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

Fernandes, Letícia; Santos, Sara; Barandas, Marília; Folgado, Duarte; Leonardo, Ricardo; Santos, Ricardo; Carreiro, André V.; Gamboa, Hugo

doi:10.3390/s20226664

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…Based on the application of wireless communication, the two wireless nodes can be set to a visible environment [10][11][12]. In this case, a system state model based on the kinematics principle is established in this paper as the following:…”

Section: Genetic Optimizationmentioning

confidence: 99%

Multi-Source Data High-Performance Indoor Positioning considering Genetic Optimization Neural Network Algorithm

Chu

Zhang

Chen

et al. 2022

Mathematical Problems in Engineering

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In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior.

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Section: Genetic Optimizationmentioning

confidence: 99%

Multi-Source Data High-Performance Indoor Positioning considering Genetic Optimization Neural Network Algorithm

Chu

Zhang

Chen

et al. 2022

Mathematical Problems in Engineering

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“…This proposal, improves the accuracy and reduces the computational cost of pedestrian localization, achieving 80 times faster pedestrian localization estimation than a pedestrian localization system based on a weighted KNN. Another interesting proposal is presented in [48], in which the authors combine CNN and LSTM to improve the accuracy in an infrastructure-free localization system. The authors of [62] use a RNN to predict the trajectory in a multi-person localization and tracking system, improving accuracy by 8% with respect to localization without the use of ML.…”

Section: A Machine Learning In Scene Analysismentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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“…WOA algorithm is combined with BP neural network, and WOA algorithm is used to adjust the parameters of BP neural network. Then the RSSI value data and corresponding parameters collected at different distances are taken as the input value of BP neural network, and the coordinates of unknown nodes are taken as the output value of BP neural network, so as to establish WOA-BP neural network model and complete the node positioning [21][22][23][24][25][26].…”

Section: Using Woa-bp To Optimize Indoor Environment Attenuation Modelmentioning

confidence: 99%

A Node Localization Algorithm based on WoaBp Optimization

Lang¹,

Sun²,

Jun³

et al. 2021

IJWMT

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With the rapid development of 5G technology, the era of interconnection of all things has arrived. At the same time, a variety of hardware and software are getting more and more location information through sensors, and the accuracy of location information is increasingly important. Because traditional positioning relies on satellite signals, it achieves good results outdoors without obstruction, but indoors, due to the obstruction of various walls, such as Beidou satellite navigation system and U.S. Global Positioning System, it is difficult to meet the accuracy requirements for indoor positioning. Therefore, how to improve the positioning accuracy of indoor nodes has become a research hotspot in the field of wireless sensor. In order to improve the indoor positioning accuracy, this paper combines artificial neural network, intelligent optimization algorithm and node positioning to improve the accuracy of indoor positioning. One of the essences of the neural network is to solve the regression problem. Through the analysis of indoor node positioning, it can be concluded that the accuracy of distance-based positioning method lies in finding the relationship between signal strength and distance value. Therefore, the neural network can be used to regression analysis of signal strength and distance value and generate related models. In order to further improve the accuracy and stability of indoor node positioning, a method combining whale optimization algorithm with neural network is proposed. By using the whale optimization algorithm to find the optimal parameters of the neural network model, the training accuracy and speed of the neural network are improved. Then, using the excellent fitting ability of the neural network, the mapping relationship between RSSI value and distance value of indoor nodes is fitted, and the corresponding regression analysis model is generated, which can minimize the noise problem caused by abnormal signal attenuation and reduce the indoor positioning error. Finally, the data is processed by the neural network to get the parameters needed in the positioning algorithm. The experimental results show that the node positioning model based on the optimized neural network and the single optimization algorithm has significantly improved the positioning accuracy and stability.

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

Cited by 10 publications

References 22 publications

Multi-Source Data High-Performance Indoor Positioning considering Genetic Optimization Neural Network Algorithm

Multi-Source Data High-Performance Indoor Positioning considering Genetic Optimization Neural Network Algorithm

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

A Node Localization Algorithm based on WoaBp Optimization

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