A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Fingerprinting-Based Positioning Algorithms <s> The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> Fingerprinting-Based Positioning Algorithms <s> We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy. <s> BIB002
As long as the fingerprint database is generated, manifold positioning algorithms can be applied according to application 8 Mobile Information Systems requirements, for instance, deterministic approach like kNN applied by RADAR BIB001 , and probabilistic approaches using Bayesian theorem BIB002 . By combining the other sensor information or floor plan, the positioning solution can further apply the scheme such as EKF, particle filter, or SLAM.