Maximum Correntropy Square Root Gauss-Hermite Quadrature Information Filter for Multiple Sensor Estimation
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s00034-023-02408-0.pdf
Reference25 articles.
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3. I. Arasaratnam, S. Haykin, Square-root quadrature Kalman filtering. IEEE Trans. Signal Process. 56(6), 2589–2593 (2008). https://doi.org/10.1109/TSP.2007.914964
4. M.S. Arulampalam, S. Maskell, N. Gordon et al., A tutorial on particle filters for online nonlinear/non-Gaussian bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002). https://doi.org/10.1109/78.978374
5. G. Bernardi, T.V. Waterschoot, J. Wouters et al., Adaptive feedback cancellation using a partitioned-block frequency-domain Kalman filter approach with PEM-based signal prewhitening. IEEEACM Trans. Audio Speech Lang. Process.. (2017). https://doi.org/10.1109/TASLP.2017.2716188
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1. Improved maximum correntropy cubature Kalman and information filters with application to target tracking under non‐Gaussian noise;International Journal of Adaptive Control and Signal Processing;2024-01-17
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