EP-FPG applied to RSSI-Based Wireless Indoor Localization

Abstract

Wireless Localization based on Received Signal Strength Indication (WL-RSSI) consists of predicting the localization of a particular device given the radio signals it receives. WL-RSSI methods are suitable for specific scenarios where Global Positioning System (GPS) is unstable or unavailable, such as indoor localization. Developing more efficient WL-RSSI methods is necessary to supplement GPS localization in such applications. Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is an optimization strategy that aims at better exploring the network weight-space when compared to methods such as Backpropagation (BP). Feedforward neural network trained by hybrid Particle swarm optimization and Gravitational search algorithm (FPG) is a kind of machine learning model with better exploring ability in the solution search space compared with conventional neural network training methods such as Backpropagation (BP). This article investigates a method to solve the slow convergence problem of conventional FPG and further improve its performance. Extreme Learning Machines (ELMs) are used to pre-train initial particles of the FPG (EP-FPG). This article also presents the application of EP-FPG to classification and regression WL-RSSI problems. Experimental results demonstrate that the proposed EP-FPG performs better on WL-RSSI problems than conventional FPG and BP.