Page 138 - 《爆炸与冲击》2026年第5期

第 46 卷 罗瑶嘉，等： 基于变分模态分解处理的冲击波压力长短期记忆网络系统建模 第 5 期

A sinusoidal signal generator was used to obtain pressure acquisition waveforms in the range of 0.1–10 Hz; the amplitudes
were converted into decibels to form the low-frequency magnitude-frequency characteristic curve, and a frequency-domain
rational function fitting procedure was applied to establish the low-frequency transfer function. Using this transfer function,
low-frequency dynamic compensation was performed on the reconstructed signal, and the compensated low-frequency signal
was combined with the original sensor response to construct an input-output dataset that simultaneously preserved the
compensated dynamic information and the original response characteristics. On the basis of this dataset, SSA was further used
to optimize key LSTM hyperparameters, including the number of hidden units, the maximum number of training epochs, and
the initial learning rate, and an LSTM network was trained to model the nonlinear, time-dependent, and memory-dependent
behavior of the acquisition system, thereby achieving fusion modeling of high- and low-frequency dynamic characteristics
within a unified framework. Simulation analyses and live explosion tests demonstrated that, compared with the traditional
inverse-filtering compensation method, the proposed approach reduced the mean absolute percentage error (MAPE) between
the compensated signal and the reference pressure curve by approximately 75% and decreased oscillation residuals by about
38%, satisfying the accuracy requirements for input pressure signals; compared with a single LSTM-based modeling approach,
the VMD-LSTM fusion model reduced the overall modeling error to 13%, indicating improved accuracy and robustness. These
results indicate that the SSA-optimized VMD decomposition, transfer-function-based low-frequency compensation, and SSA-
tuned LSTM fusion modeling together provide an effective full-band modeling strategy, and that the proposed framework
offers a robust solution for accurate dynamic characteristic modeling and compensation in shock wave pressure sensor
acquisition systems.
Keywords: shock wave pressure; dynamic compensation; variational mode decomposition; long short-term memory

冲击波作为弹药爆炸及武器系统作用过程中产生的核心毁伤元，是引发人员致伤效应与工程结构
失效的关键因素。随着科学技术的快速发展，军工领域不断突破技术瓶颈，而爆炸冲击波动力学研究作
为毁伤评估体系的重要基础，其压力场时空分布特征的精准表征已成为现代武器效能定量化研究的焦
点问题。这一需求对冲击波压力测试系统提出了严苛的动态响应要求，需在瞬态过程中实现超压信号
的高保真捕获。
在压力信号采集系统中，良好的动态特性是保证冲击波采集信号精度的关键。胡勇等 [1] 在冲击波
检测综述中提到采集系统传感器要求除宽频带外，还应具有高频响、良好的检测线性度特性（能可靠记
录冲击波压力的变化情况）、高信噪比、高灵敏度和良好的抗干扰特性。但通常系统受测量精度和制作
[2]
工艺的影响，其动态性能往往无法满足动态测试要求 。在这种背景下，对现有压力采集信号进行去噪
和补偿是改善系统性能的一条有效途径。
传统的动态补偿方法如频域修正法 [3-5] 、反卷积法 [6] 等，需要首先进行系统的精确实验标定与建模，
这不仅对标定装置有较高的要求，而且受限于线性假设下的建模精度 。近年来，随着优化算法的迭代
[7]
演进，基于智能寻参 [8-9] 的动态误差补偿机制已成为提升系统非线性失真抑制能力的重要技术路径。除
此之外，神经网络在动态信号建模领域展现出显著潜力，其使复杂信号的重构与预测精度得到进一步提
升。孙传猛等 [10] 通过卷积神经网络重构爆炸冲击波，信号曲线重构良好；于浩等 [11] 基于改进的重构技
术对爆炸场超压、动压信号进行了威力重构，重构精度有了显著提高。Yao 等 [12] 使用经验模态分解与双
向长短期记忆网络建立了逆传感网络，实现了对激波管压力阶跃信号的高精度重建；Lu 等 [13] 通过变分
模态分解-粒子群优化与长短期记忆网络相结合的模型预测离心泵压力脉动信号，使均方误差降为
2
31.45 Pa 。以上研究均证明了神经网络在动态信号建模应用领域的潜力，同样表明对于不同频域叠加的
信号采用模态分解处理后可以使神经网络预测精度较单个神经网络精度更高。
综上所述，基于融合启发式算法，近些年信号补偿与建模方法可以突破传统线性校正的模型阶次限制，
实现算法的自适应参数寻优与多目标约束优化。但目前的研究多是基于实验室环境采集的动态响应特征
进行建模，再将模型代入实测响应信号当中补偿处理，缺乏直接提取实测冲击波响应特征进行训练的方法。

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