Unmanned aerial vehicle communication link blocking method based on lead code injection

Abstract

The invention relates to the technical field of wireless communication network security, and discloses an unmanned aerial vehicle communication link blocking method based on preamble injection, which comprises the following steps of: analyzing physical layer parameters of a target spread spectrum communication link to generate and inject a specific signal which is only formed by continuous preambles and does not contain a synchronization word and an effective load; and a closed-loop adaptive optimization mechanism is established, the mechanism performs adaptive updating on strategies such as interference intensity and duty ratio according to real-time link feedback and unit power consumption cost rules, and the core of a blocking task is converted from energy confrontation depending on high power to state interaction with a receiver by using communication protocol characteristics, so that the interference intensity and the duty ratio of the blocking task are greatly improved. And in combination with real-time optimization of a closed-loop adaptive framework on an interference strategy, link blocking with both accuracy and high energy efficiency is realized under the conditions of reducing energy consumption and not depending on strict time synchronization.

Description

Technical Field

[0001] This invention relates to a method for blocking UAV communication links based on preamble injection, belonging to the field of wireless communication network security technology. Background Technology

[0002] Currently, with the widespread application of drone technology, ensuring the security and controllability of its communication links has become a practical requirement in the field of cyberspace security. To achieve reliable communication with low power consumption over long distances, drone systems typically adopt spread spectrum communication methods, represented by linear frequency modulation spread spectrum technology. This method achieves better anti-interference capabilities by spreading signal energy over a wider spectrum. To address the potential misuse of drones, the industry has developed a variety of countermeasures. Among them, communication jamming has become a widely used non-contact area control method. Its basic technical principle is to reduce the signal-to-noise ratio at the input of the target receiver by emitting jamming signals, thereby disrupting the normal operation of the communication link.

[0003] Currently, mainstream communication jamming technologies are generally based on the principle of energy suppression, that is, to overwhelm legitimate signals by transmitting broadband noise with a power much higher than that of the target signal. Although this method is direct, its own technical principle determines an inherent engineering constraint: the effective blocking effect is directly related to the high transmission power. Simply improving the power efficiency of jamming equipment does not change the technical essence of this energy countermeasure. On the contrary, it may exacerbate the occupation of the electromagnetic spectrum and the interference to surrounding non-target systems. This strong coupling relationship of energy effect has become a common technical limitation restricting the development of UAV countermeasure technology towards low power consumption and high precision.

[0004] Specifically, existing technologies have the following shortcomings: 1. High interference energy overhead, posing challenges to the energy supply and thermal management of equipment in applications requiring continuous blocking, limiting their deployment on lightweight platforms; 2. Insufficient precision of interference methods, as broadband energy coverage can affect non-target devices, which is inconsistent with the requirements of refined spectrum resource management, and the interference methods themselves are easily detected; 3. Relatively static countermeasure logic, often using fixed transmission parameters, which is difficult to effectively adapt to the dynamic changes in link characteristics of UAVs in high-speed movement and complex electromagnetic environments. Meanwhile, to cope with complex channel environments, the signal processing and synchronization technologies of UAV communication systems are constantly evolving, further highlighting the limitations of traditional energy-suppression jamming methods in terms of technical concept. For example, Chinese invention patent CN119520211B discloses a MIMO-OFDM time-frequency synchronization method suitable for UAVs in strong multipath channels. This method aims to achieve high-precision time-frequency synchronization in strong multipath channels by designing an orthogonal preamble sequence independent of the number of antennas at the transmitting end and employing an algorithm combining coarse and fine synchronization at the receiving end. The emergence of such technologies signifies that the anti-jamming capability of UAV communication links has evolved from simply relying on the spreading gain of the physical layer to relying on more sophisticated signal structures and synchronization protocols. However, this also reveals the shortcomings of existing jamming technologies: simple energy suppression cannot fundamentally disrupt this synchronization process based on specific sequence identification and related processing. Once the receiver successfully locks the preamble under the protection of this complex synchronization algorithm, subsequent communication links are difficult to effectively block. Therefore, the focus of jamming technology must shift from energy countermeasures to precise intervention at the communication protocol level. Therefore, the technical problem to be solved by this invention is to find a new technical path that no longer relies on energy advantage to suppress signals, but starts from the internal working mechanism of the communication protocol to achieve effective blocking of specific spread spectrum communication links with lower resource consumption. Summary of the Invention

[0005] This invention provides a method for blocking UAV communication links based on preamble injection. Its main purpose is to solve the problems of high power consumption, low accuracy and lack of adaptability caused by existing communication jamming technologies that rely on energy suppression.

[0006] To achieve the above objectives, this invention provides a method for blocking UAV communication links based on preamble injection, the method comprising: Step 1: Passively monitor the wireless channel on which the target UAV is communicating, and extract a set of physical layer parameters of the spread spectrum communication link used by the target UAV from the monitored signal. The set of physical layer parameters includes the spreading factor, channel bandwidth, center frequency and sampling rate. Step 2: Based on a set of physical layer parameters parsed in Step 1, a continuous preamble interference signal is generated locally. The generation rule of the continuous preamble interference signal is: it is composed of multiple basic chirp signals that match a set of physical layer parameters and spliced ​​end to end in the time domain, and does not contain any synchronization words for frame synchronization or payloads carrying user information. Step 3: Establish a closed-loop adaptive optimization mechanism for the continuous preamble interference signal injection process. The operating rules of this closed-loop adaptive optimization mechanism are as follows: Construct an action triplet consisting of interference signal type, interference intensity, and transmit duty cycle; and within a preset control period, form an observation window in time by adjusting the transmit duty cycle. Based on a feedback signal representing the link packet error rate obtained within the observation window, follow an evaluation rule that correlates the packet error rate with the interference intensity to represent the unit power blocking cost, and select an action triplet that aims to minimize the output value of the evaluation rule as the updated value to be applied to the generation and injection of continuous preamble interference signals in the next control period.

[0007] Preferably, the evaluation rule in step 3 is specifically represented by a cost function. The calculation method is as follows: ;in, The link packet error rate, measured as a feedback signal within the observation window; Sets the ratio of the interference intensity of the continuous preamble interference signal to the channel noise power within the current control cycle.

[0008] Preferably, the step of selecting the action triplet in step 3 is specifically implemented using a confidence upper bound algorithm based on the multi-armed gambling machine theory; this implementation includes: discretizing the continuous value range of the interference intensity and the transmission duty cycle into a finite number of candidate value points, and combining them with the discrete candidate values ​​of the interference signal type to form a finite set of candidate actions; In each control cycle, based on the confidence upper bound algorithm, a candidate action that minimizes the confidence upper bound value is selected from the candidate action set and used as the value of the updated action triplet.

[0009] Preferably, the execution of the confidence upper bound algorithm also includes a grid encryption mechanism. The operating rules of this mechanism are as follows: as the number of execution rounds of the control cycle increases, the number of candidate numerical points when discretizing the continuous value range of interference intensity and transmission duty cycle is gradually increased according to a preset incremental rule; so that the discrete grid density of the candidate action set increases over time, thereby making the value of the action triplet gradually approach the optimal solution in the continuous space.

[0010] Preferably, when the method is applied to a scenario where the target UAV uses frequency hopping communication, it further includes the following steps: in step 1, the frequency hopping pattern of the target UAV communication link is further parsed; during the execution of steps 2 and 3, the center frequency of the continuous preamble interference signal is controlled so that it switches synchronously with the frequency hopping pattern to ensure that the preamble can be effectively injected on each frequency hopping channel.

[0011] Preferably, the step of obtaining a feedback signal characterizing the link packet error rate in step 3 specifically involves: counting the number of acknowledgment messages that failed to be received from the target UAV and the number of negative acknowledgment messages that were received within the observation window, and calculating the ratio between the two, using this ratio as the link packet error rate.

[0012] Preferably, the step of parsing a set of physical layer parameters in step 1 further includes: performing a fast Fourier transform on the monitoring signal, determining the channel bandwidth and sampling rate by analyzing its spectral structure; obtaining estimated values ​​of time offset and frequency offset by performing autocorrelation calculation on the preamble segment of the monitoring signal, and performing time and frequency correction on the local signal generation module based on the estimated values.

[0013] Preferably, in step 2, the basic chirp signal is the basic uplink chirp signal, and its single-symbol duration is... Depend on The calculation shows that, among which For spreading factor, This refers to the channel bandwidth.

[0014] Preferably, in step 2, after generating the continuous preamble interference signal, a power normalization step is also included. This step adjusts the average power of the generated baseband waveform signal to a uniform power. Subsequently, the adaptive optimization mechanism in step 3 performs final amplitude scaling on the normalized signal based on the updated interference intensity.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. By generating and injecting a specific signal containing only continuous preamble after parsing the physical layer parameters of the target link, a link blocking method is established that keeps the target receiver in a state of synchronization waiting for a long time. This method utilizes the inherent characteristics of the spread spectrum communication protocol in the receiving and demodulation process, changing the core of the blocking task from relying on the transmit power to counter the legitimate signal through energy, to using the protocol characteristics to interact with the receiver's state. As a result, the amount of transmit energy required for effective blocking no longer depends directly on the transmit power of the target signal, but on the signal characteristics required to maintain the protocol state of the target receiver.

[0016] 2. The aforementioned signal injection process is combined with an adaptive optimization process based on real-time link feedback. In this process, the type and intensity of the interference signal and the transmission duty cycle are selected as a whole and continuously adjusted according to the actual link blocking effect. In this way, the generation and transmission of the interference signal is no longer a preset open-loop action, but constitutes a response system with environmental awareness and closed-loop correction capabilities. This system can continuously find and converge to a transmission resource consumption level that can maintain the target link blocking state in a dynamically changing communication environment.

[0017] 3. The combination of the two mechanisms described above enables the method claimed in this invention to avoid the technical path of maintaining a high transmission power for a long time in order to ensure the blocking effect in traditional noise suppression methods, and also bypasses the technical requirements of other precise interference methods to capture the target signal and maintain strict time synchronization in order to achieve effective intervention. This method achieves precise action on specific targets through parameter matching, simplifies the dependence on time synchronization by continuously occupying the receiver protocol state, and optimizes the resource consumption of the entire process in real time through an adaptive learning framework. Finally, it forms a communication link blocking technology solution that combines accuracy, low complexity and high energy efficiency in dynamic resource-constrained scenarios such as UAV communication. Attached Figure Description

[0018] Figure 1 This is the spectrum diagram of the simulated interference signal in MATLAB for this invention; Figure 2 This is a schematic diagram of the real-time interference environment in the wireless communication of this invention; Figure 3 This is a comparison chart of the single-frequency data packet reception rate of the present invention; Figure 4 This is a comparison chart of the bit error rates of the present invention; Figure 5 This is a comparison chart of data packet reception rates under simulated frequency hopping scenarios according to the present invention; Figure 6 This is a comparison chart of bit error rates under the simulated frequency hopping scenario of this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the UAV communication link blocking method based on preamble injection claimed in this invention clearer, the technical solution of this invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] The method for blocking UAV communication links based on preamble injection claimed in this invention comprises three core technical stages: a passive parameter parsing stage for the target link, a protocol-specific continuous preamble interference signal generation stage, and a closed-loop adaptive interference strategy optimization stage based on real-time link feedback. These three stages together constitute a communication link blocking process from environmental perception to precise execution and intelligent optimization, addressing the technical problem of low-power, high-efficiency blocking of communication links for UAVs using spread spectrum communication in complex electromagnetic environments. Especially in scenarios requiring continuous communication control of specific areas, existing energy-suppression-based interference methods are limited by their energy consumption and electromagnetic compatibility issues. To address this challenge, the method claimed in this invention is configured to first acquire the target link parameters, which provides a basis for subsequent generation and injection of protocol-consistent interference signals.

[0021] Step 1 of the method, namely the passive listening and physical layer parameter parsing stage, is executed in a ground control station equipped with a software radio platform. This platform is configured with a receiving front-end capable of covering the 2.4GHz and 5.8GHz UAV communication frequency bands, with an effective signal processing bandwidth of not less than 20MHz and a sampling rate of... Not less than 40 MS / s; when the target UAV enters the controlled area and establishes communication, the platform initiates passive listening to capture the air interface wireless signal and transmits the received baseband signal. Represented as target signal With additive white Gaussian noise The superposition, that is ; to extract a set of physical layer parameters of the target spread spectrum communication link from the mixed signal, i.e., {spreading factor} Channel bandwidth Center frequency and sampling rate The system first performs a Fast Fourier Transform (FFT) on the captured digital baseband signal to perform spectral analysis, and determines the channel bandwidth by identifying the frequency range where the signal energy is concentrated on the spectrum. For example, if the signal energy is mainly distributed between 910MHz and 911MHz, then determine The frequency was 1 MHz; subsequently, to determine the spreading factor... The system performs autocorrelation calculations on the preamble segment of the monitored signal. Since the preamble has a periodically repeating chirped structure, the time interval between the occurrence of autocorrelation peaks corresponds to the duration of a single chirped symbol. Given This relationship, in the known... and measured Then, the spreading factor can be solved inversely. For example, if measured It is 1.024ms, and it is known that... If the frequency is 125kHz, then it can be calculated that... Thus determine The value is 7; simultaneously, the peak position obtained from the autocorrelation calculation also provides the time offset. The frequency shift can be estimated by performing a derotation operation on the chirped signal. Based on the estimation, these two offsets are used to perform time and frequency correction on the local signal generation module. Through this series of processes, the system obtains a set of physical layer parameters that match the target link.

[0022] Step 2 of the method, namely the generation stage of the continuous preamble interference signal, aims to generate a specific interference signal consisting only of continuous preambles, without synchronization words or payloads, to address the protocol flow where the spread spectrum communication receiver must first complete preamble acquisition and synchronization before demodulating subsequent data. This signal occupies the receiver's synchronization processing resources, causing it to remain in a synchronization waiting state for an extended period, thereby achieving link blocking at the protocol level. Therefore, the system calls the local baseband modulation module, utilizing a set of physical layer parameters parsed in step 1 { }, generates a basic chirp signal, which is a standard uplink chirp signal with a single symbol duration of} Depend on The calculation shows that the signal generation process specifically involves concatenating multiple, for example eight, fundamental chirp signals that match the physical layer parameters in the time domain to form a preamble sequence. This process does not add any synchronization words for frame synchronization or payloads carrying user information; to form a continuous interference stream, the system uses this preamble sequence The waveform is cyclically tiled on the time axis to form the baseband waveform of the continuous preamble interference signal. Before being injected into the channel, the baseband waveform signal needs to undergo a power normalization step, which adjusts its average power to unity power so that the adaptive optimization mechanism in step 3 can adjust the power based on the real-time updated interference intensity. The system performs a final amplitude scaling on the normalized signal, thereby generating an interference signal that is consistent with the target signal in terms of physical layer characteristics, but retains only the preamble part at the protocol level. The injection of this signal prevents the target receiver from completing frame synchronization, thus blocking the communication process.

[0023] Step 3 of the method, namely the establishment and operation phase of the closed-loop adaptive optimization mechanism, aims to address the problem that changes in link quality caused by dynamic wireless channels and UAV mobility make it difficult for fixed interference strategies to simultaneously achieve both blocking effectiveness and energy efficiency. To minimize the energy consumed by interference while maintaining blocking effectiveness, this method establishes a closed-loop adaptive optimization mechanism for the interference signal injection process. The core of this mechanism lies in modeling the selection of interference parameters as a sequential decision problem under a multi-armed gambling machine (MAB) model. The system first constructs a mechanism based on the interference signal type, interference intensity, and other parameters. The action triplet formed by the transmission duty cycle and the continuous range of values ​​for the interference intensity and the transmission duty cycle are discretized into a finite number of candidate numerical points. For example, The value range [-20dB, 10dB] is discretized into 7 points: {-20, -15, -10, -5, 0, 5, 10}. The value range (0, 1) of the transmission duty cycle is discretized into 5 points: {0.2, 0.4, 0.6, 0.8, 1.0}. This forms a finite set containing 35 candidate actions. Within a preset control period, such as 100ms, the system uses a confidence upper bound (UCB) algorithm based on the multi-armed gambling machine theory to select an action from this set of candidate actions for execution. Simultaneously, an observation window is formed in time by adjusting the transmission duty cycle. Within the observation window, the system counts the number of acknowledgment messages (ACK) that were not successfully received from the target UAV and the number of negative acknowledgment messages (NACK) that were received, and calculates the ratio between the two. This ratio is used to characterize the link packet error rate. The system receives a feedback signal; based on this feedback signal, it follows an evaluation rule that correlates the packet error rate with the interference intensity to characterize the blocking cost per unit power consumption. This rule is specifically expressed as a cost function. The calculation method is as follows: ;in, The link packet error rate measured within the observation window. The system sets the ratio of the interference intensity of continuous preamble interference signals to the channel noise power within the current control cycle; the system then calculates this ratio. The average cost of the selected action is updated, and in the next control cycle, an action triplet that aims to minimize the output value of the evaluation rule is selected according to the confidence upper bound algorithm. This triplet is then used as the updated value for the generation and injection of interference signals in the next control cycle. In addition, the mechanism also includes a grid encryption mechanism. As the number of execution rounds of the control cycle increases, the system gradually increases the number of candidate numerical points when discretizing the continuous value range of interference intensity and transmission duty cycle according to a preset incremental rule. This makes the discrete grid density of the candidate action set increase over time, thereby making the value of the action triplet gradually approach the solution in the continuous space. Through this closed-loop learning and real-time optimization, this method can continuously find and converge to the lowest transmission resource consumption level that can maintain the target link blocking state in a dynamically changing communication environment.

[0024] It should be noted that the method claimed in this invention is applicable to scenarios where the target UAV uses frequency hopping communication. In this scenario, the step of parsing the physical layer parameters in step 1 further includes parsing the frequency hopping pattern of the target UAV's communication link. Accordingly, during the execution of steps 2 and 3, the system controls the center frequency of the generated continuous preamble interference signal to synchronously switch with the parsed frequency hopping pattern, so as to ensure that the preamble can be effectively injected on each frequency hopping channel, thereby maintaining the effectiveness of link blocking in a more complex communication environment.

[0025] Example 1: In a large-scale event security scenario, an airspace surveillance system detected a drone using frequency-hopping spread spectrum communication approaching the boundary of a temporarily designated no-fly zone. The drone was flying with an irregular trajectory, and the spectrum resources occupied by its communication link partially overlapped with the public frequency band used for on-site media broadcasting. This situation rendered traditional broadband noise suppression methods for communication blocking unusable due to potential interference with normal communication. Furthermore, the drone's high-speed maneuverability and the potential for dynamic changes in link parameters posed challenges to the real-time performance and stability of the blocking effectiveness. To address this situation, an on-site drone communication link blocking system was activated. The system first entered a passive listening and physical layer parameter parsing phase, capturing and analyzing the drone's communication signals. Within the initial few communication cycles, the system not only parsed a set of physical layer parameters for the spread spectrum communication link used by the drone, including the spreading factor, but also... 9 and channel bandwidth The frequency hopping pattern of the target signal was identified and locked at 250kHz. This parameter acquisition provides a basis for generating protocol-specific continuous preamble jamming signals, enabling the jamming signal to match the target signal at the physical layer, which is a prerequisite for low-power protocol state occupancy. Next, the system enters the generation and injection stage of continuous preamble jamming signals. Based on the parsed parameters and frequency hopping pattern, a continuous preamble jamming signal without synchronization words and payload, consisting only of multiple basic chirp signals, is generated. The center frequency of this jamming signal is controlled to synchronously follow the frequency hopping pattern of the target. The signal injection in this stage does not use a fixed high power value, but is initially taken over by a closed-loop adaptive optimization mechanism. The accurate generation of the jamming signal provides the possibility of low-power blocking, while the adaptive strategy optimization is responsible for finding and maintaining the lowest resource consumption point to achieve this possibility in the dynamic environment.

[0026] In the initial stage of interference injection, the system's closed-loop adaptive optimization mechanism is activated, using a preset, low interference intensity. The value and 50% transmit duty cycle are started, and the link packet error rate is monitored through the observation window. The changes; in the initial few control cycles, the system observed Failed to reach the 99% blocking threshold, cost function The calculation results indicate that the current strategy has a high unit power blocking cost. Therefore, the confidence upper bound algorithm based on multi-armed gambling machine theory tends to explore actions with higher confidence levels in subsequent action triple selection. Or, it can initiate actions with a duty cycle; as the strategy is iteratively updated, the system discovers that when... After reaching a certain level, It quickly climbed to over 99%, at which point the algorithm began to explore how to maintain that level. While maintaining a horizontal level, the system gradually reduces the transmission duty cycle to further compress total energy consumption. This process resolves the contradiction in traditional jamming methods: the energy waste caused by maintaining high transmission power for extended periods to cope with worst-case channel conditions versus the potential for blocking failure due to reduced power to save energy. It does not directly counteract signal energy but blocks communication by altering the internal operating state of the UAV receiver. Ultimately, before the UAV leaves the no-fly zone, the entire system operates stably at a dynamically optimized equilibrium point, maintaining continuous blocking of the high-speed frequency-hopping UAV's communication link with a transmission power far lower than that required by traditional noise suppression and a discontinuous transmission duty cycle, without any observable impact on media broadcast signals in adjacent frequency bands. The application of this method transforms the task of UAV communication link blocking from an energy-based countermeasure process relying on transmission power and signal power into a process of state interaction with the receiver using protocol characteristics. Its resource consumption no longer directly depends on the transmission power of the target signal but on the signal characteristics required to maintain the target receiver's protocol state. The resource consumption of the entire process is further optimized in real time through an adaptive learning framework.

[0027] Example 2: To quantitatively compare the energy efficiency of the method claimed in this invention with traditional broadband noise suppression methods, and to verify its power consumption performance in achieving the same communication link blocking effect, a radio frequency conducted signal test environment based on a software-defined radio platform was built. This test platform consists of three independent software-defined radio transceiver modules: module A as the legitimate signal transmitter, module B as the legitimate signal receiver, and module C as the interference signal injector. The three modules are connected to a precision adjustable attenuator via a coaxial cable to simulate signal and interference conditions of different intensities. The entire platform is placed inside an electromagnetic shielding box to eliminate the influence of electromagnetic signals from the external environment. During the test, module A was configured to continuously transmit a set of test data packets using a spread spectrum communication system. Its physical layer parameters were fixed as follows: center frequency 915MHz, spreading factor... The channel bandwidth is 7. The frequency is 125kHz, and the transmit power is 0dBm; module B uses this to receive data and calculate the link packet error rate in real time. With the symbol error rate SER; module C is configured to inject two different types of interference signals depending on the test group.

[0028] The experiment was conducted in two groups. The first group served as a control group, where module C was configured to generate and inject Gaussian white noise interference signals with a bandwidth covering the entire 125kHz communication channel. The second group was the experimental group using the method of this invention, where module C was configured to generate and inject continuous preamble interference signals consistent with the physical layer parameters of the target signal. In both experimental groups, the injection strength of the interference signal, i.e., the ratio of the interference signal power to the channel noise power, was measured. Starting from -30dBm, the signal was gradually increased to 30dBm in 5dB increments. At each interference level, module A sent 1000 test data packets, which were recorded and statistically analyzed by module B. Regarding SER; it should be noted that the number of data packets per test point is set to 1000. This is based on balancing the confidence level of the statistical results with the time consumed in a single trial. This sample size can ensure that, at a 95% confidence level, [the data will be sufficient to achieve the desired results]. The measurement error is controlled within 3%.

[0029] Experimental data show that the two interference methods differ in their blocking effectiveness (see Table 1). In the control group, when the Gaussian white noise interference intensity... When the value is below 5 dBm, the impact on the communication link is limited. Maintain at a low level; only when the interference strength exceeds the legal signal power, for example... When it reaches 15dBm, The fact that it only rose to nearly 100% indicates that the effectiveness of the noise interference method directly depends on energy suppression; in contrast, in the sample group of this invention, when the intensity of the continuous preamble interference signal... When it is -15dBm, It has exceeded 90%, when When it reaches -10dBm, This means that 100% was achieved, thus blocking the communication link. This data difference indicates that the continuous preamble interference signal utilizes the receiver's protocol processing mechanism to prevent it from entering the data demodulation stage by continuously occupying its synchronization demodulation window, rather than simply relying on energy to overwhelm the signal. Therefore, it can achieve blocking under conditions far below the target signal power.

[0030] Table 1: Comparison of experimental data on the impact of different interference methods on communication link performance.

[0031]

[0032] The test results confirm that, in order to achieve To achieve 100% link blocking effectiveness, the required interference intensity for the test group using the method of this invention is... The value is -10dBm, while the control group using Gaussian white noise interference needs to increase the interference intensity to 20dBm; this data shows that, under the same communication link conditions, the method claimed in this invention has an advantage of at least 30dB in energy consumption compared to the noise interference method, demonstrating that the method can effectively cut off the spread spectrum communication link of the UAV while having low power consumption.

[0033] To further verify the adaptive advantages of the method claimed in this invention in dealing with dynamically changing channels, especially when compared with traditional broadband noise suppression schemes using fixed transmit power, the following comparative example 1 was designed.

[0034] Comparative Example 1: To simulate the real-world scenario where the communication link quality of a UAV fluctuates dynamically due to changes in distance and attitude during actual flight, this comparative example tests the traditional broadband noise suppression method under the RF conducted test environment of Example 2. The test settings for this comparative example, except for the interference signal generation and injection strategy, are strictly consistent with the test group of Example 2. Specifically, module A, as the legitimate signal transmitter, continuously transmits signals with a center frequency of 915MHz and a spreading factor of [missing information]. 7. Channel bandwidth The test data packet is 125kHz; module B acts as the receiver; module C acts as the interference injector. The core difference between this comparative example and the present invention is that module C is configured to generate and inject a fixed-power Gaussian white noise interference signal with a bandwidth covering the entire 125kHz channel, and no power adjustment is performed throughout the test. During the test, the channel attenuation is dynamically adjusted within a 60-second test cycle by controlling the precision adjustable attenuator between module A and module B through program control, in order to simulate the scenario where the signal strength of the target UAV signal at the receiver (module B) fluctuates between -5dBm and 10dBm. According to the data in Table 1 of Example 2, in order to achieve link blocking for most of the time, the interference strength of the traditional noise suppression method (module C) is... It was set to a fixed, high value of 20 dBm, and the link packet error rate of the receiving end (module B) was recorded every 5 seconds during the test. The test results are shown in Table 2.

[0035] Table 2: Performance of fixed power noise interference under dynamic signal strength.

[0036]

[0037] The experimental results of Comparative Example 1 show that the conventional technique of fixed-power broadband noise suppression reveals significant flaws in its inherent static countermeasure logic when facing scenarios with dynamically changing target signal strength. To cope with the strongest signal (e.g., 10dBm), the interference strength must be set at an extremely high level. However, during the majority of the time when the signal is weak, this setting results in significant energy waste and unnecessary electromagnetic spectrum occupation. More importantly, even with an interference strength as high as 20dBm, the link blocking effect still deteriorates or even fails when the target signal strength instantaneously increases to above 8dBm (e.g., at the 30th second). (The percentage dropped to 75.4%), which proves that without a closed-loop adaptive optimization mechanism based on real-time link feedback as provided in this invention, conventional energy suppression methods, in their technical principles, cannot take into account both the reliability of the blocking and the efficiency of energy use, and in particular, cannot adapt to the dynamic characteristics of UAV communication links in practical applications.

[0038] Example 3: This example combines Figures 1 to 6 This section describes a method for blocking UAV communication links based on preamble injection, as follows: Figure 1 As shown in the figure, the horizontal axis represents time in milliseconds (ms), and the vertical axis represents frequency in megahertz (MHz). The shades of color represent the power-to-frequency ratio in decibels (dB / Hz). The figure shows that the interference signal is continuous in the time domain and exhibits a series of periodically repeating linear frequency-modulated chirp structures within a specific frequency range of around -3.5MHz. This intuitively demonstrates the physical characteristic that the signal consists only of a continuous preamble and is uninterrupted.

[0039] like Figure 2 As shown in the figure, there is a signal transmitter with a transmission power of [value missing]. One interfering party has a transmission power of and a receiver, and the signal received by the receiver. It is the sender signal. Interference signals With channel noise The superposition of , its mathematical expression is: The receiver is processing the signal. After processing, the error detection stage outputs a bit error rate (SER) that characterizes the communication quality.

[0040] like Figure 3 As shown, the horizontal axis represents signal strength, with units of... The vertical axis represents the data packet reception rate, expressed as a percentage (%). The graph contains two curves: a solid line with circular markers representing the reception rate (%) of continuous preamble signal data packets, and a solid line with cross markers representing the reception rate (%) of white noise data packets. The experimental results clearly show that when continuous preamble interference is used, the data packet reception rate is reduced to -10%. The signal strength has dropped sharply to near zero, while with white noise interference, the signal strength has increased to near zero when it exceeds +10. Only then did the data packet reception rate begin to decline.

[0041] like Figure 4 As shown, with bit error rate (%) as the vertical axis, signal strength... The horizontal axis represents the bit error rate performance under two interference methods. The results in the figure show that the bit error rate (BER) of the continuous preamble signal is highest when the signal strength reaches -10%. (The dashed line with circular markers indicates the BER percentage is lower.) At that point, the bit error rate had already climbed to 100%, while the white noise bit error rate (the dashed line with the cross mark) would only be available when the signal strength increased to +20%. At that time, the bit error rate only reached 100%, which shows that the method of the present invention can cause complete link errors at an interference intensity far lower than that required by the traditional white noise method, thus proving its high efficiency.

[0042] like Figure 5 As shown, the vertical axis represents the data packet reception rate (%), and the horizontal axis represents the signal strength. The changing trends of the solid line with circular markers on the LoRa data packet reception rate % curve and the solid line with cross markers on the white noise data packet reception rate % curve in the figure verify that: at -10 At signal strengths of +15, the method of this invention can compress the data packet reception rate to an extremely low level, while the white noise method is completely ineffective at this strength with a 100% reception rate, requiring the strength to be increased to +15. Only after these steps can a significant blocking effect begin to appear.

[0043] like Figure 6 As shown, the vertical axis represents the bit error rate (%), and the horizontal axis represents the signal strength. The comparison between the dashed line with circular markings representing the bit error rate (%) of the continuous preamble signal and the dashed line with cross markings representing the bit error rate (%) of white noise in the figure clearly demonstrates once again that the interference method based on preamble injection used in this invention achieves a much lower interference intensity than traditional energy suppression methods (-10). vs+20 This can cause symbol-level errors in the communication link and reach a 100% bit error rate, thereby achieving efficient link blocking.

[0044] Example 4: To determine a set of initial parameters for the closed-loop adaptive optimization mechanism in the method claimed in this invention, so that it has a definite starting point when deployed in a new operating area with unknown communication environment characteristics, the system executes an automated offline calibration process; if the action triples of the multi-armed gambling machine model in this mechanism, especially the interference strength... Improperly set search intervals: too narrow an interval may miss solutions, while too wide an interval will reduce convergence speed due to too many candidate actions, thus affecting the system's blocking efficiency and energy consumption in the early stages of the task. The offline calibration process is conducted in an RF conducted test environment, where module A and module B establish a communication link with representative physical layer parameters. After the calibration process starts, module C, acting as the interference signal injector, first generates a continuous preamble interference signal matching the parameters of the communication link and injects it with an initial interference strength of -30dBm at 100% transmit duty cycle. Subsequently, the system increases the interference strength in 1dB increments. The link packet error rate is measured by module B at each strength level. During this process, the system records make The interference intensity that first exceeds 10% is defined as the lower limit of the effective interference range. And record The interference intensity that first exceeds 99.9% is defined as the stable blocking point. Based on these two data points obtained through actual measurements, the system will conduct subsequent online optimizations. The dynamic search range is set to [ The upper limit of this range is increased by 3dB to cover the higher intensity requirements that may be caused by channel environment fluctuations. Through this calibration process, the search space of the system's core parameters is transformed from a range that depends on the setting to an interval determined by actual measurement of the response of a specific link.

[0045] After defining the search interval, the system uses a preset number of discretization points, such as 10, to... The interval and the (0, 1] interval of the launch duty cycle are uniformly discretized to form a finite set of candidate actions. Before actual operation, the internal state of each candidate action arm in this multi-armed gambling machine model is uniformly initialized. Specifically, the average cost of each action arm is set to an initial value of 0, and its selection count is also counted as 0. This initialization operation ensures that all candidate actions have an equal chance of being explored in the initial stage of optimization. After the system enters the online working state, it follows a defined sequential decision-making process to update action selection in each control cycle. The process is as follows: First, update the total number of execution cycles. The second step is to process each action arm in the candidate action set. If it is selected a number of times If the value is 0, then the corresponding action arm is selected for execution; otherwise, its known average cost is used. Number of times selected Calculate its upper confidence bound. The third step is to select the arm with the smallest [feature / size] from all the actuators. Value of the moving arm As the execution action of the current cycle; fourth step, the system follows the action arm The corresponding interference intensity In conjunction with the transmission duty cycle, a continuous preamble interference signal is generated and injected; in the fifth step, at the end of this cycle, the system obtains the link packet error rate. As feedback; the sixth step is to calculate the immediate cost of the current period based on the feedback value. Step 7: Use the immediate cost For the selected actuator arm average cost and the number of times it was selected The system is then updated, and subsequently enters the next control cycle, repeating the above steps. By executing this calibration and initialization process, the method claimed in this invention enables its core adaptive optimization mechanism to have its internal model and key parameters set through a standardized and reproducible engineering operation when facing any new task scenario. This avoids performance uncertainties caused by improper manual parameter setting, ensuring that the entire system has a stable and efficient starting point when put into use.

[0046] Example 5: When the method claimed in this invention is deployed over a long period in a complex urban electromagnetic environment, the operating condition it faces is that the background radio noise level is not constant and may change due to the increase or decrease of surrounding wireless communication services. If this change is not adapted to by the system, it may lead to the interference intensity determined in the offline calibration phase. The overall shift in the search interval renders the original optimal action triplet no longer applicable, thereby reducing the long-term operating efficiency of the closed-loop adaptive optimization mechanism.

[0047] To address this environmental change, the closed-loop adaptive optimization mechanism of this method also integrates an online performance monitoring and model reset procedure. This procedure continuously calculates a blocking cost per unit power consumption based on a sliding time window during normal system operation. long-term average This value is used as the performance baseline for stable system operation under the current environment. The system also sets a performance degradation judgment threshold. If, over multiple consecutive control cycles, the average cost of short-term measurements consistently exceeds a preset multiple of this performance baseline, the system determines that the current electromagnetic environment has changed and automatically triggers an online recalibration. This recalibration process temporarily interrupts interference injection and re-executes the determination of the effective interference range lower limit through interference intensity scanning. With stable blocking point An automated calibration process is needed to obtain data adapted to the current new environment. Search range; after the new search range is determined, the system will reset the internal state of all candidate action arms in the multi-armed gambling machine model, and resume the execution of closed-loop adaptive interference injection based on the new candidate action set. This procedure enables the entire system to have self-awareness and autonomous adaptation to environmental changes, so that it can still maintain its link interruption and optimize energy consumption in unattended long-term deployment scenarios.

[0048] Example 6: In actual deployment, especially in situations with low signal-to-noise ratio or co-channel and adjacent-channel interference, the parameter set obtained in the initial passive monitoring and physical layer parameter parsing stages of the method claimed in this invention may be uncertain. If closed-loop adaptive optimization is started directly based on these uncertain parameters, interference failure may occur due to parameter mismatch, resulting in a waste of energy and time resources in the initial stage. This is an operational risk that needs to be avoided before the system enters formal operation.

[0049] To verify the initially resolved physical layer parameters, the system executes a pre-processing, low-power parameter verification procedure before entering the complete closed-loop adaptive optimization mechanism. After this procedure is initiated, the system first verifies the initially resolved physical layer parameter set, such as the spreading factor. =8, channel bandwidth =250kHz}, generating a fixed-duration, continuous preamble interference signal baseband waveform with normalized average power; subsequently, the system uses a fixed, low interference intensity. After amplitude scaling of the signal, a short-duration probe pulse is injected into the channel. The value of this interference intensity is determined according to the lower limit of the effective interference range in the calibration process described in Example 3. The purpose is to create an observable disturbance to the target link rather than to completely block it; within a preset observation window after the probe pulse is injected, the system switches to passive listening mode and uses the link packet error rate as the basis for detection. The change is used as the basis for judgment; if it is within the observation window, If the instantaneous value of the parameter exceeds a preset verification threshold compared to the baseline value before injection, the system determines that the initially parsed physical layer parameters are correct and initiates the subsequent closed-loop adaptive optimization process based on this parameter set; if no change is observed... In response to the corresponding changes, the system determines that the initial parameters are incorrect, discards the parameter set, and returns to the passive listening and physical layer parameter parsing stage to obtain new candidate parameters and repeat the verification procedure. This pre-parameter verification procedure, through a low-energy probe and feedback observation, forms a closed loop for verifying the correctness of physical layer parameter parsing. It establishes the operation of the entire method on a verified parameter basis, thereby improving the success rate and resource utilization efficiency of subsequent adaptive interference injection.

[0050] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for blocking UAV communication links based on preamble injection, characterized in that, The method includes: Step 1: Passively monitor the wireless channel on which the target UAV is communicating, and extract a set of physical layer parameters of the spread spectrum communication link used by the target UAV from the monitored signal. The set of physical layer parameters includes the spreading factor, channel bandwidth, center frequency and sampling rate. Step 2: Based on a set of physical layer parameters parsed in Step 1, a continuous preamble interference signal is generated locally. The generation rule of the continuous preamble interference signal is: it is composed of multiple basic chirp signals that match a set of physical layer parameters and spliced ​​end to end in the time domain, and does not contain any synchronization words for frame synchronization or payloads carrying user information. Step 3: Establish a closed-loop adaptive optimization mechanism for the continuous preamble interference signal injection process. The operating rules of this closed-loop adaptive optimization mechanism are as follows: Construct an action triplet consisting of interference signal type, interference intensity, and transmit duty cycle; and within a preset control period, form an observation window in time by adjusting the transmit duty cycle. Based on a feedback signal representing the link packet error rate obtained within the observation window, follow an evaluation rule that correlates the packet error rate with the interference intensity to represent the unit power blocking cost, and select an action triplet that aims to minimize the output value of the evaluation rule as the updated value to be applied to the generation and injection of continuous preamble interference signals in the next control period.

2. The method for blocking UAV communication links based on preamble injection according to claim 1, characterized in that, In step 3, the evaluation rule is specifically represented by a cost function. The calculation method is as follows: ;in, The link packet error rate, measured as a feedback signal within the observation window; Sets the ratio of the interference intensity of the continuous preamble interference signal to the channel noise power within the current control cycle.

3. The method for blocking UAV communication links based on preamble injection according to claim 1, characterized in that, Step 3, which involves selecting the action triplet, is implemented using a confidence upper bound algorithm based on the multi-armed gambling machine theory. This implementation includes: discretizing the continuous range of values ​​for interference intensity and transmission duty cycle into a finite number of candidate value points, and combining them with discrete candidate values ​​for the interference signal type to form a finite set of candidate actions; in each control cycle, based on the confidence upper bound algorithm, selecting the candidate action that minimizes the confidence upper bound value from the set of candidate actions, and using it as the value of the updated action triplet.

4. The method for blocking UAV communication links based on preamble injection according to claim 3, characterized in that, The execution of the confidence upper bound algorithm also includes a grid encryption mechanism. The operating rules of this mechanism are as follows: as the number of execution rounds of the control cycle increases, the number of candidate numerical points when discretizing the continuous value range of interference intensity and transmission duty cycle is gradually increased according to a preset incremental rule; so that the discrete grid density of the candidate action set increases over time, thereby making the value of the action triplet gradually approach the optimal solution in the continuous space.

5. The method for blocking UAV communication links based on preamble injection according to claim 1, characterized in that, When this method is applied to a scenario where the target UAV uses frequency hopping communication, it also includes the following steps: In step 1, the frequency hopping pattern of the target UAV communication link is further parsed; during the execution of steps 2 and 3, the center frequency of the continuous preamble interference signal is controlled so that it switches synchronously with the frequency hopping pattern to ensure that the preamble can be effectively injected on each frequency hopping channel.

6. The method for blocking UAV communication links based on preamble injection according to claim 1, characterized in that, The specific steps for obtaining a feedback signal characterizing the link packet error rate in step 3 are as follows: count the number of acknowledgment messages that failed to be received from the target UAV and the number of negative acknowledgment messages that were received within the observation window, calculate the ratio between the two, and use this ratio as the link packet error rate.

7. The method for blocking UAV communication links based on preamble injection according to claim 1, characterized in that, The step of parsing a set of physical layer parameters in step 1 further includes: performing a fast Fourier transform on the monitoring signal, determining the channel bandwidth and sampling rate by analyzing its spectral structure; obtaining estimated values ​​of time offset and frequency offset by performing autocorrelation calculation on the preamble segment of the monitoring signal, and performing time and frequency correction on the local signal generation module based on the estimated values.

8. The method for blocking UAV communication links based on preamble injection according to claim 1, characterized in that, In step 2, the basic chirp signal is the basic uplink chirp signal, and its single-symbol duration is... Depend on The calculation shows that, among which For spreading factor, This refers to the channel bandwidth.

9. A method for blocking UAV communication links based on preamble injection according to claim 1, characterized in that, In step 2, after generating the continuous preamble interference signal, a power normalization step is also included. This step adjusts the average power of the generated baseband waveform signal to a uniform power. Subsequently, the adaptive optimization mechanism in step 3 performs the final amplitude scaling on the normalized signal based on the updated interference intensity.

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