A Ground Moving Target Detection Method for Seismic and Sound Sensor Based on Evolutionary Neural Networks  
[PERSON]  
College of Intelligent Science and Technology, National University of Defense Technology,  
Changsha 410003, China  
1  
[PERSON]  
Correspondence: [EMAIL_ADDRESS] College of Intelligent Science and Technology, National University of Defense Technology,  
Changsha 410003, China  
1  
[PERSON]  
College of Intelligent Science and Technology, National University of Defense Technology,  
Changsha 410003, China  
1  
Footnote 2: email: {xinging2012,x  
(DL). The threshold method is to set a fixed or variable feature threshold based on experience. When the feature value exceeds the set threshold, it is considered that there is a target intrusion. The threshold method has high computational efficiency, but its classification accuracy is easily affected by environmental noise and cannot be applied to the urban environment, which has a large amount of noise caused by human activity. Compared with the threshold method, the machine learning (ML) method has higher anti-interference ability and classification accuracy. TML classification methods include support vector machine (SVM) [13; 14; 15], K-nearest neighbor (KNN) [16; 17], decision tree (DT) [18], naive Bayes (NB) [7], artificial neural network (ANN) [10], and the Gaussian mixture model (GMM) [19]. DL methods for ground target recognition based on sound and seismic signals include CNN [20; 21; 22; 23] and RNN [24; 25]. Though the ML method improves accuracy compared with the threshold method, it also increases the complexity of the network and the hardware cost of the deployment model, making it difficult for unattended ground sensors to meet the requirements of long-term work. In order to overcome this problem, we propose an evolutionary neural network approach to ground target recognition, combining genetic algorithms and neural networks to select the best feature extraction methods, to optimize the neural network structure, and to achieve high recognition accuracy with minimum feature vector and network complexity. The machine learning methods for moving target recognition based on sound and seismic signals are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Feature Extraction Method** & **Classification Method** \\ \hline
2017, [PERSON] et al. [13] & MFCC & SVM \\
2021, [PERSON] et al. [14] & Fractal dimension & SVM \\
2019, [PERSON] et al. [15] & Wavelet energy ratio, zero-crossing rate, mean value, peak & SVM \\
2011 [PERSON] et al. [16] & Index, SD, and waveform index & KNN \\
2013 [PERSON] et al. [17] & Wavelet packet node energy & KNN \\
2010, [PERSON] et al. [18] & Wavelet statistics, spectral statistics, cadence, and kurtosis & DT \\
2011, [PERSON] et al. [7] & Energy of voice spectra, cadence, and frequency rhythm & NB \\
2017, [PERSON] et al. [10] & Automatic extraction & ANN \\
2010, [PERSON] et al. [19] & Gait frequency and temporal pattern & GMM \\
2021, [PERSON] et al. [20; 22] & Compression sensing & CNN \\
2019, [PERSON] et al. [21] & MFCC & CNN \\
2019, [PERSON] et al. [23] & Automatic extraction & CNN \\
2019, [PERSON] et al. [24] & Automatic extraction & RNN \\
2021, [PERSON] et al. [25] & Automatic extraction & RNN \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of references on machine learning methods for moving target recognition based on sound and seismic signals.  
Figure 1: UGS principle of operation.  
The rest of this paper is structured as follows. Section 2 introduces the necessity of signal preprocessing and the methods of signal preprocessing. Section 3 focuses on the principle of the evolutionary neural network, optimal feature extraction methods, and neural network structure. Section 4 presents a moving target sound and seismic signal dataset, and compares the experimental results of the proposed method with those of TML and DL methods. Section 5 provides our conclusions.