Stochastic output delay identification of discrete-time Gaussian systems
Automatica, ISSN: 0005-1098, Vol: 109, Page: 108499
2019
- 21Citations
- 10Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In this paper we propose a solution to the problems of detecting a generally correlated stochastic output delay sequence of a linear system driven by Gaussian noise. This is the model for uncertain observations resulting from losses in the propagation channel due to fading phenomena or packet dropouts that is common in wireless sensor networks, networked control systems, or remote sensing applications. The solution we propose consists of a nonlinear detector which identifies online the stochastic delay sequence. The solution provided is optimal in the sense that minimizes the probability of error of the delay detector. Finally, a filtering stage fed with the information given by the detector can follow to estimate the state of the system. Numerical simulations show good performance of the proposed method.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0005109819303607; http://dx.doi.org/10.1016/j.automatica.2019.108499; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85069879832&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0005109819303607; https://api.elsevier.com/content/article/PII:S0005109819303607?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0005109819303607?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.automatica.2019.108499
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know