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Application of Key AI Technologies in Weaponry
Author: Military Eagle Think Tank Source: Military Eagle Dynamics
The application of artificial intelligence technologies in weaponry is mainly reflected in several aspects: pattern recognition (intelligent perception), expert systems (intelligent decision-making), deep learning (intelligent decision-making), and motion control (intelligent feedback).
(1) Application of Pattern Recognition in Weaponry
Pattern recognition is one of the technological approaches for computers to simulate human sensory organs and perceive various external stimuli, including speech recognition, machine vision, and text recognition. Pattern recognition technology helps weaponry achieve automatic target recognition (ATR) capability.
In pattern recognition, machine vision can automatically receive and interpret images of real scenes through optical non-contact sensing devices to obtain information for system control. For example, DARPA’s DARPA “Heart Eye” project and the “Image Perception, Analysis, and Utilization” project developed a machine vision system with “dynamic information perception capability” to deconstruct dynamic objects using convolutional neural network image recognition technology, transforming the information in images into the computer’s “knowledge”. In actual combat, the pattern recognition system observes the dynamic video information of targets and, with the help of neural networks and specialized machine vision hardware, can automatically identify potential threats in complex battlefield environments, providing reference information for target strikes.
(2) Application of Expert Systems in Weaponry
Expert systems (ES) are a type of computer intelligent program system with specialized knowledge, using specific knowledge and experience provided by experts in a particular field, employing reasoning techniques in artificial intelligence to solve and simulate various complex problems usually solvable only by experts. It is currently one of the most active and effective branches in the field of artificial intelligence. An expert system generally consists of a knowledge base, database, reasoning mechanism, explanation mechanism, knowledge acquisition, and user interface (Figure 2).
Figure 2 Basic Structure of Expert Systems
Applying expert systems to weaponry enables real-time battlefield situation assessment capabilities. Proven expert knowledge about typical battlefield situations and damage assessment of weapons during wartime is mathematically described and composed into a database and knowledge base. In combat, weaponry receives information from space-based, air-based, sea-based, or ground control stations, and geographic information obtained by its sensors, as well as sound waves, radio waves, visible light, infrared, and laser information emitted by enemy weapons, which is compared with information in the database and knowledge base. With the help of automatic reasoning technology in artificial intelligence, the computer quickly processes this information to identify threats in the battlefield environment and interacts with experts and commanders through the user interface.
Expert systems can be combined with data storage and communication network technologies for various field military systems, such as airborne early warning and control systems on aircraft, the US Navy’s “Aegis” warships, and reconnaissance satellites, helping to determine the location and intentions of enemy forces. The US Navy utilizes networked expert systems to provide a common operational picture for all forces in the operational area, enabling collaborative combat capabilities. The most famous is the intelligent C3I information system developed by the United States, which possesses “personality” and human “characteristics” and “intelligence”, familiar with the commander’s temperament, thinking habits, and other emotional characteristics, assisting the commander in battlefield situation assessment within minutes or even seconds.
DARPA proposed the “Deep Green” system (Figure 3), which can predict instantaneous changes on the battlefield, helping commanders think ahead and determine whether to adjust plans, focusing attention on decision-making choices rather than on the details of plan formulation.
Figure 3 Conceptual Diagram of “Deep Green”
(3) Application of Deep Learning in Weaponry
Deep learning technology is based on multi-layer neural networks that can learn abstract concepts, integrate self-learning, and converge relatively quickly. It mimics the mechanisms of the human brain and can perform highly abstract feature tasks in artificial intelligence, such as speech recognition, image recognition and retrieval, and natural language understanding. Deep learning has multiple layers of nodes and connections, through which it perceives different abstract features at each level, with each layer being higher than the previous one, all achieved through self-learning. Representative projects include DARPA’s project for synthetic aperture radar “Target Recognition and Adaptation in Adverse Environments”, which applies the latest research results in deep learning and is expected to automatically locate and identify targets in synthetic aperture radar images, enhancing pilots’ situational awareness.
Applying deep learning technology to target identification and positioning in weaponry is expected to achieve automatic target recognition and real-time situational awareness. A deep neural network model with multiple hidden layers is employed, utilizing hidden layers to convert the raw input of target information into shallow features, mid-level features, and high-level features layer by layer until the final positioning and operational situational awareness of the target is achieved.
(4) Application of Motion Control in Weaponry
Motion control technology integrates artificial intelligence perception, decision-making, and feedback, including individual motion control and collective motion control, mainly applied in robotics and unmanned systems. Individual motion control is represented by the US four-legged “Big Dog” robot (Figure 4) and the bipedal humanoid “Atlas” robot, which come equipped with numerous sensors to monitor body posture and acceleration, joint movement, engine speed, and parameters of internal hydraulic mechanisms. Through advanced learning algorithms, these robots can continuously accumulate experience, autonomously avoid obstacles, and navigate increasingly complex terrains, enabling combat capabilities in high-risk battlefield environments.
Figure 4 Structure and Sensor Distribution of the “Big Dog” Robot
Collective motion control also includes unmanned system swarm control and collaborative technology between unmanned and manned systems. Unmanned system swarm control allows unmanned systems to autonomously form collaborative plans based on tasks and changes in external environments, exhibiting decentralization and non-linearity (Figure 5), thereby exponentially increasing the combat effectiveness of weaponry. The US successfully completed operational tests of the unmanned boat “Swarm” technology. A swarm of 13 unmanned boats autonomously discovered targets, devised action plans, and successfully intercepted target vessels. Missile unmanned swarm operations involve equipping missiles with tactical data links, enabling real-time information transmission between missiles and between missiles and launch platforms during the target attack process, timely conveying detection information to enhance penetration probability and achieve “tactical stealth”, thereby expanding the results of combat (Figure 6).
With the advancement of artificial intelligence technologies, continuous improvements in computer processing speeds, and the development and application of cutting-edge foundational technologies such as new materials and new processes, weaponry based on artificial intelligence is expected to evolve towards greater autonomy and miniaturization. Advancements in nano-electronic technology and micro (nano) electromechanical technology are driving the development of nano synthetic aperture radar and intelligent microelectromechanical navigation systems, which are expected to qualitatively change the guidance, navigation, and propulsion aspects of weaponry, pushing weaponry based on artificial intelligence towards greater miniaturization.
Figure 5 Decentralization and Non-linearity in Swarm Operations
Figure 6 Unmanned Missile Swarm Operations
