Highly Accurate and Efficient Wireless Indoor Positioning System

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In this project we develop a new algorithm for Peytech for indoor localization and positioning. The company has developed its hardware and software for such purpose. However, the existing software is not satisfying the desired customers' requirements when it comes to accuracy and processing time. The existing hardware has the potential to gather enough of information for this purpose and the goal of this project is to design a novel machine learning based algorithm to fulfill the localization constraints and requirements. The algorithm will be the pioneer hybrid approach that takes advantages of both available Received Signal Strength (RSS) and Time of Arrival (ToA) for efficient positioning. The proposed project has five main steps: 1) Modeling the environments requirements in software, 2) Creating an innovative approach that optimally employs the data gathered from the sensors, 3) Development of an algorithm that estimates the object positions with the required accuracy during the desirable processing time, 4) Deploying the algorithm in the company's software, test and feedback for any possible modifications, 5) Final report preparation. This project enables the costumers to utilize their production stages and their warehouse with a reliable and cost effective tracking method. Consequently effective localization and positioning process at the early or ending stages of production benefits the manufacturers. This will eventually benefit not only the industry but also the costumers of these facilities. The HQP involved in this project will be exposed to a practical industrial problem that requires knowledge and implementation of machine learning, data analysis, and localization techniques.