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Subdiffusive Source Sensing by a Regional Detection Method

1
School of Computer Science, China University of Geosciences, Wuhan 430074, China
2
School of Engineering (MESA-Lab), University of California, Merced, CA 95343, USA
*
Author to whom correspondence should be addressed.
Current address: No. 388 Lumo Road, Hongshan District, Wuhan 430074, China.
Sensors 2019, 19(16), 3504; https://doi.org/10.3390/s19163504
Received: 1 July 2019 / Revised: 1 August 2019 / Accepted: 1 August 2019 / Published: 10 August 2019
(This article belongs to the Special Issue Sensors In Target Detection)
PDF [389 KB, uploaded 19 August 2019]

Abstract

Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give some preliminary notions such as source, detection and regional spy sensors, etc. Secondly, we investigate the characterizations of regional strategic sensors and regional spy sensors. A regional detection approach on how to solve the source sensing problem of the considered system is then presented by using the Hilbert uniqueness method (HUM). This is to identify the unknown source only in a subregion of the whole domain, which is easier to be implemented and could save a lot of energy resources. Numerical examples are finally included to test our results.
Keywords: source sensing; time fractional diffusion systems; regional detection method; strategic sensors; spy sensors source sensing; time fractional diffusion systems; regional detection method; strategic sensors; spy sensors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Song, W.; Ge, F.; Chen, Y. Subdiffusive Source Sensing by a Regional Detection Method. Sensors 2019, 19, 3504.

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