Motion Target Localization Method for Step Vibration Signals Based on Deep Learning

• Published in: Applied sciences Vol. 14; no. 20; p. 9361
• Main Authors: Chen, Rui, Zhu, Yanping, Chen, Qi, Zhu, Chenyang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Algorithms; Analysis; Classification; Deep learning; end-to-end; Fault diagnosis; footstep vibration; indoor localization; Localization; Methods; motion target trajectory; Neural networks; Privacy; Propagation; Researchers; Sensors; Signal processing; Vibration; visual tracking; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14209361

To address the limitations of traditional footstep vibration signal localization algorithms, such as limited accuracy, single feature extraction, and cumbersome parameter adjustment, a motion target localization method for step vibration signals based on deep learning is proposed. Velocity vectors are used to describe human motion and adapt it to the nonlinear motion and complex interactions of moving targets. In the feature extraction stage, a one-dimensional residual convolutional neural network is constructed to extract the time–frequency domain features of the signals, and a channel attention mechanism is introduced to enhance the model’s focus on different vibration sensor signal features. Furthermore, a bidirectional long short-term memory network is built to learn the temporal relationships between the extracted signal features of the convolution operation. Finally, regression operations are performed through fully connected layers to estimate the position and velocity vectors of the moving target. The dataset consists of footstep vibration signal data from six experimental subjects walking on four different paths and the actual motion trajectories of the moving targets obtained using a visual tracking system. Experimental results show that compared to WT-TDOA and SAE-BPNN, the positioning accuracy of our method has been improved by 37.9% and 24.8%, respectively, with a system average positioning error reduced to 0.376 m.