Self calibration of the anchor nodes for UWB-IR TDOA based indoor positioning system

Vashistha, Ankush; Gupta, Ankur Kumar; Law, Choi Look

doi:10.1109/wf-iot.2018.8355163

Cited by 17 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Synchronization is again crucial in Vashistha et al [15], wherein anchors range with each other according to a predefined transmission scheme. In this case, their location is estimated using Differential TDOA (DTDOA).…”

Section: Self-calibration For Uwb Anchor Nodesmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Self-calibration For Uwb Anchor Nodesmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In fact, positioning any other mobile tag can be achieved by using the newly localized anchors. Various self-calibration approaches using UWB are reported in seven of the studies in Table 2 [3,38,78,80,90,103,113]. All of them are designed and/or validated using UWB indoor localization solutions.…”

Section: Self-calibration With Uwbmentioning

confidence: 99%

“…The idea to adopt TDOA is also used in [103], wherein a sequential and centralized low-cost self-calibration scheme is designed. Assuming the nodes are synchronized, the authors let the anchors range with each other (using a predefined transmission scheme) and by further computing the Differential TDOA (DTDOA), any anchor can be calibrated and its coordinates used in the positioning of mobile nodes (sequential solution).…”

Section: Self-calibration With Uwbmentioning

confidence: 99%

Self-calibration and Collaborative Localization for UWB Positioning Systems

et al. 2021

View full text Add to dashboard Cite

Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

show abstract

“…The fact that most large-scale testbeds employ 802.15.4 DSSS has motivated researchers to create their own UWBenabled setups. In [10], autonomous anchor calibration is studied. Tag localisation is the focus in [11] while [12] aims at enhancing the performance of fall detection solutions by localising the monitored elderly and providing the companion robot with the position where the suspected fall took place.…”

Section: Related Workmentioning

confidence: 99%

“…Tag localisation is the focus in [11] while [12] aims at enhancing the performance of fall detection solutions by localising the monitored elderly and providing the companion robot with the position where the suspected fall took place. Unfortunately, in addition to using homemade devices, the testbeds used in [10] and [11] involve heterogeneous node architectures, i.e. the tags can only perform transmission and not receive data.…”

Section: Related Workmentioning

confidence: 99%

LocURa: A New Localisation and UWB-Based Ranging Testbed for the Internet of Things

Bossche

Dalce

González

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

As many applications of the Internet of Things assume the knowledge of mobile object position, localisation has become a hot topic in the academic research community as well as for industrial R&D departments. Although distance measurements (ranging) and localisation can be studied though simulations, using real nodes provides more accurate and realistic results, especially when the nodes are deployed in the target environment. In order to support this effort, the paper presents LocURa4IoT, a new Localisation and UWB-based Ranging testbed for the Internet of Things. The testbed is made of 20 nodes based on Decawave DW1000 UWB-based transceiver and Arduino-compliant micro-controllers. The nodes' control infrastructure is presented in the paper, as well as a few results obtained with LocURa. Two open datasets are also published with the paper.

show abstract

Self calibration of the anchor nodes for UWB-IR TDOA based indoor positioning system

Cited by 17 publications

References 14 publications

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

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

Self-calibration and Collaborative Localization for UWB Positioning Systems

LocURa: A New Localisation and UWB-Based Ranging Testbed for the Internet of Things

Contact Info

Product

Resources

About