MVPTransformer: Long-Term Multivariate Time-Series Pattern for Plasma Array-Based Intelligent Protection Against HPM

High-power microwaves (HPMs) are short-pulse electromagnetic waves in the microwave spectrum, characterized by intense impact energy that can cause damage to both electronic devices and human organs. In recent years, with the rapid advancement of data-driven artificial intelligence paradigms, achieving intelligent protection against HPM has emerged as a critical research challenge. The key to enabling intelligent analysis of plasma-based HPM protection lies in accurately modeling the complex multiphysics interactions between HPM and plasma. Existing methods have made progress in predicting the response of plasma characteristic parameters (such as electron density and electric intensity) under HPM exposure and in imputing missing values. However, it primarily focuses on short-term variations of single-feature variables. In this work, we propose a novel time-series modeling approach that integrates multiperspective feature extraction with multihead attention-based information fusion, complemented by an autonomous thinking strategy (ATS). This framework enhances the precise capture of long-term multivariate time-series patterns, and our experiments demonstrate its effectiveness. Furthermore, this study specifically investigates the coupling effects between HPM and a cylindrical plasma array (CPA). By conducting case studies and comparing our method with three state-of-the-art time-series models, we validate the superior accuracy and generalizability of our proposed approach.