Research on deception countermeasures for distributed UAV swarm navigation

With the continuous development of artificial intelligence technology, distributed drone swarm combat has become a new combat style. Faced with the huge threat posed by swarm operations, and in view of the current problems of high cost-effectiveness and poor effectiveness of UAV swarm countermeasures, the distributed UAV swarm navigation deception countermeasures were studied. This paper summarizes the development status of distributed UAV swarms, analyzes the intelligent behavioral characteristics of distributed UAV swarms, studies the deception mechanism of distributed UAV swarm navigation, and designs a distributed UAV swarm navigation deception countermeasures. Based on the control method, the development trend of UAV swarm navigation deception countermeasures is prospected, providing a reference for future responses to distributed UAV swarm threats.

With the new round of innovation in swarm intelligence, system integration, network communication and collaborative control technologies, the autonomy and intelligence of UAV swarms have achieved rapid development. UAV swarm technology based on distributed architecture is in Economic and people's livelihood, military and national defense and other fields have attracted much attention. Distributed UAV swarm refers to a complex system composed of distributed semi-autonomous or autonomous individual UAVs interconnected through a network. Among them, individual drones have certain sensing, computing, communication, decision-making and execution capabilities. Based on the structural characteristics of functional distribution and decentralization, the distributed UAV swarm is based on the autonomous capabilities of a single machine, supported by the connectivity capabilities between machines, and with iterative intelligence as the core. It is reconfigurable, It has the characteristics of strong invulnerability, high flexibility and robust self-healing. At the same time, with its advantages of small size, low cost, large number, diverse functions, and distributed coordination, distributed drone swarms have become a hot research topic in swarm intelligence today. All fields have broad application prospects and practical value. However, due to the abuse of swarm technology, the risks and security threats brought by distributed drone swarms have become increasingly prominent, posing a great threat to important personnel, sensitive areas and key facilities. Faced with the huge threat posed by distributed drone swarms, the current main countermeasures can be divided into three categories: swarm-to-swarm, hard countermeasures and soft countermeasures. Among them, swarm-to-swarm refers to the use of drone swarms to fight against enemy drone swarms; hard countermeasures mainly include Phalanx interception weapons, directed energy weapons, electromagnetic railguns, and "curtain" weapons. Interception, etc.; soft countermeasures mainly include radio suppression, radio blocking and remote control protocol takeover, etc. The above countermeasures only counteract the entire swarm system through fire damage and communication interference against a single UAV, which have shortcomings such as low damage probability, extremely high countermeasure cost-effectiveness, and poor countermeasure effect. Navigation deception is a soft countermeasure with controllable countermeasures. Corresponding simulated deception signals can be designed for the UAV satellite navigation module to deceive and guide the swarm to a designated area safely and controllably, while avoiding collateral damage. At the same time, there is the possibility of capturing enemy drone swarms. In this context, this article faces the practical needs of UAV swarm countermeasures and focuses on researching distributed UAV swarm navigation deception and countermeasures methods to provide strong support for the future development of distributed UAV swarm countermeasures technology. drone jammer

Analysis of the current situation of distributed drone swarms

The distributed UAV swarm has a distributed architecture. Through local interaction between machines, it generates group intelligence through independent decision-making and information sharing. It improves the detection and strike accuracy of targets through mutual cooperation between machines, and has independent and efficient collaboration. The ability to perform combat missions. Since the distributed swarm does not rely on the swarm brain, the communication links are dispersed and the communication volume is small, it is robust to the failure of some nodes, can effectively overcome the weaknesses of the centralized swarm, and will play a prominent advantage in future operations.

Different from UAV pseudo-swarms and centralized UAV swarms, due to decentralization and inter-machine collaboration, each node in the distributed UAV swarm network has network communication capabilities without the need for centralized control by a central node. , more advanced group intelligence such as self-organization and win-win cooperation emerge through simple interactions between machines. In recent years, countries have gradually shifted their research focus to distributed drone swarms. In 2016, the United States successfully completed the "Partridge" UAV swarm flight test, as shown in Figure 2. The test released a swarm of "Partridge" micro-UAVs through manned airborne platforms such as the F-16 without pre-set procedures. Relying on information sharing and coordinated decision-making between machines, the swarm successfully reached the predetermined target location and executed Situational awareness tasks. In 2019, Yemen's Houthi armed forces launched a swarm of 10 drones to attack Saudi Arabia's Abqaiq refinery and Khurais oil field. In the same year, the "Changlianzhisheng" Unmanned Bee Swarm Joint Action Challenge was held in China, focusing on the distributed autonomous collaboration of bee swarms, automatic task planning and allocation, dynamic reconstruction of bee swarm networks, cross-domain joint collaboration of unmanned platforms, and tactics. Ability levels such as integration and innovation with technology.