@Author: WANG Shixiong (Email: s.wang@u.nus.edu; wsx.gugo@gmail.com)
@Affiliate: Department of Industrial Systems Engineering and Management, National University of Singapore
@Date: First Uploaded July 9, 2022
MATLAB Version: 2019B or later
Online supplementary materials of the paper titled
Distributionally Robust State Estimation for Nonlinear Systems
Published in the IEEE Transactions on Signal Processing (DOI: 10.1109/TSP.2022.3203225)
By Shixiong Wang
From the Department of Industrial Systems Engineering and Management, National University of Singapore
Codes
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[1] Time Series Example
- The folder contains all the source codes for the time series example.
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[2] Target Tracking Example
- The folder contains all the source codes for the target tracking example.
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[3] Maximum Entropy Distributions
- The folder contains all the source codes for the online supplementary materials. Specifically, the codes can generate Figures 7-9 and Tables IV-VI.
See Also
Distributionally Robust State Estimation for Linear Systems Subject to Uncertainty and Outlier
Robust State Estimation for Linear Systems Under Distributional Uncertainty
Warrant
Files/codes here are allowed to be edited, distributed, and re-used for any academic/teaching purpose without any warranty. However, you are strongly suggested sharing your codes with publics if you are planning to use codes here. Let's work together to guarantee the reproducibilty of experiments and the verifiability of claims in publications. We believe that this is meaningful to facilitate future research of the signal processing community.
Disclaimer
Note that the mentioned reproducibilty and verifiability do not necessarily guarantee the (absolute) correctness of academic claims in a scitific publication. Future research may deny or modify or improve the philosophies, methods, models, and/or claims conveyed in this article. But readers should not try to "find bones from an egg", and codes here are just for their reference, not for their unfriendly criticism. Of course, the authors are open to learn and friendly comments are always welcomed.