Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Indoor Location Based on Inertial Sensor Technology <s> We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone’s accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that “sense” these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1.69m. War-driving is not necessary, neither are floorplans ‐ the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment. <s> BIB001 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Indoor Location Based on Inertial Sensor Technology <s> Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building. <s> BIB002 </s> Indoor Fingerprint Positioning Based on Wi-Fi: An Overview <s> Indoor Location Based on Inertial Sensor Technology <s> The earth's magnetic field needs no transmitter infrastructure and can be measured everywhere. Therefore its use in fingerprinting technology for indoor-positioning and navigation seems to be useful. In this work the magnetic field is used in combination with Wi-Fi and RFID in the fingerprinting approach. The idea is to first calculate an approximate solution with Wi-Fi and then refine it with the magnetic field. Additionally RFID can also be used in this approach to further refine the approximate solution from Wi-Fi. To analyze the achievable accuracies static and kinematic test measurements are done. In the static test measurements the positions are calculated by using different sensors (Smartphone and low-cost IMU), different distance criteria and different signal sources. The smallest positioning errors (about 2m) occur by using all signal sources (magnetic field, Wi-Fi, RFID) and the Mahalanobis-distance. At the kinematic test measurements a Kalman Filter is used. A motion model delivers values for the system equation and the observations are retrieved by using magnetic field- and Wi-Fi-Fingerprinting. The kinematic positioning errors do not exceed 6m. <s> BIB003
Inertial sensors make full use of micro-sensors based on the micro-electro-mechanical system (MEMS), which is a combination of micro-sensors, such as accelerometers, gyroscopes, magnetic compasses, etc. Currently, inertial sensors based on indoor positioning technology are mainly divided into two categories BIB001 . One is based on Newton's law of motion, and obtains the speed and position of movement by calculating acceleration. Since data drift occurs when this method is used in practice, the cumulative error of multiple calculations can reach tens of meters or even hundreds of meters within a few seconds. Another method is pedestrian dead reckoning (PDR), which uses the number of steps that pedestrians walk to gain results. Due to its high accuracy, it has become an area of much interest in the assisted positioning technology field. The UnLoc positioning system based on track estimation was designed by Wang  at Duke University, and utilizes the accelerometer and magnetic compass combined with dead reckoning. Wang  used an accelerometer to judge the number of steps and direction of the moving user, before using relevant techniques to obtain the user's location information. Zhang BIB002 used an improved algorithm of PDR. In the PDR algorithm, the traditional methods of calculating steps are the zero-crossing method and peak detection algorithm. Both methods have a fixed threshold value and require a steady speed. The method proposed in reference BIB002 used dynamic threshold and time window to measure the number of steps. It could self-regulate based on the state of the environment and pedestrians. In the same reference BIB002 , the experimental results showed that the average positioning error of the method could be maintained around 0.57 m, with a maximum positioning error of only 1.4 m. The Zee positioning system proposed by Rai et al. BIB003 used gyroscopes, accelerometers, and electronic compasses to predict the number of steps in combination with particle filtering.