TIPS: Transformer Based Indoor Positioning System Using Both CSI and DoA of WiFi Signal

Zhang, Zhongfeng; Du, Hongxin; Choi, Seungwon; Cho, Sung Ho

doi:10.1109/access.2022.3215504

Cited by 13 publications

(8 citation statements)

References 42 publications

(55 reference statements)

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“…We compared the Euclidean distance error using existing commercialized machine learning algorithms and displayed the results in Fig. 10, which suggests the feasibility of the [41,42,43,44].…”

Section: ) Indoor Localization Errorsmentioning

confidence: 96%

DeepRSSI: Generative Model for Fingerprint-Based Localization

Yoon,

Jung,

Kim

2024

IEEE Access

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In this paper, we present an innovative methodology for generating virtual received signal strength indicator (RSSI) fingerprint maps to improve indoor localization systems and wireless communication systems using RSSI. Focusing on the challenge of extensive labor and time required in traditional data collection, we propose a generative model that combines customized attention mechanism with a conditional variational autoencoder (cVAE), leveraging datasets compiled from direct measurements of RSSI values from different access points in a real-world indoor environment. Our model uniquely synthesizes high-quality virtual RSSI maps, significantly reducing the need for extensive physical data collection while enhancing the accuracy and efficiency of indoor positioning systems. By integrating measured data with innovative data generation techniques, our approach offers a novel solution to indoor localization challenges. In addition, this model can augment high-quality synthetic data for indoor wireless signals to expand the volume of available data. We quantitatively demonstrate the effectiveness of our model, showing an average improvement of over 40% in Euclidean distance errors across several machine learning algorithms compared to existing methods. Our experiments validate that the virtual RSSI fingerprint map yields accurate position estimates, with performance enhancements observed in algorithms that confirm the utility in real-world scenarios. The contribution of our research improves indoor localization systems by improving indoor positioning accuracy and addresses the limitations of traditional fingerprinting methods, setting the stage for future innovations in wireless communication.

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Section: ) Indoor Localization Errorsmentioning

confidence: 96%

DeepRSSI: Generative Model for Fingerprint-Based Localization

Yoon,

Jung,

Kim

2024

IEEE Access

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“…Frequency Identification (RFID), Bluetooth, WiFi fingerprinting, etc. Recently, [3], [9] has shown that WiFi with channel state information can be utilized for Indoor Positioning System (IPS) and have the same positioning cost and its accuracy is not inferior to UWB. Non-line-of-sight (NLoS) conditions are a major hindrance in achieving high positioning accuracy [10], [11].…”

Section: Beside This Various Alternative Technologies Have Been Exten...mentioning

confidence: 99%

Novel Fine-Tuned Attribute Weighted Naïve Bayes NLoS Classifier for UWB Positioning

et al. 2023

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In this paper, we propose a novel Fine-Tuned attribute Weighted Naïve Bayes (FT-WNB) classifier to identify the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) for UltraWide Bandwidth (UWB) signals in an Indoor Positioning System (IPS).The FT-WNB classifier assigns each signal feature a specific weight and fine-tunes its probabilities to address the mismatch between the predicted and actual class. The performance of the FT-WNB classifier is compared with the state-of-the-art Machine Learning (ML) classifiers such as minimum Redundancy Maximum Relevance (mRMR)-k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Neural Network (NN). It is demonstrated that the proposed classifier outperforms other algorithms by achieving a high NLoS classification accuracy of 99.7% with imbalanced data and 99.8% with balanced data. The experimental results indicate that our proposed FT-WNB classifier significantly outperforms the existing state-of-the-art ML methods for LoS and NLoS signals in IPS in the considered scenario.

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“…Methods such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) have been extensively tested by the DL community [5]. The Transformer network [6] offers several advantages over other sequence-processing neural networks like Recurrent Neural Networks (RNN) [7] and Long-Short Term Memory (LSTM) [8] and has been effectively deployed in applications for natural language processing [9,10,11], computer vision [12], and tested for indoor localization using CSI [13,14]. The Transformer uses an attention mechanism to capture pairwise relationships while effectively learning long-range dependencies in the input.…”

Section: Introductionmentioning

confidence: 99%

HyTra: Hyperclass Transformer for WiFi Fingerprinting-based Indoor Localization

Nasir,

Esguerra,

Faye

et al. 2024

TESEA

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The emerging demand for a variety of novel Location-based Services (LBS) by consumers and industrial users is driven by the rapid and extensive proliferation of mobile smart devices. Sensors embedded in smart devices or machines provide wireless connectivity and Global Positioning System (GPS) capability, and are co-utilized to acquire location-linked data which are algorithmically transformed into reliable and accurate location estimates. GPS is a mature and reliable technology for outdoor localization but indoor localization in a complex multi-storey building environment remains challenging due to fluctuations in wireless signal strength arising from multipath fading. Location-linked data from wireless access points (WAPs) such as received signal strength (RSS) are acquired as numerical sequences. By conceptualizing a fixed order sequence of WAP measurements as a sentence where the RSS from each WAP are words, we may leverage on recent advances in artificial intelligence for natural language processing (NLP) to enhance localization accuracy and improve robustness against signal fluctuations. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer neural network which learns the relative positions of wireless access points (WAPs) through multiple learnable embeddings. We propose a second network, HyTra-HF, which improves upon HyTra by applying a hierarchical relationship between location classes. We test our proposed networks on public and private datasets varying in sizes. HyTra-HF outperforms existing deep learning solutions by obtaining 96.7\% accuracy for the floor classification task on the UJIIndoorloc dataset. HyTra-HF is amenable to deep model compression and achieves accuracy of 95.95\% with over ten-fold reduction in model size using Sparsity Aware Orthogonal (SAO) initialization and has the best-in-class accuracy for the sparse model.

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TIPS: Transformer Based Indoor Positioning System Using Both CSI and DoA of WiFi Signal

Cited by 13 publications

References 42 publications

DeepRSSI: Generative Model for Fingerprint-Based Localization

DeepRSSI: Generative Model for Fingerprint-Based Localization

Novel Fine-Tuned Attribute Weighted Naïve Bayes NLoS Classifier for UWB Positioning

HyTra: Hyperclass Transformer for WiFi Fingerprinting-based Indoor Localization

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