Abstract
RFID (Radio Frequency Identification) technology is an automatic identification technology that has received widespread attention from indoor positioning researchers due to its high stability and low power consumption. We proposes a multimodal data indoor positioning algorithm model based on RFID and WiFi, named Multimodal Indoor Location Network (MMILN). The common deep learning paradigms, Embedding and Pooling are used to process and pretrain different modalities of data, in order to obtain more data features. At the same time, in order to overcome the limitations of the received signal strength, WiFi hotspot names are introduced as another modality of data to compensate for the instability of a single signal. In order to better utilize the different modalities of data, we designs a location activation unit based on the idea of attention mechanism to calculate the weighted sum of the collected signals. In addition, we designs an adaptive activation function, SoftReLU, to better assist model training and prediction, given the special characteristics of the indoor positioning task and data. Experimental results show that after introducing the position activation unit and the adaptive activation function, the mean absolute error of indoor positioning decreases to 0.178 m, which is a 40.86% reduction compared to the baseline model, significantly improving the accuracy of indoor positioning.
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Acknowledgment
This paper is supported by the 2021 Fujian Foreign Cooperation Project (No. 2021I0001): Research on Human Behavior Recognition Based on RFID and Deep Learning; 2021 Project of Xiamen University (No. 20213160A0474): Zijin International Digital Operation Platform Research and Consulting; State Key Laboratory of Process Automation in Mining & Metallurgy, Beijing Key Laboratory of Process Automation in Mining & Metallurgy (No. BGRIMM-KZSKL- 2022-14): Research and application of mine operator positioning based on RFID and deep learning; National Key R&D Program of China-Sub-project of Major Natural Disaster Monitoring, Early Warning and Prevention (No. 2020YFC1522604): Research on key technologies of comprehensive information application platform for cultural relic safety based on big data technology.
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Shi, C. et al. (2023). Research on Indoor Positioning Algorithm Based on Multimodal and Attention Mechanism. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_3
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