Localization of Hostile Launch Points and Rapid Inversion of Friendly Firing Conditions Based on Deep Learning and Ensemble Optimization

In modern military operations, the localization of enemy launch points and the rapid inversion of friendly firing conditions are critical for enhancing response speed and the effectiveness of fire strikes. To improve the accuracy and efficiency of ballistic trajectory prediction and firing condition inversion, this paper proposes an intelligent prediction framework that fuses deep learning with ensemble optimization. First, a TCN-BiGRU-Attention model is designed for the localization of enemy launch points. Subsequently, a Particle Swarm Optimization - Extreme Learning Machine - Adaptive Boosting (PSO-ELM-AdaBoost) ensemble algorithm is adopted to rapidly derive the optimal firing conditions for striking the identified enemy launch points. Simulation results demonstrate that the TCN-BiGRU-Attention model reduces the comprehensive prediction error regarding range and lateral deviation to 7.89 m in the enemy launch point localization task. Furthermore, the PSO-ELM-AdaBoost algorithm exhibits superior performance in the firing condition inversion task; the prediction errors for the initial firing angle and initial velocity are only 0.06° and 0.058 m/s, respectively, representing accuracy improvements of 50% and 18% compared to the traditional ELM method. By constructing a dual-stage "trajectory prediction-parameter inversion" architecture, this study reduces the single-computation time to the millisecond level, significantly enhancing computational efficiency. These results indicate that the proposed framework possesses potential applicability under simulation validation conditions, providing methodological references and theoretical support for the design of sensing data fusion, real-time reasoning, and decision support modules in future C4ISR and Military Internet of Things (MIoT) systems.