Efterfrågan och tillhandahållandet av platsberoende tjänster blir allt större vilket i sin tur skapar intresse för billiga och skalbara tekniker i alla möjliga olika miljöer. Särskilt intressant blir tekniker som är lätta att installera på nya platser och vars hårdvarukomponenter är enkla och billiga. I denna rapport presenteras en experimentiell systemteknisk metod för positionsberäkning i inomhusmiljöer, specifikt de som på grund av lokala elektromagnetiska fält, rörliga större föremål eller oregelbundna ytor skapar störningar som gör det svårt att utföra förlitlig positionering. Systemet utgörs av ett antal wifi-routrar samt en signalmottagre kopplad till en dator med systemets mjukvarukomponent installerad. Resultatet bedömdes utifrån en förväntad nivå av korrekthet, närmare bestämt att minst hälften av systemets bedömningar inte har fel med mer än två meter, samt en övre gräns på högst tre meters fel i minst 90 procent av fallen. För att möta målsättningen utrustades mjukvaran med komponenter tänkta att minimera effekten av störningar. Ett Kalmanfilter ger en bättre tolkning av inkommande mätdata medan en för området vanlig estimeringsalgoritm, så kallad Location Fingerprinting, förstärks med en experimentell uppsättning artificiella neurala neuronnät. Som rapporten kommer visa möter systemet som helhet utmaningen och presterar initialt bättre än väntat (hälften av bedömningarna har ett fel på 1,5 meter eller lägre) men även att det beshöver testas i så många nya miljöer som möjligt så att det kan gå att dra slutsatser om dess mer generella användbarhet. / The demand for and supply of location based services (LBS) is constantly growing, which in turn leads to an unquenchable thirst for affordable, scalable localisation solutions in all kinds of surroundings. Technical solutions that are easy to set up at a new location and whose hardware components are simple and affordable, are especially of interest.This paper describes an experimental system designed for positioning a client in particularly challenging indoor environments – wether it's due to local electromagnetic fields, large moving objects or slanted surfaces, basically whatever could create difficulties in radiowave based positioning. This system consists of a number of wifi routers and a signal receiver connected to a computer running the central software component. The results were assessed out of an expected level of accuracy, namely that no more than half of the estimates are off by two meters or more, with an upper limit of no more than 90 percent of the estimates being off by three meters or more. In order to achieve this, the software includes algorithms designed to lessen the effect of signal disruption. A Kalman filter gives the system a better interpretation of sensor data, while the (for the field) common estimation method of Location Fingerprinting gets reinforced by an experimental array of artificial neural networks. As this paper will show, the system will within the initial testing fulfill the set criteria to satisfaction, however it will need future trials in a row of varying environments so as to give an indication of its general usefulness.

The vision of ubiquitous computing is to seamlessly integrate information processing into everyday objects and activities. Part of this integration is an awareness on the part of a system of its user's context. Context can be composed of several variables --- such as a user's current activity, goals, or state of mind --- but location (both past and present) is almost always a key component. Determining location outdoors has become quite simple and pervasive with today's low-cost handheld Global Positioning System (GPS) receivers. Technologies enabling the location of people and objects to be determined while indoors, however, have lagged due to their extensive infrastructure requirements and associated cost. Just as GPS receivers utilize radio signals from satellites to triangulate their position, an indoor real-time locating system (RTLS) must also make use of some feature of the environment to determine the location of mobile units. Since the signal from GPS satellites is not sufficiently strong to penetrate the structure of a building, indoor RTLS systems must either use some existing feature of the environment or generate a new one. This typically requires a large amount of infrastructure (e.g. specialized RF receivers, additional 802.11 access points, RFID readers, etc.) to be deployed, making indoor RTLSs impractical for the home. While numerous techniques have been proposed for locating people and objects within a building, none of these has yet proven to be a viable option in terms of cost, complexity of installation, and accuracy for home users. This dissertation builds on work by Patel et al. in which the home power lines are used to radiate a low-frequency wireless RF signal that mobile tags use for location fingerprinting. Leveraging the existing power line permits this system to operate on far less additional infrastructure than existing solutions such as cellular (GSM and CDMA), 802.11b/g, and FM radio based systems. The contributions of this research to indoor power line-based RTLS are threefold. First, I examine the temporal stability of a power line based RTLS system's output. Fingerprinting-based RTLS relies upon some feature of the environment, such as the amplitude of an RF signal, to be stable over time at a particular location (temporal stability), but to change in space (spatial differentiability). I show that a power line-based RTLS can be made much more resistant to temporal instability in individual fingerprint components by utilizing a wide-band RF fingerprint. Next, I directly compare the temporal stability of the raw features used by various fingerprinting based indoor RTLSs, such as cellular, 802.11b/g, and FM radio. In doing so, I show that a power line based indoor RTLS has an inherent advantage in temporal stability over these other methods. Finally, I characterize the power line as a receiving antenna for low-powered wireless devices within the home, thus allowing the power line to not only transmit the RF signals used for fingerprinting, but also to receive the sensed features reported by location tags. Here, I show that the powerline is a viable receiver for these devices and that the globally available 27.12 MHz ISM band is a good choice of frequency for communications.

Radio-frequency (RF) fingerprinting is a process that uses the minute inconsistencies among manufactured radio transmitters to identify wireless devices. Coupled with location fingerprinting, which is a machine learning technique to locate devices based on their radio signals, it can uniquely identify and locate both trusted and rogue wireless devices transmitting over the air. This can have wide-ranging applications for the Internet of Things, security, and networking fields. To contribute to this effort, this research first builds a software-defined radio (SDR) testbed to collect an RF dataset over LTE and WiFi channels. The developed testbed consists of both hardware which are receivers with multiple antennas and software which performs signal preprocessing. Several features that can be used for RF device fingerprinting and location fingerprinting, including received signal strength indicator and channel state information, are also extracted from the signals. With the developed dataset, several data-driven machine learning algorithms have been implemented and tested for fingerprinting performance evaluation. Overall, experimental results show promising performance with a radio fingerprinting accuracy above 90\% and device localization within 1.10 meters.

The history of Software-Defined Radios (SDRs) epitomizes innovation in wireless communication. Initially serving military needs, SDRs swiftly transitioned to civilian applications, revolutionizing communication. This thesis explores SDR applications such as Spectrum Scanning Systems, Contraband Cellphone Detection, and Human Activity Recognition via Wi-Fi signals. SDRs empower Spectrum Scanning Systems to monitor and analyze radio frequencies, optimizing spectrum allocation for seamless wireless communication. In Contraband Cellphone Detection, SDRs identify unauthorized signals in restricted areas, bolstering security efforts by thwarting illicit cellphone usage. Human Activity Recognition utilizes Raspberry Pi 3B+ to track movement patterns via Wi-Fi signals, offering insights across various sectors. Additionally, the thesis conducts a comparative analysis of Wi-Fi-based Human Activity Recognition and Radar for accuracy assessment. SDRs continue to drive innovation, enhancing wireless communication and security in diverse domains, from defense to healthcare and beyond.