Model-based Sparse Information Recovery by Collaborative Sensor Management. Xiao, H., Bar-Shalom, Y., & Chen, X. In ASME Dynamic Systems and Control Conference, October, 2018. ![Model-based Sparse Information Recovery by Collaborative Sensor Management [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper 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). Assuming the fast signal is an autoregressive process with known parameters, 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.
@inproceedings{Hui_DSCC2018,
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). Assuming the fast signal is an autoregressive process with known parameters, 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},
Booktitle = {{ASME} Dynamic Systems and Control Conference},
Date-Modified = {2018-08-01 12:16:33 -0400},
Keyword = {sparse sensing, irregular sampling, collaborative sensing},
Month = {October},
Title = {Model-based Sparse Information Recovery by Collaborative Sensor Management},
Url = {https://www.researchgate.net/publication/325556564_Model-based_Sparse_Information_Recovery_by_Collaborative_Sensing},
Year = 2018,
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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). Assuming the fast signal is an autoregressive process with known parameters, 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\tBooktitle = {{ASME} Dynamic Systems and Control Conference},\n\tDate-Modified = {2018-08-01 12:16:33 -0400},\n\tKeyword = {sparse sensing, irregular sampling, collaborative sensing},\n\tMonth = {October},\n\tTitle = {Model-based Sparse Information Recovery by Collaborative Sensor Management},\n\tUrl = {https://www.researchgate.net/publication/325556564_Model-based_Sparse_Information_Recovery_by_Collaborative_Sensing},\n\tYear = 2018,\n\tBdsk-File-1 = 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