Tomography-Inspired Infrared Indoor Localization and Tracking Based on Optical Shadow Imaging

Abstract

Indoor positioning systems represent a fundamental tool for enhancing efficiency, safety, and user experience in a wide range of applications requiring location awareness. Specifically, privacy concerns and the need for unobtrusive monitoring in sensitive environments, such as eldercare or child safety, motivate the development of nonvisual indoor tracking systems. This article proposes an indoor infrared (IR) tomographic framework that estimates and tracks human presence by exploiting signal occlusions between spatially distributed light-emitting diode (LED)–photodetector (PD) links. By modeling IR shadowing as a function of voxel-level occupancy, the system reconstructs 3-D spatial maps without relying on cameras or identity-revealing sensors. A regularized least-squares approach is then employed to obtain robust static occupancy estimates. Furthermore, the proposed framework is extended to dynamic scenarios, where Kalman filtering is employed to achieve reliable tracking of subjects from the reconstructed spatial centroids. Numerical results demonstrate subdecimeter localization accuracy, scalability with sensor density, and improved robustness with respect to representative indoor positioning benchmarks. Moreover, a small-scale hardware prototype experimentally validates the core static tomographic reconstruction stage using multiple 3-D objects with different geometries.