Decoys draw drones to “wrong positions” by mimicking high-value military targets, forcing drone operators to waste limited resources on false locations while the actual unit remains hidden [1, 2]. This strategy is a critical component of operational resilience and survivability, aiming to divert an adversary’s attention and exhaust their flight time or munitions [2, 3].
Specific techniques for drawing drones to incorrect positions include:
- Physical and Visual Imitation
False Targets: Soldiers create imitations of vehicles and “fire means,” such as mock machine-gun nests or mortar positions, to distract aerial reconnaissance [2].
Demonstrating Presence: By setting up equipment and positions in the “wrong direction,” units can lead drone operators away from their intended path or real mission area [2].
Light Deception: During night operations, soldiers may intentionally commit light-masking violations at decoy sites—such as using flashlights or lighting fires—to entice drone operators to “fall” into attacking the wrong area [4]. - Electronic and Signal Deception
False Clusters: Drones with radio reconnaissance systems “calculate” positions by looking for signal concentrations [5]. Soldiers create false clusters by placing multiple active GSM terminals (phones or tablets), Wi-Fi, or Bluetooth-emitting hardware away from their true location [5, 6].
Mimicking Activity: These signal clusters make a decoy site appear like a command post or troop gathering, “arousing the interest of the enemy” and forcing them to investigate [5]. - Multi-Spectral Validation
Use of Smoke: Smoke screens are deployed at “wrong positions” or along false routes of movement to validate decoys [7]. This can simulate damage to equipment or active engineering works, making a false electronic signature appear more realistic to a drone operator [6, 7].
Decoy Drones: Large numbers of unarmed decoy drones can be launched to draw enemy fire and exhaust their air defense magazines [8, 9]. This forces defenders to use expensive interceptors on “nothing interesting,” leaving them vulnerable to subsequent waves of drones carrying actual payloads [9]. - Tactical Discipline
For decoys to be effective, the real unit must maintain strict silence and invisibility [1, 5]. This includes keeping all personal devices in “flight mode” and masking real communication links like Starlink terminals so that only the decoy’s signature is visible to enemy sensors [5, 10, 11].
The Silent Sentry: Active Versus Passive Radar Mechanics
The fundamental difference between active and passive radar systems lies in whether the system emits its own electromagnetic energy to detect objects.
Functional Mechanism
Active Radar: These systems are sophisticated sensors that actively emit radio waves [1]. These waves travel through the air, reflect off objects in their path, and return to the sensor [1]. By analyzing these returned signals, active radar determines an object’s presence, precise position, and movement [1, 2].
Passive Radar: Unlike active systems, passive radar does not emit any signals [2]. Instead, it detects and analyzes reflections from external third-party signals already present in the environment, such as commercial broadcast or communication signals [2].
Stealth and Survivability
Active Radar: Because active radar must transmit signals to function, it is less stealthy [2]. Its emissions act as a detectable beacon that can be identified by an adversary’s electronic reconnaissance systems, potentially making the radar unit a target for anti-radiation strikes [2, 3].
Passive Radar: Passive systems are considered inherently stealthy [2]. Because they produce no detectable sensor emissions, they are suitable for sensitive or covert operations where it is vital to avoid alerting drone operators or enemy forces to counter-drone activities [2, 4]. They offer a survivable alternative to active radars in high-threat environments [3].
Operational and Regulatory Differences
Precision and Tracking: Active radar is known for providing precise target detection and tracking [2]. It is “technology-agnostic,” meaning it detects a physical object regardless of whether that object is emitting its own signals (such as “dark drones” that fly autonomously) [2, 5, 6].
Licensing: In the United States, active radar systems that monitor ground or airspace are subject to Federal Communications Commission (FCC) license requirements, whereas passive systems typically do not face these same transmission-related restrictions [1].
Range and Coverage: Some specialized passive systems, such as the Czech VERA-NG, are capable of tracking drones from dozens of kilometres away, providing extensive surveillance coverage without revealing the defender’s position [7].
Stealth Surveillance: The Mechanics of Passive Radar Detection
Passive radar systems identify threats without emitting their own signals by detecting and analysing reflections from external “signals of opportunity” already present in the environment [1]. While active radar must transmit radio waves and listen for the returned echo to determine an object’s position, passive radar acts strictly as a high-sensitivity receiver [1, 2].
The mechanism for identifying threats includes the following elements:
Utilising Ambient Signals: Passive radar relies on third-party electromagnetic energy that is already saturating the area, such as commercial broadcast (radio and TV) or communication signals [1].
Detection of Reflections: When a threat, such as a drone, flies through these ambient signals, the signals reflect off the physical object [1]. The passive radar sensor captures these reflected waves and compares them to the original reference signal to “see” the object [1].
Tracking and Coverage: Advanced systems, like the Czech VERA-NG, are capable of using these reflections to track drones from dozens of kilometres away, providing extensive surveillance without revealing the defender’s own location [3].
Inherent Stealth: Because the system produces no detectable sensor emissions of its own, it is inherently stealthy [1]. It does not act as an electronic beacon for adversary reconnaissance or anti-radiation missiles, making it a highly survivable alternative to active radar in high-threat environments [1, 4].
Technology-Agnostic Nature: Like traditional radar, passive radar identifies the physical presence of an object rather than its communication link [1, 5]. This allows it to identify “dark drones” that are flying autonomously without emitting the radio frequency (RF) signals that standard RF sensors rely on [1, 6].
In a broader integrated counter-UAS framework, passive radar data is often combined with other sensors—such as acoustic arrays or thermal cameras—using sensor fusion to eliminate false positives and provide a clear, composite visual of the detected threat [7, 8].
Tactical Dominance through Radio Frequency Drone Detection
Frequency analyzers (often categorized as Radio Frequency or RF sensors) protect a position by monitoring the electromagnetic spectrum to detect, identify, and track the radio signals transmitted between a drone and its control station [1-3]. These devices provide an early warning of a drone’s presence, often detecting a threat by its sound or signal before it is visually identifiable [4, 5].
By analyzing unique radio signatures, these systems can identify a drone’s make and model, and in some instances, technical details such as its serial number, MAC address, or Remote ID [6-8]. This information allows personnel to distinguish between friendly and malicious drones and assess the threat level based on the aircraft’s known payload, range, and speed capabilities [7-9]. Furthermore, frequency analyzers can often locate the drone operator’s position, enabling a unit to target the source of the threat directly [1, 5, 10].
Key tactical advantages of using these systems include:
Inherent Stealth: Frequency analyzers are “passive,” meaning they do not emit signals of their own; this allows them to detect threats without alerting the drone operator or revealing the defender’s location to enemy electronic reconnaissance [1, 3, 11].
Operational Resilience: The combination of a frequency analyzer and an anti-drone gun can make a military position “unmanageable” for enemy aerial reconnaissance [12].
Non-Line-of-Sight Detection: Unlike cameras, RF-based detection can identify drones even when they are physically obscured by terrain or operating in low-visibility conditions like darkness or fog [13, 14].
Deception Mitigation: Integrated systems can use frequency analysis to detect “spoofing,” where a drone pilot intentionally reports a false location within their communication signal to evade tracking [15].
To maintain protection, it is recommended that operators camouflage themselves and their equipment, as anti-drone units are considered high-priority targets for enemy forces [12, 16]. Additionally, while these devices are highly effective against most drones, they may face visibility gaps when encountering “dark drones” that fly autonomously to GPS waypoints without emitting radio signals [2, 17, 18].
Detecting and Countering GNSS Spoofing in Drone Operations
Drone operators can identify if they are being targeted by Global Navigation Satellite System (GNSS) spoofing—a tactic where an adversary transmits false navigation signals—by monitoring for specific anomalies in their drone’s telemetry and flight behavior [1, 2].
Based on the sources, identifying spoofing involves looking for the following indicators:
- Significant Telemetry Discrepancies
The most clear sign of spoofing is a sudden, illogical shift in the drone’s reported position. Spoofing “tricks” a drone’s navigation computer into accepting false positioning data, which often results in:
Impossible Locations: The drone may report coordinates that are hundreds of miles away from its true physical location [2].
Altitude Manipulation: Operators may notice sudden, unexplained changes in altitude data, which is sometimes manipulated by spoofing networks to induce a crash [2]. - Erratic Flight Behavior and “Drifting”
When a drone’s legitimate GPS/GNSS signal is overpowered or replaced by a spoofed signal, it loses its ability to maintain a precise flight path.
Aimless Drifting: Instead of following its intended route or hovering in place, the drone may begin to drift aimlessly [1].
Loss of Waypoint Control: For “dark drones” or autonomous systems programmed to travel to specific GPS waypoints, spoofing will cause them to fly off course or fail to reach their target [2, 3]. - Receiver Logic Errors and Data Corruption
Sophisticated electronic warfare methods may involve more than just false coordinates; they can also target the internal processing of the drone.
Logic Overload: Adversaries may send corrupted data packets designed to overload the drone’s receiver logic, which can lead to system instability or a total loss of the navigation link [4]. - Sudden Activation of Safety Protocols
Drones are often programmed with safety protocols to prevent them from becoming dangerous if their navigation or control links are disrupted.
Uncommanded Landing: If a drone perceives its navigation link is untrustworthy or lost, it may enter a “pre-programmed safety mode” [5, 6].
Forced “Return-to-Home”: The drone might automatically trigger a return-to-home sequence, but due to the spoofed coordinates, it may fly toward a location designated by the adversary rather than the operator’s actual launch site [5].
Alternatives to GNSS Reliance
Because spoofing has become a frequent threat in conflict zones—leading to significant “collateral damage” even for civilian maritime and aviation sectors—modern drone designs are shifting toward systems that do not rely on satellite navigation [2, 7]. Operators can avoid spoofing entirely by using:
Fibre-Optic Tethers: These provide a high-bandwidth physical link that is immune to spoofing and electronic noise [7, 8].
Onboard AI: Using machine learning and vision-based target recognition, drones can navigate and strike targets autonomously even if all external GPS signals are severed [7-9].
Silent Sentinels: The Evolution of Spoof-Resistant Drones
Yes, there are specific categories of drones designed to be immune to GNSS (Global Navigation Satellite System) spoofing and electronic warfare. As adversaries increasingly utilize sophisticated spoofing networks like Ukraine’s Pokrova to mislead navigation, drone developers have shifted toward technologies that bypass the electromagnetic spectrum entirely. [1], [2]
The sources identify the following types of drones as being immune or highly resistant to spoofing:
- Fibre-Optic Tethered Drones
These drones are physically connected to their operators via a lightweight optical cable that carries control commands and sensor data. [3]
Total Immunity: Because the connection is wired, the drone does not rely on radio signals or GPS for its navigation or control link. [4], [5]
Stealth and Security: These platforms are immune to both jamming and spoofing, and they cannot be intercepted without physical access to the cable. [4], [6]
Operational Use: In conflict zones like Ukraine, these are used for observation and targeting in areas saturated with heavy electronic warfare where standard radio-controlled drones would be instantly disabled. [3] - Drones with Onboard AI and Vision-Based Navigation
Advanced drones are increasingly being fitted with onboard artificial intelligence and machine learning models that allow them to navigate without any external link. [7], [6]
GPS Independence: By using vision-based target recognition and edge-computing chips, these drones can identify, track, and strike targets even if all GPS and control signals are severed. [8], [9], [6]
Autonomous “Kill-Loops”: The operator provides mission parameters before take-off, and the drone executes the flight autonomously. [7] In Ukraine, experimental FPV platforms and modified loitering munitions are already testing these autonomous behaviours to counter spoofing and jamming. [10], [8] - Pre-Programmed Autonomous Systems (“Dark Drones”)
Some autonomous systems, often referred to as “dark drones,” are programmed to fly to specific waypoints without maintaining an active communication link. [11], [12]
Resilience via Mission Profile: Systems like the Shahed-type loitering munitions (designated as Geran-2 in Russian service) rely heavily on pre-programmed flight paths. [8], [13]
Limited Vulnerability: While many “dark drones” still rely on GPS waypoints—which can be spoofed—those equipped with inertial navigation or the aforementioned vision-based AI are much harder to trick into flying off course. [11], [8], [6]
Comparison of Resilient Technologies
Technology
Navigation Signal
Spoofing/Jamming Resilience
Typical Use
Fibre-Optic Tether
Light (via wire)
Complete Immunity
Stationary overwatch and heavy EW zones. [3], [4]
Onboard AI
Visual/Local
Independent of GPS
Loitering munitions and autonomous strike missions. [9], [6]
Frequency Hopping
Wireless RF
Resistant to Jamming (but still susceptible to spoofing)
Tactical mesh networks and mobile operations. [14], [15]
Unmasking the Shadows: Multimodal Detection of Low-Altitude Drones
Yes, drones can be detected even when they use terrain masking, although this tactic is specifically designed to exploit the “blind spots” of certain sensors. While flying at low altitudes or behind geographical features like hills and mountains can hide a drone from traditional line-of-sight systems, a multi-sensor approach can still identify and track these threats. [1-4]
The effectiveness of detection depends on the sensor technology being employed:
- Acoustic Sensors (Beyond-Line-of-Sight)
Acoustic sensors are one of the most effective tools for countering terrain masking. Unlike radar or cameras, these systems do not require a direct line-of-sight to the target. [4, 5]
Mechanism: They utilize arrays of sensitive microphones to “hear” the unique sound signatures generated by a drone’s motors and propellers. [5]
Obstruction Penetration: Sound waves can travel around or through obstructions—such as buildings, thick foliage, or mountainous terrain—that would block radar waves or visual optics. [4, 5]
Environmental Resilience: These sensors can sometimes exceed the detection range of optics in conditions like thick fog or total darkness. [4] - Radar Systems and “Blind Spots”
Radar remains the primary tool for long-range detection, but it is the sensor most susceptible to terrain masking. [3, 6]
Blind Spots: Radar systems have frequent blind spots at low altitudes where the signal is obscured by the curve of the earth or environmental clutter. [1, 3]
Terrain Challenges: In mountainous regions, drones can stay below the radar’s line-of-sight by using the relief of the terrain as a physical shield. [2]
Advanced Solutions: Modern systems, such as the Czech VERA-NG (a passive electronic support system), are designed to mitigate these gaps and can track drones from dozens of kilometres away. [7] - Radio Frequency (RF) Sensors
RF sensors monitor the electromagnetic spectrum for the communication links between a drone and its operator. [3, 8]
Passive Detection: Because they “listen” for signals rather than emitting them, they can identify a threat even if it is not visually identifiable. [8, 9]
Gap regarding “Dark Drones”: While RF sensors are excellent for identifying a drone’s make and model, they have a major visibility gap regarding “dark drones” that fly autonomously to GPS waypoints or use fiber-optic tethers, as these do not emit the radio signals the sensors rely on. [1, 3] - The Role of Sensor Fusion
The most reliable way to detect drones using terrain masking is through sensor fusion. By delivering the combined output of disparate sensor types (RF, radar, acoustic, and cameras) to a “single pane of glass” dashboard, counter-UAS platforms like DedroneTracker.AI or Anduril’s Lattice can close individual visibility gaps. [10-13]
Automatic Cueing: For example, an acoustic sensor might provide the initial detection of a drone behind a hill, which then cues a radar or EO/IR camera to immediately visualize and track the target as soon as it emerges from the “mask” and enters their line-of-sight. [14-16]
Reduced False Positives: AI-driven fusion algorithms correlate data from these different sources to virtually eliminate false positives (like birds) while locating the drone with high accuracy. [17, 18]
Silent Sentinels: The Mechanics of Passive Radar Detection
Passive radar systems typically detect and analyse reflections from external “signals of opportunity” that already saturate the environment [1, 2]. According to the sources, these commercial signals primarily include:
Broadcast Signals: This includes commercial radio and television transmissions [1, 2].
Communication Signals: These encompass various existing signals from public or private communication networks [1, 2].
By acting strictly as a high-sensitivity receiver for these third-party signals, passive radar can identify the physical presence of a threat, such as a drone, as it flies through and reflects these ambient waves [1, 2]. This method provides several tactical advantages:
Inherent Stealth: Because the system produces no detectable sensor emissions of its own, it does not reveal the defender’s location to adversary reconnaissance or anti-radiation missiles [1, 2].
Technology-Agnostic Detection: It identifies the physical airframe of an object rather than its communication link, allowing it to detect “dark drones” that fly autonomously without emitting the radio frequency (RF) signals that standard RF sensors depend on [1].
Long-Range Coverage: Specialized systems, such as the Czech VERA-NG, are capable of utilizing these reflections to track targets from dozens of kilometres away [1, 3].
Silent Vigilance: The VERA-NG Passive Drone Surveillance System
The VERA-NG is an advanced passive electronic support system developed by the Czech defense technology firm ERA [1]. It is primarily used to detect and track unmanned aircraft systems (UAS) across large distances [1].
Key Capabilities and Features:
Long-Range Tracking: The system is capable of tracking drones from dozens of kilometres away, providing extensive surveillance coverage of the airspace [1].
Detection of Small Drones: Unlike some traditional systems that struggle with small targets, the VERA-NG is designed to provide surveillance even for small drones [1].
Inherent Stealth: Because it is a passive system, it does not emit any detectable electromagnetic signals of its own [2]. This makes it inherently stealthy and ideal for sensitive or covert operations, as it does not alert adversaries to its presence or reveal the defender’s position [2].
Survivable Alternative: Long-range passive sensors are considered a highly survivable alternative to active radar systems for finding drones, enhancing the defender’s overall tactical advantage [3].
Strategic Integration
The VERA-NG has been tested with the Czech Armed Forces specifically for its effectiveness in detecting UAS over wide areas [1]. In a modern multi-layered defence strategy, such high-resolution passive sensors are critical for filling visibility gaps where active radars might be vulnerable to being targeted or where “dark drones” (autonomous drones that do not emit radio signals) are operating [3, 4].
Leave a Reply