Ensemble learning particle swarm optimization for real-time UWB indoor localization

Cai, Xufen; Ye, Long; Zhang, Qin

doi:10.1186/s13638-018-1135-0

Cited by 23 publications

(13 citation statements)

References 47 publications

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“…Then we use the Lagrange duality method to solve the convex problem. Lastly, we find the parameters w, w 0 that minimizes w by (5). arg min w,w 0…”

Section: Fuse Learning Methods 1) Svm Model For the Nlos And Los Cmentioning

confidence: 99%

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Own¹,

Hou²,

Tao³

2019

IEEE Access

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Today, indoor localization technology based on WiFi signals has become more and more popular and applicable. It not only facilitates people's lives but also creates enormous economic value. However, during the propagation of the WiFi signal, it is easily interfered by obstacles, and the signal fluctuation is significant, resulting in low accuracy of positioning. To overcome these problems, we reduce the influence of environmental factors firstly. Then the positioning accuracy is improved by using the SVM model to distinguish the NLOS or LOS environment and employing the capsule networks to derive the users' positions with the WiFi 2.4G and 5G signals. As we all know, the WiFi 2.4G signal has excellent penetrability and is less affected by obstacles, while the WiFi 5G signal has excellent stability and small fluctuations. Therefore, we use the advantages of these two kinds of signals to derive the optimal suggestion by the capsule neural network, which is the learning system with minimum data sets needed. The experimental results show that the positioning effect of the two signals simultaneously is better than the positioning effect of a single signal. We also compare with the traditional indoor positioning methods and use the simulation data to carry out the robustness test, and the positioning accuracy reached 0.99 m in the field environment finally.INDEX TERMS Indoor localization, NLOS and LOS channel propagation condition, WiFi 2.4G and WiFi 5G, SVM, capsule network.

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“…Then we use the Lagrange duality method to solve the convex problem. Lastly, we find the parameters w, w 0 that minimizes w by (5). arg min w,w 0…”

Section: Fuse Learning Methods 1) Svm Model For the Nlos And Los Cmentioning

confidence: 99%

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Own¹,

Hou²,

Tao³

2019

IEEE Access

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“…In order to overcome the malfunctions in GPS positioning and to perform accurate positioning in a sophisticated indoor environment, systems called Indoor Positioning Systems (IPSs) are introduced. Some of the most popular technologies of this type are the infrared connection, WI-FI and above all Wi-Fi fingerprinting, which is often employed in the development of smart phone applications [2,3,4], Bluetooth, ZigBee, ultrasound, radio frequency identification (RFID), and ultra-wideband (UWB) [5,6], which offers higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Methods for developing an indoor navigation system

2021

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The report offers methods for developing an indoor navigation system. For this purpose, popular navigation applications have been analysed. We have been strongly motivated by the fact that no universal methods have been established that are applicable with this kind of projects. Very often the case is that new methods are formed in view of a specific process and they are based on a particular technology and on the related organisation, design and other considerations. This is also confirmed by a conducted experiment with a commercial product for indoor navigation. The methods offered include map digitising, determining a user’s location, and selecting the shortest path. This is achieved by applying elements of graph theory, databases, XML and QR code. The methods offered are universal and could be applied to any type of building. The methods have been approbated in the building of a mobile application for the needs of the University of Mining and Geology, Sofia.

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“…For example, Duru et al [5] investigated position-measuring algorithms for tracking an object with UWB sensors. Cai et al [4] developed an ensemble learning particle swarm optimization algorithm for indoor target detection and showed that it almost correctly detected target positions in empty indoor environment Although their method may be useful to identify each tag position, it is not evaluated for environment with Non-Line-of-Sight condition and multi-path problem yet, and the purpose of the algorithm is not to detect the stay regions of the target. Alhadhrami et al [1] used a UWB positioning system to develop a voice-guide service for a blind person.…”

Section: Introductionmentioning

confidence: 99%

Extracting Stay Regions from UWB Indoor Trajectory and its Evaluation

Hayama

Takahashi

Nagatomo

2020

International Journal of Service and Knowledge Management

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Location-based technology is key for ubiquitous society. To enhance the location-based services, recognizing the places and the patterns where a person and an object have visited in addition to geographical locations is needed. Although some researchers have developed methods that extract outdoor stay regions from GPS trajectories to recognize the visiting places, there is no technology that recognizes stay regions, such as spatial location in a living house and an office building, from indoor trajectories. Technology for extracting indoor stay regions is required to achieve more intelligent indoor-location-based services, such as smart-home and smart-office. Therefore, we developed a method which extracts stay regions from an ultra-wideband (UWB) indoor trajectory. An UWB indoor positioning technology provides location information with a few-tens-of-centimeters error. Our developed method was evaluated comparing with conventional methods.

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Ensemble learning particle swarm optimization for real-time UWB indoor localization

Cited by 23 publications

References 47 publications

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Methods for developing an indoor navigation system

Extracting Stay Regions from UWB Indoor Trajectory and its Evaluation

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