In the target recognition and precision strike missions of war robots, the application of artificial intelligence algorithms needs to combine real-time, robustness and multi-modal data processing. The following classifies the core algorithms and their functions from a technical point of view： ###1. Target recognition optimization algorithm 1. **Multi-scale inspection architecture** -**YOLOv7/Transformer-Based Detectors**: Through the fusion of convolution and self-attention mechanisms, real-time detection of targets of different scales is realized. YOLOv7's gradient flow optimization improves the recognition rate of small targets, while the long-range dependency modeling capabilities of Visual Transformer (ViT) can reduce complex background interference. -Feature pyramid network (FPN)**: Adopts a cross-level feature fusion strategy to combine low-resolution semantic information with high-resolution spatial information from the perspective of drones to improve the accuracy of long-distance target recognition. 2. **Dynamic environmental adaptation technology** -**Meta-learning (MAML)**: Quickly adapt to the new battlefield environment through a small number of samples. For example, when migrating between desert and urban scenes, the model parameters can converge within 10-100 iterations. -**Online incremental learning**: The elastic weight curing (EWC) algorithm is used to achieve 30 frames per second processing on the embedded GPU platform while maintaining the continuous learning ability of new camouflage targets. ###2. Precise strike decision-making algorithm 1. **Multi-restraint intensive learning** -**PPO-MPC hybrid architecture**: The combination of near-end strategy optimization (PPO) and model predictive control (MPC) optimizes the strike sequence under the constraints of firepower consumption. Experiments show that the architecture can still maintain a 92% task completion rate at 80% interference intensity. -**Hierarchical intensive learning**: The upper-level strategy plans strategic goals (such as prioritizing the destruction of air defense systems), the lower-level Q network controls specific ballistic parameters, and the dual time-scale design makes the decision-making delay less than 50ms. 2. **Ballistic prediction algorithm** -**Neural Differential equation (Neural ODE)**: Construct an aerodynamic model, predict the trajectory of a high-speed maneuvering target through an adaptive step size solver, and the prediction error is less than 0.3rad when the wind speed changes by 20m/s. ### 3. Multi-modal sensor fusion 1. **Cross-modal alignment technology** -**Deep correlation analysis (DCA)**: Use a typical correlation analysis network to align the feature space of infrared thermal images and visible light images, and increase the recognition confidence from 0.68 to 0.91 in the smoke interference environment. -**Millimeter wave radar point cloud processing**: The PointNet++ architecture is used to extract sparse point cloud characteristics, and the attention gating mechanism is combined to achieve millimeter-level accuracy estimation of the velocity vector of the moving target. ###4. Adversarial defense system 1. **Confrontation sample detection** -**Randomized Smoothing authentication (Randomized Smoothing)**: Gaussian noise disturbance is applied to the input data, and the detection rate of adversarial samples is 99.2% on the CIFAR-10-C battlefield data set, and the FPGA hardware acceleration reduces the computational overhead by 40%. 2. **Electromagnetic confrontation strategy** - **Frequency agility driven by Q learning**: A dynamic frequency hopping mode is constructed in the 2.4-5.8GHz frequency band, and the interference channel is avoided within 30µs through the Q table update strategy, and the communication recovery time is shortened to 1/5 of the traditional method. ###5. Group collaborative combat algorithm 1. **Distributed task allocation** -**Improved Hungarian algorithm**: The simulated annealing mechanism is introduced to solve the NP-difficult problem of multi-robot task allocation, and the task allocation time of 50 combat units is reduced from 1200ms to 280ms. 2. **Group situational awareness** - **Graph attention network (GAT)**: Construct a battlefield relationship map, the nodes represent the status of the combat unit, and the edge weights dynamically update the threat assessment values to achieve a 37% increase in the accuracy of group threat propagation modeling. ### Techno-ethical boundaries It should be noted that the algorithm design should comply with Article IV of the Convention on Certain Conventional Weapons, and the international law compliance verification layer should be embedded in the target identification module, such as blocking strike instructions to medical facilities through the rule engine. At the same time, interpretable AI (XAI) technologies, such as inter-layer correlation dissemination (LRP), are used to ensure that the decision-making process meets the transparency requirements of the Madrid Principles. The current technological bottleneck lies in the real-time fusion of multi-source heterogeneous data in a complex electromagnetic environment, as well as the controllability research of the emergence behavior of group intelligence. In the next step, we can explore the combination optimization algorithm enabled by quantum computing to improve the efficiency of large-scale collaborative combat while ensuring the Nash equilibrium.