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Schön, T.B., Gustafsson, F. and Nordlund, P.-J. (2005) Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models. IEEE Transactions on Signal Processing, 53, 2279-2289.
https://doi.org/10.1109/TSP.2005.849151

has been cited by the following article:

• TITLE: Hamiltonian Servo: Control and Estimation of a Large Team of Autonomous Robotic Vehicles

  AUTHORS: Vladimir Ivancevic, Peyam Pourbeik

  KEYWORDS: Team of UGVs, Kalman Servo, Hamiltonian Control, Bayesian Estimation

  JOURNAL NAME: Intelligent Control and Automation, Vol.8 No.4, November 30, 2017

  ABSTRACT: This paper proposes a novel Hamiltonian servo system, a combined modeling framework for control and estimation of a large team/fleet of autonomous robotic vehicles. The Hamiltonian servo framework represents high-dimensional, nonlinear and non-Gaussian generalization of the classical Kalman servo system. After defining the Kalman servo as a motivation, we define the affine Hamiltonian neural network for adaptive nonlinear control of a team of UGVs in continuous time. We then define a high-dimensional Bayesian particle filter for estimation of a team of UGVs in discrete time. Finally, we formulate a hybrid Hamiltonian servo system by combining the continuous-time control and the discrete-time estimation into a coherent framework that works like a predictor-corrector system.