Low-altitude unmanned aerial vehicle electromagnetic interference complexity early warning method and system based on fast FFT algorithm

Abstract

The invention relates to the technical field of electromagnetic spectrum monitoring and signal processing, and discloses a low-altitude unmanned aerial vehicle electromagnetic interference complexity early warning method and system based on a fast FFT algorithm. According to the method, space electromagnetic signals are collected in real time, FFT frequency domain analysis is carried out to obtain spectrum data, unmanned aerial vehicle signal frequency and power and left and right interference signal frequencies and interference energy are extracted based on the spectrum data, the frequency coincidence ratio and the energy coincidence ratio are calculated in an unmanned aerial vehicle system working frequency band, and objective complexity is obtained through the product of the frequency coincidence ratio and the energy coincidence ratio. And determining an electromagnetic interference complexity grade by combining a preset grade division standard look-up table, and further outputting an alarm signal and / or prompt information. The system comprises an electromagnetic signal acquisition unit, a signal processing unit and an early warning output unit, wherein the signal processing unit completes parameter extraction, overlap ratio calculation, complexity calculation and grading output. According to the scheme, complexity grading evaluation with low calculation amount is realized, and the method is suitable for real-time early warning of a low-altitude unmanned aerial vehicle scene.

Description

A method and system for early warning of electromagnetic interference complexity of low-altitude UAVs based on fast FFT algorithm Technical Field

[0001] This invention relates to the field of electromagnetic spectrum monitoring and signal processing technology, and in particular to a rapid frequency domain analysis method and its implementation system for electromagnetic interference complexity assessment and early warning in low-altitude unmanned aerial vehicle (UAV) application scenarios. Background Technology

[0002] In the field of electromagnetic signal processing, especially for the safe operation of low-altitude unmanned systems such as drones, real-time and accurate complexity assessment of the surrounding electromagnetic interference environment has always been a key focus and challenge in technical research. Early assessment methods often relied on qualitative descriptions such as "strong," "medium," and "weak," lacking precise quantitative parameters as support. This resulted in highly subjective and inconsistent assessment results, making it difficult to provide a reliable basis for flight control decisions. Subsequent attempts at quantitative assessment, such as methods based on multiple independent parameters (e.g., time domain, frequency domain, and energy domain occupancy) and classifying levels according to national military standards, have made progress in quantifying indicators. However, in practical applications, when multiple indicators overlap or are inconsistent, these methods cannot make effective comprehensive judgments, revealing the deficiency of insufficient overall assessment model integrity.

[0003] To overcome the challenges of multi-parameter comprehensive evaluation, more advanced solutions have emerged in existing technologies, such as the invention patent application "Method for Evaluating the Objective Complexity of Electromagnetic Interference Based on a Fast S-Transform Time-Frequency Spatial Model" (CN108469560A) filed by the inventor in 2018. This method obtains a two-dimensional time-frequency matrix by performing a fast S-transform on the signal, thereby enabling the simultaneous extraction and calculation of three evaluation parameters: time-domain occupancy, frequency-domain occupancy, and energy occupancy. It innovatively multiplies these three parameters to obtain a comprehensive objective complexity value. Then, the interference level is determined by looking up a table. This technical approach effectively integrates multi-dimensional information using a time-frequency spatial model, solves the evaluation dilemma when parameters intersect, and achieves an important leap from qualitative to quantitative analysis.

[0004] However, the Fast S-Transform algorithm upon which this scheme relies is essentially a complex transformation for time-frequency localization analysis of the signal. Despite its "fast" name, its computational complexity is still significantly greater than that of the classic Fast Fourier Transform (FFT). This higher computational complexity results in relatively long parameter extraction and evaluation times, making it difficult to meet the stringent requirements of millisecond-level real-time response when facing scenarios with rapidly changing electromagnetic environments during UAV flight. Furthermore, for steady-state or quasi-steady-state interference signals commonly found in UAV communication links, performing a complete time-frequency analysis is sometimes unnecessary and may even lead to redundant consumption of computational resources.

[0005] Therefore, the industry urgently needs a new assessment method that can significantly improve computing efficiency and reduce hardware resource consumption while ensuring the accuracy and comprehensiveness of the assessment, so as to truly meet the real-time early warning needs of lightweight drone platforms. Summary of the Invention

[0006] The present invention aims to overcome the shortcomings of the prior art and solve the technical problem of how to quickly, quantitatively, and in real time assess the complexity of electromagnetic interference faced by low-altitude UAVs and provide automated early warning, so as to avoid flight safety accidents caused by assessment delays or ambiguous judgments.

[0007] To address the aforementioned problems, this invention provides a method for early warning of electromagnetic interference complexity of low-altitude unmanned aerial vehicles (UAVs) based on a fast FFT algorithm, comprising:

[0008] S1. Real-time acquisition of spatial electromagnetic signals, and frequency domain analysis of the spatial electromagnetic signals using the Fast Fourier Transform (FFT) algorithm to obtain spectrum data;

[0009] S2. Extract UAV signal parameters and interference signal parameters based on the spectrum data, wherein the UAV signal parameters include at least the UAV signal frequency and the UAV signal power, and the interference signal parameters include at least the left and right interference signal frequencies and the interference signal energy;

[0010] S3. Based on the interference signal parameters and the operating frequency band of the UAV system, calculate the frequency overlap of the interference signal within the operating frequency band of the UAV system, and calculate the energy overlap based on the relationship between the interference signal energy and the reference energy of the UAV main signal.

[0011] S4. Obtain the objective complexity by multiplying the frequency overlap degree and the energy overlap degree. The electromagnetic interference complexity level is determined by looking up a table based on a preset electromagnetic interference level classification standard.

[0012] S5. Execute the early warning rule based on the electromagnetic interference complexity level, and output an alarm signal and / or output a prompt message.

[0013] As an optional implementation, step S2 includes: performing peak detection and interpolation on the spectrum data to obtain the UAV signal frequency and the UAV signal power;

[0014] And using the UAV signal frequency as the center, the left and right interference signal frequencies are searched in the frequency domain range on both sides.

[0015] As an optional implementation, the frequency overlap calculation in step S3 includes: within the operating frequency band of the UAV system, making a judgment on the spectrum data based on a power threshold, counting the set of effective occupied frequency points exceeding the power threshold, and characterizing the frequency overlap by the effective occupied bandwidth corresponding to the set of effective occupied frequency points.

[0016] As an optional implementation, the calculation of energy overlap in S3 includes: within the operating frequency band of the UAV system, summing the frequency power of the interference signals to obtain the total energy of the interference signals, and using the ratio of the total energy of the interference signals to the reference energy of the UAV main signal as the energy overlap.

[0017] As an optional implementation, in step S4, the objective complexity is calculated. At that time, the temporal domain occupancy and spatial domain occupancy are preset to 100%, and the objective complexity is determined based on the frequency overlap and energy overlap under preset conditions. .

[0018] As an optional implementation, the electromagnetic interference level classification standard includes at least a classification based on objective complexity. The intervals are classified according to hierarchical rules, and the objective complexity is... It is divided into multiple quantitative levels, which correspond to at least one qualitative level among general complexity, mild complexity, moderate complexity, and severe complexity.

[0019] On the other hand, the present invention also provides a low-altitude UAV electromagnetic interference complexity early warning system based on a fast FFT algorithm for implementing the above method, comprising:

[0020] An electromagnetic signal acquisition unit is used to acquire spatial electromagnetic signals in real time and output time-domain sampled data.

[0021] The signal processing unit, connected to the electromagnetic signal acquisition unit, performs FFT frequency domain analysis on the time-domain sampled data to obtain spectral data, extracts UAV signal parameters and interference signal parameters based on the spectral data, calculates frequency overlap and energy overlap, and obtains the objective complexity based on the product of the frequency overlap and energy overlap. The electromagnetic interference complexity level is determined by looking up a table based on a preset electromagnetic interference level classification standard.

[0022] The early warning output unit is connected to the signal processing unit and is used to output alarm signals and / or prompt information based on the electromagnetic interference complexity level.

[0023] As an optional implementation, the signal processing unit includes:

[0024] The FFT analysis module is used to perform an FFT on the time-domain sampled data to output spectral data;

[0025] The parameter extraction module, connected to the FFT analysis module, is used to extract the UAV signal frequency, UAV signal power, left and right interference signal frequencies, and interference signal energy from the spectrum data.

[0026] The overlap calculation module, connected to the parameter extraction module, is used to calculate the frequency overlap and the energy overlap.

[0027] The classification module, connected to the overlap calculation module, is used to obtain the objective complexity based on the product of the frequency overlap and the energy overlap. The complexity level of the electromagnetic interference is determined by referring to a table.

[0028] As an optional implementation, the early warning output unit includes at least one output interface, which includes any one or more of an audible and visual alarm interface, a display interface, and a communication interface;

[0029] The communication interface is used to send the alarm signal and / or prompt information to the UAV control module and / or the ground display terminal.

[0030] This invention replaces the Fast S-Transform with the Fast Fourier Transform as the core analysis tool by employing the Fast Fourier Transform (FFT) algorithm, which has extremely low computational complexity, reducing the computational load from O(N) to O(N). 2 The complexity of electromagnetic interference is reduced from the logN level to the O(Nlog2N) level, thus achieving millisecond-level real-time assessment of electromagnetic interference complexity and completely solving the problem of response lag in previous solutions. This invention defines two key quantitative indicators: frequency overlap and energy overlap. These directly focus on the occupancy of the interference signal within the UAV's operating frequency band and its relative energy intensity, covering the core interference dimension while avoiding the computational burden of time-domain analysis. A single objective complexity is obtained by multiplying these two indicators. The values ​​are mapped to a clear grading standard table. This method not only inherits the advantages of multi-parameter comprehensive decision-making, but also has a clearer and more direct decision-making logic. This invention also greatly reduces the dependence on hardware systems. Since it only requires frequency domain analysis and energy calculation, there is no need to equip additional hardware for measuring complex indicators such as time and spatial domains, which simplifies the entire system structure and significantly reduces power consumption and cost.

[0031] This invention is particularly suitable for lightweight unmanned aerial vehicle (UAV) platforms with limited computing resources and extremely high real-time requirements, providing a fast, quantitative, and reliable online early warning and decision support method for their flight safety in complex electromagnetic environments. Attached Figure Description

[0032] 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.

[0033] Figure 1 is a flowchart illustrating the electromagnetic interference complexity early warning method for low-altitude UAVs based on the fast FFT algorithm in Embodiment 1 of the present invention.

[0034] Figure 2 is a schematic diagram of the simulation results of spectrum analysis of UAV signals and multiple interference signals in the 2.4GHz band in Embodiment 1 of the present invention;

[0035] Figure 3 is a structural block diagram of the low-altitude UAV electromagnetic interference complexity early warning system based on the fast FFT algorithm in Embodiment 2 of the present invention.

[0036] Figure 4 is a schematic diagram of the electromagnetic interference assessment experiment and hardware connection for verifying the method of the embodiment of the present invention. Detailed Implementation

[0037] Example 1:

[0038] This embodiment aims to address the problem that existing technologies based on complex time-frequency analysis methods such as the S-transform have high computational complexity and cannot meet the real-time early warning requirements of lightweight UAVs. By introducing the Fast Fourier Transform (FFT) and constructing a two-dimensional evaluation model based on frequency overlap and energy overlap, a rapid quantitative evaluation of electromagnetic environment complexity at the millisecond level is achieved.

[0039] As shown in Figure 1, the method provided in this embodiment mainly includes five core steps, the details of which are as follows.

[0040] First, step S1 is executed to acquire spatial electromagnetic signals in real time. The spatial electromagnetic signals are then analyzed in the frequency domain using a Fast Fourier Transform (FFT) algorithm to obtain spectral data. During this process, to ensure comprehensive capture of the electromagnetic environment in which the low-altitude UAV is located, the system continuously samples analog and digital signals in the airspace using omnidirectional or directional antennas. Let the complex electromagnetic signal sequence obtained from discrete sampling be... ,in The value range is 0, 1, 2, ... -1, here This represents the number of sampling points. To obtain the frequency domain distribution characteristics of this signal, this embodiment does not use the traditional Discrete Fourier Transform (DFT) for direct calculation, because the complexity of direct calculation is as high as O(N). 2 This approach is insufficient to meet the real-time requirements of UAV flight. Instead, this embodiment employs a fast discrete Fourier transform (FFT) algorithm based on butterfly operations, the core of which lies in utilizing the rotation factor. The periodicity and symmetry of the FFT decompose the DFT of a long sequence into a combination of DFTs of shorter sequences. Specifically, the expression for the Fast FFT is:

[0041] ;

[0042] In the formula, is the rotation factor, which is used to characterize the unit root in the discrete Fourier transform; The exponential term is determined by the frequency domain index k. With time domain index To be determined jointly; For signal The discrete Fourier transform result is the spectrum data we need; This represents the number of sampling points; The imaginary unit is used. Through this recursive decomposition, the computational complexity is significantly reduced to O(Nlog₂N), resulting in a qualitative leap in the efficiency of spectrum analysis and laying a data foundation for subsequent real-time parameter extraction.

[0043] After obtaining the spectrum data, step S2 is executed next to extract the UAV signal parameters and interference signal parameters based on the spectrum data. The UAV signal parameters include at least the UAV signal frequency and UAV signal power, and the interference signal parameters include at least the left and right interference signal frequencies and interference signal energy. The specific operation logic is as follows: first, the one-dimensional frequency domain sequence after FFT transformation... Perform amplitude spectrum analysis, amplitude spectrum It intuitively reflects the signal at different frequency points. The energy distribution at the location. In this embodiment, the UAV target signal is accurately located by using a peak detection algorithm, combined with interpolation processing when necessary. The frequency of the UAV signal. This refers to the main frequency of the UAV target signal, that is, the frequency location where the UAV signal energy is most concentrated. Its mathematical expression is defined as:

[0044] ;

[0045] In the formula, A pre-defined set of drone signal frequencies for search.

[0046] Accordingly, the target signal power of the UAV This refers to the drone signal at the 1st The power at each frequency point is calculated using the following formula:

[0047] ;

[0048] Based on this, in order to evaluate the overall strength of the drone signal, we define The reference energy for the UAV signal is obtained by integrating or summing the power over the set of UAV signal frequencies, i.e. ,in This represents the sampling time interval.

[0049] Meanwhile, after determining the location of the UAV's main signal, this embodiment searches for potential interference signals within both directions of the UAV signal frequency. The extraction of interference signal parameters is divided into two directions: left interference and right interference. The frequency of the left interference signal... This refers to the main frequency of the interference signal on the left, and its expression is: In the formula, For the set of frequencies of left-hand interference signals, This is the FFT spectrum of the left interference signal.

[0050] Similarly, the frequency of the right interference signal This refers to the main frequency of the interference signal on the right, and its expression is: In the formula, This is the set of frequencies of the right-hand interference signal. The FFT spectrum of the right-hand interference signal is given. For characterizing the intensity of the interference signal, the power of the interference signal is... This refers to the interference signal at the 1st The power at each frequency point is also expressed based on the FFT amplitude calculation: Furthermore, in order to comprehensively quantify the energy impact of the interference, this embodiment defines the energy of the interference signal. It refers to the total energy of the left and right interference signals within the system's operating frequency band, used to assess the degree of energy impact of interference on UAV signals. Its expression is: In the formula, This represents the set of frequencies of all detected valid interference signals. Through the above steps, we have completed the transformation and extraction of key frequency domain feature parameters from the original time-domain signal.

[0051] Then, in step S3, based on the interference signal parameters and the UAV system's operating frequency band, the frequency overlap of the interference signal within the UAV system's operating frequency band is calculated, and the energy overlap is calculated based on the relationship between the interference signal energy and the UAV's main signal reference energy. This step is the core evaluation logic of this embodiment, aiming to eliminate invalid spatial and temporal redundancy indicators and focus on the two most direct interference dimensions: frequency and energy. The frequency overlap... This refers to the proportion of frequencies occupied by interfering signals within the operating frequency band of the UAV system, i.e., the degree to which the frequency of the interfering signal overlaps with the operating frequency band of the UAV. In actual calculations, not all weak signals are included in the interference; instead, a preset power threshold is used. The spectrum data is analyzed to determine the effective occupied frequencies exceeding a certain threshold. The expression for calculating frequency overlap is:

[0052] ;

[0053] In the formula, The bandwidth of the operating frequency band of the unmanned aerial vehicle (UAV) system; This is the sum of the effective bandwidth occupied by the interfering signals within the operating frequency band. This represents the FFT frequency resolution. This metric intuitively reflects how much of the UAV's communication resources are being occupied by interference signals in the frequency domain.

[0054] On the other hand, the energy overlap This refers to the ratio of the total energy of the interference signal to the reference energy of the UAV signal. It is used to measure the suppression capability of the interference signal at the energy level, and its expression is:

[0055] ;

[0056] The larger the ratio, the stronger the interference signal is relative to the drone signal, and the higher the risk of the signal being overwhelmed or erroneous.

[0057] After completing the quantization of the above two dimensions, step S4 is executed to obtain the objective complexity based on the product of the frequency overlap and the energy overlap. The electromagnetic interference complexity level is determined by looking up a table based on a preset electromagnetic interference level classification standard. Here, objective complexity is introduced. As the sole comprehensive evaluation indicator, its calculation formula is:

[0058] ;

[0059] Typically, for the purpose of standardization and grading, The value will be normalized or mapped to between 0% and 100%. The model is designed so that it only works when the interference signal overlaps with the UAV's operating frequency band. >0), and also possesses a certain energy intensity ( A signal greater than 0 volts to pose a substantial threat to drone communication. If either of these two factors is absent—for example, if the frequency bands do not overlap at all, or the energy is extremely low—then the product of the two factors is negligible. Approaching 0 accurately reflects a low-risk state. To calculate... The values ​​are converted into intuitive warning levels. This embodiment establishes a set of electromagnetic interference level classification standards. As shown in Table 1 below, this standard converts objective complexity... It is divided into different intervals, corresponding to different qualitative and quantitative levels.

[0060] Table 1: Electromagnetic Interference Level Classification Standards in this Embodiment (%)

[0061]

[0062] It should be noted that, in order to verify the adaptability of this embodiment to different parameter ranges, we can list three typical implementation scenarios.

[0063] In the first case, when the calculated objective complexity... When the value is 5% (in the range of 0 < (≤10) According to the table, the electromagnetic interference complexity level is quantitative level 1, which belongs to the "general complexity" in the qualitative level, which means that the environment is safe and the drone can operate normally.

[0064] In the second scenario, when the calculated objective complexity... When the value is 45% (in the range of 40 < (For ranges ≤50), the electromagnetic interference complexity level is determined to be quantitative level 5 by referring to the table, which belongs to "mildly complex". At this time, the system may prompt the operator to pay attention to the signal quality.

[0065] The third scenario involves extremely harsh environments, where the calculated objective complexity... When the value is 75% (in the range of 70 < (≤ 80) The electromagnetic interference complexity level is determined to be quantitative level 8 by referring to the table, which belongs to "severely complex". This indicates that the communication link faces a great risk of interruption.

[0066] Finally, step S5 is executed, which involves implementing early warning rules based on the electromagnetic interference complexity level and outputting alarm signals and / or prompting information. Specifically, the system presets one or more alarm thresholds. For example, when the complexity level reaches "moderate complexity" or "severe complexity," an alarm is issued via an audible and visual alarm, and instructions are sent to the UAV flight control system through the communication interface, prompting the flight control personnel to take measures such as forced landing, returning to base, or switching frequency bands, thereby avoiding safety accidents caused by communication interruption.

[0067] To more intuitively demonstrate the application effect of this embodiment in a real-world scenario, Figure 2 shows a specific application example. In this scenario, the operating frequency band of a certain type of UAV data link is used as the evaluation object. Its operating frequency band is set to 2.410GHz to 2.430GHz, with a bandwidth of 20MHz and a center frequency of approximately 2.420GHz (i.e., the UAV signal in Figure 2). At this time, there are three interference signals in space: interference B, interference C, and interference D.

[0068] The center frequency of interference B is 2.410 GHz, the center frequency of interference C is 2.440 GHz, and the center frequency of interference D is 2.500 GHz.

[0069] According to the processing logic of this embodiment, FFT analysis is performed first, and then parameters are extracted. When calculating the frequency overlap, the system will find that the main spectrum of interference B falls entirely within the UAV's operating frequency band (2.410-2.430GHz), therefore its bandwidth is fully included. Although the center frequency of interference C is 2.440 GHz, the left part of its spectrum extends into the operating frequency band of the UAV (the part above 2.430 GHz is not included, and the part below 2.430 GHz is included), therefore part of its bandwidth is included. The center frequency of interference D is 2.500 GHz, and its spectrum is entirely outside of 2.430 GHz; therefore, it has little effect on... The contribution is 0, meaning it is automatically filtered out by this algorithm.

[0070] The system then calculates the ratio of the energy of the effective interference (interference B and interference C in the in-band portion) to the energy of the UAV signal. Based on the formula, the frequency overlap is calculated. Further calculations of the signal energy are performed, assuming the UAV main signal reference energy is... The total energy of interference B and interference C within the working band Then the degree of energy overlap This leads to the objective complexity. According to Table 1, this complexity value falls into the medium to high category. Based on the warning rules of this embodiment, the system will automatically output an alarm signal and prompt for appropriate countermeasures on the control interface.

[0071] This application example demonstrates that even in the presence of multiple interferences, the method of the present invention can quickly and quantitatively identify interferences that pose a substantial threat to the assessment object (such as B and C), and effectively filter out irrelevant interferences (such as D), thus realizing a closed-loop assessment from frequency domain analysis to hierarchical early warning.

[0072] Furthermore, to highlight the advantages of this invention over the prior art, Table 2 below compares the computational complexity of the 512-point radix-2 FFT transform used in this invention with that of the traditional fast S-transform.

[0073] Table 2: Comparison of time and computational complexity of radix-2 FFT transform and fast S-transform for 512-point UAV signals.

[0074]

[0075] As can be seen from the data in Table 2 above, the equivalent total number of operations for the Fast S-Transform is as high as over 7 million, while the FFT algorithm used in this invention requires less than 7,000 operations, a difference of three orders of magnitude (approximately 1,000 times) in computation time. This means that this invention can complete a full evaluation loop in an extremely short time, greatly freeing up the computing resources of the onboard processor and making it possible to achieve millisecond-level real-time early warning on low-power hardware.

[0076] In summary, the method in this embodiment, through the efficient FFT algorithm combined with the innovative frequency-energy two-dimensional overlap model, not only solves the problems of redundancy of indicators and difficulty in cross-evaluation of parameters in the traditional "four-domain method", but also achieves an exponential improvement in computational efficiency while ensuring the accuracy of evaluation. It is particularly suitable for the field of electromagnetic protection of low-altitude UAVs with stringent requirements for real-time performance and power consumption.

[0077] Example 2:

[0078] This embodiment provides a low-altitude UAV electromagnetic interference complexity early warning system based on a fast FFT algorithm for implementing the method described in Embodiment 1. As shown in Figure 3, the system mainly consists of three core units in terms of hardware logic: an electromagnetic signal acquisition unit, a signal processing unit, and an early warning output unit. These units are electrically connected via a data bus or signal lines to collaboratively complete the physical acquisition, digital processing, and result execution of signals.

[0079] First, the electromagnetic signal acquisition unit is the sensing front end of the entire system, used to acquire spatial electromagnetic signals in real time and output time-domain sampled data. This unit typically includes a broadband receiving antenna, a low-noise amplifier (LNA), a mixer, and a high-speed analog-to-digital converter (ADC). The receiving antenna is responsible for capturing weak electromagnetic waves in space. After amplification and down-conversion, the ADC converts the analog signal into a digital time-domain sequence. The data is then transmitted to the backend signal processing unit. In the experimental scenario shown in Figure 4, devices such as a spectrum analyzer act as this acquisition unit, capturing real electromagnetic environment data.

[0080] Secondly, the signal processing unit is the core computing hub of the system, and it is connected to the electromagnetic signal acquisition unit via data transfer. Physically, this unit can employ a digital signal processor (DSP), a field-programmable gate array (FPGA), or a high-performance embedded processor (ARM). Functionally, the signal processing unit further includes an FFT analysis module, a parameter extraction module, an overlap calculation module, and a classification module. Specifically, the FFT analysis module receives time-domain sampled data and performs the aforementioned butterfly operation to output spectral data. Its output is connected to the input of the parameter extraction module. The parameter extraction module is responsible for scanning the spectrum data and locking the UAV signal frequency through a peak search algorithm. and its power Simultaneously search for interference signal frequencies on both the left and right sides. , and its energy The output of this module is connected to the overlap calculation module, transmitting the extracted parameters. The overlap calculation module calculates the frequency overlap of the interference signal within the UAV's operating frequency band according to the formula in Example 1. and energy overlap Finally, the rating module receives these two overlap indices and calculates their product to obtain the objective complexity. And according to the electromagnetic interference level classification standard table (as shown in Table 1) preset in the memory, the current electromagnetic interference complexity level is found and determined.

[0081] Finally, the warning output unit is connected to the output of the signal processing unit to output alarm signals and / or prompts based on the electromagnetic interference complexity level. As shown in Figure 3, the warning output unit is designed with multiple output interfaces to adapt to different application scenarios. Specifically, it may include an audible and visual alarm interface, a display interface, and a communication interface. The audible and visual alarm interface can be connected to a buzzer or LED warning light, which will directly issue an audible and visual signal to warn the on-site operator when the complexity level exceeds a preset threshold (e.g., reaching level 6 "moderate complexity"). The display interface is used to connect to a ground-based display terminal or an airborne screen to display the current spectrum (as shown in Figure 2) and the calculated... The values ​​and specific complexity levels (such as "medium complexity") provide visualized decision support for technical personnel. The communication interface is mainly used to send the alarm signals and prompts to the UAV control module to realize automated interference avoidance logic, such as triggering the UAV's automatic return-to-home or hovering procedures.

[0082] In actual installation and deployment, the above system exhibits a high degree of integration. The logic sections of the signal processing unit and the early warning output unit can be integrated onto the same circuit board, forming a lightweight airborne module that can be directly installed inside the UAV fuselage and interact with the flight control system via an internal bus; alternatively, it can function as part of a ground station, receiving spectrum data transmitted from the UAV via a data transmission link for remote processing. The experimental scenario shown in Figure 4 verifies the feasibility of the system. By simulating interference signals of different frequencies and intensities through a signal source, the system can accurately reflect the current interference level on the display terminal. This is not limited to laboratory environments but is also applicable to complex outdoor electromagnetic countermeasures exercises or actual operational scenarios.

[0083] The system described in this embodiment, through the rational division and efficient interconnection of hardware modules, solidifies complex electromagnetic environment assessment algorithms into a real-time processing flow. Its compact structure and rich interfaces not only enable it to operate independently providing audible and visual warnings, but also allow for deep integration with UAV flight control systems, forming a closed-loop safety protection system. Compared to traditional equipment that requires expensive, bulky high-performance computers to run the S-transform algorithm, this system significantly reduces hardware costs and power consumption, improves the portability and practicality of the equipment, and effectively solves the technical challenge of low-altitude UAVs lacking effective warning methods in complex electromagnetic environments.

Claims

1. A method for early warning of electromagnetic interference complexity of low-altitude UAVs based on a fast FFT algorithm, characterized in that, include: S1. Real-time acquisition of spatial electromagnetic signals, and frequency domain analysis of the spatial electromagnetic signals using the Fast Fourier Transform (FFT) algorithm to obtain spectrum data; S2. Extract UAV signal parameters and interference signal parameters based on the spectrum data, wherein the UAV signal parameters include at least the UAV signal frequency and UAV signal power, and the interference signal parameters include at least the left and right interference signal frequencies and interference signal energy; S3. Calculate the frequency overlap of the interference signal within the operating frequency band of the UAV system based on the interference signal parameters and the UAV system operating frequency band, and calculate the energy overlap based on the relationship between the interference signal energy and the UAV main signal reference energy; S4. Obtain the objective complexity by multiplying the frequency overlap degree and the energy overlap degree. S5. Based on the preset electromagnetic interference level classification standard, the electromagnetic interference complexity level is determined by looking up a table; S6. Based on the electromagnetic interference complexity level, the early warning rule is executed, and an alarm signal and / or a prompt message is output.

2. The method for early warning of electromagnetic interference complexity of low-altitude UAVs based on the fast FFT algorithm as described in claim 1, characterized in that, Step S2 includes: performing peak detection and interpolation on the spectrum data to obtain the UAV signal frequency and the UAV signal power; and searching for left and right interference signal frequencies in the frequency domain on both sides of the UAV signal frequency as the center.

3. The method for early warning of electromagnetic interference complexity of low-altitude UAVs based on the fast FFT algorithm as described in claim 1, characterized in that, Step S3, calculating the frequency overlap, includes: within the operating frequency band of the UAV system, making a judgment on the spectrum data based on a power threshold, statistically analyzing the set of effective occupied frequency points exceeding the power threshold, and characterizing the frequency overlap by the effective occupied bandwidth corresponding to the set of effective occupied frequency points.

4. The method for early warning of electromagnetic interference complexity of low-altitude UAVs based on the fast FFT algorithm as described in claim 1, characterized in that, The calculation of energy overlap in S3 includes: within the operating frequency band of the UAV system, summing the frequency power of the interference signals to obtain the total energy of the interference signals, and using the ratio of the total energy of the interference signals to the reference energy of the UAV main signal as the energy overlap.

5. The method for early warning of electromagnetic interference complexity of low-altitude UAVs based on the fast FFT algorithm as described in claim 1, characterized in that: In step S4, the objective complexity is calculated. At that time, the temporal domain occupancy and spatial domain occupancy are preset to 100%, and the objective complexity is determined based on the frequency overlap and energy overlap under preset conditions. 。 6. The method for early warning of electromagnetic interference complexity of low-altitude UAVs based on the fast FFT algorithm as described in claim 1, characterized in that: The electromagnetic interference level classification criteria include at least a classification based on objective complexity. The intervals are classified according to hierarchical rules, and the objective complexity is... It is divided into multiple quantitative levels, which correspond to at least one qualitative level among general complexity, mild complexity, moderate complexity, and severe complexity.

7. A low-altitude UAV electromagnetic interference complexity early warning system based on a fast FFT algorithm for implementing the method of any one of claims 1-6, characterized in that, include: An electromagnetic signal acquisition unit is used to acquire spatial electromagnetic signals in real time and output time-domain sampled data. The signal processing unit, connected to the electromagnetic signal acquisition unit, performs FFT frequency domain analysis on the time-domain sampled data to obtain spectrum data, extracts UAV signal parameters and interference signal parameters based on the spectrum data, calculates frequency overlap and energy overlap, and obtains the objective complexity based on the product of the frequency overlap and energy overlap. The electromagnetic interference complexity level is determined by looking up a table based on a preset electromagnetic interference level classification standard; the early warning output unit is connected to the signal processing unit and is used to output alarm signals and / or prompt information based on the electromagnetic interference complexity level.

8. The low-altitude UAV electromagnetic interference complexity early warning system based on the fast FFT algorithm as described in claim 7, characterized in that, The signal processing unit includes: an FFT analysis module for performing an FFT on the time-domain sampled data to output spectrum data; a parameter extraction module connected to the FFT analysis module for extracting the UAV signal frequency, UAV signal power, left and right interference signal frequencies, and interference signal energy from the spectrum data; an overlap calculation module connected to the parameter extraction module for calculating the frequency overlap and the energy overlap; and a rating module connected to the overlap calculation module for obtaining the objective complexity based on the product of the frequency overlap and the energy overlap. The complexity level of the electromagnetic interference is determined by referring to a table.

9. The low-altitude UAV electromagnetic interference complexity early warning system based on the fast FFT algorithm as described in claim 7, characterized in that: The early warning output unit includes at least one output interface, which includes any one or more of an audible and visual alarm interface, a display interface, and a communication interface; wherein, the communication interface is used to send the alarm signal and / or prompt information to the UAV terminal control module and / or the ground terminal display terminal.

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