Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> (1) Deterministic Denoising <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> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> (1) Deterministic Denoising <s> This study proposes a clustering-based Wi-Fi fingerprinting localization algorithm. The proposed algorithm first presents a novel support vector machine based clustering approach, namely SVM-C, which uses the margin between two canonical hyperplanes for classification instead of using the Euclidean distance between two centroids of reference locations. After creating the clusters of fingerprints by SVM-C, our positioning system embeds the classification mechanism into a positioning task and compensates for the large database searching problem. The proposed algorithm assigns the matched cluster surrounding the test sample and locates the user based on the corresponding cluster's fingerprints to reduce the computational complexity and remove estimation outliers. Experimental results from realistic Wi-Fi test-beds demonstrated that our approach apparently improves the positioning accuracy. As compared to three existing clustering-based methods, K-means, affinity propagation, and support vector clustering, the proposed algorithm reduces the mean localization errors by 25.34%, 25.21%, and 26.91%, respectively. <s> BIB002
Channel Propagation Model Denoising: More than 90% of indoor channel propagation models are empirical models. The model is based on a large number of prior data, which has a high reliability. The channel propagation model is used to model the indoor space, and a correction factor is introduced to eliminate the influence of indoor areas (such as walls, doors, and windows) on the radio frequency signal, so the denoising of the channel propagation model performs well. The channel propagation model denoising uses fingerprint data as a parameter to be calculated in the channel propagation model after the end of the offline phase. The channel propagation model identifies the effective and invalid data, thus removing the noise. Channel propagation model denoising is a commonly used deterministic denoising method; however, this method mainly relies on the establishment of the channel propagation model, so the channel propagation model directly affects the denoising effect. The radar BIB001 system used the channel propagation model to establish the fingerprint database, and at the same time, the radar system did the denoising operation to the fingerprint data. Median Filter: Median filtering is a non-linear filtering denoising method that uses the median value of the neighborhood noise point to replace it. Lim et al. BIB002 chose median filtering to denoise the fingerprint database. The median filtering can effectively solve the short-term effect of fast fading on signal strength. Neighborhood Mean Filtering: Like median filtering, neighborhood mean filtering is also a common denoising method. After finding the noise point of the fingerprint database, the value is estimated by using the average of the values of non-noise point in the neighborhood to achieve filtering  . In reference  , after the neighborhood filtering method was used to denoise the fingerprint database, the average location error decreased by 13%, and the error in the 6 m above the fixation point decreased by 46%.