Self-calibration of anchor positions for indoor localization

Yu, Wentao; Zhao, Xin; Sun, Guangyi

doi:10.1109/robio.2017.8324479

Cited by 5 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar experimental results as in [90] are found in [113]. A two-process algorithm called SELF-CAL is proposed.…”

Section: Self-calibration With Uwbsupporting

confidence: 71%

“…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%

“…We have shown that a great number of solutions that primarily focus on UWB already exists [3,9,20,38,63,78,80,90,100,101,103,107,113]. However, none of them in fact makes use of UWB-specific features (see Section 4) to boost the performance.…”

Section: Collaborative Localization With Uwbmentioning

confidence: 99%

See 2 more Smart Citations

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

“…Similar experimental results as in [90] are found in [113]. A two-process algorithm called SELF-CAL is proposed.…”

Section: Self-calibration With Uwbsupporting

confidence: 71%

Section: Self-calibration With Uwbmentioning

confidence: 99%

See 1 more Smart Citation

Self-calibration and Collaborative Localization for UWB Positioning Systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Yu et al [18] describes an algorithm called SELF-CAL, which is composed of two separate steps. Firstly, authors apply Markov State Transition Equation (MSTE) to calculate the state vectors of all coordinates of the anchors and secondly the position of the anchors are estimated by implementing an iterative trilateral localization technique.…”

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

“…The n self-positioning base stations are sorted according to the x -axis coordinates in the coordinate system from small to large; the distance between the n self-positioning base stations and the two base stations with known positions is calculated by the Least Square method to obtain the position coordinates. Yu et al [ 12 ] describe a self-calibration method for base station’s position for indoor positioning. The Markov State Transition Equation is used to calculate the state vector of all the coordinates between the base stations.…”

Section: Introductionmentioning

confidence: 99%

TOF-Based Fast Self-Positioning Algorithm for UWB Mobile Base Stations

Han

Zhang

Lai³

et al. 2021

Sensors

View full text Add to dashboard Cite

To solve the problem of heavy workload and high cost when acquiring the position of Ultra-Wideband (UWB) mobile base stations in sports fields, a fast self-positioning algorithm for UWB mobile base stations algorithm based on Time of Flight (TOF) is proposed. First, according to the layout of the base stations in the sports field, the local coordinate system is determined, and an equation based on the ranging information between the base stations is established; the Least Square method is used to calculate the coordinates of each base station, and the Newton Iteration method is used to converge the positioning results. Then the origin and propagation law of positioning error, as well as the method of reducing the positioning error are analyzed. The simulation data and experimental results show that the average positioning accuracy of the mobile base station is within 0.05 m, which meets the expected accuracy of the base station position measurement. Compared with traditional manual measurement methods, base station self-positioning can effectively save deployment time and reduce workload.

show abstract

Self-calibration of anchor positions for indoor localization

Cited by 5 publications

References 9 publications

Self-calibration and Collaborative Localization for UWB Positioning Systems

Self-calibration and Collaborative Localization for UWB Positioning Systems

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

TOF-Based Fast Self-Positioning Algorithm for UWB Mobile Base Stations

Contact Info

Product

Resources

About