Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices

Bregar, Klemen; Mohorčič, Mihael

doi:10.1109/access.2018.2817800

Cited by 150 publications

(134 citation statements)

References 15 publications

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“…By using the raw primary signal strengths received at multiple detectors as the inputs, the CNN model can learn a better mapping relationship between the raw data and the detection results as well as achieve a better spectrum detection performance compared with the SVM model. Other applications of deep learning for the perception include joint channel estimation and signal detection [50], link adaption [51], waveform recognition [52], and radio localization [53]. Fig.…”

Section: B Learning From Radio Environmentmentioning

confidence: 99%

20 Years of Evolution From Cognitive to Intelligent Communications

Qin

Zhou

Zhang

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AIenabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.

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Section: B Learning From Radio Environmentmentioning

confidence: 99%

20 Years of Evolution From Cognitive to Intelligent Communications

Qin

Zhou

Zhang

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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“…In recent years, more and more machine-and deep-learning (DL) methods have been applied to positioning problems with various sensor signals [13][14][15]. Feature-based ML and DL approaches have both been used to identify LOS/NLOS and other propagation conditions for UWB positioning system [16][17][18][19][20][21]. CIRs have been used to estimate errors and signal quality and to enhance classic tracking techniques such as Bayesian filters [22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Kram

Stahlke

Feigl

et al. 2019

Sensors

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Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system.

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“…A necessary condition for equation (5) is that g(·) should be differentiable with respect to all trainable variables. Therefore, we mainly focus on linear trilateration methods such as the linear-least square (LS) or the weighted linear-least (WLS) methods that calculate the coordinates of the device using matrix operations [19]- [21]. For the same reason, the extended Kalman filter (EKF) is also compatible with the proposed method, because it consists of matrix operations.…”

Section: B Cost Functionsmentioning

confidence: 99%

Unsupervised Learning Technique to Obtain the Coordinates of Wi-Fi Access Points

Choi

Talwar

2019

2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Given that the accuracy of range-based positioning techniques generally increases with the number of available anchor nodes, it is important to secure more of these nodes. To this end, this paper studies an unsupervised learning technique to obtain the coordinates of unknown nodes that coexist with anchor nodes. As users use the location services in an area of interests, the proposed method automatically discovers unknown nodes and estimates their coordinates. In addition, this method learns an appropriate calibration curve to correct the distortion of raw distance measurements. As such, the positioning accuracy can be greatly improved using more anchor nodes and well-calibrated distance measurements. The performance of the proposed method was verified using commercial Wi-Fi devices in a practical indoor environment. The experiment results show that the coordinates of unknown nodes and the calibration curve are simultaneously determined without any ground truth data.

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Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices

Cited by 150 publications

References 15 publications

20 Years of Evolution From Cognitive to Intelligent Communications

20 Years of Evolution From Cognitive to Intelligent Communications

UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Unsupervised Learning Technique to Obtain the Coordinates of Wi-Fi Access Points

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