Cooperative Passive Pedestrian Detection and Localization Using a Visible Light Communication Access Network

Abstract

Visible light communication (VLC) systems are promising candidates for future indoor access and peer-to-peer networks. The performance of these systems, however, is vulnerable to line of sight (LOS) link blockage due to objects inside the room. Considering pedestrians as the most common VLC links blocking obstacles, we develop a probabilistic passive pedestrian detection and localization method. Our method takes advantage of the blockage status of VLC LOS links between the user equipment (UE) and transceivers on the ceiling to passively detect a single pedestrian, modeled as a cylinder with a random radius. The VLC network gathers the blockage status and computes the geometry of the LOS link graph through a cooperative scheme between VLC device-equipped users inside the room. We also develop a mathematical framework to obtain an optimum solution for estimating the location and size of the object and conclude with a sub-optimum estimation by simplifying the problem to a quadratic programming approach. Simulation results show that using a 5 × 5 grid of transceivers on the ceiling and as few as eight UEs, the root-mean-squared error in estimating the center and radius of the object can be less than 5 cm and 3 cm, respectively.

Optical Wireless Communications: Illuminating the Path for Cooper's Law

“…OWCbased object detection typically involves the transmission and reception of optical signals and the subsequent analysis of received data to identify objects. For example, a network of optical receivers is proposed in [347] to detect the presence of a pedestrian using a probabilistic model. Also, a map of an indoor space can be developed from VLC signals as described in [348], while deciphering the human posture and vital signs were explored in [349] and [350], respectively.…”

Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

“…Visible light-based passive sensing techniques are categorized into two groups (adapted from [25]): (i) general visible light-based (unmodified lighting), assuming either a fully passive device-free object [26][27][28][29] or a device-equipped object [30][31][32][33], and (ii) VLC-based techniques with an active transmitter and a device-free object [4,13,15,[34][35][36][37][38][39][40][41]. Most of these sensing techniques focus on developing algorithms for high-level applications, such as occupancy detection [36,39,41], gesture recognition [15,34], and fall detection [40]. A fully passive occupancy detection study with an accuracy beyond 90% was conducted in [26], exploiting reverse-biased LED luminaires as photodetectors for sensing.…”