Enhanced Area Estimation Algorithms for Indoor Wireless Localization

Abstract

We enhance the area average (AA) algorithm based on received signal strength (RSS) fingerprinting in Wi-Fi infrastructure by utilizing map information. The area searching (AS) algorithm adopts the concept of Cell-ID method before fingerprinting is proposed to select appropriate areas to reduce the estimation error. The fusion-based RSS distance computation (FRD) scheme can represent the RSS distance more accurate when both 2.4 GHz and 5 GHz are considered. Experiment results validate that the enhanced algorithms achieve above 90% of area estimation accuracy in the area with access points in four different testing environments.