Adaptive Kernel Kalman Filter
| Autor: | Ian K. Proudler, James R. Hopgood, Mengwei Sun, Michael Davies |
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| Rok vydání: | 2023 |
| Předmět: |
Signal Processing (eess.SP)
Computer science Hilbert space Kalman filter Adaptive kernel Kalman filter non-linear dynamic systems sequential Bayesian filters kernel Kalman rule Adaptive filter kernel mean embedding symbols.namesake Filter (video) Kernel (statistics) Signal Processing FOS: Electrical engineering electronic engineering information engineering symbols Probability distribution Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering Particle filter Algorithm Reproducing kernel Hilbert space |
| Zdroj: | Sun, M, Davies, M E, Proudler, I & Hopgood, J R 2023, ' Adaptive Kernel Kalman Filter ', IEEE Transactions on Signal Processing, vol. 71, pp. 713-726 . https://doi.org/10.1109/TSP.2023.3250829 |
| ISSN: | 1941-0476 1053-587X |
| DOI: | 10.1109/tsp.2023.3250829 |
| Popis: | Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs, some particles, in the data space, are used to capture the properties of the dynamical system model. Specifically, particles are generated and updated in the data space, while the corresponding kernel weight mean vector and covariance matrix associated with the feature mappings of the particles are predicted and updated in the RKHSs based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced particle numbers, by comparing with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF). For example, compared with the GPF, the proposed approach provides around 5% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles. Comment: The manuscript has been accepted for publication as a regular paper in the IEEE Transactions on Signal Processing |
| Databáze: | OpenAIRE |
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