A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

Abstract

When employing penetration ammunition to strike multi-story buildings, the detection methods using acceleration sensors suffer from signal aliasing, while magnetic detection methods are susceptible to interference from ferromagnetic materials, thereby posing challenges in accurately determining the number of layers. To address this issue, this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion, utilizing both the temporal convolutional network (TCN) and the long short-term memory (LSTM) recurrent network. By leveraging the strengths of these two network structures, the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process, establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate. A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates, capturing the multi-source physical field signals and their patterns during the penetration process. The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting, respectively. The model's predictive performance is evaluated under various operating conditions, including different ratios of added noise to random sample positions, penetration speeds, and spacing between target plates. The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms, 0.12 ms, and 0.16 ms, respectively, confirming the robustness of the proposed model. Moreover, the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.