Multimodal sensor selection for multiple spatial field reconstruction
- Authors: Nguyen, Linh , Thiyagarajan, Karthick , Ulapane, Nalika , Kodagoda, Sarath
- Date: 2021
- Type: Text , Conference paper
- Relation: 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 p. 1181-1186
- Full Text: false
- Reviewed:
- Description: 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. © 2021 IEEE.
Multivariate versus univariate sensor selection for spatial field estimation
- Authors: Nguyen, Linh , Thiyagarajan, Karthick , Ulapane, Nalika , Kodagoda, Sarath
- Date: 2021
- Type: Text , Conference paper
- Relation: 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 p. 1187-1192
- Full Text: false
- Reviewed:
- Description: The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Matérn cross-covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice. © 2021 IEEE.