Stochastic differential equations (3rd ed.): an introduction with applications
Stochastic differential equations (3rd ed.): an introduction with applications
Learning nonlinear dynamical systems using an EM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
Monte Carlo smoothing with application to audio signal enhancement
IEEE Transactions on Signal Processing
A survey of data smoothing for linear and nonlinear dynamic systems
Automatica (Journal of IFAC)
Gaussian filtering and smoothing for continuous-discrete dynamic systems
Signal Processing
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This article considers the application of the unscented transformation to approximate fixed-interval optimal smoothing of continuous-time non-linear stochastic dynamic systems. The proposed methodology can be applied to systems, where the dynamics can be modeled with non-linear stochastic differential equations and the noise corrupted measurements are obtained continuously or at discrete times. The smoothing algorithm is based on computing the continuous-time limit of the recently proposed unscented Rauch-Tung-Striebel smoother, which is an approximate optimal smoothing algorithm for discrete-time stochastic dynamic systems.