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]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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