Target Recognition Method Based on Impulse Response Signal

This paper addresses the issues of short impulse response signal duration and susceptibility to noise interference by using the Leaky Integrate-and-Fire (LIF) neuron model to simulate the charging and discharging process for impulse signal generation. In addition, an Impulse Response Signal-Convolutional Spiking Neural Network (IRS-CSNN) is developed to exploit the time-frequency characteristics of the target impulse response signal for target identification. First, the signal sequence of pitch and azimuth angle changes is subjected to time-frequency analysis, from which the time-frequency features are extracted and fed into the IRS-CSNN with Time-Frequency Spatial Attention Mechanism (TF-SAM) to facilitate target classification and identification. The experimental results show that this approach effectively utilizes the feature information in the impulse response signal and exploits the feature extraction capabilities of the CSNN. The average recognition accuracy for targets at different angles under noise-added conditions is 89.93%, an improvement of 1.87%-4.34% over that of a similarly structured CNN and other machine learning models. These results confirm the effectiveness of the proposed method.