UWB-based Indoor Localization Using Ray-tracing Algorithm

Hyun, Jieum; Oh, Taekjun; Lim, Hyungtae; Myung, Hyun

doi:10.1109/urai.2019.8768568

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…The application of a more sophisticated positioning algorithm, also taking into account DMC and possibly prior scenario geometry information, e.g., for ray-tracing as shown, for example, in [ 35 ], is likely to further improve the obtained results. An extension of the model to the third spatial (vertical) dimension might lead to improvement.…”

Section: Discussionmentioning

confidence: 99%

Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment

Neunteufel

Grebien

Arthaber

2022

Sensors

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The localization of iot nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as rss or aoa, are inherently prone to multipath fading. Especially for tof measurements, the low available transmit bandwidth of the used transceiver hardware is problematic. In our previous work on this topic we showed that wideband signal generation on narrowband low-power transceiver chips is feasible without any changes to existing hardware. Together with a fixed wideband receiving anchor infrastructure, this facilitates tdoa and aoa measurements and allows for localization of the fully asynchronously transmitting nodes. In this paper, we present a measurement campaign using a receiver infrastructure based on sdr platforms. This proves the actual usability of the proposed method within the limitations of the bandwidth available in the ism band at 2.4GHz. We use the results to analyze the effects of possible anchor placement schemes and scenario geometries. We further demonstrate how this node-to-infrastructure-based localization scheme can be supported by additional node-to-node rss measurements using a simple clustering approach. In the considered scenario, an overall positioning rmse of 2.19m is achieved.

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Section: Discussionmentioning

confidence: 99%

Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment

Neunteufel

Grebien

Arthaber

2022

Sensors

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“…The experimental results show that the positioning accuracy of this algorithm is within 40 cm in non-line-of-sight environments. Hyun et al 13 applied the ray tracing algorithm in a fixed scenes to UWB NLOS recognition, and the experimental results showed that the error of the algorithm was stable at about 25 cm. Li et al 14 proposed a positioning method that achieves an accuracy of less than 20 cm.…”

Section: Introductionmentioning

confidence: 99%

Clustering Fusion Algorithm for ImprovedAccuracy in UWB Indoor Positioning

Guo,

Yin,

Song

et al. 2024

Preprint

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Due to the presence of non-line-of-sight (NLOS) obstacles, the localization accuracy in ultra-wideband (UWB) wireless indoor localization systems is typically substantially lower. To minimize the influence of these environmental factors and improve the accuracy of indoor wireless positioning, a novel fusion optimization algorithm is proposed in this paper, which combines the density-based spatial clustering algorithm with noise (DBSCAN) and particle swarm optimization (PSO) algorithm. The positioning error of this algorithm remains is stable within 3 cm in static positioning scenarios, and can achieve high accuracy in NLOS environments.

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“…In the event of accidents in these areas, it is difficult to accurately locate them, so drones can play a role in this situation, replacing traditional fixed APs and solving the problem of zero-point coverage. In [7], the researchers proposed a new UAV localization method that uses ultra-wideband radio signals as localization signals and can effectively improve the localization performance in non-line-of-sight situations by applying the correction values of ray-tracing algorithms to ultra-wideband ranging data. However, this approach requires the deployment of multiple positioning base stations to collect enough signal data to enable the positioning of the UAV.…”

Section: Introductionmentioning

confidence: 99%

Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection

Pan

Tan

et al. 2023

Drones

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To improve the limited number of fixed access points (APs) and the inability to dynamically adjust them in fingerprint localization, this paper attempted to use drones to replace these APs. Drones have higher flexibility and accuracy, can hover in different locations, and can adapt to different scenarios and user needs, thereby improving localization accuracy. When performing fingerprint localization, it is often necessary to consider various factors such as environmental complexity, large-scale raw data collection, and signal strength variation. These factors can lead to high-dimensional and complex nonlinear relationships in location fingerprints, thereby greatly affecting localization accuracy. In order to overcome these problems, this paper proposes a kernel global locally preserving projection (KGLPP) algorithm. The algorithm can reduce the dimensionality of location fingerprint data while preserving its most-important structural information, and it combines global and local information to avoid the problem of reduced information and poor dimensionality reduction effects, which may arise from considering only one. In the process of location estimation, an improved weighted k-nearest neighbor (IWKNN) algorithm is adopted to more accurately estimate the target’s position. Unlike the traditional KNN or WKNN algorithms, the IWKNN algorithm can choose the optimal number of nearest neighbors autonomously, perform location estimation and weight calculation based on the actual situation, and thus, obtain more-accurate location estimation results. The experimental results showed that the algorithm outperformed other algorithms in terms of both the average error and localization accuracy.

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UWB-based Indoor Localization Using Ray-tracing Algorithm

Cited by 17 publications

References 10 publications

Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment

Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment

Clustering Fusion Algorithm for ImprovedAccuracy in UWB Indoor Positioning

Drone-Assisted Fingerprint Localization Based on Kernel Global Locally Preserving Projection

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