Indoor localization in NLOS conditions using asynchronous WSN and neural network

Abstract

Development of technologies for accurate localization of objects in indoor environments can transform wide application domains like healthcare, warehouses and fitness industries. In this paper, we present a novel neural network and asynchronous wireless sensor network (WSN) based indoor localization scheme. A custom designed ultrasonic trans-receiver serves as the back bone of the localization scheme. In addition to the experiments on hardware, we utilize Locusim, an acoustic simulator to augument extensive analysis on Non line of Sight (NLOS) conditions. We demonstrate that the neural network based localization scheme can provide an accuracy suitable for most of the real world applications even under NLOS conditions.