While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no apriori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device.

A technique is proposed to improve the accuracy of indoor positioning systems based on WIFI radio-frequency signals by using dynamic access points and fingerprints (DAFs). Moreover, an indoor position system that relies solely in DAFs is proposed. The walking pattern of indoor users is classified as dynamic or static for indoor positioning purposes. I demonstrate that the performance of a conventional indoor positioning system that uses static fingerprints can be enhanced by considering dynamic fingerprints and access points. The accuracy of the system is evaluated using four positioning algorithms and two random access point selection strategies. The system facilitates the location of people where there is no wireless local area network (WLAN) infrastructure deployed or where the WLAN infrastructure has been drastically affected, for example by natural disasters. The system can be used for search and rescue operations and for expanding the coverage of an indoor positioning system.

The demand for accurate localization under indoor environments has increased dramatically in recent years. To be cost-effective, most of the localization solutions are based on the WiFi signals, utilizing the pervasive deployment of WiFi infrastructure and availability of the WiFi enabled mobile devices. In this thesis, we develop completed indoor localization solutions based on WiFi fingerprinting and machine learning approaches with two types of WiFi fingerprints including received signal strength indicator (RSSI) and channel state information (CSI). Starting from the low complexity algorithm, we propose a soft range limited K nearest neighbours (SRL-KNN) to address spatial ambiguity and the fluctuation of WiFi signals. SRL-KNN exploits RSSI and scales the fingerprint distance by a range factor related to the physical distance between the user’s previous position and the reference location in the database. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Besides, the idea of the soft range limiting factor can be applied to all of the existed probabilistic methods, i.e., parametric and nonparametric methods, to improve their performances. A semi-sequential short term memory step is proposed to add to the existed probabilistic methods to reduce their spatial ambiguity of fingerprints and boost significantly their localization accuracy. In the following research phase, instead of locating user's position one at a time as in the cases of conventional algorithms, our recurrent neuron networks (RNNs) solution aims at trajectory positioning and takes into account of the relation among RSSI measurements in a trajectory. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. Next, the problem of localization using only one single router is analysed. CSI information will be adopted along with RSSI to enhance the localization accuracy. Each of the reference point (RP) is presented by a group of CSI measurements from several WiFi subcarriers which we call CSI images. The combination of convolutional neural network (CNN) and LSTM model is proposed. CNN extracts the useful information from several CSI values (CSI images), and then LSTM will exploit this information in sequential timesteps to determine the user's location. Finally, a fully practical passive indoor localization is proposed. Most of the conventional methods rely on the collected WiFi signal on the mobile devices (active information), which requires a dedicated software to be installed. Different from them, we leverage the received data of the routers (passive information) to locate the position of the user. The localization accuracy is investigated through experiments with several phones, e.g., Nexus 5, Samsung, Iphone and HTC, in hundreds of testing locations. The experimental results demonstrate that our proposed localization scheme achieves an average localization error of around 1.5 m when the phone is in idle mode, and approximately 1 m when it actively transmits data. / Graduate

In this report it is evaluated whether a higher dimensional fingerprint vector increases accuracy of an algorithm for indoor localisation. Many solutions use a Received Signal Strength Indicator (RSSI) to estimate a position. It was studied if the use of the Channel State Information (CSI), i.e. the channel’s frequency response, is beneficial for the accuracy.The localisation algorithm estimates the position of a new measurement by comparing it to previous measurements using k-Nearest Neighbour (k-NN) regression. The mean power was used as RSSI and 100 samples of the frequency response as CSI. Reduction of the dimension of the CSI vector with statistical moments and Principal Component Analysis (PCA) was tested. An improvement in accuracy could not be observed by using a higher dimensional fingerprint vector than RSSI. A standardised Euclidean or Mahalanobis distance measure in the k-NN algorithm seemed to perform better than Euclidean distance. Taking the logarithm of the frequency response samples before doing any calculation also seemed to improve accuracy. / I denna rapport utvärderas huruvida data av högre dimension ökar noggrannheten hos en algoritm för inomhuspositionering. Många lösningar använder en indikator för mottagen signalstyrka (RSSI) för att skatta en position. Det studerades studerade om användningen av kanalens fysikaliska tillstånd (CSI), det vill säga kanalens frekvenssvar, är fördelaktig för noggrannheten.Positioneringsalgoritmen skattar positionen för en ny mätning genom att jämföra den med tidigare mätningar med k-Nearest Neighbour (k-NN)-regression. Medeleffekten användes som RSSI och 100 sampel av frekvenssvaret som CSI. Reducering av CSI vektornsdimension med statistiska moment och Principalkomponentanalys(PCA) testades. En förbättring av noggrannheten kunde inte observeras genom att använda data med högre dimension än RSSI. Ett standardiserat Euklidiskt eller Mahalanobis avståndsåatt i k-NN-algoritmen verkade prestera bättre än Euklidiskt avstånd. Att ta logaritmen av frekvenssvarets sampel innan andra beräkningar gjordes verkade också förbättra noggrannheten.

In order to investigate what place WiFi fingerprinting has in pedestrian-oriented planning, an interview study was carried out by conducting 12 semi-structured interviews with different actors working in pedestrian-related fields. The actors represent both the private and the public sector, and work in various geographical scales. The interviews investigate what pedestrian-related questions actors work with, what data their work requires and what methods they use to gather the data. For each of these three categories, topics that appeared in over half of the interviews were analyzed qualitatively. With this analysis, relevant applications of WiFi fingerprinting in pedestrian-oriented planning today are identified, namely determining capacity and dimensions, determining the spatial layout of small-scale environments, measuring congestion and providing validation data. The data needs not filled by WiFi fingerprinting are found to be spot-specific information suitable for qualitative analysis, visitor composition and network level flows and movements. Lastly, WiFi fingerprinting can be combined with other data sources to complement its drawbacks as a piece in a puzzle, such as using spot visits to better understand the reasons behind the flow data.