Benchmarking of Localization Solutions: Guidelines for the Selection of Evaluation Points

Djaja-Jośko

et al. 2020

Sensors

Localization systems are the source of data that allows to evaluate elderly person’s behaviour, to draw conclusions concerning his or her health status and wellbeing, and to detect emergency situations. The article contains a description of a system intended for elderly people tracking. Two novel solutions have been implemented in the system: a hybrid localization algorithm and a method for wireless anchor nodes synchronization. The algorithm fuses results of time difference of arrival and received signal strength measurements in ultrawideband (UWB) and Bluetooth Low Energy (BLE) radio interfaces, respectively. The system allows to change the intensity of UWB packets transmission to adapt localization accuracy and energy usage to current needs and applications. In order to simplify the system installation, communication between elements of the system infrastructure instead of wire interfaces is performed over wireless ones. The new wireless synchronization method proposed in the article consists in retransmission of UWB synchronization packets by selected anchor nodes. It allows for extension of the system coverage, which is limited by the short range of UWB transmission. The proposed solution was experimentally verified. The synchronization method was tested in a laboratory, and the whole system’s performance was investigated in a typical flat. Exemplary results of the tests performed with older adult participation in their own homes are also included.

“…Three narrowband technologies (Zigbee, Wi-Fi, and Bluetooth) were investigated in Reference [24]. Multilateration and Viterbi algorithms were used for position calculation.…”

Section: Related Workmentioning

confidence: 99%

UWB/BLE Tracking System for Elderly People Monitoring

Djaja-Jośko

et al. 2020

Sensors

“…This number is not fixed and should be adjusted to reach statistically significant results for the localization system, test environment, and the test and evaluation procedure. Guidelines for the selection of evaluation points are provided by de Poorter et al [ 44 ]. The poses can be selected based on various criteria, such as random grid-based sampling or by defining an application-dependent path.…”

Section: Test and Evaluation With The Tande 4iloc Frameworkmentioning

confidence: 99%

Meaningful Test and Evaluation of Indoor Localization Systems in Semi-Controlled Environments

Schyga

Hinckeldeyn

Kreutzfeldt

2022

Sensors

Despite their enormous potential, the use of indoor localization systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of test and evaluation (T&E) methodologies. The heterogeneous nature of ILS, their influences, and their applications pose various challenges for the design of a methodology that provides meaningful results. Methodologies for building-wide testing exist, but their use is mostly limited to associated indoor localization competitions. In this work, the T&E 4iLoc Framework is proposed—a methodology for T&E of indoor localization systems in semi-controlled environments based on a system-level and black-box approach. In contrast to building-wide testing, T&E in semi-controlled environments, such as test halls, is characterized by lower costs, higher reproducibility, and better comparability of the results. The limitation of low transferability to real-world applications is addressed by an application-driven design approach. The empirical validation of the T&E 4iLoc Framework, based on the examination of a contour-based light detection and ranging (LiDAR) ILS, an ultra wideband ILS, and a camera-based ILS for the application of automated guided vehicles in warehouse operation, demonstrates the benefits of T&E with the T&E 4iLoc Framework.

“…1b). Since it is important to make sure that our assumption is relevant [29], we extended the calculation to a bigger subset of measurement but this time without filtering settings and/or LOS conditions. More precisely, we look at 104 different measurements carried out for a single node.…”

Section: Averaging Over Multiple Rangesmentioning

confidence: 99%

UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach

et al. 2021

Ultra-wideband (UWB) positioning performance is highly related to the accuracy of the coordinates of the fixed anchor nodes, which form the system infrastructure. The process of determining the position of the anchors is called calibration. In an anchor-based system, it is crucial for the fixed nodes to know their locations with the highest possible accuracy. However, in certain situations, it is almost impossible to perform the calibration manually, e.g., during emergency interventions. Moreover, calibration is always delicate and time-consuming. We designed an effortless and accurate selfcalibration algorithm that does not require any manual intervention to precisely pinpoint the position of the anchors. This paper presents an innovative algorithm that combines machine learning and exploits the time resolution capabilities of UWB with adaptive physical settings to enable the automatic calibration of the fixed anchor nodes, even in realistic NLOS (non-line-of-sight) conditions. The self-calibration algorithm combines iterative gradient descent to pinpoint the positions of the anchors and uses error detection and correction from a convolutional neural network. Moreover, the algorithm can use a different set of settings for each anchor pair. This is done to ensure the most robust and accurate communication between nodes. Extensive measurements were carried out to allow anchors to estimate distances among each others. Distances were then combined and processed by the self-calibration algorithm. Experimental evaluation in two complex and large environments with many obstacles and reflections shows that accuracy reached by the algorithm is about 2.4 cm on average and 95th percentile is 5.7 cm, in best case. The results refer to the relative positions among the anchors. Results prove that in order to precisely calibrate the anchors nodes in an UWB positioning system, high correctness can be obtained by combining the accuracy of UWB together with deep learning and adaptive PHY modulation schemes.