Ding Zhenke, Deng Zhongliang, Hu Enwen, Liu Bingxun, Zhang Zhichao, Ma Mingyang
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2024 Oct 16;24(20):6669. doi: 10.3390/s24206669.
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.
集成传感器的智能手机在人们的生活中发挥着重要作用，在先进的多传感器融合导航系统中，单个传感器信息的使用至关重要。由于环境不同，传感器的权重也会不同，这也会影响多源融合定位的方法和结果。基于智能手机传感器的多源数据，本研究探索了五种信息——全球导航卫星系统（GNSS）、惯性测量单元（IMU）、蜂窝网络、光学传感器和Wi-Fi传感器——来表征数据的时间、空间和数学统计特征，并构建了一个多尺度、多窗口和上下文连接的场景感知模型，以准确检测室内、半室内、室外和半室外空间的环境场景，从而为多传感器导航系统中的多传感器定位提供良好的基础。检测环境场景为多传感器融合定位提供了环境定位基础。该模型主要分为四个部分：基于多传感器的数据挖掘、多尺度卷积神经网络（CNN）、结合上下文信息的双向长短期记忆（BiLSTM）网络以及元启发式优化算法。
