The goal of this paper is to investigate Ultra Wide-Band (UWB) localization with Time Difference of Arrival (TDoA) processing at the anchors. We consider scenarios where the anchors are placed very close to each other and the target to be localized is around the group of anchors. All target-anchor communications are assumed to be in Line-Of-Sight (LOS). Since our analysis shows that symmetries in anchors' placement, with respect to the target position, degrade the positioning accuracy of standard algorithms, we propose to use a Subset Selection (SS) strategy, where position estimates obtained with properly selected subsets of asymmetric anchors are fused together to get the final localization output. Our results show improved localization accuracy with respect to the use of all anchors, especially in estimating the angle of arrival. Finally, we analyze the impact of an inaccurate time synchronization among the anchors, deriving guidelines for hardware implementation.

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In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra Wide-Band (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three "hybrid" radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively.

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