A Collaborative Sensing and Model-based Realtime Recovery of Fast Temporal Flows from Sparse Measurements. Xiao, H., Bar-Shalom, Y., & Chen, X. IEEE Transactions on Industrial Electronics, 2019. in productionabstract bibtex This paper considers the real-time recovery of a fast discrete signal (e.g., updated every T seconds) by using sparsely sampled sensor measurements whose sampling intervals are much larger than T (e.g., MT and NT, where M and N are integers). When the fast signal is an autoregressive moving average process, we propose an online information recovery algorithm that reconstructs the missing, fast time series by a complementary modulation of the sensor speeds MT and NT, and by a model-based fusion of the sparsely collected data. We provide the collaborative sensing design, parametric analysis, existence of solutions, and optimization of the algorithm. Application to a closed-loop disturbance rejection problem reveals the feasibility to reject fast disturbance signals fully with only slow sensors in real time, and in particular, the rejection of narrow-band disturbances whose frequencies are much higher than the Nyquist frequencies of the sensors.
@article{Hui_CS_J2018,
Abstract = {This paper considers the real-time recovery of a fast discrete signal (e.g., updated every T seconds) by using sparsely sampled sensor measurements whose sampling intervals are much larger than T (e.g., MT and NT, where M and N are integers). When the fast signal is an autoregressive moving average process, we propose an online information recovery algorithm that reconstructs the missing, fast time series by a complementary modulation of the sensor speeds MT and NT, and by a model-based fusion of the sparsely collected data. We provide the collaborative sensing design, parametric analysis, existence of solutions, and optimization of the algorithm. Application to a closed-loop disturbance rejection problem reveals the feasibility to reject fast disturbance signals fully with only slow sensors in real time, and in particular, the rejection of narrow-band disturbances whose frequencies are much higher than the Nyquist frequencies of the sensors.},
Author = {Hui Xiao and Yaakov Bar-Shalom and Xu Chen},
Date-Added = {2018-04-10 02:43:36 +0000},
Date-Modified = {2019-07-14 21:50:35 -0400},
Journal = {IEEE Transactions on Industrial Electronics},
Keyword = {sparse sensing, irregular sampling, collaborative sensing},
Note = {in production},
Rating = {5},
Title = {A Collaborative Sensing and Model-based Realtime Recovery of Fast Temporal Flows from Sparse Measurements},
Year = 2019,
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Application to a closed-loop disturbance rejection problem reveals the feasibility to reject fast disturbance signals fully with only slow sensors in real time, and in particular, the rejection of narrow-band disturbances whose frequencies are much higher than the Nyquist frequencies of the sensors.","author":[{"firstnames":["Hui"],"propositions":[],"lastnames":["Xiao"],"suffixes":[]},{"firstnames":["Yaakov"],"propositions":[],"lastnames":["Bar-Shalom"],"suffixes":[]},{"firstnames":["Xu"],"propositions":[],"lastnames":["Chen"],"suffixes":[]}],"date-added":"2018-04-10 02:43:36 +0000","date-modified":"2019-07-14 21:50:35 -0400","journal":"IEEE Transactions on Industrial Electronics","keyword":["sparse sensing"," irregular sampling"," collaborative sensing"],"note":"in production","rating":"5","title":"A Collaborative Sensing and Model-based Realtime Recovery of Fast Temporal Flows from Sparse 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= {This paper considers the real-time recovery of a fast discrete signal (e.g., updated every T seconds) by using sparsely sampled sensor measurements whose sampling intervals are much larger than T (e.g., MT and NT, where M and N are integers). When the fast signal is an autoregressive moving average process, we propose an online information recovery algorithm that reconstructs the missing, fast time series by a complementary modulation of the sensor speeds MT and NT, and by a model-based fusion of the sparsely collected data. We provide the collaborative sensing design, parametric analysis, existence of solutions, and optimization of the algorithm. Application to a closed-loop disturbance rejection problem reveals the feasibility to reject fast disturbance signals fully with only slow sensors in real time, and in particular, the rejection of narrow-band disturbances whose frequencies are much higher than the Nyquist frequencies of the sensors.},\n\tAuthor = {Hui Xiao and Yaakov Bar-Shalom and Xu Chen},\n\tDate-Added = {2018-04-10 02:43:36 +0000},\n\tDate-Modified = {2019-07-14 21:50:35 -0400},\n\tJournal = {IEEE Transactions on Industrial Electronics},\n\tKeyword = {sparse sensing, irregular sampling, collaborative sensing},\n\tNote = {in production},\n\tRating = {5},\n\tTitle = {A Collaborative Sensing and Model-based Realtime Recovery of Fast Temporal Flows from Sparse Measurements},\n\tYear = 2019,\n\tBdsk-File-1 = 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Y.","Chen, X."],"key":"Hui_CS_J2018","id":"Hui_CS_J2018","bibbaseid":"xiao-barshalom-chen-acollaborativesensingandmodelbasedrealtimerecoveryoffasttemporalflowsfromsparsemeasurements-2019","role":"author","urls":{},"metadata":{"authorlinks":{"chen, x":"https://xchen-me.github.io/"}}},"bibtype":"article","biburl":"https://xchen-me.github.io/bibtex_XuChen_UConn.bib","creationDate":"2021-03-19T05:11:49.023Z","downloads":0,"keywords":["sparse sensing"," irregular sampling"," collaborative sensing"],"search_terms":["collaborative","sensing","model","based","realtime","recovery","fast","temporal","flows","sparse","measurements","xiao","bar-shalom","chen"],"title":"A Collaborative Sensing and Model-based Realtime Recovery of Fast Temporal Flows from Sparse Measurements","year":2019,"dataSources":["hHZrcJyhveBr8MCqA"]}