Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments

作者：Tronarp Filip^*; Garcia Fernandez Angel F; Sarkka Simo

来源：IEEE Signal Processing Letters, 2018, 25(3): 408-412.

DOI：10.1109/LSP.2018.2794767

• Iterative methods
• nonlinear/non-Gaussian systems
• state estimation
• statistical linear regression (SLR)

This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalizations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearization filter, and the iterated posterior linearization smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have a similar or superior performance to competing algorithms.

• 出版日期2018-3