Design of a Multi-Source Sensor Data Cascade Dimensionality Reduction Classification Fuser [一种多源传感器数据层叠降维分类融合器设计]

作者：Ye C.; Guo H.; Chen H.; Yang Y.

来源：Chinese Journal of Sensors and Actuators, 2022, 35(8): 1117-1122.

DOI：10.3969/j.issn.1004-1699.2022.08.016

• autoencoder
• data classification and fusion
• deep learning
• feature extraction
• multi source sensor
• Softmax classifier

It is difficult to classity multi-source sensor data accurately which have nonlinear and high-dimensional characteristics. After direct data fusion,the noise is large and the usability is reduced. Therefore,a multi-source sensor data cascade classification dimension reduction fusion method is proposed. A cascade automatic dimensionality reduction classifier(SAESM)based on deep learning is designed. SAESM and Softmax classifier are combined to extract the data features of source sensors and distinguish the data attribute categories in the cluster. A cluster head node that can represent the data category is allocated to the collection composed of different categories of data and information of all the sensor nodes is integrated by the head node and transmitted to the sink node uniformly. The sink node performs parameter fusion processing on the information table integrated by the cluster head node to complete multi-source sensor data fusion. The experimental results show that the number of correctly classified samples with according to multi-source sensor data feature extraction is high,and the amount of noise data is effectively reduced after fusion.

• 单位
  华南理工大学