This paper has been accepted for publication in the International Technical Meeting (ITM) of the Institute of Navigation (ION), Dana Point, CA, USA, January 2015. Adaptive Integrated Indoor Pedestrian Tracking System Using MEMS sensors and Hybrid WiFi/Bluetooth-Beacons With Optimized Grid- based Bayesian Filtering Algorithm Mohamed M. Atia, Queen’s University Umar Iqbal, Queen’s University Sidney Givigi, Royal Military College of Canada Aboelmagd Noureldin, Royal Military College of Canada Michael Korenberg, Queen’s University BIOGRAPHY (IES) Mohamed M. Atia is currently Research Associate and Deputy Director of Navigation & Instrumentation Lab in Royal Military College of Canada. He received a Ph.D. degree (Computer and Electrical Engineering) from Queen’s University in 2013. He has several years of industry experience in software systems design and development. He has 40+ international publications, one book chapter, and several patent applications in the in the area of multi-sensor integrated navigation systems. He applies advanced signal processing, estimation theory, and artificial intelligence in the field of positioning, tracking, and navigation systems for a wide range of platforms/environments. Umar Iqbal is currently Research Associate and Lecturer in Electrical and Computer Eng. Department in Royal Military College of Canada. He has MSc. and PhD degrees from Queen’s University in 2009 and 2013 respectively. Since 2009, he has been publishing extensively in the area of land-vehicles navigation using several multi-sensors fusion techniques. His current research interest includes RISS/GNSS integrated navigation for land vehicles and INS/GPS. Sidney Givigi received his B.Sc. in Computer Science and an M.A.Sc. in Electrical Engineering from the Federal University of Espirito Santo, Brazil. He also received his Ph.D. in Electrical and Computer Engineering from Carleton University, Canada. In 2009, he joined the Department of Electrical and Computer Engineering of the Royal Military College of Canada (RMCC) as an assistant professor. Sidney’s research interests are mainly focused on autonomous systems, especially the decentralized control of multiple vehicles, learning and adaptation of autonomous robots and modeling of complex systems with Game Theory. Aboelmagd Noureldin is a cross-appointment professor at the Departments of Electrical and Computer Engineering of both Queen’s University and the Royal Militar y College (RMC) of Canada. He is also the founder of the NavINST research group at RMC. He holds B.Sc. degree in Electrical Engineering (1993) and M.Sc. degree in Engineering Physics (1997) from Cairo University, Giza, Egypt. In addition, he holds Ph.D. degree in Electrical and Computer Engineering (2002) from the University of Calgary, Alberta, Canada. Michael Korenberg received the B.Sc., M.Sc. (Mathematics), and Ph.D. (Electrical Engineering) degrees from McGill University, Montreal, Quebec. He has been on Faculty at Queen's University since 1983, and is currently a Professor in the Department of Electrical and Computer Engineering. His research interests include development of practical methods for representing/identifying nonlinear systems of unknown structure and for time-series analysis. ABSTRACT With recent dramatic increase in sensors deployments and processing nodes, accurate indoor positioning, tracking, and navigation is becoming achievable. Among many platforms that need to be localized and tracked are pedestrians. A reliable indoor pedestrians tracking has a wide range of applications such as healthcare, retail, rescue missions and context-awareness applications. This paper introduces a calibration-free hybrid indoor positioning system that utilizes inertial sensors (INS), wireless local area networks (WLAN), and low-cost Blue- tooth low-energy (BLE) wireless beacons. BLE beacons are becoming very popular in retails and they can be easily installed in any indoor environment. To deal with