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A Novel Adaptive Variational Bayesian Filter for Underwater Localization System with Unknown Noise Statistics
- Citation Author(s):
-
HaoqianHuang
Hohai University - Submitted by:
- Haoqian Huang
- Last updated:
- Mon, 08/07/2023 - 23:54
- DOI:
- 10.21227/23a4-tb38
- Data Format:
- License:
33 Views- Categories:
- Keywords:
Abstract
These data is state estimation accuracy of the proposed algorithm When the adjust factor is 1
These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.
The data are explained as follows:
rmse_ckf_1,rmse_ukf_1,rmse_vakf_1,rmse_vakfpr_1,rmse_okf_1 are the position accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
rmse_ckf_2,rmse_ukf_2,rmse_vakf_2,rmse_vakfpr_2,rmse_okf_2 are the velocity accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
Through this set of data, it can be seen that the proposed algorithm is effective in complex noise situations. The proposed algorithm will have higher state estimation accuracy, which verifies its performance.
These data is state estimation accuracy of the proposed algorithm When the adjust factor is 1
These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.
The data are explained as follows:
rmse_ckf_1,rmse_ukf_1,rmse_vakf_1,rmse_vakfpr_1,rmse_okf_1 are the position accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
rmse_ckf_2,rmse_ukf_2,rmse_vakf_2,rmse_vakfpr_2,rmse_okf_2 are the velocity accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
Through this set of data, it can be seen that the proposed algorithm is effective in complex noise situations. The proposed algorithm will have higher state estimation accuracy, which verifies its performance.
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