Square root receding horizon information filters for nonlinear dynamic system models
MetadataShow full item record
New nonlinear filtering algorithms are designed based on a receding horizon strategy, i.e., a finite impulse response (FIR) structure, and square root information filtering to achieve high accuracy and good performance in empirical error covariance tests. The new nonlinear receding horizon filters reduce approximation errors in nonlinear filtering by considering a set of recent observations with non-informative initial conditions. By applying information filtering, we are able to manage the non-informative initial conditions, and thus propose the square root version of the algorithm as a means of retaining the positive definiteness of the error covariance. Based on the proposed strategy, we then implement known nonlinear filtering frameworks. Simulation results confirm that the new nonlinear receding horizon filters outperform existing nonlinear filters in well-known nonlinear examples. © 2012 IEEE.
Showing items related by title, author, creator and subject.
Song, I.; Kim, Du Yong; Shin, V.; Jeon, M. (2012)In this study, the authors consider the receding horizon filtering problem for discrete-time linear systems with state and observation time delays. Novel filtering algorithm is proposed based on the receding horizon ...
Suenaga, Hiroaki; Smith, A. (2011)We examine the volatility dynamics of three major petroleum commodities traded on the NYMEX: crude oil, unleaded gasoline, and heating oil. Using the partially overlapping time-series (POTS) framework of Smith (2005), we ...
Tran, The Truyen; Luo, W.; Phung, D.; Gupta, S.; Rana, S.; Kennedy, R.; Larkins, A.; Venkatesh, S. (2014)Background: Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity ...