Russia Builds Drone Acoustic Detection Network

According to a report by Reference News on September 28, the Russian newspaper "Izvestia" reported on September 22 that Russia has developed an acoustic detection system for drones. The entire system consists of a network of autonomous sensors, some of which can be air-dropped and quickly deployed to designated areas. According to the development team, the system can identify drone types in real-time and, with specific sensor deployment plans, can also estimate the drone's approximate flight speed and direction. Experts say this technology can be used for early warning of approaching long-range drones, enhancing the protection capabilities of key Russian industries and infrastructure.

The "Spider" system is a comprehensive acoustic detection sensor complex that can classify and identify airborne threats based on sound source characteristics. Once a drone's sound signal is captured, the sensors can quickly identify the type and transmit the detection data back to the command center in real-time.

The system is currently in the preparation stage for mass production. Project development leader Mikhail Buyoniy stated, "Relying on neural network technology, this system has autonomous learning capabilities, and when new types of drones appear, it can recognize them through iterative training."

Compared to radar stations and large electro-optical detection equipment, acoustic sensors consume very little energy. When using batteries, they can operate for nearly a month without recharging; when paired with solar photovoltaic panels, they can achieve nearly permanent operation.

The "Spider" system is equipped with multiple types of sensors, including fixed manually deployed sensors and drone air-dropped sensors. Among them, the air-dropped sensors come in two configurations: one is "dart-like," which can penetrate the ground and secure itself upon landing; the other is "hedgehog-like," which relies on a specialized needle structure to attach to branches, suspending the device in the air.

Buyoniy explained, "The higher the sensor is set up, the better it captures abnormal sound sources, and the signal can also be transmitted over long distances."

According to development test data, the system can detect first-person view (FPV) drones at a distance of up to 200 meters, and for large drones using fuel engines, the detection distance exceeds 1000 meters. If a multi-row network deployment mode is used, the system can calculate the flight speed and movement direction of aerial targets.

The development team stated that the system can be deployed around key industries and infrastructure. Importantly, the detection network can extend to cover hundreds or even thousands of kilometers, depending solely on the number of sensors deployed. Therefore, a deep detection and early warning defense line can be constructed against potential attack routes of enemy drones.

The system has strong survivability, and the destruction of a few sensors will not affect the normal operation of the entire detection network.

Military expert Yuri Lyamin said, "This system is almost impossible to destroy. No one would use expensive long-range missiles to target one or two low-cost, easily replaceable sensors."

Another advantage of the system is that sensors can be deployed in remote areas such as forests, swamps, and riverbeds.

Lyamin stated, "Such autonomous detection equipment is the only feasible means to monitor these areas. Currently, the enemy mainly relies on such terrain to plan long-range drone attack routes."

Military expert Dmitry Korneyev stated that real-time understanding of the flight routes, numbers, and types of drones is an important condition for defending against attacks.

This early warning capability is particularly critical for mobile firepower units. After obtaining intelligence on approaching drones in advance, they can quickly move forward to implement interception.

Lyamin pointed out that acoustic detection methods also have shortcomings, as the detection effectiveness of the equipment can be easily affected by weather and terrain conditions. Strong headwinds, heavy rain, and other severe weather can reduce the detection range for drones.

To address this flaw, the density of sensor deployment can be increased. These sensors are relatively inexpensive and can be deployed in large quantities to form a wide-ranging monitoring network.

Military experts emphasize that a multi-layered early warning system must be established to counter drone attacks. The optimal model is a combination of various monitoring methods that complement and enhance each other: relying on radar stations for comprehensive airspace monitoring, using electro-optical detection equipment for visual target identification, and then employing acoustic sensors for supplementary detection in specific directions. (Translated by Wei Lianglei)