Estimation-algorithms includes MATLAB functions for the EKF, UKF, Particle Filter, and their computationally efficient variants.
Two newly developed UKFs:
- Single Propagation Unscented Kalman Filter (SPUKF)
- Extrapolated Single Propagation Unscented Kalman Filter (ESPUKF)
and a new variant of the Particle Filter: Extrapolated Single Propagation Particle Filter (ESP-PF)
These new algorithms use the Single Propagation Technique to significantly reduce the processing time of the UKF and the Particle Filter.
To use EKF, UKF, particle_filter, SPUKF, ESPUKF and ESPPF functions, add Estimation-algorithms folder in Set Path.
Further details about the functions can be found using help command, for example: execute help EKF to see details about the EKF function
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S. K. Biswas, L. Qiao and A. G. Dempster, "A Novel a Priori State Computation Strategy for the Unscented Kalman Filter to Improve Computational Efficiency," in IEEE Transactions on Automatic Control, vol. 62, no. 4, pp. 1852-1864, April 2017. doi: 10.1109/TAC.2016.2599291
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S. K. Biswas and A. G. Dempster, “Approximating Sample State Vectors Using the ESPT for Computationally Efficient Particle Filtering,” IEEE Transactions on Signal Processing, vol. 67, no. 7, pp. 1918–1928, Apr. 2019.
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S. K. Biswas, L. Qiao, and A. G. Dempster, “A quantified approach of predicting suitability of using the Unscented Kalman Filter in a non-linear application,” Automatica, vol. 122, p. 109241, Dec. 2020, doi: 10.1016/j.automatica.2020.109241.
S. K. Biswas, L. Qiao and A. G. Dempster, "A Novel a Priori State Computation Strategy for the Unscented Kalman Filter to Improve Computational Efficiency," in IEEE Transactions on Automatic Control, vol. 62, no. 4, pp. 1852-1864, April 2017. doi: 10.1109/TAC.2016.2599291
S. K. Biswas and A. G. Dempster, “Approximating Sample State Vectors Using the ESPT for Computationally Efficient Particle Filtering,” IEEE Transactions on Signal Processing, vol. 67, no. 7, pp. 1918–1928, Apr. 2019.