A New Scene Sensing Model Based on Multi-Source Data from Smartphones

Smartphones with integrated sensors play an important role in people's lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-10, Vol.24 (20), p.6669
Main Authors: Ding, Zhenke, Deng, Zhongliang, Hu, Enwen, Liu, Bingxun, Zhang, Zhichao, Ma, Mingyang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Altitude
Artificial satellites
Classification
CNN
Data mining
Datasets
Electronics in navigation
GNSS
Machine learning
Mathematical optimization
multi-source sensor
Neural networks
Satellites
scene classification
Sensors
Smart phones
Smartphones
Telecommunication systems
Wi-Fi
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Summary:Smartphones with integrated sensors play an important role in people's lives, and in advanced multi-sensor fusion navigation systems, the use of individual sensor information is crucial. Because of the different environments, the weights of the sensors will be different, which will also affect the method and results of multi-source fusion positioning. Based on the multi-source data from smartphone sensors, this study explores five types of information-Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), cellular networks, optical sensors, and Wi-Fi sensors-characterizing the temporal, spatial, and mathematical statistical features of the data, and it constructs a multi-scale, multi-window, and context-connected scene sensing model to accurately detect the environmental scene in indoor, semi-indoor, outdoor, and semi-outdoor spaces, thus providing a good basis for multi-sensor positioning in a multi-sensor navigation system. Detecting environmental scenes provides an environmental positioning basis for multi-sensor fusion localization. This model is divided into four main parts: multi-sensor-based data mining, a multi-scale convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network combined with contextual information, and a meta-heuristic optimization algorithm.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24206669