[13]  Fred Daum,et al.  Exact particle flow for nonlinear filters , 2010, Defense + Commercial Sensing.

[14]  Fred Daum,et al.  Nonlinear filters with log-homotopy , 2007, SPIE Optical Engineering + Applications.

[15]  D. Crisan,et al.  Approximate McKean–Vlasov representations for a class of SPDEs , 2005, math/0510668.

[16]  Sean P. Meyn,et al.  Feedback Particle Filter , 2013, IEEE Transactions on Automatic Control.