Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Disadvantages of Wi-Fi Fingerprint Positioning Technology <s> Indoor positioning systems that make use of received signal strength based location fingerprints and existing wireless local area network infrastructure have recently been the focus for supporting location-based services in indoor and campus areas. A knowledge and understanding of the properties of the location fingerprint can assist in improving design of algorithms and deployment of position location systems. However, most existing research work ignores the radio signal properties. This paper investigates the properties of the received signal strength reported by IEEE 802.11b wireless network interface cards. Analyses of the data are performed to understand the underlying features of location fingerprints. The performance of an indoor positioning system in terms of its precision is compared using measured data and a Gaussian model to see how closely a Gaussian model may fit the measured data. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Disadvantages of Wi-Fi Fingerprint Positioning Technology <s> Positioning in indoor wireless environments is growing rapidly in importance and gains commercial interests in context-awareness applications. The essential challenge in localization is the severe fluctuation of receive signal strength (RSS) for the mobile client even at a fixed location. This work explores the major noisy source resulted from the multipath in an indoor wireless environment and presents an advanced positioning architecture to reduce the disturbance. Our contribution is to propose a novel approach to extract the robust signal feature from measured RSS which is provided by IEEE 802.11 MAC software so that the multipath effect can be mitigated efficiently. The dynamic multipath behavior, which can be modeled by a convolution operation in the time domain, can be transformed into an additive random variable in the logarithmic spectrum domain. That is, the convolution process becomes a linear and separable operation in the logarithmic spectrum domain and then can be effectively removed. To our best knowledge, this work is the first to enhance the robustness to a multipath fading condition, which is common in the environments of an indoor wireless LAN (WLAN) location fingerprinting system. Our approach is conceptually simple and easy to be implemented for practical applications. Neither a new hardware nor an extra sensor network installation is required. Both analytical simulation and experiments in a real WLAN environment demonstrate the usefulness of our approach to significant performance improvements. The numerical results show that the mean and the standard deviation of estimated error are reduced by 42% and 29%, respectively, as compared to the traditional maximum likelihood based approach. Moreover, the experimental results also show that fewer training samples are required to build the positioning models. This result can be attributed to that the location related information is effectively extracted by our algorithm. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Disadvantages of Wi-Fi Fingerprint Positioning Technology <s> Considering that indoor positioning applications based on wireless local area network location fingerprinting would be mainly used on the mobile devices. This paper investigates the differences of received signal strength indication (RSSI) between different smart phones and the distributions of RSSI are also analyzed. The statistical analysis of experimental results shows the differences of RSSI between different smart phones are not trivial. Nearly 65% of the RSSI histograms are significantly peaked relative to Gaussian distribution and 65% of them are left-skewed distribution. Therefore, taking skewness and kurtosis coefficients into account, Gaussian distribution is not sufficient to ensure an accurate modeling of the RSSI. The impacts of human behavior on RSSI distribution are explored and two types of human behavior are revealed to be the cause of bi-modal distribution. The statistical data analysis could enable smart phone indoor positioning systems designers to improve positioning performance and to model location fingerprinting based indoor positioning systems. <s> BIB003 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Disadvantages of Wi-Fi Fingerprint Positioning Technology <s> For outdoors, Global Network Navigation Systems have become the standard mechanism of positioning; but for indoor environments, there is still no standard system. Several mechanisms have been proposed and, among them, some of the more extended are those that use Wi-Fi signal for positioning, and mainly, those known as Wi-Fi fingerprinting. However, actual Wi-Fi mechanisms depends on environmental conditions: instability of Wi-Fi, differences in sensors of Smartphones, etc. One of these uncontrolled effects is the interaction between RF and human bodies. Human bodies interact with Wi-Fi radiation and, therefore, affects the signal received. <s> BIB004 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Disadvantages of Wi-Fi Fingerprint Positioning Technology <s> This paper proposes a RSSI of geomagnetic field-based indoor positioning method that takes advantage of the dependence of RSSI of geomagnetic field on the type of building material. Buildings are usually constructed with H-beam or reinforced concrete. However, most researches on RSSI of geomagnetic field-based indoor positioning do not consider the extent of geomagnetic field disturbance by building material. Based on the finding that RSSI of geomagnetic field is more affected by H-beam building than reinforced concrete building, and by applying interpolation, the proposed positioning method achieved efficient database generation. Experiments demonstrated that the proposed method not only efficiently reduced DB generation costs but also was faster. <s> BIB005
Wi-Fi fingerprint positioning technology needs many algorithms as support, so computation consumption and algorithm complexity is relatively high. Additionally, as the method requires a large amount of a priori information as data support, the preliminary work has a high cost factor. Since the method is modeled by the strength of each AP signal received at a certain location, it is also influenced by the environment. Once the AP location or the indoor environment changes, data re-acquisition and re-modeling are needed. The indoor environment is complex, so radio frequency signals will be affected by multipath effects, so there may be errors in the measured signal intensity. As 70% of the human body is composed of water, it can partly absorb the radio frequency signal of 2.4 GHz BIB004 . Since people spend most of their daily time in an indoor space, the human body has a significant impact on radio frequency signal by causing the decline and uncertainty of the signal in varying degrees. Walters BIB003 suggested that the location of the human body in an indoor space, the distance between people and APs and human body orientation could influence signal propagation. Walters found in their experiments that when there were moving bodies in indoor space, the average signal strength declined and the standard deviation increased significantly. Additionally, when mobile devices were completely blocked by human bodies, the signal strength would decrease significantly by around 10 dBm. (2) Effects of the Multipath Effect on Signal Strength Since many obstacles (like windows, doors, and walls) exist in indoor space, the radio frequency signal will propagate through different paths, which causes the signal to reach the receiver at different times. A different receiving time causes different phase, which can be superposed and cause signal distortion. This phenomenon above is called the multipath effect. Multipath is the main factor affecting indoor signal strength and the multipath effect is different in diverse environments due to their different distribution, construction, main materials BIB005 , and so on. To minimize its impact, researchers have used the channel propagation model. Fang et al. BIB002 found that the multipath effect could be divided into two parts: one part is the convolution filter effect caused by multi-scattering in an indoor environment, and the other part is communication additive noise. They modeled both effects with the logarithmic propagation model and reduced the probability of the standard deviation of estimate error from 42% to 29%, respectively. It is worth mentioning that, due to the multipath effect, there is time-duration exiting while measuring the RSS, which also has an impact on RSS measurement. Ignoring the time-duration leads to incorrect and unstable RSS measurements. Thus, it is necessary to keep measuring for a period, like 20 to 30 s, depending on the indoor environment BIB001 .