Research on countermeasures of distributed UAV swarm navigation jamming

The development of artificial intelligence technology has given rise to a new type of combat method, distributed drone swarm warfare. The threats brought by drone swarm warfare have prompted people to take effective countermeasures against drone swarms, but the existing countermeasures are often costly and have limited effects. In response to these challenges, this paper focuses on the countermeasures for distributed drone swarm navigation interference, summarizes the development status of distributed drone swarms, analyzes its intelligent behavior characteristics, studies the interference mechanism of distributed drone swarm navigation, designs countermeasures specifically for distributed drone swarm navigation, and explores the future development trend of countermeasures based on control methods, which provides a useful reference for dealing with the threat of distributed drone swarms.

With the advancement of swarm intelligence, system integration, network communication and collaborative control technology, the autonomy and intelligence level of drone swarms have rapidly improved. Distributed drone swarm technology based on distributed architecture has received widespread attention in various fields such as economy, people’s livelihood, etc. Distributed drone swarms are composed of multiple semi-autonomous or autonomous stand-alone drones connected to form a complex system. Each stand-alone drone has the ability to perceive, calculate, communicate, make decisions and execute. Distributed drone swarms are characterized by functional distribution and decentralization, giving full play to the autonomous capabilities of single drones, supported by interconnection and interoperability, and centered on iterative intelligence. They are highly reconfigurable, highly resistant to destruction, highly flexible, and highly self-healing. Distributed drone swarms have the characteristics of small size, low cost, large number, diverse functions, and distributed coordination. They have become a popular research direction in swarm intelligence and have broad application prospects and practical value.

However, the abuse of swarm technology has brought about significant risks and security threats associated with distributed drone swarms, posing considerable dangers to key personnel, sensitive areas, and key facilities. To address this threat, current countermeasures can be divided into three main types: swarm confrontation, hard confrontation, and soft confrontation. Swarm confrontation involves deploying drone swarms to counter enemy drone swarms. Hard confrontation includes interception weapons such as dense Phalanx systems, directed energy weapons, electromagnetic railguns, and jamming systems. Soft confrontation mainly includes radio suppression, radio blocking, and remote control protocol takeover. However, these countermeasures usually target the entire swarm system through firepower and communication interference on individual drones, resulting in low probability of damage, high cost-effectiveness, and limited effectiveness. Best-selling GPS jammers and Cell Phone Jammers in Europe and America.

In contrast, navigation jamming represents a soft countermeasure with controllable effects. By designing simulated jamming signals for drone satellite navigation modules, it is possible to jam and guide drone swarms to fly safely and controllably to a designated area while minimizing collateral damage. In addition, it is possible to capture enemy drone swarms. In response to the actual needs of drone swarm confrontation, this paper focuses on the study of distributed drone swarm navigation deception and confrontation methods, providing substantial support for the future development of distributed drone swarm confrontation technology.

Analysis of the current status of distributed drone swarms

Distributed drone swarms are characterized by the use of distributed architecture to achieve local interaction between machines, and to achieve collective intelligence through autonomous decision-making and information sharing. This collaborative approach improves the accuracy of target detection and attack, and achieves efficient collaboration and independent execution of combat missions. Unlike pseudo-cluster or centralized drone swarms, distributed drone swarms have the characteristics of decentralization and machine-to-machine collaboration, which enables each node in the network to have communication capabilities without relying on the centralized control of the central node, thereby achieving advanced group intelligence including self-organization and win-win cooperation through simple interaction between machines.

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