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 mov- ing targets remains challenging. Various factors can degrade the estimation accuracy, including the Doppler effect, interference, and high noise. This thesis addresses the challenges of indoor localization and tracking systems and proposes several solutions. Using a novel signal design, which we named Differential Zadoff-Chu, we developed al- gorithms that accurately estimate the distances of static and moving targets, even un- der random Doppler shifts. We then developed a high-resolution multi-target ranging algorithm that estimates the ranges to targets at proximity based on the Levenberg- Marquardt algorithm. These ranging algorithms require a line of sight (LOS) between the transmitter and the receiver. Therefore, we designed an algorithm to classify re- ceived signals as LOS and non-LOS by exploiting a room’s geometry. Transforming distances into a 2D or 3D location and orientation requires solving an optimization problem. We propose using three nodes arranged as an isosceles triangle to deter- mine the position and orientation of a target. Utilizing the geometry of the isosceles triangle, we developed a highly accurate location and orientation estimation algo- rithm by solving a constrained optimization problem. Finally, we propose a Kalman filter to improve the tracking accuracy of moving targets even under non-LOS condi- tions. This 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 a moving target’s position and orientation. We validated the proposed algorithms via numerical simu- lations and real experiments using low-cost ultrasound hardware. The results showed that the proposed algorithms outperformed current state-of-the-art in accuracy and complexity.