Indoor Human Localization Using Electrostatic Sensors and Compressive Sensing Techniques

Abstract

Indoor human localization is of great significance in a variety of applications, including navigation, healthcare, security, and many other location-based services. This article presents a passive indoor localization method that exploits the varying electric fields naturally generated by human activities. An array of electrostatic sensors capable of passive, long-range sensing is developed using charge amplifiers. Human localization is formulated as an inverse problem that aims to reconstruct the charge distribution within the target area from sensor measurements. The spatial sensitivity matrix is preprocessed using QR factorization, and then, compressive sensing is used to find the sparse solution. Experiments were conducted in an office environment of $4.2\times 4.2$ m. Results obtained show that the localization accuracy is location-dependent and a median error less than 0.26 m has been achieved. Although the sensor signals are vulnerable to a variety of factors, the localization method exhibits strong robustness against environmental and subject changes.