Hi-Loc: Hybrid Indoor Localization via Enhanced 5G NR CSI

Ruan, Yongle; Chen, Liang; Zhou, Xin; Guo, Guangyi; Chen, Ruizhi

doi:10.1109/tim.2022.3196748

Cited by 36 publications

(24 citation statements)

References 42 publications

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“…As can be seen from Table 2 , the global positioning results based on single sensors are all poor, while the maximum positioning error of the 5G and geomagnetism fusion indoor positioning method proposed in this paper was 1.47 m and the average positioning error was 0.49 m. It can also be seen from Figure 12 that the positioning errors after fusion are mostly distributed within 1 m. Compared to Hi-Loc [ 11 ], our proposed algorithm does not differ much in localization accuracy in approximately the same scenario with a single 5G localization, but after fusion, our localization accuracy improvement over the Hi-Loc algorithm is about 24.6%. Therefore, our proposed 5G and geomagnetism fusion positioning method can make up for the single sensor deficiencies, improve indoor positioning accuracy and provide reliable global position estimation for subsequent local and global fusion positioning.…”

Section: Experimental Results and Analysismentioning

confidence: 89%

“…Gao [ 37 ] generated 5G CSI through a simulation approach and used ray-tracing channel models to achieve indoor and outdoor localization, but the effectiveness of the method has not been verified in real-world scenarios. Hi-Loc [ 11 ] realized indoor positioning based on a convolutional neural network and long and short term memory through independently studied 5G CSI transceiver equipment, and achieved meter-level positioning accuracy. Kia [ 38 ] used real 5G CSI data based on ray tracing to achieve intercity fingerprint matching localization by means of a convolutional neural network algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…With the large-scale popularization and application of 5G, the localization methods based on 5G New Radio (NR) have attracted huge amount of attention [ 10 ]. Channel state information (CSI) can reflect the fine-grained signal propagation characteristics of wireless communication links, and it has been proven to favor indoor positioning methods based on 5G signals in the case of multiple subcarriers of CSI [ 11 ]. However, in dynamic scenarios, CSI is prone to signal buildup and is affected by signal fluctuations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization

Yang

Cheng

Jia

et al. 2023

Sensors

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For positioning tasks of mobile robots in indoor environments, the emerging positioning technique based on visual inertial odometry (VIO) is heavily influenced by light and suffers from cumulative errors, which cannot meet the requirements of long-term navigation and positioning. In contrast, positioning techniques that rely on indoor signal sources such as 5G and geomagnetism can provide drift-free global positioning results, but their overall positioning accuracy is low. In order to obtain higher precision and more reliable positioning, this paper proposes a fused 5G/geomagnetism/VIO indoor localization method. Firstly, the error back propagation neural network (BPNN) model is used to fuse 5G and geomagnetic signals to obtain more reliable global positioning results; secondly, the conversion relationship from VIO local positioning results to the global coordinate system is established through the least squares principle; and finally, a fused 5G/geomagnetism/VIO localization system based on the error state extended Kalman filter (ES-EKF) is constructed. The experimental results show that the 5G/geomagnetism fusion localization method overcomes the problem of low accuracy of single sensor localization and can provide more accurate global localization results. Additionally, after fusing the local and global positioning results, the average positioning error of the mobile robot in the two scenarios is 0.61 m and 0.72 m. Compared with the VINS-mono algorithm, our approach improves the average positioning accuracy in indoor environments by 69.0% and 67.2%, respectively.

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Section: Experimental Results and Analysismentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization

Yang

Cheng

Jia

et al. 2023

Sensors

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“…, respectively. Finally, same as we evaluated (19) and (21) for the macroscopic learning part, we also evaluate z (v) i for the online and z(v) NN i for the target neural networks of the microscopic learning part. The loss function for learning the micro-fading channel characteristics is calculated over all the subcarrier reprentations N for each view, and is written as…”

Section: E Micro-fading Level Representationsmentioning

confidence: 99%

“…Lastly, by comparison to CNNs, transformers are less computationally costly and have faster training and inference times [17]. We note that historically, the attention mechanism was used on top of convolutional feature maps, which is reflected in recent growing literature in wireless localization [20], or even a combination with long short-term memory (LSTM) [21].…”

mentioning

confidence: 99%

Self-Supervised and Invariant Representations for Wireless Localization

Salihu¹,

Schwarz²,

Rupp³

2023

Preprint

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In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned representations are easily transferable to new environments and ready to use for other wireless downstream tasks. To the best of our knowledge, the proposed method is the first joint-embedding self-supervised approach to forsake the dependency on contrastive channel estimates. Our approach outperforms fully-supervised techniques in small data regimes under fine-tuning and, in some cases, linear evaluation. We assess the performance in centralized and distributed massive MIMO systems for multiple datasets. Moreover, our method works indoors and outdoors without additional assumptions or design changes.

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Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Lyu,

Chan,

Hung

et al. 2023

Advanced Intelligent Systems

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Indoor positioning system (IPS) technologies have widespread applications in logistics, intelligent manufacturing, healthcare monitoring, etc. The recently released Bluetooth low‐energy (BLE) 5.1 specification enables in‐phase and quadrature‐phase (I/Q) data measurements. It allows angle of arrival estimation and becomes a natural choice for IPS implementation. Conventional BLE 5.1 IPSs use multiple anchors to provide massive redundancy to improve system robustness. It however demands effective approaches to leverage redundancy. Besides, interference due to various environmental factors can introduce severe errors to I/Q data and affect positioning accuracy. Facing these challenges, herein, a novel deep learning‐based multianchor BLE 5.1 IPS is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, a novel attentional filtering network tailored to infer high‐quality I/Q sample data is developed and a spatial regularization loss incorporating spatial location relationships to strengthen the feature embedding discrimination is proposed. Two multianchor BLE 5.1 I/Q sample datasets are developed and released for public download. Numerical experiments are carried out to compare the proposed method with previous BLE 5.1 IPS methods and methods utilizing other radio frequency data. Results indicate that the proposed method consistently achieves submeter accuracy and significantly outperforms the state‐of‐the‐art approaches.

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Hi-Loc: Hybrid Indoor Localization via Enhanced 5G NR CSI

Cited by 36 publications

References 42 publications

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization

A Novel Deep Learning Approach to 5G CSI/Geomagnetism/VIO Fused Indoor Localization

Self-Supervised and Invariant Representations for Wireless Localization

Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

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