Indoor physical field estimation from sparse sensor observations

Abstract

A thesis submitted to the Nanyang Technological University in partial fulfillment of the requirements for the degree of Doctor of Philosophy the minimum eigenspace is shown to be monotonically decreasing w.r.t. the worst case error variance (WCEV) of the estimated parameters. We select the sensing location whose observation vector has the maximum projection onto the minimum eigenspace of the current dual observation matrix. The proposed MPME is shown to be one of the most computationally efficient algorithms. Our Monte-Carlo simulations show that MPME outperforms the state of the art, especially when the number of available sensors is very limited. The MPME algorithm can be easily used for the indoor physical field reconstruction, but the current methods cannot because of their high storage requirement and high computation cost.