@Author: WANG Shixiong (Email: [email protected]; [email protected])

@Affiliate: Department of Industrial SystemsEngineering and Management, National University of Singapore

@Date: First Uploaded on 3 Nov 2020; Last Updated on 22 Sep 2021

MATLAB Version: 2019B or later

DRSE: Distributionally Robust State Estimation

Online supplementary materials of the paper titled

Robust State Estimation for Linear Systems Under Distributional Uncertainty

Published in the IEEE Transactions on Signal Processing (DOI: 10.1109/TSP.2021.3118540)

By Shixiong Wang, Zhongming Wu, and Andrew Lim

From the Department of Industrial Systems Engineering and Management, National University of Singapore (S. Wang and A. Lim); and the School of Management Science and Engineering, Nanjing University of Information Science and Technology (Z. Wu).

[1] Data

• This folder contains the raw data to reproduce Figures and Tables.

[2] Source Codes

• The folder "[1] Main" contains the codes to generate Figures 1-3 and Tables 1-5 in the main body of the paper.

  • The folder "[1] Source Code - True Structure" contains the codes to generate Figure 1 and Tables 1-4.

  • The folder "[2] Source Code - Fake Structure" contains the codes to generate Figure 2 and Tables 5.

  • The folder "[3] Source Code - Gamma_2" contains the codes to generate Figure 3.

• The folder "[2] Supplementary" contains the codes to generate Figures 1, 2 in the oneline supplementary materials.

  • The folder "[1] Source Code - Uncertainty - 4)" contains the codes to generate Figure 1.

  • The folder "[2] Source Code - Uncertainty - 5)" contains the codes to generate Figure 2.

See Also

Distributionally Robust State Estimation for Linear Systems Subject to Uncertainty and Outlier

Warranty

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.