In recent years, the rapid advancement of unmanned aerial vehicle (UAV) technology has led to widespread adoption of civilian drones across various sectors, including delivery, surveillance, agriculture, and entertainment. However, this proliferation has also introduced significant security risks, such as unauthorized flights in restricted airspace, privacy invasions, and potential threats to critical infrastructure. As a result, the development and deployment of counter-drone systems have become imperative to mitigate these risks. Numerous counter-drone technologies, based on principles like radio frequency (RF) jamming, laser disruption, net capture, and spoofing, have emerged, each with distinct advantages and limitations. Selecting an appropriate counter-system for specific environments—such as airports, urban areas, or sensitive electromagnetic zones—remains a challenge due to the lack of standardized evaluation frameworks. In this article, I propose a comprehensive evaluation method for civilian drone counter systems, drawing from experimental data and fuzzy mathematical techniques to assess performance across multiple criteria. The goal is to provide a robust tool for comparing different technologies and guiding decision-makers in choosing optimal solutions for diverse scenarios.
The evaluation of civilian drone counter systems is multifaceted, requiring consideration not only of immediate effectiveness but also of collateral damage, usability, and hardware reliability. To address this, I define four primary evaluation indicators: disposal effect, secondary disasters, ease of use, and equipment quality. Each of these indicators is further broken down into secondary metrics, forming a hierarchical structure that captures the complexities of real-world operations. For instance, disposal effect encompasses success rate, disposal time, disposal outcome, and effective range, while secondary disasters include fire risk, impact force,坠落 range (i.e., the area where a disabled drone may fall), and electromagnetic pollution. These indicators are tailored to contexts like civil aviation airports or electromagnetically sensitive zones, where safety and minimal interference are paramount. It is important to note that environmental factors, such as extreme weather, can influence certain metrics; thus, the model assumes controlled, non-extreme conditions for accurate assessment.
To quantify these indicators, I employ a fuzzy comprehensive evaluation method, which handles the inherent uncertainties and subjective judgments in assessing civilian drone counter systems. The process begins with defining an evaluation semantic set: V = {very poor, poor, moderate, good, excellent}, represented numerically as V = {υ1, υ2, υ3, υ4, υ5}. Each secondary indicator is associated with a fuzzy membership function that maps raw data—such as disposal time in seconds or fire area ratio—to degrees of membership in these semantic categories. For example, the membership function for “very poor” (υ1) might be a piecewise linear function that assigns full membership to values below a threshold and decreases linearly to zero. These functions are developed using the Delphi method, incorporating expert opinions from fields like drone countermeasures, aviation safety, and electromagnetic engineering. The membership functions for the five semantic levels are expressed as follows:
$$
\mu_1(x) = \begin{cases}
1, & x < x_0 \\
\frac{x_1 – x}{x_1 – x_0}, & x_0 \leq x \leq x_1 \\
0, & x > x_1
\end{cases}
$$
$$
\mu_2(x) = \begin{cases}
\frac{x – x_0}{x_1 – x_0}, & x_0 \leq x \leq x_1 \\
\frac{x_2 – x}{x_2 – x_1}, & x_1 \leq x \leq x_2 \\
0, & \text{otherwise}
\end{cases}
$$
$$
\mu_3(x) = \begin{cases}
\frac{x – x_1}{x_2 – x_1}, & x_1 \leq x \leq x_2 \\
\frac{x_3 – x}{x_3 – x_2}, & x_2 \leq x \leq x_3 \\
0, & \text{otherwise}
\end{cases}
$$
$$
\mu_4(x) = \begin{cases}
\frac{x – x_2}{x_3 – x_2}, & x_2 \leq x \leq x_3 \\
\frac{x_4 – x}{x_4 – x_3}, & x_3 \leq x \leq x_4 \\
0, & \text{otherwise}
\end{cases}
$$
$$
\mu_5(x) = \begin{cases}
0, & x < x_3 \\
\frac{x – x_3}{x_4 – x_3}, & x_3 \leq x \leq x_4 \\
1, & x > x_4
\end{cases}
$$
Here, x represents the raw value of an indicator, and x0, x1, x2, x3, x4 are thresholds determined through expert consensus. For instance, in assessing disposal time for civilian drones, x0 might be 0 seconds, x1 = 10 seconds, x2 = 30 seconds, x3 = 60 seconds, and x4 = 120 seconds, reflecting typical operational expectations. The membership vector for an indicator, denoted as γ = (γμ1, γμ2, γμ3, γμ4, γμ5), is derived by evaluating the raw data against these functions. Aggregating such vectors across all secondary indicators forms a fuzzy evaluation matrix R, where each row corresponds to an indicator’s membership distribution.
However, not all indicators contribute equally to the overall evaluation. To account for this, I use the fuzzy analytic hierarchy process (FAHP) to assign weights. This method improves upon traditional AHP by simplifying consistency checks and providing a more robust weighting mechanism. For each primary indicator, a fuzzy complementary matrix is constructed by comparing the relative importance of its secondary indicators using a 0.1–0.9 scale, where 0.5 denotes equal importance, values closer to 0.9 indicate greater importance, and values closer to 0.1 indicate lesser importance. For example, under the primary indicator “disposal effect” for civilian drones, the secondary indicators—success rate, disposal time, disposal outcome, and effective range—are compared pairwise to form a matrix. This matrix is then transformed into a fuzzy consistent matrix, from which weights are calculated. Let me illustrate with a generic fuzzy complementary matrix F for a set of elements U1, U2, …, Un:
| Element | U1 | U2 | … | Un |
|---|---|---|---|---|
| U1 | f11 | f12 | … | f1n |
| U2 | f21 | f22 | … | f2n |
| … | … | … | … | … |
| Un | fn1 | fn2 | … | fnn |
Where fij represents the importance of Ui relative to Uj. The fuzzy consistent matrix Q is derived with elements qij computed as:
$$
q_{ij} = \frac{\sum_{k=1}^{n} f_{ik} – \sum_{k=1}^{n} f_{jk}}{2n} + 0.5
$$
The weight vector W for the secondary indicators is then obtained by normalizing the row sums of Q. For instance, if we denote qm as the sum of all elements in Q, the weight for the i-th indicator is:
$$
w_i = \frac{\sum_{j=1}^{n} q_{ij}}{qm}
$$
This process is repeated for all primary indicators, and similarly, weights for the primary indicators themselves are determined by comparing them against each other. The resulting weight sets are used to adjust the fuzzy evaluation matrix, yielding a comprehensive evaluation vector B = W × R, where W is the aggregated weight vector and R is the matrix of membership vectors. Finally, to facilitate comparison between different civilian drone counter systems, the evaluation vector is converted to a percentage score using a weighted average method. Let S = {0, 25, 50, 75, 100} correspond to the semantic set V. The overall score A is calculated as:
$$
A = \frac{\sum_{i=1}^{5} b_i s_i}{\sum_{i=1}^{5} b_i}
$$
Where bi are the components of vector B. This score provides a quantitative measure of performance, enabling stakeholders to rank and select counter systems effectively.
To validate this evaluation method, I conducted a series of disposal experiments targeting civilian drones using two representative counter-technologies: laser strike and radio frequency interference. These technologies were chosen because they exemplify “hard kill” and “soft kill” approaches, respectively, and are widely used in real-world scenarios. The experiments were performed in a controlled outdoor environment with clear weather and minimal wind to reduce environmental variables. The target civilian drone was a commercially available quadcopter, typical of “low, slow, and small” drones that pose common security threats. For each counter system, ten disposal trials were conducted, and data for all secondary indicators were meticulously recorded.
In the laser strike experiments, the counter system employed a high-energy laser beam to target the civilian drone’s critical components, such as the battery or motors, from a distance of 650 meters. The drone was initially hovering at 80 meters altitude. Upon laser engagement, the time to disable the drone, the outcome (e.g.,摧毁, controlled descent, or drift), and any secondary effects like fire or坠落 range were noted. The results, as summarized in the table below, show high success rates but varying degrees of collateral risk. For instance, in some trials, the laser ignited the drone’s battery, creating a fire hazard, while in others, the drone drifted unpredictably due to partial damage. Electromagnetic pollution was negligible, as lasers do not emit radio waves, confirmed by spectrum analysis showing minimal noise elevation in frequency bands relevant to civil aviation (e.g., 1–1.6 GHz).
| Trial | Success | Disposal Time (s) | Outcome | Fire Risk | Impact Force | 坠落 Range (m) |
|---|---|---|---|---|---|---|
| 1 | Yes | 7 | Destroyed | Medium | Low | 2 |
| 2 | Yes | 9 | Destroyed | Medium | Low | 4 |
| 3 | Yes | 4 | Drifted | High | Moderate | 120 |
| 4 | Yes | 6 | Destroyed | Low | Low | 2 |
| 5 | Yes | 7 | Destroyed | Medium | Low | 5 |
| 6 | Yes | 11 | Destroyed | Medium | Low | 3 |
| 7 | Yes | 8 | Destroyed | High | Low | 4 |
| 8 | Yes | 4 | Destroyed | High | Medium | 10 |
| 9 | Yes | 29 | Drifted | High | Moderate | 20 |
| 10 | Yes | 19 | Destroyed | Medium | Medium | 15 |
The radio frequency interference experiments involved two subtypes: control signal jamming (targeting 2.4 GHz and 5.8 GHz bands) and positioning signal jamming (targeting 1.5 GHz GNSS bands). For control signal jamming, trials were conducted at distances of 220 meters, 120 meters, and 50 meters between the jammer and the civilian drone, with the drone operator stationed 50 meters from the drone. The goal was to disrupt the command link, forcing the drone to hover, return to home, or drift. Results indicated that effectiveness increased with proximity; at 50 meters, the jammer consistently triggered return-to-home behavior, while at longer ranges, impacts were minimal. For positioning signal jamming, trials at 220 meters showed mixed outcomes, with most causing the drone to drift or descend uncontrollably. Importantly, spectrum monitoring revealed significant electromagnetic pollution in the 1.55–1.6 GHz band during positioning jamming, with noise levels rising up to 95 dBμV, posing risks to nearby navigation systems. In contrast, control signal jamming had negligible impact on these frequencies, as shown in the table below summarizing key metrics.
| Jamming Type | Distance (m) | Success Rate | Avg. Disposal Time (s) | Common Outcome | Electromagnetic Pollution |
|---|---|---|---|---|---|
| Control Signal | 220 | 0% | >60 | No effect | Low |
| Control Signal | 120 | 50% | 47 | Drift/Return | Low |
| Control Signal | 50 | 100% | 40.4 | Return to home | Low |
| Positioning Signal | 220 | 90% | 51.6 | Drift/Descent | High |
Using the collected data, I applied the fuzzy comprehensive evaluation method to assess each counter system. The weights for secondary indicators were derived through FAHP, based on expert input prioritizing factors like success rate and minimal secondary disasters for civilian drone countermeasures in sensitive areas. For example, under “disposal effect,” success rate received the highest weight (0.3), followed by disposal outcome (0.2563), disposal time (0.2375), and effective range (0.2063). Under “secondary disasters,”坠落 range and electromagnetic pollution were weighted equally highest (0.2875 each), reflecting concerns about safety and interference. The primary indicator weights were set as: disposal effect (0.3125), secondary disasters (0.275), ease of use (0.2125), and equipment quality (0.2). These weights emphasize that while effectiveness is crucial, minimizing harm and ensuring practicality are vital for civilian applications.
The raw data from experiments were normalized using the membership functions. For instance, a disposal time of 7 seconds in laser trials might correspond to membership vectors like (0, 0, 0.2, 0.8, 0) for the semantic set, indicating “good” performance. Aggregating these across all indicators and applying weights yielded the following comprehensive evaluation vectors for the three counter systems: laser strike, control signal jamming, and positioning signal jamming. The vectors, denoted as B, represent the degree of membership in each semantic category:
$$
B_{\text{laser}} = (0.1511, 0.1169, 0.1306, 0.0841, 0.5173)
$$
$$
B_{\text{control jamming}} = (0.0878, 0.0508, 0.4529, 0.0958, 0.3127)
$$
$$
B_{\text{positioning jamming}} = (0.2200, 0.2002, 0.2186, 0.0732, 0.2881)
$$
Converting these to percentage scores using S = {0, 25, 50, 75, 100} gave the final evaluations: laser strike scored 70.16, control signal jamming scored 62.48, and positioning signal jamming scored 57.84. These results align with experimental observations—laser systems offer high effectiveness but pose fire and safety risks, whereas RF jamming methods are less invasive but vary in reliability and electromagnetic impact. The scores provide a quantifiable means to compare technologies; for instance, in environments where electromagnetic pollution is unacceptable, such as near airports, positioning jamming might be discouraged despite its moderate effectiveness against civilian drones.
This evaluation method offers several advantages for assessing civilian drone counter systems. First, it integrates multiple criteria into a single framework, balancing technical performance with practical concerns. Second, the use of fuzzy logic accommodates the uncertainties inherent in real-world operations, such as varying drone models or environmental conditions. Third, the FAHP-based weighting ensures that expert knowledge is systematically incorporated, enhancing objectivity. However, limitations exist. The model’s accuracy depends on the quality of membership functions and weights, which require continuous refinement through broader expert consultation. Additionally, the experiments involved a limited number of trials (ten per system), which may not capture all variability; future work should expand datasets to improve robustness. Environmental factors like wind or precipitation were controlled but could affect metrics like坠落 range in practice, suggesting the need for scenario-specific adjustments.
In conclusion, I have presented a fuzzy comprehensive evaluation method for civilian drone counter systems, grounded in experimental data from laser and RF jamming technologies. The approach leverages fuzzy mathematics to handle subjective judgments and produces quantitative scores that facilitate comparison and selection. As the threat landscape from civilian drones evolves, with advancements in autonomy and evasion techniques, such evaluation frameworks will become increasingly important for security planners. Future research could extend this model to include dynamic scenarios, such as swarming drones or adaptive countermeasures, and incorporate machine learning to automate weight calibration. By continually refining these methods, we can enhance the safety and efficiency of counter-drone operations, ensuring that responses to unauthorized drone activities are both effective and proportionate. Ultimately, this work contributes to the broader goal of securing airspace while fostering responsible innovation in civilian drone technologies.