With the prevalence of smartphones, watches, and Internet of Things (IoT) devices, the ability to track their positions is becoming increasingly important. For many indoor positioning systems (IPSs), providing an uninterrupted flow of information in real-time, effective, and low power are the main criterion. As a result, a solution is needed to withstand these conditions. This study is conducted to fill this gap and propose new solutions for an old question. In this thesis, three main indoor positioning systems are proposed. Primarily, the focus is on creating an IPS based on a smart-card functioning at 125 kHz. This system is a proximityactivated card with a magnetic transmitter that is designed to transmit data to a server. The study revealed an accuracy of 30 cm in 70% of the observations by a sigmoid function-activated neural network. Another positioning system is studied by introducing a non-blocking access control scenario. This system is a hybrid based on a magnetic positioning system and a visual systemfor the surveillance of the area. Using a Global Nearest Neighbor algorithm, a merging algorithm is developed and implemented to allocate the data collected from themagnetic system and the camera to a certain person. This system reached an accuracy of 97% in various testing scenarios. Despite the accuracy of these systems, magnetic-based systems are impossible to integrate with smartphones and other dominant radio frequency systems such as WLAN. While having no multipath effect and functioning in certain difficult environments, such as next to brick walls or corners, these systems are prone to huge metallic bodies. Therefore, a new system is introduced based on Bluetooth Direction Finding (BLE-DF). This technology utilizes seamless integration with numerous IoT and smart devices, offering swift performance and low power consumption while also boasting highly precise direction-finding capabilities. This thesis studies two main BLE-DF architectures. The design characteristics of the designed antenna arrays, a signalmodel for the arrays, and the BLE-DF are proposed in chapters 4 to 5. The performance of the arrays in an uncontrolled environment revealed that our proposed antenna arrays are up to 88% more accurate than an out-of-the-shelf array. Different angle estimation algorithms and a data modification process are proposed which caused up to 40% increase in precision. Different structures of antennas and patterns are studied. A precision of 3.7◩ is achieved using a URA array that is equivalent to 30 cm in a range of 5m. Finally, a positioning algorithm is presented that proves the same accuracy of approximately 30 cm. Such a high accuracy enables new applications. While the achieved accuracy is very promising, like any other technology, the BLE-DF has its own limitations. For instance, the system is prone to multipath which is inevitable in indoor spaces. Therefore, several methods are proposed to compensate for this effect, such as using GaussianMixtureModels (GMM) and spatial smoothing that could increase precision by 85% and, 40% respectively. Finally, this dissertation tries to smooth the path of future interested researchers by contributing to a better understanding of the performance of our proposed systems as low-power, sub-meter accuracy IPSs.