This paper proposes a barrier-modulated probabilistic super-twisting guidance (BMPSTG) for robust three-dimensional maneuvering target interception under measurement uncertainty and packet loss. A stochastic barrier-adaptive mechanism inspired by tunneling dynamics is introduced to reshape the sliding variable evolution, enabling accelerated convergence and reduced chattering compared with conventional super-twisting schemes. To enhance disturbance reconstruction accuracy, a composite observer integrating a finite-time disturbance observer (FTDO) and a high-order extended state observer (HESO) is developed. The FTDO ensures fast transient estimation, while the HESO improves steady-state precision and robustness to noise. Simulation results demonstrate that the proposed FTDO–HESO structure outperforms both standalone FTDO and conventional FTDO–ESO configurations in terms of estimation accuracy and guidance performance. A probability-coupled gain adaptation strategy further adjusts control gains according to engagement states, improving robustness under aggressive target maneuvers. Monte Carlo simulations verify that the proposed method achieves high interception accuracy with stable convergence and reduced control effort in complex engagement scenarios.

Multi-missile interception of maneuvering targets requires accurate prediction and impact-time coordination under fixed missile speed. This study couples LSTM-IMM tracking with active curvature planning. LSTM generates a time-varying IMM transition matrix from measurement features, and the guidance law converts fixed-speed coordination into curvature and normal-acceleration allocation. Comparisons, sensitivity tests, Monte Carlo simulations, and five engagement scenarios verify tracking, mode recognition, real-time feasibility, and interception without speed regulation.