Observability analysis for soil moisture estimation⁎. Issue 2 (December 2017)

• Record Type: Journal Article
• Authors: Nahar, Jannatun; Liu, Jinfeng; Shah, Sirish L.
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 2(2017)
• Page Start: 110
• Page End: 114
• Publication Date: 2017-12
• Subjects: Observability -- state estimation -- extended Kalman filter -- agriculture -- nonlinear systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.12.020
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The knowledge of soil moisture is important in studying climatology, earth science and most importantly irrigation decision support systems, but is often hard to determine since it is not possible to use critical measurements including moisture sensors all over the entire agricultural grid sector. As a result, soil moisture at unmeasured region needs to be estimated, which can be done using state estimators such as Kalman based estimators. The model that is used to represent water transfer between atmosphere, plant and soil, also known as agro-hydrological model, is highly nonlinear. Since 'strong' rather than 'weak' observability of the system ensures better performance of Kalman based estimators to develop a reliable soil moisture estimation algorithm, the main objective of this study is to discuss observability analysis of this nonlinear agro-hydrological system. The study was performed using synthetic data. The extended Kalman filter (EKF) was chosen as the state estimator. As would be expected, the results show that the EKF performance is better in cases where the system is 'strongly' observable.