Artificial intelligence-empowered applications, countermeasures, and challenges in battlefield environment information for aviation and aerospace transition zones

The AATZ (aviation and aerospace transition zones), defined as the atmospheric layer between 50 and 250 km altitude, has been increasingly recognized as a critical domain for next-generation strategic competition. Unlike conventional near space studies limited to 20~100 km, this work systematically extended the analytical boundary upward to encompass the thermosphere–ionosphere coupling region (100~250 km)—a vacuum-dominated regime previously underrepresented in comprehensive reviews. In response to the growing deployment of hypersonic vehicles, low-Earth-orbit reconnaissance platforms, and advanced electronic warfare systems, the AATZ is no longer treated merely as a passive operational backdrop; rather, its highly dynamic atmospheric density, plasma irregularities, and complex electromagnetic propagation characteristics are now actively exploited as tactical variables in information confrontation.

To address the challenges posed by this high-dimensional, nonlinear, and rapidly evolving environment, AI(artificial intelligence) has been investigated as a transformative enabler across the full “perception–fusion–prediction–countermeasure” chain.

First, deep learning architectures—including encoder-decoder networks and convolutional-recurrent hybrids—have been employed to achieve efficient end-to-end inversion of key environmental parameters such as neutral density, electron concentration, and wind velocity from sparse and heterogeneous observations (e.g., GNSS(global navigation satellite system) radio occultation, radiosonde data, and airglow imagery). Second, multi-source data from ground-based radar, satellite remote sensing, and meteorological reanalysis are integrated through a fusion framework combining graph neural networks and attention mechanisms. This approach supports the construction of a mission-oriented digital twin of the battlefield environment and ensures consistency in high-dimensional situational awareness. Third, by embedding physical constraints such as mass conservation, energy conservation, and ion-neutral particle coupling dynamics into AI-driven meteorological models, the accuracy of short-term (within 6 hours) environmental forecasts has been significantly enhanced, achieving minute-level temporal resolution and spatial accuracy below 100 km. Finally, autonomous decision-making capabilities are developed through reinforcement learning and generative adversarial frameworks to support adaptive electronic countermeasures, including real-time waveform deception, cognitive jamming, and trajectory-aware interference, thus realizing “one-target-one-policy” and “one-moment-one-strategy” intelligent confrontation paradigms.

It has been established that AI not only enhances the fidelity and timeliness of environmental characterization but also transforms passive environmental knowledge into an active combat advantage. The resulting information warfare chain enables dynamic adaptation to ionospheric scintillation, thermospheric drag anomalies, and sporadic-E layer disruptions. These phenomena were historically sources of uncertainty but are now leveraged as natural masking conditions for stealth penetration or communication denial.

Nevertheless, several critical bottlenecks were identified that impede the transition of AI from laboratory demonstration to operational deployment. These include: 1) inherent uncertainty in multi-sensor perception due to observational sparsity and calibration drift; 2) weak interpretability of deep predictive models, which leads to “black-box” decisions incompatible with high-stakes military operations; 3) poor cross-domain transferability when models trained in simulation fail to generalize under real-world perturbations or adversarial conditions; and 4) severe data scarcity caused by international restrictions on high-altitude measurements, which fundamentally limits the training robustness of domestically developed AI systems.

To address these challenges, four strategic pathways are proposed to bridge the gap between technical feasibility and combat credibility. First, trustworthy AI must be prioritized through the integration of physical laws into neural architectures, such as PINNs(physics-informed neural networks) and neural operators, to ensure consistency with first principles. Second, standardized evaluation benchmarks and adversarial testing environments should be established to enable interoperability across services and platforms. Third, indigenous data acquisition capabilities must be accelerated via high-altitude long-endurance platforms, hypersonic testbeds, and quantum-enabled remote sensing. Fourth, AI is expected to evolve into a cross-domain intelligent hub that fuses electromagnetic, kinetic, and cyber information to support advanced concepts such as cognitive electronic warfare and cooperative space-air interception.

In conclusion, AI is no longer merely a supporting tool but a core driver reshaping aerospace warfare. Only through systematic, interdisciplinary collaboration can the leap from “technologically usable” to “operationally trustworthy” be achieved, thereby securing strategic initiative in this decisive high frontier.