⚡ electrical engineering

A Multi-physics Simulation Framework for High-power Microwave Counter-unmanned Aerial System Design and Performance Evaluation

This paper presents a comprehensive multi-physics simulation framework that utilizes Monte Carlo analysis to design and evaluate the effectiveness of high-power microwave (HPM) systems for neutralizing small unmanned aerial systems (sUAS) by modeling electromagnetic coupling and semiconductor damage probabilities.

Original authors: Akbar Anbar Jafari, Gholamreza Anbarjafari

Published 2026-02-10

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CC BY 4.0

Original authors: Akbar Anbar Jafari, Gholamreza Anbarjafari

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The "Invisible Lightning Bolt" vs. The Drone Swarm

Imagine you are trying to protect a castle, but instead of knights on horses, the invaders are thousands of tiny, autonomous, buzzing mechanical wasps (drones). Traditional ways to stop them—like shooting them down with bullets or using "jamming" to cut their radio signals—aren't working. Some of these drones are "smart" enough to fly themselves without any radio connection, and shooting them down creates a mess of falling debris.

This paper describes a way to build a "High-Power Microwave" (HPM) system. Think of this not as a radio jammer, but as a precision-guided, invisible lightning bolt that doesn't break the drone's "brain" (the software), but instead physically fries its "nervous system" (the wires and chips).

1. How it Works: The "Overloaded Circuit" Metaphor

Most drones are built with cheap, unshielded electronics. The researchers explain that these drones have tiny wires running through them that act like accidental antennas.

The Analogy: Imagine a drone is like a small house with delicate lightbulbs. A radio jammer is like someone shouting loudly outside the house to confuse the residents. An HPM system, however, is like a massive surge of electricity hitting the power lines. The residents might still know what to do, but the lightbulbs themselves literally pop and melt.

The paper uses math to show that when the microwave energy hits these tiny wires, it creates a "voltage spike." If that spike is big enough, it punches a hole through the microscopic components (the "gate oxides") inside the drone's chips, rendering the drone a useless piece of plastic instantly.

2. The "Flashlight vs. Laser" Strategy (CW vs. Pulsed)

The researchers looked at two ways to deliver this energy:

Continuous Wave (CW): This is like holding a high-powered flashlight steady on a target. It’s consistent, but it takes time to "heat up" the electronics enough to break them.
Pulsed Mode: This is like a strobe light on steroids. Instead of a steady beam, you send out incredibly intense, lightning-fast bursts of energy.

The Finding: The paper discovers that Pulsed Mode is the winner. Even if you use the same amount of total energy, sending it in quick, violent "punches" (pulses) is much more effective at breaking the drone's electronics from a further distance than a steady stream. It’s the difference between heating a piece of ice with a warm hair dryer (CW) versus hitting it with a hammer (Pulsed).

3. The "Reality Check" (The Monte Carlo Simulation)

In a perfect math equation, everything goes exactly where you point it. In the real world, things are messy. The drone might be tilted, the wind might shake the antenna, or the wires might be a different length.

To account for this, the authors ran a "Monte Carlo Simulation."
The Analogy: Imagine trying to predict if a professional basketball player will make a shot. A simple math model says, "He's a pro, he'll make it." But a Monte Carlo simulation is like simulating 10,000 games where the player is slightly tired, the wind is blowing in the gym, the ball is slightly deflated, and the hoop is wobbling.

By running these 10,000 "messy" simulations, the researchers found that the system is actually less effective than simple math suggests. They realized that because drones can be oriented in any direction, the "invisible lightning" might miss the most sensitive wires. This tells engineers they need to build much more powerful systems than they originally thought to guarantee a "kill."

4. Safety: The "Invisible Fence"

Because this system uses massive amounts of microwave energy, it can't just be fired anywhere. The paper calculates "Exclusion Zones."

The Analogy: It’s like a giant, invisible fence. If you are standing too close to the beam, the energy could be harmful to humans. The researchers provide a "map" so operators know exactly how far away people need to be to stay safe, ensuring the "lightning bolt" stays focused on the drones and doesn't accidentally cook the people trying to defend the castle.

Summary

The paper provides a "blueprint" and a "stress test" for a new kind of defense. It proves that by using intense pulses of microwave energy, we can create a way to "zap" drone swarms out of the sky by breaking their internal electronics, providing a fast, non-explosive way to protect sensitive areas.

Technical Summary: A Multi-Physics Simulation Framework for HPM Counter-UAS Design

1. Problem Statement

The rapid proliferation of small unmanned aerial systems (sUAS) has created a significant asymmetric threat in both commercial and military sectors. Traditional counter-UAS (C-UAS) methods face critical limitations:

Kinetic solutions (missiles/nets) are expensive and suffer from limited magazine depth.
RF Jamming is ineffective against autonomous drones that do not rely on external communication links (e.g., GPS-independent or fiber-optic guided drones).
High-Energy Lasers (HEL) require precise dwell time and clear atmospheric conditions, making them difficult to use against swarms.

There is an urgent need for a non-kinetic, physics-layer neutralization method—specifically High-Power Microwaves (HPM)—that can induce irreversible semiconductor damage regardless of the drone's software or communication protocols. However, existing literature lacks a unified, reproducible framework that links RF source characteristics to actual semiconductor failure probabilities.

2. Methodology

The authors developed a comprehensive multi-physics simulation framework implemented in Python. The methodology integrates five distinct physical layers:

RF Propagation & Antenna Modeling: Uses the Friis transmission relation and parabolic reflector gain equations to model the electric field ( $|E|$ ) decay over distance.
Electromagnetic (EM) Coupling: Models unshielded drone wiring harnesses as short dipoles. It incorporates a resonance-enhanced coupling model, accounting for the fact that wires near the half-wavelength ( $\lambda/2 \approx 6.12\text{ cm}$ at $2.45\text{ GHz}$ ) experience significantly higher induced voltages.
Semiconductor Damage Modeling: Employs a sigmoid (logistic) probability function to model the likelihood of failure for five critical subsystems (GPS, Flight Controller, ESC, Camera, and BMS) based on established CMOS latchup and gate oxide breakdown thresholds.
Stochastic Uncertainty Quantification: Instead of relying on deterministic "best-case" calculations, the framework uses a 10,000-trial Monte Carlo analysis. This incorporates random variations in transmitter power, antenna pointing errors (Rayleigh distribution), polarization mismatch, and target wire orientation.
System-Level Integration: Calculates the total "kill probability" ( $P_{system}$ ) as the probability that at least one critical subsystem fails.

3. Key Contributions

Unified Analytical Model: A complete mathematical chain from the RF source to the induced voltage at the target's internal circuitry.
Subsystem-Resolved Damage Model: A parameterized model for five specific drone components, allowing for more granular performance evaluation.
Stochastic Performance Prediction: The introduction of Monte Carlo simulations to provide realistic kill probabilities with 95% confidence intervals, accounting for real-world "jitter" and orientation randomness.
Parametric Design Tools: Provision of design maps (power vs. aperture) and safety exclusion zone calculations compliant with ICNIRP 2020 guidelines.
Open Science: Full release of simulation source code to ensure reproducibility.

4. Key Results

Deterministic vs. Stochastic Discrepancy: The study found that deterministic models overestimate kill probability by approximately 1.5–1.6×. For example, at 30m, the deterministic model predicts a 43.5% kill probability, while the Monte Carlo simulation predicts only 25.2%.
Baseline Performance: For a 25 kW CW (Continuous Wave) system with a 60 cm dish, the kill probability is 51.4% at 20 m, dropping to 13.1% at 40 m.
Pulsed vs. CW Operation: Pulsed operation is significantly more effective. A 500 kW peak power pulse (1% duty cycle) extends the 90% kill range from ~18 m to ~88 m, while maintaining a manageable average power (5 kW) that allows for air cooling instead of liquid cooling.
Vulnerability Identification: The simulation confirmed that ESC signal wires (typically 5–8 cm in length) are the most vulnerable entry point due to resonance-enhanced coupling at 2.45 GHz.
Safety & Thermal: The framework established that at 25 kW CW, the general public exclusion zone is 161 m. It also identified that pulsed operation significantly reduces thermal management requirements.

5. Significance

This research provides a vital engineering tool for the design of next-generation directed energy weapons. By proving that stochastic analysis is mandatory for realistic mission planning, the paper prevents designers from overestimating system effectiveness. The findings regarding pulsed operation offer a clear technical pathway to increasing engagement ranges without requiring massive increases in average power consumption or cooling infrastructure, making HPM systems more viable for mobile, rapid-deployment counter-drone applications.