CSI-fingerprinting based human indoor localization in noisy environments using time-invariant CNN
Indoor localization in noisy environments
Indoor localization, the process of determining precise indoor locations, is crucial for enhancing productivity, safety, and user experience in various sectors, including construction, industry, and retail. In Hitachi, we use indoor localization technology to efficiently collect field data for our Worksite-Augmenting Metaverse system, directly supporting frontline workers with information with spatial accuracy. Traditional methods of indoor localization, such as trilateration, which depend on measuring Wi-Fi signal strength (RSSI), require clear sightlines and numerous access points, making them impractical in obstacle-rich environments. Fingerprinting techniques using Wi-Fi Channel State Information (CSI) have emerged as promising alternatives, as CSI provides unique signal patterns for each location. However, real-world conditions often produce noisy, fluctuating CSI data, reducing accuracy (Fig. 1). In this blog I would like to introduce a new method designed to handle noisy CSI signals effectively.
