A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

Yaro, Abdulmalık Shehu; Maly, Filip; Pražák, Pavel

doi:10.3390/s23052545

Cited by 16 publications

(27 citation statements)

References 80 publications

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“…Based on the propagation speed and time difference of UWB signals in space, the actual position of the label can be calculated, thus achieving high-precision three-dimensional indoor positioning. Compared to other indoor positioning technologies, UWB indoor positioning technology has the following advantages [1][2]:…”

Section: Design Of Methodologymentioning

confidence: 99%

“…However, these products all have some drawbacks. RFID systems use GPS systems to locate luggage, but GPS accuracy can only reach up to 3-5m, which cannot allow tourists to accurately locate the specific location of their luggage [1]; The Bluetooth connection used by AirTag is prone to interference and requires binding with Apple phones. If there are no Apple products within a certain range, the positioning function of AirTag will not work.4.…”

Section: Introductionmentioning

confidence: 99%

“…1 (𝑏 1 − 𝑥 0 ) + 𝑎 2 (𝑏 2 − 𝑦 0 ) + 𝑎 3 (𝑏 3 − 𝑧 0 ) 𝐹 = (𝑥 0 − 𝑏 1 ) 2 + (𝑦 0 − 𝑏 2 ) 2 + (𝑧 0 − 𝑏 3 )2 …”

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UL-TDOA airport baggage positioning system based on UWB and Wi-Fi technologies

Liao,

Chen,

Huang

et al. 2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

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With the increasing convenience of transportation in recent years, the daily passenger flow of the airport has increased significantly. This has put forward higher requirements for baggage management at airports. In order to ensure the airport manages checked baggage and helps passengers find their baggage easily and conveniently, this paper presents an airport baggage positioning system using a wireless communication system. First, signals of both UWB and Wi-Fi frequencies are transmitted through the transmitter on the luggage. Then, based on UL-TDOA technology, the specific location of the baggage is calculated by the time difference between the different base stations receiving the signal. The system designed in this paper has obtained good simulation accuracy through MATLAB simulation tests. Compared with other similar products or methods in the specific environment, by higher positioning accuracy and other advantages.

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Section: Design Of Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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UL-TDOA airport baggage positioning system based on UWB and Wi-Fi technologies

Liao,

Chen,

Huang

et al. 2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

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“…Kalman filter [ 21 , 32 ] is a widely used method for filtering signals in IPS. In addition, machine learning [ 7 , 10 , 14 , 29 , 33 ] and deep learning [ 12 , 30 , 34 ] methods are being employed to optimize signal features which are not covered by standard localization techniques. Network issues, environmental factors affecting signal quality, and hardware malfunction can impact collected data.…”

Section: Introductionmentioning

confidence: 99%

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Garcia,

Inoue

2024

Sensors

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In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

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“…Furthermore, there is no research yet that focuses on the derivation of a CRLB for such fused algorithms. Other methods for data fusion of multiple measurements incorporate machine learning, for example, methods based on channel state information [ 25 , 26 ] or support vector regression [ 27 , 28 ]. Machine learning algorithms need training data to work, which is not necessary for our proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

Wideband TDoA Positioning Exploiting RSS-Based Clustering

Fuchs

Wielandner

Neunteufel

et al. 2023

Sensors

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The accuracy of radio-based positioning is heavily influenced by a dense multipath (DM) channel, leading to poor position accuracy. The DM affects both time of flight (ToF) measurements extracted from wideband (WB) signals—specifically, if the bandwidth is below 100 MHz—as well as received signal strength (RSS) measurements, due to the interference of multipath signal components onto the information-bearing line-of-sight (LoS) component. This work proposes an approach for combining these two different measurement technologies, leading to a robust position estimation in the presence of DM. We assume that a large ensemble of densely-spaced devices is to be positioned. We use RSS measurements to determine “clusters” of devices in the vicinity of each other. Joint processing of the WB measurements from all devices in a cluster efficiently suppresses the influence of the DM. We formulate an algorithmic approach for the information fusion of the two technologies and derive the corresponding Cramér-Rao lower bound (CRLB) to gain insight into the performance trade-offs at hand. We evaluate our results by simulations and validate the approach with real-world measurement data. The results show that the clustering approach can halve the root-mean-square error (RMSE) from about 2 m to below 1 m, using WB signal transmissions in the 2.4 GHz ISM band at a bandwidth of about 80 MHz.

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A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

Cited by 16 publications

References 80 publications

UL-TDOA airport baggage positioning system based on UWB and Wi-Fi technologies

UL-TDOA airport baggage positioning system based on UWB and Wi-Fi technologies

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Wideband TDoA Positioning Exploiting RSS-Based Clustering

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