# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
Other studies have utilized Sparse Autoencoders (SAEs) to extract signal features and combined them with backpropagation neural networks (BPNNs) to build indoor positioning models, achieving precise output of two-dimensional coordinates [21]. However, these methods uniformly treat localization as a classification task, dividing the localization scene into several regions and assigning individuals to the center of a particular region based on classification outcomes. Nevertheless, given that walking is inherently a continuous dynamic process, the discrete nature of classification methods struggles to capture the dynamic trends and subtle variations in human walking poses while overlooking the temporal correlations between successive footsteps. Consequently, in practical scenarios demanding higher localization accuracy, relying solely on the region assignment of individual footprints fails to provide continuous and precise estimates of the moving target's position. Currently, a considerable number of researchers have transformed the problem of continuous target pose estimation into a data sequence learning task. For example, researchers [22] utilized 2D pose sequences as input to their models,encoding the extracted time-series features through LSTM to output predicted 3D poses. [PERSON] [23] proposed constructed multiple neural network models, including ResNets, using IMU data to regress walking speed and direction changes, achieving high-precision trajectory prediction. Based on the above research, this paper proposes a motion target localization method for step vibration signals based on deep learning. The main work includes the following:  
1. Introducing the Channel Attention Mechanism (CAM) [24], one-dimensional residual neural network (1D-ResNet), and bidirectional long short-term memory network (Bi-LSTM) in building a combined network for the task of feature extraction of multidimensional footstep vibration signals. This aims to fully explore the potential pedestrian motion information in the footstep signals. Additionally, using velocity vectors to describe human motion better adapts to nonlinear motion and complex interaction situations. 2. Collecting footstep vibration signal data from multiple subjects walking on different paths and combining it with a visual tracking system to obtain real trajectory information of individuals. The actual walking speed of pedestrians is used as the signal label to construct a footstep vibration signal dataset, which is made publicly available for use by other researchers. 3. Through experimental verification, our method in this paper has been shown to have higher positioning accuracy compared to traditional methods, such as WT-TDOA and SAE+BPNN, achieving continuous and accurate estimates of motion target positions. The remainder of the paper is structured as follows: In Section 2, we describe the location scenario and sensor arrangement. In Section 3, we describe our detailed method of localizing motion targets using footstep-induced floor vibration, and in Section 4, we describe the experiments we conducted, including test set experiment, ablation experiment, and comparative experiment. Finally, we present future work and conclusions in Section 5.