Indoor Location Estimation by Using Maximum Likelihood Estimation Based Algorithm on Small Cell Networks

This thesis presents a new framework for indoor localization using Third Generation(3G) Universal Mobile Telephone System (UMTS) Femtocell. The fingerprinting technique is applied to collect the Received Signal Strength Indication(RSSI) values through an Android User Equipment(UE) and data is processed in real time using MQTT server. To achieve better Radio Frequency (RF) planning and optimization for the placement of Femto Access Point(FAP), statistical analysis is performed by normalizing and calculating the Mean Squared Error(MSE) of the acquired data. To maximize the success rate in finding the location of the person, Maximum likelihood Estimation(MLE) based algorithm is used for tracking. Simulation was carried out both for 1 million samples and real life test using 100 samples. To make it more accurate and efficient, MLE based algorithm is developed and simulated in MATLAB. Both simulation approaches resulted in good success rates. A case study is also part of this thesis, case study discuss about the real life project and deployment setup for indoor positioning estimation and localization techniques using multiple FAP's.