Double-Layer Cubature Kalman Filter for Nonlinear Estimation
Feng Yang1,2,3, Yujuan Luo4,5, Litao Zheng6,7
1School of Automation, Northwestern Polytecnical University, Xi'an 710129, China. yangfeng@nwpu.edu.cn.
Sensors (Basel, Switzerland)
|March 1, 2019
Summary
A new double-layer cubature Kalman filter (DLCKF) improves state estimation accuracy and reduces computational load in nonlinear systems, outperforming existing filtering methods.
Area of Science:
- Engineering
- Computer Science
- Mathematics
Background:
- Cubature Kalman filter (CKF) struggles with strongly nonlinear systems.
- Cubature particle filter (CPF) faces high computational complexity due to stochastic sampling.
Purpose of the Study:
- To introduce a novel filtering algorithm, the double-layer cubature Kalman filter (DLCKF).
- To address the limitations of existing filters in nonlinear system state estimation.
Main Methods:
- The DLCKF represents prior distributions using weighted deterministic sampling points.
- An inner CKF updates each deterministic sampling point.
- An outer CKF mechanism provides final state estimations.
Main Results:
- The DLCKF demonstrates high estimation accuracy.
- The DLCKF exhibits low computational complexity compared to other algorithms.
- Simulations validate the proposed algorithm's performance.
Conclusions:
- The DLCKF offers a superior balance of accuracy and computational efficiency for nonlinear systems.
- This novel approach enhances state estimation in challenging dynamic environments.
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