Active Countermeasures for Drones Based on Radio Frequency Fingerprinting and Protocol Cracking

Abstract

This invention discloses a proactive countermeasure method for unmanned aerial vehicles (UAVs) based on radio frequency fingerprinting and protocol cracking, belonging to the field of UAV countermeasure technology. This invention aims to solve the technical problems in existing UAV countermeasure technologies, such as the difficulty in simultaneously implementing precise protocol cracking attacks and ensuring the survival of the countermeasure system itself, and the ease with which the attack process exposes the electromagnetic position for tracking and attack. The method involves: first, intercepting the communication signal of the target UAV, extracting its radio frequency fingerprint characteristics, analyzing its time-varying patterns, and constructing a radio frequency fingerprint perturbation model; then, based on this model, performing fingerprint modulation on forged control commands to generate and transmit an attack signal; during the attack process, continuously monitoring airspace radiation source signals, and upon identifying a threatening radiation source, quickly switching to generate and transmitting a camouflage signal simulating the radar echo characteristics of the target UAV. This invention achieves synergy between countermeasure attacks and stealth protection, and can be widely applied to proactive UAV countermeasure operations in various low-altitude security protection scenarios.

Description

Technical Field

[0001] This invention relates to the field of drone countermeasures technology, specifically to a proactive drone countermeasures method based on radio frequency fingerprinting and protocol cracking. Background Technology

[0002] The rapid development of drone technology has led to its widespread application in fields such as aerial photography, surveying, and agricultural plant protection. However, at the same time, drones pose a threat to public safety, privacy, and critical infrastructure. To address this challenge, various research institutions and enterprises have been conducting research on drone countermeasures, with main technical approaches including spectrum jamming, GNSS navigation deception, protocol cracking and command injection, and physical capture and destruction. Among these, protocol cracking and command injection technology has attracted widespread attention due to its ability to achieve precise control over target drones (such as forced landing and expulsion), and is considered one of the most promising "soft-kill" countermeasures.

[0003] Among existing drone countermeasure technologies, patent publication number CN121012593A discloses a vehicle-mounted integrated drone detection and countermeasure system and its working method. This scheme acquires the radio frequency fingerprint features of the drone through a radio detection unit to generate target identification results. When an illegal drone intrusion is detected, a directional jamming unit is driven to transmit a jamming signal towards the target drone based on the threat level. This scheme achieves a preliminary combination of drone identification and jamming based on radio frequency fingerprints, but its countermeasures remain at a coarse-grained jamming level, unable to implement precise protocol-level control of the drone, and the radiation exposure problem of the countermeasure system itself during jamming is not effectively solved. Patent publication number CN120897195A discloses a radio frequency fingerprint hiding method based on feature compensation. This scheme uses a joint compensation model at the transmitter end to compensate for carrier frequency offset, I / Q imbalance, and power amplifier nonlinear distortion to hide the radio frequency fingerprint, thereby preventing illegal receivers from identifying and tracking the transmitter. This solution focuses on how tracked individuals can hide their fingerprints to evade identification. Its core technology lies in privacy protection rather than proactive attacks, and it does not involve how to use fingerprint features to crack protocols and take over target drones.

[0004] While the aforementioned existing technologies have each made some progress in specific areas, none have resolved the core contradiction faced by countermeasure systems when launching protocol-breaking attacks: when a countermeasure system emits protocol-breaking signals to take over a target drone, its own radio frequency radiation characteristics expose its location in electromagnetic space, making it highly susceptible to detection and targeted strikes by anti-radiation drones or electronic reconnaissance systems with radiation source tracking capabilities. In other words, existing countermeasure systems cannot simultaneously achieve precise attacks and ensure their own survival. The root cause of this contradiction lies in the fact that existing technologies treat attack and self-defense as independent or even conflicting tasks, failing to utilize the resources generated during the attack process to address self-protection issues. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a proactive countermeasure method for drones based on radio frequency fingerprinting and protocol cracking. By constructing a closed-loop collaborative mechanism for attack and stealth, it can achieve a high success rate in protocol cracking attacks while effectively avoiding the risk of the countermeasure system itself being tracked and located, thereby solving the technical problem of attacking and being exposed in the prior art.

[0006] To solve the above-mentioned technical problems, this invention provides the following technical solution: a method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking, the method comprising the following steps:

[0007] Step 1: Intercept the communication signal of the target UAV, extract the radio frequency fingerprint feature of the target UAV from the communication signal, analyze the time-varying law of the radio frequency fingerprint feature under different flight states of the target UAV, and construct the radio frequency fingerprint perturbation model of the target UAV.

[0008] Step 2: Generate a forged control command based on the preset countermeasure target, use the radio frequency fingerprint perturbation model to perform fingerprint modulation on the baseband waveform of the forged control command, generate an attack signal with the physical layer fingerprint characteristics of the target UAV, and transmit the attack signal to the target UAV.

[0009] Step 3: During the transmission of the attack signal, continuously monitor radiation source signals in the surrounding airspace to identify whether there are any threatening radiation sources with radiation source tracking characteristics.

[0010] Step 4: When the threat radiation source is identified, stop transmitting the attack signal, and call the radio frequency fingerprint disturbance model to generate a camouflage signal that simulates the radar echo characteristics of the target UAV, and transmit the camouflage signal in the direction of the threat radiation source.

[0011] By constructing a radio frequency fingerprint perturbation model of the target UAV and using this model to perform fingerprint modulation on forged control commands to generate attack signals, while continuously monitoring radiation source signals during the transmission of attack signals and switching to a camouflage signal when a threatening radiation source is identified, the countermeasure system can dynamically switch to a stealth state according to environmental threats while carrying out precise protocol cracking attacks. This effectively avoids the risk of being tracked and located by anti-radiation weapons, achieving synergy between attack and self-defense.

[0012] Further, in step one, constructing the radio frequency fingerprint perturbation model of the target UAV specifically includes: extracting the nonlinear distortion coefficient of the power amplifier, the local oscillator phase noise trajectory, the I / Q imbalance parameters, and the transient turn-on characteristics of the target UAV from the communication signal; performing correlation analysis between the extracted radio frequency fingerprint features and the hovering state, acceleration state, turning state, or load change state of the target UAV to obtain the perturbation time series data of the radio frequency fingerprint features changing over time; and constructing the radio frequency fingerprint perturbation model based on the perturbation time series data.

[0013] By extracting the nonlinear distortion coefficient of the power amplifier, the local oscillator phase noise trajectory, the I / Q imbalance parameters, and the transient turn-on characteristics from the communication signal, and correlating these features with the hovering, acceleration, turning, or load change states of the target UAV, the perturbation time-series data of the RF fingerprint features over time can be obtained. This enables the construction of a more accurate RF fingerprint perturbation model that reflects the time-varying laws of the target UAV, providing a high-fidelity simulation basis for subsequent fingerprint modulation and improving the realism of the attack signal.

[0014] Furthermore, in step two, the baseband waveform of the forged control command is fingerprinted using the radio frequency fingerprint perturbation model. Specifically, this includes: performing dynamic pre-distortion processing on the instantaneous phase of the forged control command based on the phase noise trajectory in the radio frequency fingerprint perturbation model; and performing nonlinear mapping processing on the signal amplitude of the forged control command based on the nonlinear characteristics of the power amplifier in the radio frequency fingerprint perturbation model.

[0015] By dynamically predistorting the instantaneous phase of the forged control command based on the phase noise trajectory in the radio frequency fingerprint perturbation model, and performing nonlinear mapping processing on the signal amplitude based on the nonlinear characteristics of the power amplifier, the generated attack signal can highly reproduce the unique fingerprint of the target UAV in terms of physical layer micro-features, thereby effectively penetrating the physical layer authentication of the UAV with anti-spoofing mechanisms and improving the success rate of command injection.

[0016] Furthermore, in step four, before transmitting the attack signal to the target drone, the method further includes: upconverting the fingerprint-modulated attack signal to a carrier frequency consistent with the communication link of the target drone, and matching the bandwidth parameters of the communication link;

[0017] By upconverting the fingerprint-modulated attack signal to a carrier frequency consistent with the target drone's communication link and matching the bandwidth parameters, it is possible to ensure that the attack signal is completely consistent with the target drone's legitimate communication signal in terms of macroscopic frequency domain parameters. This avoids the target drone or pilot's terminal identifying it as external interference due to abnormal frequency points or bandwidth, further enhancing the stealth and effectiveness of the attack.

[0018] Furthermore, in step two, the preset countermeasure targets include driving away the target drone, forcing the target drone to land, or taking over control of the target drone. The forged control command is generated based on reverse analysis of the communication protocol between the target drone and the pilot's terminal.

[0019] By setting specific countermeasure targets such as driving away, forced landing, or takeover, and generating corresponding forged control commands based on reverse analysis of the communication protocol between the target drone and the pilot, the system can accurately respond to different threat levels and countermeasure needs, enabling it to flexibly cope with diverse scenarios.

[0020] Furthermore, in step three, identifying whether there are threatening radiation sources with radiation source tracking characteristics specifically includes: analyzing whether there are continuous radar-like scanning pulse signals or high-precision direction-finding signals in the radiation source signal, detecting the intensity change trend of the radiation source signal, and comparing the characteristics of the radiation source signal with a preset radiation source characteristic database.

[0021] By analyzing whether there are continuous radar-like scanning pulses or high-precision direction-finding signals in the radiation source signals, detecting the trend of signal intensity changes, and comparing the signal characteristics with a preset radiation source characteristic database, it is possible to accurately identify threatening radiation sources with radiation source tracking characteristics, providing a reliable trigger basis for the timely activation of stealth camouflage by the countermeasure system.

[0022] Furthermore, in step four, the camouflage signal simulating the radar echo characteristics of the target UAV is generated by calling the radio frequency fingerprint perturbation model. Specifically, this includes: generating an amplitude modulation signal simulating the radar cross section scintillation characteristics of the target UAV based on the radio frequency fingerprint perturbation model; and generating a phase modulation signal simulating the micro-Doppler effect characteristics generated by the rotor rotation of the target UAV based on the radio frequency fingerprint perturbation model.

[0023] By generating amplitude modulation signals that simulate the scintillation characteristics of the radar cross section of a target UAV and phase modulation signals that simulate the micro-Doppler effect of rotor rotation based on the radio frequency fingerprint perturbation model, the camouflage signal can be made highly similar to the target UAV in terms of radar echo properties. This can effectively deceive threat sources such as anti-radiation UAVs, causing them to direct their attacks toward false targets and ensuring the survival of the countermeasures system.

[0024] Furthermore, in step four, after transmitting the camouflage signal toward the direction of the threat radiation source, the method further includes: when the threat radiation source signal is detected to have disappeared or moved away, re-intercepting the communication signal of the target drone, updating the radio frequency fingerprint perturbation model, and resuming the transmission of the fingerprint-modulated attack signal toward the target drone.

[0025] By re-intercepting the target UAV's communication signal and updating the radio frequency fingerprint disturbance model after the threat radiation source signal has disappeared or moved away, and then resuming the transmission of the attack signal, the countermeasure system can quickly return to the attack state after the threat is eliminated, and ensure that subsequent attack signals are always adapted to the latest flight status and fingerprint characteristics of the target UAV, thus maintaining continuous combat effectiveness.

[0026] Furthermore, in step one, the interception of the target drone's communication signal is achieved through a broadband receiving array, which is also used to continuously monitor radiation source signals in the surrounding airspace during the transmission of the attack signal.

[0027] By employing a broadband receiving array to simultaneously intercept the target UAV's communication signals and continuously monitor the signals from radiation sources in the surrounding airspace, the system hardware architecture can be simplified, the equipment complexity reduced, and the wide coverage and high sensitivity of signal reception ensured, thereby improving the system's integration and real-time response capabilities.

[0028] Furthermore, in step four, the process of stopping the transmission of the attack signal and switching to the transmission of the spoof signal is completed within microseconds or milliseconds.

[0029] By controlling the process of stopping the transmission of attack signals and switching to transmitting camouflage signals to be completed within microseconds or milliseconds, it is possible to ensure rapid state switching when the threat radiation source approaches, minimize the system's exposure window, and significantly enhance the survivability of the countermeasure system in dynamic battlefield environments.

[0030] Compared with existing technologies, this active countermeasure method for drones based on radio frequency fingerprinting and protocol cracking has the following advantages:

[0031] I. This invention constructs a radio frequency fingerprint perturbation model of the target UAV, and uses this model to generate attack signals by fingerprint modulation of forged control commands. Simultaneously, it continuously monitors airspace radiation source signals during the transmission of attack signals. When a threatening radiation source with tracking characteristics is identified, the attack is quickly stopped and a camouflage signal is generated. This invention can achieve high success rate protocol cracking and precise active countermeasures against the target UAV, while dynamically avoiding the risk of the countermeasure system itself being tracked and located by radiation sources. This effectively solves the technical problems in the prior art where it is difficult to balance precise attack with self-survival and the attack exposes the electromagnetic position. It achieves closed-loop synergy between counterattack and stealth protection, and greatly improves the survivability of the countermeasure system in complex electromagnetic environments.

[0032] Second, this invention extracts multi-dimensional radio frequency fingerprint features of the target UAV and constructs a refined radio frequency fingerprint disturbance model by associating the time-varying patterns of its different flight states. Based on this model, the phase and amplitude of the forged control commands are precisely modulated, which enables the attack signal to highly reproduce the inherent attributes of the target UAV in terms of physical layer features. This effectively penetrates the physical layer authentication of the UAV with anti-spoofing mechanisms, thereby significantly improving the success rate of protocol cracking and command injection, achieving precise countermeasures against the target UAV in multiple modes, while reducing the interference of the countermeasure signal to the surrounding electromagnetic environment, and adapting to diverse low-altitude protection scenarios.

[0033] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0035] Figure 1 This is a flowchart illustrating the overall process of the method according to an embodiment of the present invention.

[0036] Figure 2 This is a schematic diagram of the drone radio frequency fingerprint feature extraction and matching identification process according to an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of the drone communication protocol cracking and proactive countermeasure execution process according to an embodiment of the present invention. Detailed Implementation

[0038] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0039] Example

[0040] In related technologies, the illegal low-altitude flight of drones poses significant security risks to critical areas of civil aviation safety and the protection of personal privacy. Existing drone countermeasures mostly employ passive jamming methods, using high-power radio frequency suppression across the entire frequency band to drive away or force drones to land. These solutions suffer from drawbacks such as uncontrollable interference range, potential for collateral damage to nearby legitimate wireless equipment, low countermeasure efficiency, and inability to precisely counter specific drone models. Some solutions incorporate drone radio frequency identification (RFID) technology, but rely solely on conventional communication frequency bands and signal standards for identification. This makes them ineffective against newer drones employing frequency-hopping encryption protocols, resulting in insufficient identification accuracy and anti-interference capabilities, hindering stable and reliable active countermeasures.

[0041] Specifically, the UAV active countermeasure method based on radio frequency fingerprinting and protocol cracking provided by this invention achieves accurate identification of individual UAVs and models through refined extraction and matching of radio frequency fingerprints, obtains control link permissions of UAVs by targeted communication protocol cracking, and finally generates and sends appropriate active countermeasure commands to achieve precise, controllable and efficient countermeasure operations against target UAVs, while significantly reducing the impact on the surrounding electromagnetic environment.

[0042] like Figure 1 As shown, the overall process of this invention includes six core steps: radio frequency signal acquisition and preprocessing, radio frequency fingerprint feature extraction, fingerprint matching and recognition, communication protocol cracking, active countermeasure command generation and transmission, countermeasure effect monitoring and closed-loop control. The six steps are executed sequentially to form a complete active countermeasure closed loop.

[0043] The hardware system underlying this method includes an RF signal acquisition unit, a baseband signal processing unit, a fingerprint recognition computing unit, a protocol cracking computing unit, an active countermeasure signal transmission unit, and a main control unit. The RF signal acquisition unit adopts an ultra-wideband RF receiving architecture, supporting full-band signal reception from 30MHz to 6GHz, with a sampling rate of 125MSPS and a sampling precision of 16bit, enabling continuous acquisition of uplink signals and downlink signals for UAV remote control and image transmission. The baseband signal processing unit is equipped with an FPGA chip, performing downconversion filtering, synchronous demodulation, and preprocessing operations. The fingerprint recognition computing unit and protocol cracking computing unit adopt a multi-core DSP architecture, equipped with an embedded neural network acceleration module, to complete feature extraction, matching, recognition, protocol reverse engineering, and cracking operations. The active countermeasure signal transmission unit supports multi-band RF signal transmission, with continuously adjustable transmission power from 0.1W to 30W, and supports full-process processing of frequency hopping synchronization carrier modulation command encapsulation. The main control unit uses an industrial-grade embedded processor to complete the timing synchronization, status management, process scheduling, and data interaction of each unit.

[0044] In this method, all units operate using a unified clock synchronization signal with a frequency of 10MHz, provided by the temperature-controlled crystal oscillator built into the main control unit. The synchronization error is controlled within 100ns, ensuring precise synchronization of the timing of RF signal acquisition, baseband processing, feature calculation protocol cracking, and signal transmission, thus avoiding identification failure and countermeasure failure due to timing deviation.

[0045] Specifically, the implementation process of this method consists of six core steps executed consecutively, and the execution logic and operation details of each step are as follows.

[0046] The first step is the full-time acquisition and preprocessing of radio frequency signals in the target airspace.

[0047] In this embodiment, this step continues to execute after the system is powered on. The main control unit sends acquisition configuration parameters to the radio frequency signal acquisition unit. The configuration parameters include the receiving frequency band range, sampling rate, sampling accuracy, and gain level. For example, for urban low-altitude protection scenarios, the receiving frequency band range is set to the three mainstream UAV communication frequency bands: 2400MHz to 2483.5MHz, 5725MHz to 5850MHz, and 840MHz to 845MHz. Simultaneously, full-band scanning capability from 30MHz to 6GHz is reserved to handle UAV devices using non-standard frequency bands. The sampling rate is fixed at 125MSPS, the sampling accuracy is set to 16bit, and the front-end gain is set to 40dB to ensure effective reception of weak UAV radio frequency signals.

[0048] The RF signal acquisition unit continuously acquires RF signals in the target spatial domain according to the configured parameters, converts the acquired raw analog RF signals into digital baseband signals, and transmits them to the baseband signal processing unit. The baseband signal processing unit sequentially performs digital down-conversion, low-pass filtering, carrier synchronization, symbol synchronization, and signal-to-noise ratio estimation operations on the digital baseband signals. Specifically, the digital down-conversion operation shifts the RF signal to zero intermediate frequency, obtaining two baseband signals: an in-phase component and a quadrature component. The low-pass filter uses a 128th-order finite-length unit impulse response filter with a passband cutoff frequency of 20MHz and a stopband attenuation greater than 80dB, used to filter out out-of-band noise and interference signals. Carrier synchronization is achieved using a Costas ring to estimate and compensate for carrier frequency and phase offset, with the frequency offset estimation range set to ±100kHz. Symbol synchronization employs an early-late gate synchronization algorithm to lock the optimal sampling point, controlling the synchronization error within 1 / 16 of a symbol period.

[0049] After preprocessing, the baseband signal processing unit estimates the signal-to-noise ratio (SNR) of the output baseband signal. When the estimated SNR is greater than a preset SNR threshold, it is determined that a valid target signal exists in the current baseband signal. The baseband signal is then transmitted to the fingerprint recognition processing unit and the protocol cracking processing unit, respectively. For example, the preset SNR threshold is set to 6dB, which can be adaptively adjusted according to the complexity of the electromagnetic environment in the actual application scenario.

[0050] The second step is the refined extraction of the drone's radio frequency fingerprint features.

[0051] like Figure 2 As shown, the process of RF fingerprint feature extraction and matching recognition includes five sub-steps: transient and steady-state signal separation, multi-dimensional feature extraction, adaptive weighted feature fusion, cosine similarity matching, and recognition result output.

[0052] Specifically, after receiving a valid baseband signal, the fingerprint recognition processing unit first accurately detects the transient start and end points of the signal, separating the transient and steady-state segments. The transient start point detection employs a dual-sliding-window energy detection algorithm, setting two sliding windows of different lengths: a shorter window with 64 sampling points and a longer window with 512 sampling points. The energy ratio within the two windows is calculated, and when this ratio continuously exceeds a preset energy ratio threshold, the point is determined to be the transient start point. For example, the preset energy ratio threshold is set to 3.2. The transient end point is determined based on a stable symbol rate in the signal modulation format. When the symbol rate remains stable for 1024 consecutive sampling points, the point is determined to be the transient end point, thus completing the separation of the transient and steady-state segments.

[0053] In this embodiment, transient feature parameters are extracted from the separated transient segment signal. These transient feature parameters include transient rise time, transient peak amplitude, transient frequency offset rate of change, and transient phase noise features. For the separated steady-state segment signal, modulation features, symbol rate features, carrier frequency offset features, and harmonic distortion features caused by RF power amplifier nonlinearity are extracted based on the constellation diagram.

[0054] In this step, to improve the discriminative power and anti-interference capability of the RF fingerprint, an adaptive weighted fusion algorithm based on feature discriminative power is adopted to generate the final UAV RF fingerprint feature vector. The calculation formula is as follows:

[0055]

[0056] in, This represents the final generated UAV radio frequency fingerprint feature vector, which has a dimension of 128 and is used for subsequent matching and recognition operations. This vector has uniqueness and stability and can uniquely characterize the radio frequency hardware characteristics of the target UAV. This represents the summation operator, used to perform a weighted summation of multiple single-dimensional feature vectors. The index variable represents the feature dimension and is used to iterate through all single-dimensional features involved in the fusion. This represents the total number of single-dimensional features participating in the fusion, as described in this embodiment. The value of is 8, and the corresponding 8 single-dimensional features are transient rise time, transient peak amplitude, transient frequency offset change rate, transient phase noise variance, constellation diagram clustering radius, symbol rate offset, carrier frequency offset, harmonic distortion, and total energy percentage. Indicates the first The adaptive weight coefficients corresponding to each single-dimensional feature are positively correlated with the inter-class discriminative power of the corresponding feature and negatively correlated with the intra-class scatter. The sum of the weight coefficients of all features is 1. In this embodiment, the weight coefficients are adaptively assigned through a pre-trained feature discriminative power model, and the values ​​range from 0.05 to 0.35. Indicates the first The feature vector obtained after normalizing a single-dimensional feature is processed using the min-max normalization method, which maps the value range of all features to the interval between 0 and 1, eliminating the influence of differences in the dimensions of different features.

[0057] It is understandable that the adaptive weighted fusion algorithm described above can effectively amplify the contribution of highly discriminative features to the final fingerprint vector, reduce the influence of features with large dispersion and poor stability, improve the recognition accuracy and anti-interference ability of radio frequency fingerprints, and achieve stable feature extraction even in low signal-to-noise ratio environments.

[0058] After feature fusion is completed, a standardized 128-dimensional radio frequency fingerprint feature vector is generated and transmitted to the matching and recognition module to perform subsequent matching and recognition operations.

[0059] The third step is drone matching, identification, and classification based on the radio frequency fingerprint feature database.

[0060] Specifically, the fingerprint recognition processing unit pre-stores a standardized UAV radio frequency fingerprint feature library. This library contains model information, communication protocol type, control link parameters, and countermeasure strategy priority information corresponding to the standard radio frequency fingerprint feature vectors of mainstream UAV models on the market. The feature library uses an incremental update mode, allowing for the addition of fingerprint data for different UAV models through offline import or online learning.

[0061] In this embodiment, the matching and recognition operation employs a cosine similarity matching algorithm to calculate the cosine similarity value between the extracted target RF fingerprint feature vector and each standard fingerprint feature vector in the feature library. For example, the calculation range of the cosine similarity is set to 0 to 1; the closer the similarity value is to 1, the higher the matching degree between the two feature vectors.

[0062] When the calculated maximum cosine similarity value is greater than the preset similarity threshold, a successful match is determined, and the corresponding UAV model information, communication protocol type, control link parameters, and countermeasure strategy priority information are output. When the calculated maximum cosine similarity value is less than or equal to the preset similarity threshold, a match is determined to be unsuccessful, the target is marked as an unknown UAV model, a full-band protocol reverse scanning process is simultaneously initiated, and the radio frequency fingerprint feature vector of the unknown UAV is stored in a temporary feature library, awaiting subsequent offline calibration and feature library updates. For example, the preset similarity threshold is set to 0.85. This threshold can be adjusted according to the security protection level of the scenario; in high-security scenarios, the threshold can be increased to 0.92 to reduce the probability of false identification.

[0063] After the matching and identification are completed, the main control unit will send the identified drone-related parameters to the protocol cracking calculation unit to start the targeted protocol cracking process.

[0064] The fourth step involves targeted cracking of the drone communication protocol based on the identification results.

[0065] like Figure 3 As shown, the process of protocol cracking and active countermeasure command generation and execution includes six sub-steps: protocol type matching, protocol frame parsing and key cracking, frequency hopping sequence synchronization prediction, countermeasure command generation, encapsulated radio frequency signal transmission, and countermeasure effect verification.

[0066] Specifically, after receiving the UAV parameters from the main control unit, the protocol cracking unit calls the corresponding protocol cracking engine based on the matched communication protocol type. For known UAV models, the protocol cracking engine pre-stores the corresponding model's protocol frame structure encryption method, frequency hopping pattern, and key space information, enabling it to quickly crack the protocol frame parsing key and reverse-engineer the control command format.

[0067] In this embodiment, for known models of encrypted drones, a combination of known-plaintext attack and brute-force cracking is used to crack the key. Specifically, the protocol frame synchronization header address code and fixed frame structure fields of the drone communication are first obtained by parsing the preprocessed baseband signal. These fields are known plaintext information. Combined with the encryption algorithm of the corresponding model, a constraint equation for key cracking is constructed. The cracking operation adopts a multi-core parallel computing mode, dividing the key space into multiple parallel computing subspaces. Each computing core is responsible for the key traversal operation of one subspace, which greatly improves the cracking efficiency. For example, for mainstream consumer drones using 16-bit rolling keys, the cracking time of this step can be controlled within 200ms, enabling fast key cracking and synchronous updating.

[0068] For unknown drone models that fail to match, the protocol cracking unit initiates a blind parsing and reverse engineering process. First, it performs modulation system identification, symbol rate estimation, and frame structure detection on the baseband signal. It extracts the synchronization code, frame length verification method, and address field information of the protocol frame. Through data analysis of multiple consecutive frames, it reconstructs the frame structure and interaction logic of the protocol. At the same time, it uses a sliding window traversal method to crack the basic encryption rules and frequency hopping patterns of the protocol, providing a foundation for the generation of subsequent active countermeasure commands.

[0069] For UAVs employing frequency-hopping communication, in order to achieve precise synchronization between countermeasure commands and the target frequency-hopping sequence, this step uses a frequency-hopping sequence synchronization algorithm based on linear prediction to accurately predict the frequency hopping frequency and the hopping time. The calculation formula is as follows:

[0070]

[0071] in, Indicates the predicted first The carrier frequency value corresponding to each frequency hopping time slot is in MHz. This value is used to configure the carrier frequency of the active countermeasure signal transmission unit to achieve synchronization with the frequency hopping sequence of the target UAV. This represents the first result obtained through baseband signal analysis. The actual carrier frequency value corresponding to each frequency hopping time slot is in MHz. This value is the measured value obtained by the protocol cracking calculation unit through real-time signal analysis. The fixed frequency step value, expressed in MHz, represents the frequency hopping sequence. This value is obtained by fitting frequency data from multiple consecutive frequency hopping time slots. For mainstream drones, the frequency step value ranges from 1MHz to 20MHz. The index variable represents the frequency hopping time slot and is used to mark the timing position of the frequency hopping sequence. The duration of the frequency hopping time slot is determined by the frequency hopping period obtained through parsing. The frequency hopping period of mainstream UAVs ranges from 1ms to 10ms. The compensation correction value for frequency prediction is expressed in MHz. This value is obtained by fitting the prediction error of the preceding eight consecutive frequency hopping time slots and is used to eliminate the prediction error caused by frequency drift crystal oscillator deviation. In this embodiment, the compensation correction value ranges from -0.5MHz to 0.5MHz.

[0072] It is understandable that the frequency hopping sequence prediction algorithm described above can achieve accurate tracking and synchronization of the target UAV's frequency hopping sequence, with the synchronization error controlled within 10µs, ensuring that the countermeasure command can accurately fall into the target UAV's receiving time slot, and greatly improving the success rate of receiving the countermeasure command.

[0073] After the protocol is cracked, the protocol cracking unit outputs complete control protocol information for the target UAV, including the frame structure, modulation scheme, carrier frequency, frequency hopping sequence, key update rules, and definition format of valid command fields for uplink control commands. Based on the protocol cracking results, the main control unit sends countermeasure configuration parameters to the active countermeasure signal transmitting unit, initiating the generation and transmission process of active countermeasure commands.

[0074] The fifth step is the generation and precise launch of proactive countermeasure commands.

[0075] Specifically, after receiving the countermeasure configuration parameters from the main control unit, the active countermeasure signal transmitting unit first completes the parameter configuration of the radio frequency transmission link. The configuration parameters include the carrier frequency modulation method, transmit power, frequency hopping sequence, and time slot synchronization parameters of the transmitting radio frequency band. For UAVs using frequency hopping communication, the carrier frequency of each frequency hopping time slot is updated in real time according to the predicted frequency value obtained by the above frequency hopping sequence prediction formula, achieving precise synchronization with the target UAV control link.

[0076] In this embodiment, the active countermeasure command is generated according to a preset countermeasure strategy, which includes forced landing mode, drive-away mode, hovering mode, and return-to-home mode. For example, in forced landing mode, a forced landing command corresponding to the drone model is generated. The command is encapsulated according to the cracked protocol frame structure, and the correct synchronization header address code key and verification field are added to ensure that the target drone can correctly recognize and execute the command. In drive-away mode, a forced return-to-home command is generated to control the drone to return to the takeoff point, eliminating the security risk to the target airspace. In hovering mode, a hovering command is generated to control the drone to remain hovering at its current position, allowing time for subsequent handling operations.

[0077] After the active countermeasure command is generated, the active countermeasure signal transmitting unit performs baseband modulation, up-conversion, and power amplification on the command, converting it into a radio frequency signal, which is then transmitted towards the target UAV via a directional antenna. For example, the transmission power is adaptively adjusted according to the distance to the target UAV: ​​1W when the target distance is less than 500 meters, 5W when the target distance is between 500 and 1000 meters, and 10W when the target distance is greater than 1000 meters, with a maximum transmission power not exceeding 30W. This ensures the countermeasure effect while minimizing interference with the surrounding electromagnetic environment.

[0078] In this embodiment, the active countermeasure signal transmission uses a directional antenna with a beamwidth of 30 degrees. The antenna's direction can be controlled by the main control unit to aim at the location of the target drone, further reducing the coverage area of ​​the countermeasure signal and avoiding interference with surrounding legitimate wireless equipment.

[0079] The sixth step is to monitor and manage the countermeasures in real time and in a closed loop.

[0080] Specifically, while the active countermeasure command is being transmitted, the radio frequency signal acquisition unit continuously acquires radio frequency signals in the target airspace, monitoring the remote control and image transmission signal status of the target UAV in real time. When the target UAV's remote control uplink signal is interrupted, its image transmission downlink signal is interrupted, or it executes the corresponding operation according to the countermeasure command, the countermeasure operation is deemed effective. For example, after a forced landing command is transmitted, if the target UAV's altitude continues to decrease and its horizontal speed drops to 0, the forced landing command is deemed successfully executed.

[0081] In this embodiment, if no change in the target drone's status is detected for 500ms after the countermeasure command is issued, the countermeasure operation is deemed to have failed. The main control unit restarts the protocol cracking process, updates the key and frequency hopping sequence parameters, regenerates the countermeasure command, and issues it again, forming a closed-loop control process. Once the target drone is successfully countermeasured, the system stores all data from this countermeasure process, including radio frequency signal data, fingerprint feature data, protocol cracking data, countermeasure command data, and countermeasure effect data, in the system log for subsequent data analysis and feature database updates.

[0082] In this embodiment, the method supports simultaneous identification and countermeasures against multiple target drones. Specifically, when the baseband signal processing unit detects multiple valid target signals simultaneously, it uses digital beamforming technology to separate the spatial domain of the multiple target signals and performs radio frequency fingerprint feature extraction, matching, identification protocol cracking, and active countermeasures on each target signal. For multiple targets, the system executes countermeasures sequentially according to a preset countermeasure priority. The countermeasure priority is dynamically adjusted based on the target drone's distance, flight speed, and depth within the protected area; targets that are closer, fly faster, and are deeper within the protected area have a higher countermeasure priority.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking, characterized in that, The method includes the following steps: Step 1: Intercept the communication signal of the target UAV, extract the radio frequency fingerprint feature of the target UAV from the communication signal, analyze the time-varying law of the radio frequency fingerprint feature under different flight states of the target UAV, and construct the radio frequency fingerprint perturbation model of the target UAV. Step 2: Generate a forged control command based on the preset countermeasure target, use the radio frequency fingerprint perturbation model to perform fingerprint modulation on the baseband waveform of the forged control command, generate an attack signal with the physical layer fingerprint characteristics of the target UAV, and transmit the attack signal to the target UAV. Step 3: During the transmission of the attack signal, continuously monitor radiation source signals in the surrounding airspace to identify whether there are any threatening radiation sources with radiation source tracking characteristics. Step four: When the threat radiation source is identified, stop transmitting the attack signal, and call the radio frequency fingerprint disturbance model to generate a camouflage signal that simulates the radar echo characteristics of the target UAV, and transmit the camouflage signal in the direction of the threat radiation source.

2. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step one, constructing the radio frequency fingerprint perturbation model of the target UAV specifically includes: extracting the nonlinear distortion coefficient of the power amplifier, the local oscillator phase noise trajectory, the I / Q imbalance parameters, and the transient turn-on characteristics of the target UAV from the communication signal; performing correlation analysis between the extracted radio frequency fingerprint features and the hovering state, acceleration state, turning state, or load change state of the target UAV to obtain the perturbation time series data of the radio frequency fingerprint features changing over time; and constructing the radio frequency fingerprint perturbation model based on the perturbation time series data.

3. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step two, the baseband waveform of the forged control command is fingerprinted using the radio frequency fingerprint perturbation model. Specifically, this includes: performing dynamic pre-distortion processing on the instantaneous phase of the forged control command based on the phase noise trajectory in the radio frequency fingerprint perturbation model; and performing nonlinear mapping processing on the signal amplitude of the forged control command based on the nonlinear characteristics of the power amplifier in the radio frequency fingerprint perturbation model.

4. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking as described in claim 1, characterized in that, In step four, before transmitting the attack signal to the target drone, the method further includes: upconverting the fingerprint-modulated attack signal to a carrier frequency consistent with the communication link of the target drone, and matching the bandwidth parameters of the communication link.

5. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step two, the preset countermeasure targets include driving away the target drone, forcing the target drone to land, or taking over control of the target drone. The forged control command is generated based on reverse analysis of the communication protocol between the target drone and the pilot's terminal.

6. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step three, identifying whether there are threatening radiation sources with radiation source tracking characteristics specifically includes: analyzing whether there are continuous radar-like scanning pulse signals or high-precision direction-finding signals in the radiation source signals, detecting the intensity change trend of the radiation source signals, and comparing the characteristics of the radiation source signals with a preset radiation source feature database.

7. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step four, the radio frequency fingerprint perturbation model is invoked to generate a camouflage signal that simulates the radar echo characteristics of the target UAV. Specifically, this includes: generating an amplitude modulation signal that simulates the radar cross section scintillation characteristics of the target UAV based on the radio frequency fingerprint perturbation model, and generating a phase modulation signal that simulates the micro-Doppler effect characteristics generated by the rotor rotation of the target UAV based on the radio frequency fingerprint perturbation model.

8. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step four, after transmitting the camouflage signal in the direction of the threat radiation source, the method further includes: when the threat radiation source signal is detected to have disappeared or moved away, re-intercepting the communication signal of the target drone, updating the radio frequency fingerprint perturbation model, and resuming the transmission of the fingerprint-modulated attack signal to the target drone.

9. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step one, the communication signal of the target UAV is intercepted through a broadband receiving array, which is also used to continuously monitor radiation source signals in the surrounding airspace during the transmission of the attack signal.

10. The method for proactive countermeasures against drones based on radio frequency fingerprinting and protocol cracking according to claim 1, characterized in that, In step four, the process of stopping the transmission of the attack signal and switching to the transmission of the spoof signal is completed within microseconds or milliseconds.

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