Fusion of WiFi and IMU Using Swarm Optimization for Indoor Localization

Abstract

With the popularity of smart devices and growing demand of location-based services, machine learning methods have been attracting increasing attention for their potential in indoor localization. Since GPS signal has limited access in indoor environments, alternative sensing solutions have been employed, among which the integration of inertial measurement and WiFi Received Signal Strength (RSS) is the preferred choice for its low cost and pedestrian compatibility. Researchers have proposed various approaches incorporating machine learning algorithms to improve indoor localization performance, which can be broadly divided into the fingerprinting-based approaches and ranging-based approaches. However, these conventional methods still either cannot achieve satisfactory accuracy or need the assistance of other prerequisites to reduce the localization error. To address this issue, in this chapter, we propose a new indoor localization system that integrates the inertial sensing and RSS fingerprinting via a modified Particle Swarm Optimization (PSO)-based algorithm. Different from traditional methods, our proposed method improves the accuracy by a new optimization process, in which the Inertial Measurement Unit (IMU) data is translated into the displacement information serving as the soft constraints of the optimization, and the result of the RSS fingerprinting method provides a guide for the swarm search. Also, a fitness metric based on Gaussian Process Regression (GPR) is developed to evaluate the likelihood of each particle in finding the real position. Experiments are conducted in the real-world scenarios, and the results validate that the proposed approach outperforms the typical approaches by at least 15.8% with the mean localization error of 1.618 m.