Soldiers can create false electronic clusters as a form of electronic deception to mislead drones equipped with radio reconnaissance systems. Because these drones can “calculate” a unit’s position by detecting radio signals even when soldiers are visually hidden, creating fake signal signatures can divert the enemy’s attention away from a unit’s actual location [1].
The following techniques and principles are used to create and manage these clusters:
- Defining and Mimicking a “Cluster”
The Signal Threshold: In military contexts, a “cluster” is typically defined as more than three active GSM terminals (such as mobile phones or tablets) in one area [1].
Mimicking Activity: To create a convincing false cluster, soldiers place multiple active electronic devices—including phones, tablets, or Wi-Fi and Bluetooth-emitting hardware—in a single location away from their actual positions [1].
Arousing Interest: The goal is to create a concentrated area of electromagnetic activity that appears to be a command post or a troop gathering, thereby “arousing the interest of the enemy” and forcing them to investigate the wrong area [1]. - Strategic Placement and Supporting Deception
Displacement: False clusters must be established in “wrong directions” or dummy positions to draw drone surveillance and potential fire away from the real unit [2].
Layered Deception: These clusters are often more effective when paired with other decoys, such as imitation vehicles, imitation fire means (like mock machine-gun nests), or false light-masking signatures at night [2-4].
Using Smoke: Soldiers may also use smoke screens in these false positions to simulate activity or damage, further deceiving the drone operator into believing they have found a legitimate target [5]. - Maintaining Discipline at the Actual Position
To make the false clusters believable, soldiers must maintain strict electronic silence at their true location:
Flight Mode is Mandatory: All personal phones and tablets at the actual position must remain in “flight” mode [1, 6]. This prevents the enemy’s direction finders from identifying the real number of personnel at a site [2, 6].
Masking High-Priority Targets: High-signature devices that cannot always be turned off, such as Starlink terminals and generators, should be moved as far away from primary troop locations as possible and masked with specialized camouflage capes [7-9].
Avoiding “Routine”: Gathering in groups for food, guard changes, or rest creates a “predictable and vulnerable” signature that drones look for [10]. Deception only works if the real unit remains dispersed and electronically invisible [9, 10].
By creating these mock signal clusters, units can force the enemy to waste limited flight resources, time, and munitions on non-existent targets while increasing the survivability of the actual unit [2, 11].
Ghost Signals: Tactical Electronic Decoy Strategies
To create false electronic clusters for deceiving drones, soldiers use various active electronic devices that emit signals detectable by an enemy’s radio reconnaissance systems. The goal is to mimic the electromagnetic signature of a legitimate tactical unit or command post [1].
According to the sources, the following types of devices are used to form these false signatures:
Mobile Phones (GSM Terminals): In military contexts, a signal concentration of more than three active mobile phones is typically defined as a “cluster” that will arouse enemy interest [1].
Tablets: Like mobile phones, these are used as active GSM terminals to contribute to the electronic signature of a fake position [1].
Wi-Fi and Bluetooth Hardware: Drones can “calculate” positions by detecting clusters of Wi-Fi and Bluetooth signals, so active transmitters for these protocols are used to build a convincing decoy [1, 2].
Radios: Signals from various radio communication devices are often included in these clusters to simulate active military coordination [2].
Strategic Management of the Devices
To ensure these devices successfully deceive the enemy, soldiers follow specific tactical guidelines:
Active Displacement: These devices must be placed in a “wrong direction” away from actual unit positions to divert surveillance and potential fire [1, 3].
Pairing with Physical Decoys: False electronic clusters are most effective when paired with imitation objects, such as fake vehicles, mock machine-gun nests, or mortar positions [3].
Nighttime Deception: At night, these electronic decoys are often supplemented by intentional light-masking violations, such as leaving on flashlights or allowing phone screen glow in the decoy area to further draw the drone operator’s attention [4].
Maintaining Silence Elsewhere: Deception only works if the unit’s actual location remains electronically silent; all personal devices at the true position must remain in “flight mode” to avoid being detected by direction finders [1, 5].
Sonic Sentinels: Acoustic Methods for Beyond Line-of-Sight Drone Detection
Acoustic sensors detect drones beyond line-of-sight (BLOS) by monitoring the unique sound signatures produced by the drone’s motors and propellers [1-3]. While sensors like radar and cameras often require a direct path to the target, acoustic systems rely on sound waves that can travel around or through various obstructions.
According to the sources, acoustic sensors achieve BLOS detection through the following mechanisms:
Microphone Arrays: These systems use arrays of sensitive microphones to capture acoustic emissions from drones [1]. By analyzing the specific sound patterns and frequencies—which differ based on the drone model—these sensors can identify and locate a threat even when it is physically obscured [1, 2].
Acoustic Imaging: Advanced sensors create an “acoustic image” of the airspace [3]. This data-driven representation allows the system to track the path and position of a drone behind buildings, thick foliage, or mountainous terrain that would block traditional visual or infrared sensors [1, 3, 4].
Environmental Resilience: Acoustic sensors are particularly effective in conditions where line-of-sight optics fail, such as in thick fog, heavy rain, or total darkness [3, 5]. In certain instances, their ability to “hear” a drone allows them to exceed the detection range of optical sensors [3].
Passive Detection: Like passive radar, acoustic sensors are inherently stealthy because they do not emit any electromagnetic signals of their own [3]. They act strictly as receivers, detecting the drone’s own emissions without alerting the drone operator to the presence of counter-drone activities [3].
Integration and Verification
While acoustic sensors provide critical BLOS capabilities, they are typically integrated into a layered “sensor fusion” strategy [6-8]. In these systems, an acoustic sensor might provide the initial detection behind an obstruction, which then alerts the command and control (C2) platform—such as Anduril’s Lattice or DedroneTracker.AI—to cue other sensors like radar or thermal cameras once the drone enters their line-of-sight for final identification and mitigation [9-11].
Acoustic Signatures for Drone Tracking in Low Visibility
Acoustic sensors track drones in thick fog by monitoring the unique sound signatures produced by their motors and propellers, a method that is unaffected by the visual obscuration that hinders cameras and thermal imagers [1, 2]. Because sound waves can travel through fog and around physical obstructions, these sensors provide a critical tracking capability when line-of-sight is lost [2, 3].
The specific mechanisms for tracking in these conditions include:
Sound Signature Analysis: These systems utilize arrays of sensitive microphones to capture acoustic emissions [1]. Since every drone model produces distinct sound patterns and frequencies, the system can identify and locate the source by analyzing these unique audio markers [1].
Creation of an “Acoustic Image”: Advanced sensors generate an acoustic image of the airspace [3]. This data-driven representation allows the system to track the drone’s exact position and flight path in real-time, even when the environment is completely opaque to the human eye or standard optics [3].
Passive and Discrete Operation: Like passive radar, acoustic sensors are inherently stealthy [3]. They do not emit any signals (such as radio waves or laser beams) that would alert a drone operator to their presence, making them ideal for covert defensive operations in low-visibility weather [3].
Superior Range in Adverse Weather: In many instances, the ability to “hear” a drone allows these sensors to exceed the detection and tracking range of optical sensors in thick fog, heavy rain, or total darkness [3].
While highly effective in fog, acoustic sensors are typically part of a multi-sensor fusion strategy [4, 5]. In an integrated command-and-control system like DedroneTracker.AI or Anduril’s Lattice, acoustic data is correlated with other sensors—such as radar or RF detectors—to eliminate false positives and maintain a continuous track as the drone moves through different environmental conditions [5, 6].
Automated Defences: AI and the Modern Drone Kill-Chain
AI platforms can automate significant portions of the drone kill-chain in real-time, primarily to handle the speed and volume of modern aerial threats that would otherwise overwhelm human operators [1-3]. This automation is managed through integrated software platforms designed to compress the timeline between detection and neutralization [1, 4].
The role of AI in automating the kill-chain—structured as Detect, Track, Identify, and Mitigate (DTI-M)—includes the following capabilities:
- Automated Detection and Tracking
AI platforms utilize sensor fusion to ingest and correlate data from disparate sources, such as radar, radio frequency (RF) sensors, acoustic arrays, and EO/IR cameras [5-7].
Granular Identification: AI algorithms can distinguish between drones and “biological clutter” (like birds) and identify specific drone models by their radio signatures or visual characteristics [8-10].
Distributed Tracking: Advanced platforms like Anduril’s Lattice can track multiple threats simultaneously across a network of sensors, maintaining a common operating picture in real-time [4, 11, 12]. - Kill-Chain Optimization and Fire Control
Because high-speed maneuvers and drone swarms create a cognitive load that is “unsustainable” for humans, AI is used to automate fire-control decisions [2, 3, 13].
Speed to Engagement: Systems like Lattice are designed to detect, track, classify, and engage threats in seconds [4]. In US Army trials, the platform demonstrated “autonomy-enhanced fire control” and “kill-chain optimization,” successfully performing live-fire intercepts of multiple targets [11, 14].
Optimal Effector Selection: AI can analyze threat data and recommend the most effective and economically sustainable response, whether that is electronic jamming, a high-energy laser, or a kinetic interceptor [2, 12, 15]. - Human-Machine Teaming (“Man-on-the-Loop”)
While AI provides the speed for real-time automation, current military doctrines often maintain a human-in-the-loop or human-on-the-loop for lethal decisions [15-17].
Filtered Data: The platform filters out irrelevant data, presenting only critical information to the user, who then authorizes the final instruction to the interceptor or effector [18].
Exceptions for High-Speed Threats: Some modern systems, such as the Slinger turret or Bullfrog autonomous gun, are capable of fully autonomous engagement when loitering munitions are detected closing at high speeds, as there may be no time for a human to react [17, 19]. - Battlefield Implementation
Ukraine: Front-line AI applications currently focus on terminal guidance and target recognition, allowing FPV drones to complete missions autonomously even if electronic warfare severs their communication links [16, 20, 21].
“Internet of the Battlefield”: Modern strategy emphasizes software-centric “defensive shields” that are maneuverable and adaptable, allowing even small units on the move to access automated C-UAS fire control [1, 13, 22].
Despite these advancements, experts note that AI on the battlefield currently remains primarily an enabler that accelerates data processing and decision cycles rather than acting as a fully independent decision-maker for all lethal engagements [16, 22, 23].
The Invisible Pilot: GNSS Spoofing in Modern Warfare
GNSS spoofing is a sophisticated electronic warfare (EW) method that involves transmitting false navigation signals to a receiver, such as the GPS unit on a drone, to manipulate its perceived location [1, 2]. Unlike jamming, which simply overpowers signals with noise, spoofing “tricks” the drone’s navigation computer into accepting false positioning data [2].
How GNSS Spoofing Affects Drones
The primary goal of spoofing is to seize control of a drone’s navigation without necessarily severing its command link. Its effects include:
Misdirection: By injecting false coordinates, spoofing networks can mislead drones and missiles into flying off course or navigating toward “wrong positions” [2].
Induced Crashes: Sophisticated spoofing can manipulate a drone’s altitude or position data so severely that it crashes into the ground or obstacles [2, 3].
Drifting: If a drone loses its legitimate GPS signal and is fed inconsistent spoofed data, it may drift aimlessly until it exhausts its battery or fuel [3].
Detection of “Deceptive” Drones: Integrated counter-UAS sensors can use spoofing detection to identify drones that are intentionally reporting a false location within their own communication signals to evade tracking [4].
Strategic and Tactical Implementation
Nationwide Networks: Ukraine utilizes a system called Pokrova, a nationwide GNSS spoofing network designed to mislead Russian loitering munitions and missiles, forcing them to miss their intended targets [2].
Shipboard Defense: The French Navy has successfully used the “Neptune” GNSS-spoofer (developed by MC2 Technologies) on frigates to disrupt attacking drones in the Red Sea [5].
Border Security: Countries like Rwanda have reportedly deployed GPS spoofing and jamming equipment along borders to counter aerial threats in contested regions [6].
Collateral Risks
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