Fingerprint Augment Based on Super-Resolution for WiFi Fingerprint Based Indoor Localization

Lan, Tian; Wang, Xianmin; Chen, Zhikun; Zhang, Sihai

doi:10.1109/jsen.2022.3174600

Cited by 39 publications

(8 citation statements)

References 43 publications

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“…The success of machine learning (ML) algorithms highly depends on the existence of a large number of datasets, but the collection of datasets, especially labeled ones for supervised learning, could be a challenging task in applications such as large-scale invasive examinations in medical testing [18,19] and multi-building and multi-floor indoor localization for a large-scale building complex [20] due to the issues of privacy and the high labor and time costs in collecting and labeling the data. Data augmentation has become a viable solution in this regard and has been applied widely to the categorization of images [21] and texts [22].…”

Section: Data Augmentationmentioning

confidence: 99%

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Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Tang,

Li,

Kim

et al. 2024

Sensors

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Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

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Section: Data Augmentationmentioning

confidence: 99%

“…Sinha et al converted a file containing 256 RSSI values into a 16×16 image as input to a Convolutional Neural Network (CNN) [24,25]. Lan et al proposed a super-resolutionbased fingerprint augmentation framework to achieve conversion between fingerprint data and fingerprint images [20].…”

Section: Indoor Localization Data Augmentationmentioning

confidence: 99%

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Tang,

Li,

Kim

et al. 2024

Sensors

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“…Wi-Fi-based localisation has emerged as a promising and effective approach for indoor localisation. One popular method is the fingerprinting process, which involves collecting CSI/RSSI data from various locations within a building to build a comprehensive database for estimating a user's location by comparing changes in CSI data with pre-collected fingerprints [4]. A user's location is estimated by comparing changes of CSI data with the pre-collected fingerprints.…”

Section: Related Workmentioning

confidence: 99%

“…Two main categories of device-free localisation methods have been proposed fingerprinting-based and model-based schemes. Fingerprinting-based schemes extract features from channel state information (CSI) or received signal strength indicator (RSSI) to create a feature-position library [2][3][4] However, they are labour-intensive and require frequent updates as the environment changes. Conversely, model-based schemes use a specific dataset to establish the correlation between radio signals and positions [1,5,6].…”

Section: Introductionmentioning

confidence: 99%

Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information

Abuhoureyah,

Yan Chiew,

Bin Mohd Isira

et al. 2023

IET Wireless Sensor Systems

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The trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device‐free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine‐grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real‐time location‐tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location‐independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real‐world environments.

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“…Fingerprint‐based localization has become widely utilized in indoor localization due to its high accuracy. Nowadays, fingerprint‐based indoor localization methods mainly rely on Bluetooth [3], ZigBee [4], and Wi‐Fi [5]. Among them, indoor Wi‐Fi fingerprint‐based localization has become eye‐catching due to its high positioning accuracy and low material cost.…”

Section: Introductionmentioning

confidence: 99%

Received signal strength reconstruction using pix2pix generative adversarial network

Wu,

Qin,

Zhang

2023

Electronics Letters

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Positioning technology based on received signal strength (RSS) fingerprints has emerged as a vital research topic in indoor localization due to its high accuracy and low cost. Increasing the density of offline fingerprints collected can effectively improve localization accuracy in the online stage, but it will result in astronomical collection costs. RSS reconstruction using generative models is an efficient solution to dramatically reduce fingerprint collection costs while maintaining excellent accuracy. In this paper, the authors propose a pix2pix generative adversarial network based RSS reconstruction model, which can generate a dense RSS fingerprint map based on only the location information of the wireless access point. The accuracy and efficiency of the proposed model are verified in an indoor environment.

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Fingerprint Augment Based on Super-Resolution for WiFi Fingerprint Based Indoor Localization

Cited by 39 publications

References 43 publications

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information

Received signal strength reconstruction using pix2pix generative adversarial network

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