Signal Processing and Optimization Techniques for High Accuracy Indoor Localization, Tracking, and Attitude Determination
High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.
Overview
Abstract
High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of moving targets is challenging. Various factors can degrade the estimation accuracy, such as the Doppler effect, interference, and high noise. This thesis addresses the challenges in indoor localization and tracking systems and proposes several solutions. A novel signal design, which we named Differential Zadoff-Chu, allows us to develop algorithms that accurately estimate the distances of static and moving targets even under random Doppler shifts. Then, we develop a high-resolution multi-target ranging algorithm that can estimate the ranges to targets at proximity based on the Levenberg-Marquardt algorithm. These ranging algorithms require line-of-sight between the transmitter and the receiver. Therefore, we design an algorithm to classify received signals into line-of-sight and non-line-of-sight by exploiting the room's geometry. Transforming distances into 2D or 3D locations and orientations requires solving an optimization problem. We propose using three nodes arranged as an isosceles triangle to find the position and orientation of a target. Utilizing the geometry of the isosceles triangle, we develop a highly accurate location and orientation estimation algorithm by solving a constrained optimization problem. Finally, we propose a Kalman filter to improve the tracking accuracy of moving targets even under non-line-of-sight conditions. The proposed filter fuses the position and orientation estimated using our Riemannian localization algorithm with the position and orientation estimated using an inertial measurement unit (IMU) to obtain a more accurate estimate of the position and orientation of a moving target. We validate the proposed algorithms via numerical simulations and real experiments using low-cost ultrasound hardware. The results show that the proposed algorithms outperform the state-of-the-art in terms of both accuracy and complexity.
Brief Biography
Mohammed AlSharif received his B.S. degree in electrical engineering, with first honors, from King Fahd University of Petroleum and Minerals, KFUPM, Dhahran, Saudi Arabia, in 2012, and the M.S. degree in electrical engineering from King Abdullah University of Science and Technology, KAUST, Thuwal, Saudi Arabia, in 2016, where he is currently working towards his Ph.D.
