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Article summary:
1. This paper presents a method for reliably quantifying and bounding estimation errors in linear dynamic systems with uncertain time-correlated sensor errors.
2. The paper develops stationary and non-stationary Gauss-Markov process models to ensure tight upper bounds on the estimation error variance.
3. Frequency-domain analysis is used to derive both continuous and discrete time domain models, which outperform existing models in the literature.
Article analysis:
The article provides a reliable and trustworthy approach for quantifying and bounding estimation errors in linear dynamic systems with uncertain time-correlated sensor errors. The paper develops stationary and non-stationary Gauss-Markov process models to ensure tight upper bounds on the estimation error variance, using frequency-domain analysis to derive both continuous and discrete time domain models that outperform existing models in the literature. The article does not appear to be biased or one-sided, as it provides an unbiased approach for estimating errors in linear dynamic systems. Furthermore, the article does not appear to contain any unsupported claims or missing points of consideration, as it provides a detailed explanation of the proposed methodologies and their implications for safety-critical navigation applications. Additionally, there is no evidence of promotional content or partiality within the article, as it focuses solely on providing an unbiased approach for estimating errors in linear dynamic systems. Finally, possible risks are noted throughout the article, as it emphasizes the importance of accurately quantifying and bounding estimation errors in order to ensure safe navigation applications. In conclusion, this article appears to be reliable and trustworthy due its unbiased approach and emphasis on accurately quantifying and bounding estimation errors.