Bluetooth-Based WKNNPF and WKNNEKF Indoor Positioning Algorithm

Pheng, Sokliep; Li, Ji; Luo, Xiangfeng; Zhong, Yanru; Jiang, Zetao

doi:10.1109/icaci52617.2021.9435858

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Edgar S et al [ 13 ] introduced an improved hybrid technique that combines received signal strength information from available WLAN access points with wireless sensor network technology for fingerprint-based indoor positioning, effectively reducing positioning errors. Peng S et al [ 14 ] developed an algorithm that integrates Weighted K-nearest neighbors and Kalman Filtering, proposing enhancements like weighted K-nearest neighbor Particle filtering and weighted K-nearest neighbor extended Kalman filtering to lower positioning errors effectively. Yue D et al [ 15 ] proposed a navigation localization model based on an improved-bee-algorithm-optimized BP network.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices

Zhang,

Shen,

Yao

et al. 2024

Sensors

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The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm’s data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

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Section: Introductionmentioning

confidence: 99%

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices

Zhang,

Shen,

Yao

et al. 2024

Sensors

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“…However, the WKNN algorithm has the disadvantages of large time consumption and low accuracy when processing the RSSI difference [12]. Considering the inaccurate weighting of RPs by the traditional WKNN algorithm using the reciprocal of the RSSI difference, Pheng S [13] proposed an improved WKNN algorithm based on the physical distance weighting of RSSI. Bundak C E A [14] put forward an improved WKNN algorithm which, without a fixed K value, established a KNN model via support vector regression to determine the K value of each specific position, achieving higher positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost

Zhang

Chen

et al. 2023

Sensors

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Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26–45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance.

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Bluetooth-Based WKNNPF and WKNNEKF Indoor Positioning Algorithm

Cited by 2 publications

References 10 publications

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices

An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost

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