Indoor location method of interference source based on deep learning of spectrum fingerprint features in Smart Cyber-Physical systems

Chen, Yunfei; Du, Taihang; Jiang, Chundong; Sun, Shuang

doi:10.1186/s13638-019-1363-y

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Machine learning has been used for the detection and estimation of jamming attacks. In [42], an unsupervised machine learning algorithm based on a multi-layer auto-encoder is used to extract the interference source spectrum features. These features are then used to distinguish the interference sources' types and locations without labeling measured data.…”

Section: Machine Learning For Jamming Detectionmentioning

confidence: 99%

Machine learning based wireless interference estimation in a robotic force-seeking application

Candell¹,

Kashef²,

Montgomery³

et al. 2022

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Wide deployment of wireless communications plays an essential role in the vision of future cyber-physical systems (CPSs), which includes massive transfer of automation information. Many practical considerations of industrial CPSs affect the success of the deployment of industrial wireless networks, and, thus, can inhibit their widespread adoption. These considerations include multi-path propagation, network congestion, and jamming interference. Jamming is of chief concern when wireless is used for mission critical or safety integrated systems. In this paper, an experimental platform consisting of a robot arm depressing a spring mechanism with a wireless force-feedback control algorithm is constructed. The robot applies downward pressure on a spring assembly until a predetermined force is detected and transmitted successfully to the controller under varying levels of sustained interference. Machine learning is used to learn and predict the signal-to-interference level of the communication link solely using position information from an independent vision tracking system. Various supervised learning algorithms are investigated and rated according to their performance.

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Section: Machine Learning For Jamming Detectionmentioning

confidence: 99%

Machine learning based wireless interference estimation in a robotic force-seeking application

Candell¹,

Kashef²,

Montgomery³

et al. 2022

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“…First, candidate region-based object detection methods, such as Hybrid Task Cascade [13], CenterMask [14], PolyTransform [15], etc; second, regression-based object detection methods, such as YOLO [16,17], SSD [18], FPN [19], etc; third, search-based object detection methods, such as AttentionNet [20] and reinforcement learning-based object detection algorithms [21]. Many scholars have incorporated deep learning into the technical solutions for indoor positioning and navigation: A fingerprint localization algorithm based on Deep Belief Networks (DBN) with noise reduction is used to achieve target localization in specific indoor environments [22]; using deep learning methods to automatically encode and extract deep features from Wi-Fi fingerprint data, and create a deep feature location fingerprint database with one-to-many relationships for indoor localization [23]; adding the scene recognition classification process to a visual localization system [24], etc. At present, the image quality, pixel resolution, sensor, and aperture performance of the video frames obtained by the cell phone camera have been significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Mobile Phone Indoor Scene Recognition Location Method Based on Semantic Constraint of Building Map

Liu¹,

Gao²,

Luo³

et al. 2022

Preprint

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At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

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List of Deep Learning Models

Mosavi

Ardabili²,

Várkonyi-Kóczy

2020

Lecture Notes in Networks and Systems

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Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.

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Indoor location method of interference source based on deep learning of spectrum fingerprint features in Smart Cyber-Physical systems

Cited by 5 publications

References 23 publications

Machine learning based wireless interference estimation in a robotic force-seeking application

Machine learning based wireless interference estimation in a robotic force-seeking application

Mobile Phone Indoor Scene Recognition Location Method Based on Semantic Constraint of Building Map

List of Deep Learning Models

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