Application of solely self-attention mechanism in CSI-fingerprinting-based indoor localization

Nkabiti, Kabo Poloko; Chen, Yueyun

doi:10.1007/s00521-020-05681-1

Cited by 14 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These attention weights describe the relative relevance of each input element and are used to weight each element's contribution to the output.In general, allows a DL model to dynamically focus on the most important information in the input data while disregarding less relevant information. This can lead to more accurate and robust predictions, as well as increased model interpretability [155,[166][167][168].…”

Section: Attention Mechanismsmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj¹,

Himeur²,

Sohail³

et al. 2023

Preprint

View full text Add to dashboard Cite

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

show abstract

Section: Attention Mechanismsmentioning

confidence: 99%

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Kerdjidj¹,

Himeur²,

Sohail³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Channel Attention Mechanism. The attention mechanism consists of a multilayer perceptron (MLP) and a softmax layer [29]. The MLP is divided into three layers, and the number of neurons in the hidden layer of the MLP is reduced to C/r in order to reduce the number of parameters and improve the computational efficiency of the system, with r being the decay rate.…”

Section: Wavelet Transformmentioning

confidence: 99%

Wi-Exercise: An Indoor Human Movement Detection Method Based on Bidirectional LSTM Attention

Hao

Kang

Dang

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

Human motion recognition has become a hot topic in the field of human-computer interaction. Due to the traditional method of detecting human movement in indoor environments, there are still problems such as high hardware costs, influenced by the environment and the need for experimenters to be equipped with relevant equipment. With the large-scale deployment of wireless devices and the establishment of wireless network infrastructure, indoor personnel movement recognition systems based on wireless signals are gradually proposed. In this paper, we propose a device-independent motion-detection method Wi-exercise based on temporal recurrent neural networks. The acquired channel state information (CSI) is preprocessed by wavelet transform, and the output CSI matrix is filtered by the principal component analysis (PCA) to extract the feature values. The wavelet function is processed to further remove the noise also to make the action data more distinctive, and then PCA is used to track the entire CSI time series to improve and maintain its dimensional energy. The preprocessed data are subjected to feature extraction to obtain CSI of human motion and establish feature sequences, and then the action model is trained using the bidirectional long short-term memory attention (ABLSTM) mechanism to finally realize human motion recognition. Finally, the robustness of Wi-exercise is tested experimentally, and its performance is compared with that of existing recognition methods. The experimental results show that the average recognition rate of the method is 94.43% in the complex indoor environment, and the method is better than the traditional classifier method for action recognition, with lower and higher recognition accuracy.

show abstract

“…RSS is not particularly sensitive to the perception of subtle movements, which may lead to the same localization results when the target moves in a small area [10]. Compared with RSS, CSI also suffers from problems such as multipath effects and an NLOS effect, but it includes the amplitude and phase information of multiple subcarriers, which can portray the channel characteristics better than RSS, effectively improving the resolution of multipath signals and further improving the accuracy of the indoor localization [11].…”

Section: Introductionmentioning

confidence: 99%

Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

Xue,

Chen,

Chi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

With the widespread use of WiFi devices and the availability of channel state information (CSI), CSI-based device-free localization (DFL) has attracted lots of attention. Fingerprint-based localization methods are the primary solutions for DFL, but they are faced with the fingerprint similarity problem due to the complex environment and low bandwidth of the commercial WiFi. Meanwhile, fingerprints may change unpredictably due to multipath WiFi signal propagation in time-varying environments. To tackle these problems, this paper proposes an adaptive online learning DFL method, which adaptively updates the localization model to ensure long-term accuracy and adaptability. Specifically, the CSI signals of the target located at different reference points are first collected and transformed to discriminable fingerprints using the weights of Multilayer Online Sequence Extreme Learning Machine (ML-OSELM). After that, an online learning DFL model is built to adapt to the changes of the environment. Experimental results in a time-varying indoor environment validate the adaptability of the proposed method against environmental changes and show that our method can achieve 10% improvement over other methods.

show abstract

Application of solely self-attention mechanism in CSI-fingerprinting-based indoor localization

Cited by 14 publications

References 35 publications

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Uncovering the Potential of Indoor Localization: Role of Deep and Transfer Learning

Wi-Exercise: An Indoor Human Movement Detection Method Based on Bidirectional LSTM Attention

Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

Contact Info

Product

Resources

About