{"id":162615,"date":"2012-06-25T00:00:00","date_gmt":"2012-06-25T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/fm-based-indoor-localization\/"},"modified":"2018-10-16T20:33:06","modified_gmt":"2018-10-17T03:33:06","slug":"fm-based-indoor-localization","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fm-based-indoor-localization\/","title":{"rendered":"FM-based Indoor Localization"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi RSSI signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM broadcast radio signals for robust indoor fingerprinting. Because of the lower frequency, FM signals are less susceptible to human presence, multipath and fading, they exhibit exceptional indoor penetration, and according to our experimental study they vary less over time when compared to WiFi signals. In this work, we demonstrate through a detailed experimental study in 3 different buildings across the US,<br \/>\nthat FM radio signal RSSI values can be used to achieve roomlevel indoor localization with similar or better accuracy to the one achieved by WiFi signals. Furthermore, we propose to use additional signal quality indicators at the physical layer (i.e., SNR, multipath etc.) to augment the wireless signature, and show that localization accuracy can be further improved by more than 5%. More importantly, we experimentally demonstrate that the localization errors of FM andWiFi signals are independent. When FM and WiFi signals are combined to generate wireless fingerprints, the localization accuracy increases as much as 83% (when accounting for wireless signal temporal variations) compared to when WiFi RSSI<br \/>\nonly is used as a signature.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi RSSI signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM 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