Deep-Learning-Based Scenario Recognition With GNSS Measurements on Smartphones

• Published in: IEEE sensors journal Vol. 23; no. 4; pp. 3776 - 3786
• Main Authors: Dai, Zhiqiang, Zhai, Chunlei, Li, Fang, Chen, Weixiang, Zhu, Xiangwei, Feng, Yanming
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Convolutional long short-term memory (ConvLSTM); convolutional neural network (CNN); Convolutional neural networks; Deep learning; Doppler effect; Feature extraction; Global navigation satellite system; global navigation satellite system (GNSS) measurements; Machine learning; Position measurement; Satellite broadcasting; Satellites; scenario recognition; Smart phones; smartphone; Smartphones; Spatial data; Structured grids (mathematics)
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3230213

Smartphones are in everyone's hands for applications including navigation- and localization-based services, and scenario recognition is critical for seamless indoor and outdoor navigation. How to use smartphone sensing data to recognize different scenarios is a meaningful but challenging problem. To address this issue, we propose a structured grid-based deep-learning scenario recognition technique that uses smartphone global navigation satellite system (GNSS) measurements (satellite position, pseudorange, Doppler shift, and C/N0). In this work, the scenarios are grouped into four categories: deep indoors, shallow indoors, semioutdoors, and open outdoors. The proposed approach utilizes Voronoi tessellations to obtain structured-grid representations from satellite positions and performs computations using convolutional neural networks (CNNs) and convolutional long short-term memory (ConvLSTM) networks. With only spatial information being considered, the CNN model is used to extract the features of Voronoi tessellations for scenario recognition, achieving a high accuracy of 98.82%. Then, to enhance the robustness of the algorithm, the ConvLSTM network is adopted, which treats the measurements as spatiotemporal sequences, improving the accuracy to 99.92%. Compared with existing methods, the proposed algorithm is simple and efficient, using only GNSS measurements without the need for additional sensors. Furthermore, the latencies of the CNN and ConvLSTM models on a CPU are only 16.82 and 27.94 ms, respectively. Therefore, the proposed algorithm has the potential for real-time applications.