Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> AP Selection Strategy <s> This paper explores the possibility to design an indoor ultra wideband (UWB) ranging and positioning system using the received signal strength (RSS). Due to the extremely large bandwidths, the effects of small scale fading are reduced to the level where the knowledge of the path loss model (PLM) can be employed for accurate and reliable distance estimation. This approach enables trilateration based position estimation while significantly reducing the synchronization effort. A limited number of measurements is necessary in order to calibrate the PLM parameters but no extensive database of measurements is required, such as in fingerprinting methods. Based on simulated UWB channels, the effects of uncertainties in the PLM parameters on the estimated distance are characterized. Data from a UWB measurement campaign in indoor line-of-sight (LOS) scenarios are used to verify the performance of such a system. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> AP Selection Strategy <s> This paper presents an AP selection for indoor positioning based on Wi-Fi received signal strength (RSS), which is a low cost and easy-accessible technique. However, the RSS are changeable over time by multipath effect, which may affect the accuracy of positioning. First, the system uses the box plot to remove the outlier of the RSS samples from APs to smooth the RSS. Then, the standard deviation is used to analysis the variation of RSS, and selects the Top-N APs having less interference for location estimation. We will use simulations to further verify the positioning accuracy and efficiency of the proposed strategy, but also look forward to apply on related positioning services. <s> BIB002
With the constant development of Wi-Fi, AP distribution density is also rapidly increasing. Traditional indoor positioning methods can choose to use all the APs that can be detected in the environment; however, the signal strength provided by the AP can be affected by a severe multipath effect and has a low reference value which interferes with the accuracy of the fingerprint data and increases the burden of the positioning system, thus making it difficult to locate the fingerprint. Therefore, the choice of AP becomes an issue requiring further attention BIB002 . Jhuang et al.  proposed that, in the online phase, the standard error of the signal strength was used as the metric to calculate the detected signal strength of each AP and categorized them prior to selecting the top M APs as positioning APs to participate in positioning. Feng Chen [54] listed two common AP selection strategies based on the strongest received signal strength and the Fisher criterion. During the online phase, the AP selection strategy based on the strongest received signal strength chooses the M strongest signals of the AP to participate in positioning. As this method sorts all the APs based on the strength of signals every time positioning, every time positioning it picks different APs to participate in positioning based on the different locations of users. In contrast, the Fisher criterion calculates the signal strength from each AP through the fingerprint database analysis of signal distribution, as shown in Equation BIB001 . Among them, As seen in Equation (9), the smaller the denominator, the higher the probability that the fingerprint data from the same AP is similar to the signal strength obtained at the same location in the online phase. Molecules represent the changes in signal strength at different RPs to measure the ability of different APs to differentiate reference points. The Fisher criterion selects the top M ξ i of the largest Aps to participate in positioning by sorting ξ i .