Adaptive Kernel Kalman Filter

Mengwei Sun, Michael E. Davies, Ian Proudler, James R. Hopgood

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper presents a novel model-based Bayesian filter called the adaptive kernel Kalman filter (AKKF). The proposed filter approximates the arbitrary probability distribution functions (PDFs) of hidden states as empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). Specifically, particles are generated and updated in the data space to capture the properties of the dynamical system model, while the corresponding kernel weight vector and matrix associated with the particles' feature mappings are predicted and updated in the RKHS based on the kernel Kalman rule (KKR). We illustrate and confirm the advantages of our approach through simulation, offering detailed comparison with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF) algorithms.
Original languageEnglish
Title of host publication2021 Sensor Signal Processing for Defence Conference
PublisherIEEE Xplore
Publication statusE-pub ahead of print - 23 Sept 2021
EventInternational Conference in Sensor Signal Processing for Defence: : from Sensor to Decision - The Royal College of Physicians , Edinburgh, United Kingdom
Duration: 14 Sept 202115 Sept 2021


ConferenceInternational Conference in Sensor Signal Processing for Defence:
Abbreviated titleSSPD2021
Country/TerritoryUnited Kingdom
Internet address


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