Artificial Neural Network for LiDAL Systems

TL;DR

This paper presents an ANN-based approach for improving target detection and localization in indoor LiDAL systems, effectively distinguishing humans from background obstacles using pattern recognition of reflected signals.

Contribution

It introduces a novel application of artificial neural networks to enhance LiDAL system performance in indoor environments, including MIMO and MISO configurations.

Findings

ANN successfully classifies targets versus obstacles in simulations

Improved accuracy in target localization in realistic environments

Demonstrates feasibility of neural networks in LiDAL systems

Abstract

In this paper, we introduce an intelligent light detection and localization (LiDAL) system that uses artificial neural networks (ANN). The LiDAL systems of interest are MIMO LiDAL and MISO IMG LiDAL systems. A trained ANN with the LiDAL system of interest is used to distinguish a human (target) from the background obstacles (furniture) in a realistic indoor environment. In the LiDAL systems, the received reflected signals in the time domain have different patterns corresponding to the number of targets and their locations in an indoor environment. The indoor environment with background obstacles (furniture) appears as a set of patterns in the time domain when the transmitted optical signals are reflected from objects in LiDAL systems. Hence, a trained neural network that has the ability to classify and recognize the received signal patterns can distinguish the targets from the background obstacles in a realistic environment. The LiDAL systems with ANN are evaluated in a realistic indoor environment through computer simulation.