Research on Indoor Pedestrian Localization and Tracking Algorithms

I have conducted comprehensive research on indoor pedestrian localization and tracking algorithms, with in-depth analysis of several key approaches in this field. The studied algorithms include RSS-based KNN indoor localization (implementing k-nearest neighbors pattern matching using Received Signal Strength fingerprints), RSS-based Kalman filter algorithm (utilizing state-space models for recursive estimation with noise filtering capabilities), and hybrid particle filter algorithms that integrate RSS measurements with Dead Reckoning (DR) data through sequential Monte Carlo methods. I have thoroughly examined the advantages and limitations of each algorithm, evaluating their practical applicability and accuracy metrics under various conditions. My research extends to real-world implementations of these algorithms, analyzing their performance effectiveness and feasibility across different indoor environments. Through extensive investigation, I have identified unique strengths in each approach and can provide deeper insights into understanding both the capabilities and constraints of indoor pedestrian localization and tracking systems from both theoretical and implementation perspectives.