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 production
abstract   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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