LiDAL: Light Detection and Localization

TL;DR

LiDAL is an innovative indoor light-based detection and localization system that leverages VLC technology and radar concepts to detect, count, and locate people using reflected light signals, with multiple configurations and advanced detection methods.

Contribution

This paper introduces the first indoor light-based detection and localization system, LiDAL, integrating VLC, radar concepts, and novel detection techniques for accurate human localization.

Findings

LiDAL can detect and localize humans indoors using visible light reflections.

The system achieves improved accuracy with MIMO and MISO configurations.

Neural network methods effectively distinguish mobile targets from background reflections.

Abstract

In this paper, we present the first indoor light-based detection and localization system that builds on concepts from radio detection and ranging (radar) making use of the expected growth in the use and adoption of visible light communication (VLC), which can provide the infrastructure for our LiDAL system. Our system enables active detection, counting and localization of people, in addition to being fully compatible with existing VLC systems. In order to detect human (targets), LiDAL uses the visible light spectrum, it sends pulses using a VLC transmitter and analyses the reflected signal collected by a photodetector receiver. Although we examine the use of the visible spectrum here, LiDAL can be used in the infrared spectrum and other parts of the light spectrum. We introduce LiDAL with different transmitter-receiver configurations and optimum detectors considering the fluctuation of the received reflected signal from the target in the presence of Gaussian noise. We design an efficient multiple input multiple output (MIMO) LiDAL system with wide field of view (FOV) single photodetector receiver, and also design a multiple input single output (MISO) LiDAL system with an imaging receiver to eliminate the ambiguity in target detection and localization. We develop models for the human body and its reflections and consider the impact of the colour and texture of the cloth used as well as the impact of target mobility. A number of detection and localization methods are developed for our LiDAL system including cross correaltion, a background subtraction method and a neural network based method. These methods are considered to distinguish a mobile target from the ambient reflections due to background obstacles (furniture) in a realistic indoor environment.