Wireless IoT Positioning System for Accurate Localized Tracking Using Low-Cost Hardware

This paper presents the development and application of a filter-based system to enhance real-time positioning and tracking in low-cost wireless systems. The research explores the use of Kalman filters in conjunction with experimentally gathered data to estimate transmitter locations within an area, leveraging Received Signal Strength (RSS) measurements from multiple receivers. Experimental results should demonstrate the possible effectiveness of the Kalman filter in reducing noise and improving stability in the positioning estimates. The implementation process, including data acquisition via Texas Instruments SMARTRF06 development boards, is detailed. This study provides valuable insights into the application of Kalman filters in low-cost localization systems and outlines future directions for improving accuracy and system scalability.