Research on Developing an Outdoor Location System Based on the Ultra-Wideband Technology

Rykała, Łukasz; Typiak, A.; Typiak, Rafał

doi:10.3390/s20216171

Cited by 13 publications

(19 citation statements)

References 25 publications

(55 reference statements)

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“…The corresponding errors in the x - y horizontal plane are 12 cm and 20 cm, respectively. The improvement using the redundant robust estimation is about 50%, which is by far better than other approaches found in the literature that makes use of median or moving averages as in [ 13 ] that account for improvements in the range from 10 to 20%. Our positioning errors are also better than other UWB solutions using a limited number of UWB anchors but complemented with INS+GNSS, as in [ 17 ], where authors declare a horizontal error of 35 cm.…”

Section: Laboratory Experimentation Implementation Of the Ekf-basmentioning

confidence: 81%

“…When non redundancy is available, due to working only with the minimum number of ranges for trilateration, then temporal filtering, such as median filters or moving averaging are employed, as in this [ 13 ] UWB localization implementation. This kind of temporal in-range median-based solutions can circumvent the presence of sporadic outliers but fail when those errors are systematic.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Jiménez

Seco

2021

Sensors

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The location of people, robots, and Internet-of-Things (IoT) devices has become increasingly important. Among the available location technologies, solutions based on ultrawideband (UWB) radio are having much success due to their accuracy, which is ideally at a centimeter level. However, this accuracy is degraded in most common indoor environments due to the presence of obstacles which block or reflect the radio signals used for ranging. One way to circumvent this difficulty is through robust estimation algorithms based on measurement redundancy, permitting to minimize the effect of significantly erroneous ranges (outliers). This need for redundancy often conflicts with hardware restraints put up by the location system’s designers. In this work, we present a procedure to increase the redundancy of UWB systems and demonstrate it with the help of a commercial system made by Decawave. This system is particularly easy to deploy, by configuring a network of beacons (anchors) and devices (tags) to be located; however, its architecture presents a major disadvantage as each tag to be located can only measure ranges to a maximum of four anchors. This limitation is embedded in the Positioning and Networking Stack (PANS) protocol designed by Decawave, and therefore is not easy to bypass without a total redesign of the firmware. In this paper, we analyze the strategies that we have been able to identify in order to provide this equipment with multiple range measurements, and thus enable each tag to be positioned with more than four measured ranges. We will see the advantages and disadvantages of each of these strategies, and finally we will adopt a solution that we implemented to be able to measure up to eight ranges for each mobile device (tag). This solution implies the duplication of the tags at the mobile user, and the creation of a double interleaved network of anchors. The range among tags and the eight beacons is obtained through an API via a wireless BLE protocol at a 10 Hz rate. A robustified Extended Kalman filter (EKF) is designed to estimate, by trilateration, the position of the pair of mobile tags, using eight ranges. Two different scenarios are used to make localization experimentation: a laboratory and an apartment. Our position estimation, which exploits redundant information and performs outlier removal, is compared with the commercial solution limited to four ranges, demonstrating the need and advantages of our multi-range approach.

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Section: Laboratory Experimentation Implementation Of the Ekf-basmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Jiménez

Seco

2021

Sensors

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“…Indoor localization using ultra-wideband (UWB) radio is a popular research topic [ 1 , 2 , 3 , 4 , 5 , 6 ] and an off-the-shelf real-time localization system [ 7 ]. Its suitability for mobile applications and its great localization accuracy provide solutions for a wide spectrum of applications.…”

Section: Introductionmentioning

confidence: 99%

Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model

Krapež

Vidmar

Munih

2021

Sensors

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An ultra-wideband (UWB) localization system is an alternative in a GPS-denied environment. However, a distance measurement with UWB modules using a two-way communication protocol induces an orientation-dependent error. Previous research studied this error by looking at parameters such as the received power and the channel response signal. In this paper, the neural network (NN) method for correcting the orientation-induced distance error without the need to calculate the signal strength, obtain the channel response or know any parameters of the antenna and the UWB modules is presented. The NN method utilizes only the measured distance and the tag orientation, and implements an NN model obtained by machine learning, using measurements at different distances and orientations of the two UWB modules. The verification of the experimental setup with 12 anchors and a tag shows that with the proposed NN method, 5 cm better root mean square error values (RMSEs) are obtained for the measured distance between the anchors and the tag compared to the calibration method that did not include orientation information. With the least-square estimator, 14 cm RMSE in 3D is obtained with the NN model corrected distances, with a 9 cm improvement compared to when raw distances are used. The method produces better results without the need to obtain the UWB module’s diagnostics parameters that are required to calculate the received signal strength or channel response, and in this way maintain the minimum packet size for the ranging protocol.

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“…The first step in building a follow-me system is having the ability to determine the precise position of the guide in relation to the UGV. This is the premise for the study presented in the following paper, where this process forms a basis for the subsequent determination of the desired UGV trajectory [12]. A trajectory of the guide's movement is generated that connects two or more of the guide's locations.…”

Section: Introductionmentioning

confidence: 99%

Configuring a UWB Based Location System for a UGV Operating in a Follow-Me Scenario

2021

Self Cite

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Unmanned Ground Vehicles (UGV) are devices capable of performing basic working movements without the operator being in their immediate working environment. Their capabilities include but are not limited to the perception of the environment with the use of sensors, determining the platform’s position, and planning and executing its movement. Ultra Wideband (UWB) is one of the wireless communication technologies which is increasingly used in location systems. This article presents the use of UWB technology in developing a guide localization system for a UGV (one of the stages of implementing a follow-me system). The article describes tests carried out on the developed testbed. Their aim was to determine the hardware configuration of the anchor arrangement characterized by the minimum number of lost data packets during operation. In order to determine the influence of the analysed variables on the output values, the method of global sensitivity analysis for neural networks was used.

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Research on Developing an Outdoor Location System Based on the Ultra-Wideband Technology

Cited by 13 publications

References 25 publications

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Improving the Accuracy of Decawave’s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges

Distance Measurements in UWB-Radio Localization Systems Corrected with a Feedforward Neural Network Model

Configuring a UWB Based Location System for a UGV Operating in a Follow-Me Scenario

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