Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach

Wang, Xuyu; Yu, Zhitao; Mao, Shiwen

doi:10.1007/s11036-019-01302-x

Cited by 28 publications

(15 citation statements)

References 40 publications

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“…This type of imitation learning was motivated by humans' tendency to learn skills by imitating the behaviors of others, and has been widely used in autonomous driving [19], [20], wireless communication [21], [22], and smart grids [23], [24]. In BC, agents receive instructions from a hand-crafted demonstrator (which serves as training data), and then replicate actions from the expert policy.…”

Section: ) Behavior Cloning (Bc)mentioning

confidence: 99%

IADRL: Imitation Augmented Deep Reinforcement Learning Enabled UGV-UAV Coalition for Tasking in Complex Environments

Zhang

Mao

et al. 2020

IEEE Access

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Recent developments in Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have made them highly useful for various tasks. However, they both have their respective constraints that make them incapable of completing intricate tasks alone in many scenarios. For example, a UGV is unable to reach high places, while a UAV is limited by its power supply and payload capacity. In this paper, we propose an Imitation Augmented Deep Reinforcement Learning (IADRL) model that enables a UGV and UAV to form a coalition that is complementary and cooperative for completing tasks that they are incapable of achieving alone. IADRL learns the underlying complementary behaviors of UGVs and UAVs from a demonstration dataset that is collected from some simple scenarios with non-optimized strategies. Based on observations from the UGV and UAV, IADRL provides an optimized policy for the UGV-UAV coalition to work in an complementary way while minimizing the cost. We evaluate the IADRL approach in an visual game-based simulation platform, and conduct experiments that show how it effectively enables the coalition to cooperatively and cost-effectively accomplish tasks. INDEX TERMS Unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), coalition, deep reinforcement learning (DRL), imitation learning. JIAN ZHANG (Member, IEEE) received the B.Sc. and M.Sc. degrees in applied physics from Sichuan University, Chengdu, China, in 2001 and 2008, respectively, and the Ph.D. degree in electrical and computer engineering from Auburn University, Auburn, AL, USA, in 2016. He is currently an Assistant Research Professor with the RFID Laboratory, Auburn University. His main research interests include RFID technologies and applications, the Internet of Things, indoor localization, UAV, and collaborative robotics. His work focuses on improving the efficiency of supply chain management for industry and business.

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Section: ) Behavior Cloning (Bc)mentioning

confidence: 99%

IADRL: Imitation Augmented Deep Reinforcement Learning Enabled UGV-UAV Coalition for Tasking in Complex Environments

Zhang

Mao

et al. 2020

IEEE Access

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“…Additionally, a convolutional network was also considered as an approach to extract features from the used images. Finally, in [ 15 ], the fusion of magnetic and light data, using the smartphone’s sensors, was explored. This IPS used DNN, specifically Long-Short-Term-Memory (LSTM) layers, and analysed the impact of different design parameters, such as the test data size, the number of considered layers, and hidden units.…”

Section: Related Workmentioning

confidence: 99%

An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning

Fernandes

Santos

Barandas

et al. 2020

Sensors

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Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user’s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.

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“…Some scholars have applied LSTM algorithm to CSI-based device-free activity recognition method. For example, Damodaran and Schäfer [11] and Wang et al [12] used LSTM algorithm to recognize human activities and locations, respectively, and achieved high recognition accuracy. However, the above methods only used the simple single-layer one-way LSTM algorithm which is too simple to recognize complex activities.…”

Section: Introductionmentioning

confidence: 99%

Small CSI Samples-Based Activity Recognition: A Deep Learning Approach Using Multidimensional Features

Tian

Chen

et al. 2021

Security and Communication Networks

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With the emergence of tools for extracting CSI data from commercial WiFi devices, CSI-based device-free activity recognition technology has developed rapidly and has been widely used in security monitoring, smart home, medical monitoring, and other fields. However, the existing CSI-based activity recognition algorithms need a large number of training samples to obtain the ideal recognition accuracy. To solve the problem, an attention-based bidirectional LSTM method using multidimensional features (called MF-ABLSTM method) is proposed. In this method, the signal preprocessing and continuous wavelet transform algorithms are used to construct time-frequency matrix, the sample entropy is used to characterize the statistical feature of CSI amplitudes, the energy difference at a fixed time interval is used to characterize the time-domain feature of activities, and the energy distribution of different frequency components is used to characterize the frequency-domain feature of activities. By expanding the training samples with the proposed tensor prediction algorithm, the accurate activity recognition can be realized with only a few samples. A large number of experiments verify the good performance of MF-ABLSTM method.

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Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach

Cited by 28 publications

References 40 publications

IADRL: Imitation Augmented Deep Reinforcement Learning Enabled UGV-UAV Coalition for Tasking in Complex Environments

IADRL: Imitation Augmented Deep Reinforcement Learning Enabled UGV-UAV Coalition for Tasking in Complex Environments

An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning

Small CSI Samples-Based Activity Recognition: A Deep Learning Approach Using Multidimensional Features

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