Multi-Floor Indoor Localization Based on RBF Network With Initialization, Calibration, and Update

Yang, Ling; Yu, Yangkang; Li, Bofeng

doi:10.1109/twc.2021.3089202

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…Studies on floor classification using RF signals are actively in progress [ 17 , 18 ]. Because WiFi AP and BLE beacons are installed on each floor of the building, the user’s floor can be determined using the RSSI signal pattern.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, these methods are difficult to apply universally because of the need for reference barometric pressure information and the need to pre-calibrate the pressure sensor in the user's smartphone. Studies on floor classification using RF signals are actively in progress [17,18]. Because WiFi AP and BLE beacons are installed on each floor of the building, the user's floor can be determined using the RSSI signal pattern.…”

Section: Related Workmentioning

confidence: 99%

“…To address this problem, Shao proposed multiple 3D autonomous blocks through a clustering algorithm based on WiFi fingerprints using RSSI and spatial similarity [19]. The same block contains Studies on floor classification using RF signals are actively in progress [17,18]. Because WiFi AP and BLE beacons are installed on each floor of the building, the user's floor can be determined using the RSSI signal pattern.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones

Shin,

Kim,

Lee

2024

Sensors

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Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims to accurately estimate the location of various people using smartphones in a building with a cloud platform-based localization system. The proposed technology is modularized in a hierarchical structure to sequentially estimate the floor and location. This system comprises four localization modules: course level detection, fine level detection (FLD), fine location tracking (FLT), and level change detection (LCD). Each module operates organically according to the current user status. The position estimation range is defined as a total of three phases, and an appropriate location estimation module suitable for the corresponding phase operates to estimate the user’s location gradually and precisely. When the user’s floor is determined by an FLD, the two-dimensional position of the user is estimated by an FLT module that tracks the user’s position by comparing the received signal strength indicator vector sequence and radio map. Also, LCD recognizes the user’s floor change and converts the user’s phase. To verify the proposed technology, various experiments were conducted in a six-story building, and an average accuracy of less than 2 m was obtained.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones

Shin,

Kim,

Lee

2024

Sensors

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“…We run each algorithm 10 times for all test cases Our code is available online. 2 Algorithms used for comparison: We consider the following state-of-the-art algorithms where two of them are combined with our proximity-based hierarchical clustering (denoted as Prox) for fair comparison:…”

Section: A Experiments Settingsmentioning

confidence: 99%

GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals

Zhuo

Zhao

et al. 2022

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)

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We study the problem of floor identification for radiofrequency (RF) signal samples obtained in a crowdsourced manner, where the signal samples are highly heterogeneous and most samples lack their floor labels. We propose GRAFICS, a graph embedding-based floor identification system. GRAFICS first builds a highly versatile bipartite graph model, having APs on one side and signal samples on the other. GRAFICS then learns the low-dimensional embeddings of signal samples via a novel graph embedding algorithm named E-LINE. GRAFICS finally clusters the node embeddings along with the embeddings of a few labeled samples through a proximity-based hierarchical clustering, which eases the floor identification of every new sample. We validate the effectiveness of GRAFICS based on two large-scale datasets that contain RF signal records from 204 buildings in Hangzhou, China, and five buildings in Hong Kong. Our experiment results show that GRAFICS achieves highly accurate prediction performance with only a few labeled samples (96% in both micro-and macro-F scores) and significantly outperforms several state-of-the-art algorithms (by about 45% improvement in micro-F score and 53% in macro-F score).

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“…Wi-Fi technology is not affected by external factors such as light, humidity, temperature, etc., and it can penetrate obstacles well within a certain frequency band. Indoor localization based on Wi-Fi, which is provided with the above advantages, has been widely studied in numerous fields, especially fingerprinting techniques based on Received Signal Strength (RSS) [7][8][9] and Channel State Information (CSI) 10 . However, due to the large amount of collected data and the high requirements of multi-dimensional data on hardware and software, it is challenging to use CSI in real environments.…”

Section: Introductionmentioning

confidence: 99%

A Fingerprint Dictionary Processing Approach in Indoor Localization System Based on Wi-Fi

Yang,

Wang,

Cheng

et al. 2024

Preprint

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Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms often prioritize enhancing online phase accuracy, overlooking the importance of offline data preprocessing, which can negatively impact overall performance. This study introduces a novel approach called Fingerprint Dictionary Preprocessing (FDP) that employs Convolutional Dictionary Learning (CDL) to process radio map data. CDL learns a set of kernels capturing site characteristics, representing RSS values from Access Points (APs) in a sparse manner. The proposed FDP system compresses data through feature learning, reducing storage and bandwidth requirements for data transmission. In the online phase, CDL is utilized for assisting matching fingerprints against the learned dictionary, accurately locating users. The contributions of the FDP system presenting a cost-effective and practical solution for indoor localization, addressing the challenges associated with large data collection and multi-dimensional data requirements, making it a promising approach for real-world applications. We conducted experiments in two real indoor environments, and the results indicated that the proposed FDP system, whether applied to the original radio map or the preprocessed fingerprint database, led to improved localization accuracy and reduced localization time.

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Multi-Floor Indoor Localization Based on RBF Network With Initialization, Calibration, and Update

Cited by 14 publications

References 29 publications

Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones

Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones

GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals

A Fingerprint Dictionary Processing Approach in Indoor Localization System Based on Wi-Fi

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