A Typical Infrared Background Radiation Prediction Model Based on RF-VMD and Optimized Hybrid Neural Network

Abstract

The short-term prediction and adjustment of a target’s infrared radiation hold significant value in military camouflage applications. Existing radiation prediction models generally require real-time environmental and meteorological data support, resulting in lag in active camouflage. To meet the demand for active camouflage of background infrared (IR) radiation, a short-term background IR radiation prediction method based on historical data is proposed. First, a random forest (RF) is used to filter the collected multidimensional meteorological parameters. Variational mode decomposition (VMD) is applied for time-frequency analysis on these parameters, optimizing them with Bayesian algorithms and decomposing them into multivariate intrinsic mode functions (IMFs) with similar frequencies to reduce the impact of nonlinearity in the data. Based on the superimposed IMFs as inputs, a hybrid deep neural network prediction model is established. The model optimizes the CNN-LSTM network with residual connections and introduces a multi-head self-attention mechanism to enhance spatiotemporal feature extraction of the multidimensional meteorological parameters, focusing on key temporal feature regions. According to the experimental results, the constructed model demonstrates high prediction accuracy and adaptability across different background environments, with a low parameter count and fast prediction capability, meeting the practical application needs for various complex backgrounds.