The widespread adoption of Unmanned Aerial Vehicles (UAVs) in civilian domains, such as airport security and critical infrastructure protection, has introduced significant safety risks that necessitate effective countermeasures. High-Energy Laser Systems (HELSs) offer a promising defensive solution; however, when confronting large-scale malicious UAV swarms, the Dynamic Resource Target Assignment (DRTA) problem becomes critical. To address the challenges of complex combinatorial optimization problems, a method combining precise physical models with multi-agent reinforcement learning (MARL) is proposed. Firstly, an environment-dependent HELS damage model was developed. This model integrates atmospheric transmission effects and thermal effects to precisely quantify the required irradiation time to achieve the desired damage effect on a target. This forms the foundation of the HELS–UAV–DRTA model, which employs a two-stage dynamic assignment structure designed to maximize the target priority and defense benefit. An innovative MADDPG-IA (I: intrinsic reward, and A: attention mechanism) algorithm is proposed to meet the MARL challenges in the HELS–UAV–DRTA problem: an attention mechanism compresses variable-length target states into fixed-size encodings, while a Random Network Distillation (RND)-based intrinsic reward module delivers dense rewards that alleviate the extreme reward sparsity. Large-scale scenario simulations (100 independent runs per scenario) involving 50 UAVs and 5 HELS across diverse environments demonstrate the method’s superiority, achieving mean damage rates of 99.65% ± 0.32% vs. 72.64% ± 3.21% (rural), 79.37% ± 2.15% vs. 51.29% ± 4.87% (desert), and 91.25% ± 1.78% vs. 67.38% ± 3.95% (coastal). The method autonomously evolved effective strategies such as delaying decision-making to await the optimal timing and cross-region coordination. The ablation and comparison experiments further confirm MADDPG-IA’s superior convergence, stability, and exploration capabilities. This work bridges the gap between complex mathematical and physical mechanisms and real-time collaborative decision optimization. It provides an innovative theoretical and methodological basis for public-security applications.

In multi-asset UAV safety monitoring, dynamic threat assessment is challenged by delayed recognition of changes in the potentially threatened asset and unstable threat rankings under noisy observations. This paper proposes an interpretable three-dimensional dynamic threat assessment method integrating approach directionality, historical trend correction, and adaptive exponential moving average smoothing. Each UAV-protected asset pair is treated as an assessment unit to construct a many-to-many threat matrix. A basic threat score is first derived from UAV type, three-dimensional closing velocity, distance, altitude difference, and vertical approach motion. Approach directionality is then characterized using the geometric alignment between the UAV velocity and the bearing to each protected asset, with optional attitude and reachability information. The temporal trend of directionality is estimated from historical observations to capture persistent changes in approach tendency, while adaptive smoothing balances responsiveness to genuine threat transitions against suppression of noise-induced fluctuations. Comprehensive simulation experiments, including three-dimensional discrimination, Monte Carlo evaluation, observation noise, latency, missed detections, clutter, difficult motion conditions, and ablation studies, demonstrate that the proposed method provides accurate and timely threat identification while substantially improving score and ranking stability. The resulting framework offers an interpretable and computationally efficient basis for multi-UAV threat prioritization and safety-oriented situational awareness.

In multi-asset UAV safety monitoring scenarios, dynamic threat assessment is challenged by delayed recognition of protected-asset shifts and unstable threat rankings under noisy observations. To address these issues, this paper proposes an approach-directionality-based dynamic UAV threat assessment method that integrates historical trend correction and adaptive exponential moving average (EMA) smoothing. The proposed method takes each UAV–protected asset pair as the basic assessment unit and constructs a threat matrix for multiple UAVs and multiple protected assets. Based on fundamental threat factors, including UAV type, closing speed, altitude, and distance, an approach directionality factor is defined using the geometric relationship between the UAV velocity direction and the bearing direction of each protected asset. To capture shifts in the potentially affected protected asset, the historical directionality sequence is linearly fitted, and the instantaneous directionality is dynamically enhanced or suppressed according to the estimated trend. An adaptive EMA mechanism is then introduced to improve temporal stability under noisy conditions. Simulation results from turning-approach scenarios, Monte Carlo experiments, and noise robustness tests show that the proposed method identifies protected-asset shifts earlier while maintaining high protected-asset identification accuracy and ranking stability. The framework provides interpretable support for threat ranking, situational awareness, and safety-oriented decision-making in non-offensive UAV safety monitoring applications.

The emergence of laser technology has led to the gradual integration of laser weapon system (LaWS) into military scene, particularly in the field of anti-unmanned aerial vehicle (UAV), showcasing significant potential. However, A current limitation lies in the absence of a comprehensive quantitative approach to assess the capabilities of LaWS. To address this issue, a damage effectiveness characterization model for LaWS is established, taking into account the properties of laser transmission through the atmosphere and the thermal damage effects. By employing this model, key parameters pertaining to the effectiveness of laser damage are determined. The impact of various spatial positions and atmospheric conditions on the damage effectiveness of LaWS have been examined, employing simulation experiments with diverse parameters. The conclusions indicate that the damage effectiveness of LaWS is contingent upon the spatial position of the target, resulting in a diminished effectiveness to damage on distant, low-altitude targets. Additionally, the damage effectiveness of LaWS is heavily reliant on the atmospheric condition, particularly in complex settings such as midday and low visibility conditions, where the damage effectiveness is substantially reduced. This paper provides an accurate and effective calculation method for the rapid decisionmaking of the operators.

Accurately predicting high-energy laser propagation toward fast-moving aerial targets is not straightforward, since the slant range, elevation angle, atmospheric path, accumulated turbulence, and thermal blooming all change along with target motion. Because of this coupled evolution, fixed-path or quasi-static models can hardly describe, in a complete way, the time-varying loss of beam transmission and the weakening of energy concentration. To address this problem, this paper builds on established wave-optics propagation methods to develop a dynamic multi-physics framework for high-speed moving-target engagements. The central idea is to rebuild the propagation environment at every time step according to the instantaneous target position so that the optical path can be linked with height-dependent extinction, refractive-index structure, and absorption profiles; meanwhile, relative motion is represented as an effective transverse sweeping velocity, which helps reduce the accumulation effect of thermal blooming. Based on a unified split-step Fourier method, the model combines Beer–Lambert path attenuation, Kolmogorov-spectrum multilayer phase screens, and thermal-blooming phase modulation, yielding time-resolved indicators such as transmittance, received power, peak irradiance, Strehl ratio, scintillation index, pointing error, and beam-quality factor. Simulation results show that, in a 500 m static horizontal path, the attenuation module reduces to the Beer–Lambert solution, with the maximum relative error kept below ${10}^{-14}$; the turbulence phase-screen implementation is validated against the absolute Kolmogorov prediction. Over the resolved inertial interval, the ensemble-averaged structure function yields a fitted exponent $p_{\mathrm{f}\mathrm{i}\mathrm{t}}=1.671$ (theoretical: $5/3=1.667$) and a relative normalization error of $2.5\mathrm{\%}$, confirming that the generated screens reproduce both the Kolmogorov scaling and the prescribed turbulence strength. For a 1.064 μm, 2 kW Gaussian beam propagating over a 50 s trajectory of about 8 km, when the target speed increases from 0 to 300 m/s, the thermal-blooming-related beam-quality factor decreases from roughly 1.8 to 1.2, which indicates that dynamic geometry, turbulence, attenuation, and thermal blooming need to be considered together when evaluating laser propagation performance.

To address the dynamic target allocation and resource scheduling problems of multiple High-Energy Laser Systems (HELSs) in defending critical infrastructure against large-scale heterogeneous UAV swarm penetration, this paper proposes a soft–hard damage cooperative task allocation and optimization method, termed Attention-based Centralized Training and Decentralized Execution proximal policy optimization (Attn-CTDE-PPO), which integrates an attention mechanism with multi-agent reinforcement learning. First, a cooperative multi-agent sequential decision-making model for multi-HELS engagement against UAV swarms is constructed by considering continuous laser irradiation, system energy consumption, thermal accumulation limits, and soft–hard damage mechanisms. Second, a multi-head attention set encoder based on a Transformer is introduced to extract global situational features at the state level. This design enables the policy network to handle a time-varying number of targets and mitigates the explosion of hybrid action spaces. At the action level, a discrete–continuous dual-head network with a masking mechanism is proposed to achieve target allocation and laser-parameter scheduling. Furthermore, a multi-dimensional threat assessment module is developed, which can prune infeasible actions in the action space via physical rule-based masking, thereby accelerating the decision-making speed of the method. Monte Carlo simulation experiments verified the defense reliability, energy management efficiency and real-time responsiveness of the proposed method. The experimental results demonstrate that, in heterogeneous swarm scenarios with different numbers of UAVs, the proposed method effectively suppresses UAV penetration, improves the defense success rate, and reduces total system energy consumption compared with baseline algorithms.