UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Abstract

Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system.

Experimental Evaluation of IEEE 802.15.4z UWB Ranging Performance under Interference

“…The unique capabilities of UWB such as fine-granular time resolution enabled research on accuracy and reliability improvements such as the assessment of the channel response available at the transceiver [9][10][11][12][13]. An overview of the sources of TOA estimation errors is given in Figure 1.…”

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

“…• Fingerprinting [72], [73]: CIRs is assumed to be spatially significant and the relation of the signal propagation to the environment is exploited for positioning by implicit modelling using a set of pre-recorded training data. • Multipath-SLAM [74]- [77]: CIRs is used to jointly estimate the position of virtual anchors (i.e.…”

Indoor Drone Positioning: Accuracy and Cost Trade-Off for Sensor Fusion

“…For example, a large open space of a 1000 square meters would only require 4 UWB anchors in the corners to position the drone adequately, while hundreds of ArUco markers would need to be placed to cover the entire area, and their positions need to be calibrated accurately, leading to excessive personnel costs. Alternatively a collection of racks can easily be fitted with ArUco markers on known locations (e.g., on the crossings of the horizontal and vertical beams that make up the rack structure, whose location is known), while every hallway would require UWB anchors due to the electromagnetic disruption of the metal racks and Non-Line of Sight signals, requiring additional intelligent processing and anchor node selection [35].…”