Building Interior Structures Sensing Based on Bayesian Approach Exploiting Structural Continuity

Abstract

Through-the-wall building interior structure sensing has been greatly serving in various applications, including search-and-rescue operations. However, most existing methods exhibit limitations in imaging the walls and corners with good continuity and recognizable features. In this article, we consider imaging of the building interior structures by extracting the major building elements with structural continuity. Specifically, the signals from a complex building are first modeled as the superposition responses from discrete canonical scatterers, such as planar walls and wall corners. Then, a structural variational Bayesian method is designed to detect and extract these critical structures. This method improves the 1-D continuity of the walls and the 2-D continuity of the corners through a Bayesian hierarchical probabilistic model. Moreover, we incorporate the generalized approximate message-passing technique into the variational expectation maximization method to efficiently estimate the walls and corners simultaneously. Results from both simulated and real data validate the effectiveness of the proposed method in accurately extracting walls and corners with improved continuity, thereby enabling a comprehensive building structure.