Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

Zhang, Zhongfeng; Lee, Minjae; Choi, Sangdun

doi:10.3390/s21175776

Cited by 8 publications

(9 citation statements)

References 35 publications

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“…In [26], the authors propose a deep residual sharing learning based IPS which uses the 2DCNN to exploit both frequency and time features in bimodal CSI data. In [25], Zhang et al propose utilizing the trajectory CSI to enhance the robustness of the instability of RF signals in the indoor environment. They employ an 1DCNN to extract the spatial information from the trajectory CSI.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, deep learning (DL) based algorithms are gaining a keen interest in the application of IPS. Some related works [22], [23], [24], [25], [26] have shown that the DL algorithms such as dense neural network (DNN), convolutional neural network (CNN), long short-term memory (LSTM), etc. can help the IPS better capture the correlation between the fingerprint and corresponding position, which consequently enhances the accuracy compared to the conventional methods [27], [28].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Choi

et al. 2022

IEEE Access

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In a channel state information (CSI) based indoor positioning system, the positioning performance becomes susceptible to multipath fading effects especially in non-line-of-sight environments. We propose a transformer-based indoor positioning system (TIPS) to address this challenge. The proposed TIPS utilizes a self-attention mechanism to process the continuous WiFi CSI observed from predetermined routes as fingerprints in a given indoor environment. Each route is then considered a sentence, whereas the position along the route is treated as a word in terms of natural language processing. Consequently, the problem of predicting the position with the fingerprints can then be considered the task of predicting the current word with previous words, which can be efficiently solved using the proposed TIPS. In order to fully exploit the relations among positions, we propose embedding the information of the direction of arrival (DoA) on top of the collected CSI as inputs to the TIPS. Thus, the transformer of the proposed TIPS can better capture the dependencies of the positions in the route and significantly boost positioning accuracy. To exhibit the superiority of the proposed TIPS in a radio frequency (RF) environment, we demonstrate a hardware implementation of an RF testbed consisting of an emulator of WiFi access point and user equipment. Through extensive computer simulations and experimental tests, it is demonstrated that the proposed TIPS can reduce the positioning error down to 20 cm, which is a significant improvement compared to the current state-ofthe-art models.INDEX TERMS CSI, DoA, indoor positioning system, transformer.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Choi

et al. 2022

IEEE Access

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“…LSTM-based tracking methods have also been proposed for indoor environments recently. For instance, in [20], [21], continuous CSI measurements of trajectories were first represented as deep features via a CNN network and then the features were fed into an LSTM network for tracking purposes. However, these learning-based tracking methods are designed under a specific environment and their generality is poor.…”

Section: B Csi-based Trackingmentioning

confidence: 99%

“…• LSTM-F: we compare with the LSTM-based tracking method proposed in [20], [21]. Following [20], we first train an AAResCNN using all the training samples.…”

Section: B Tracking Across Environmentsmentioning

confidence: 99%

“…By using built-in IMU, such as acceleration sensor and gyroscope, step length and orientation can be estimated through the pedestrian dead reckoning (PDR) approach [19], with which a more realistic system model can be built. Some learning-based tracking [12], [20], [21] or IMU-aided tracking methods [22], [23] have recently been proposed for indoor environments. By exploring the correlation of CSI measurements or IMU data along a trajectory, these methods achieve better performance than stationary positioning systems and Bayesian filtering tracking systems.…”

Section: Introductionmentioning

confidence: 99%

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CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Zhang¹,

Sifaou²,

Li³

2022

Preprint

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Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, i.e., a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This paper presents a new localization system with high accuracy and generality. On the one hand, the receptive field of the existing convolutional neural network (CNN)-based positioning networks is limited, restricting their performance as useful information in CSI is not explored thoroughly. As a solution, we propose a novel attention-augmented Residual CNN to utilize the local information and global context in CSI exhaustively. On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible. Specifically, we remodel the tracking problem as a denoising task and solve it with deep trajectory prior. Furthermore, we investigate how the precision difference of inertial measurement units will adversely affect the tracking performance and adopt plug-and-play to solve the precision difference problem. Experiments show the superiority of our methods over existing approaches in performance and generality improvement.

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A low-cost indoor positioning system based on data-driven modeling for robotics research and education

Hong

Kyzer

et al. 2023

Robotica

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This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

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Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

Cited by 8 publications

References 35 publications

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

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

CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

A low-cost indoor positioning system based on data-driven modeling for robotics research and education

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