@article{JMiguez20,

title = {Nudging the particle Filter},

author = {O. D. Akyildiz and Joaqu\'{i}n Miguez},

doi = {https://doi.org/10.1007/s11222-019-09884-y},

year  = {2019},

date = {2019-07-13},

journal = {Statistics and Computing},

volume = {30},

pages = {305-330},

keywords = {approximation errors, Data assimilation, Model errors, Nudging, Particle filtering, Robust filtering},

pubstate = {published},

tppubtype = {article}

}

@article{10.3150/17-BEJ954,

title = {Nested particle filters for online parameter estimation in discrete-time state-space Markov models},

author = {Dan Crisan and Joaqu\'{i}n M\'{i}guez},

url = {https://doi.org/10.3150/17-BEJ954},

doi = {10.3150/17-BEJ954},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Bernoulli},

volume = {24},

number = {4A},

pages = {3039 -- 3086},

publisher = {Bernoulli Society for Mathematical Statistics and Probability},

keywords = {error bounds, model inference, Monte Carlo, Parameter estimation, Particle filtering, recursive algorithms, State space models},

pubstate = {published},

tppubtype = {article}

}

@article{Miguez2016,

title = {A Proof of Uniform Convergence Over Time for a Distributed Particle Filter},

author = {Joaqu\'{i}n M\'{i}guez and Manuel A V\'{a}zquez},

url = {http://www.sciencedirect.com/science/article/pii/S0165168415004077},

doi = {10.1016/j.sigpro.2015.11.015},

issn = {01651684},

year  = {2016},

date = {2016-05-01},

journal = {Signal Processing},

volume = {122},

pages = {152--163},

abstract = {Distributed signal processing algorithms have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters (PFs). However, most distributed PFs involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard PFs do not hold for their distributed counterparts. In this paper, we analyze a distributed PF based on the non-proportional weight-allocation scheme of Bolic et al (2005) and prove rigorously that, under certain stability assumptions, its asymptotic convergence is guaranteed uniformly over time, in such a way that approximation errors can be kept bounded with a fixed computational budget. To illustrate the theoretical findings, we carry out computer simulations for a target tracking problem. The numerical results show that the distributed PF has a negligible performance loss (compared to a centralized filter) for this problem and enable us to empirically validate the key assumptions of the analysis.},

keywords = {Convergence analysis, Distributed algorithms, Journal, Parallelization, Particle filtering, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}