Multimodal Sensor Selection for Multiple Spatial Field Reconstruction
- Resource Type
- Conference
- Authors
- Nguyen, Linh; Thiyagarajan, Karthick; Ulapane, Nalika; Kodagoda, Sarath
- Source
- 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Industrial Electronics and Applications (ICIEA), 2021 IEEE 16th Conference on. :1181-1186 Aug, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Industrial electronics
Electric potential
Uncertainty
Multimodal sensors
Conferences
Gaussian processes
Predictive models
Multimodal sensing
sensor selection
multivariate
multiple spatial fields
multivariate Gaussian process
multivariable
- Language
- ISSN
- 2158-2297
The paper addresses the multimodal sensor selection problem where selected colocated sensor nodes are employed to effectively monitor and efficiently predict multiple spatial random fields. It is first proposed to exploit multivariate Gaussian processes (MGP) to model multiple spatial phenomena jointly. By the use of the Matérn cross-covariance function, cross-covariance matrices in the MGP model are sufficiently positive semi-definite, concomitantly providing efficient prediction of all multivariate processes at unmeasured locations. The multimodal sensor selection problem is then formulated and solved by an approximate algorithm with an aim to select the most informative sensor nodes so that prediction uncertainties at all the fields are minimized. The proposed approach was validated in the real-life experiments with promising results.