An unmanned aerial vehicle jamming test method based on dynamic attenuation and power division

Abstract

The present application relates to the technical field of communication anti-jamming test, in particular to a kind of unmanned aerial vehicle interference test method based on dynamic attenuation and power division;The present application extracts multidimensional environmental characteristic vector including power spectral density, modulation feature and signal time delay.Based on the vector, a dynamic attenuation matrix is constructed, and a composite path loss model is established based on the correction of free space propagation model, which can effectively simulate the interference characteristics in the real wireless propagation scene, make the test channel more close to the actual deployment environment, and thus improve the credibility and adaptability of the interference test;The present application generates three types of signals, including environmental noise signal, deceptive jamming signal and suppressive jamming signal, based on the optimal power ratio, and forms a precise anti-interference evaluation system by combining three indicators, including communication response time delay, positioning deviation and communication interruption rate, to effectively improve the test accuracy of the robustness, stability and fault-tolerant capability of the unmanned aerial vehicle communication system in different interference environments.

Description

Technical Field

[0001] This invention relates to the field of communication anti-interference testing technology, specifically to a UAV interference testing method based on dynamic attenuation and power distribution. Background Technology

[0002] Anti-jamming (AJ) testing aims to reproduce real or extreme electromagnetic environments and evaluate the reliability and robustness of communication links under interference conditions such as noise, spoofing, and suppression.

[0003] However, most existing laboratory methods rely solely on preset attenuation or statistical distributions (such as Rayleigh and Rician), failing to capture transient environmental noise, inter-frequency coupling, and dynamic changes in multiple frequency bands, including GPS, 2.4 GHz, and 5.8 GHz. This results in significant discrepancies between the interference effects and experimental data after formal deployment.

[0004] Furthermore, traditional systems typically allocate interference power using equal power or manual adjustment methods, ignoring the signal-to-noise ratio differences and real-time channel gain of each sub-channel. This often leads to the inefficient use of high-quality channels while weak channels are over-powered, resulting in low overall interference efficiency, high additional power consumption, and difficulty in comprehensively evaluating the multi-dimensional anti-interference capabilities of the communication system.

[0005] To address the aforementioned issues, it is necessary to propose a UAV interference testing method based on dynamic attenuation and power distribution. Summary of the Invention

[0006] The purpose of this invention is to solve the problems existing in the background technology and to propose a UAV interference testing method based on dynamic attenuation and power distribution.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for testing UAV interference based on dynamic attenuation and power division includes the following steps:

[0009] Step 1: Multi-dimensional environmental signal acquisition and environmental noise signal feature extraction;

[0010] Accessing a 4-channel MIMO receiver set up in the UAV's operating environment, the system synchronously collects environmental noise signals for UAV communication across various frequency bands, including those for GPS-dedicated communication, image transmission, and remote control communication. IQ data streams from the environmental signals are extracted using Fourier transform and spectrum sensing algorithms, and environmental signal attenuation characteristic data for the UAV's operating environment are extracted, providing fundamental data for reproducing environmental noise in the entire UAV operating environment.

[0011] Extract the raw data of the time-series environmental signals.

[0012] Power spectral density (PSD) is extracted using Fourier transform and spectrum sensing algorithms.

[0013] As a preferred embodiment of the present invention, high-order cumulative feature classification is performed based on modulation recognition, and modulation recognition feature values ​​are calculated, including second-order cumulative quantities and fourth-order cumulative quantities.

[0014] As a preferred embodiment of the present invention, the delay estimation calculation for UAV communication is performed based on the generalized correlation function and the dual-channel fast Fourier transform. The specific process is as follows:

[0015] A dual-channel Fast Fourier Transform (FFT) is performed on the original signal to obtain the dual-channel FFT result. Using the dual-channel FFT result as calculation parameters, the power spectrum operator and time delay relationship function are calculated through a preset generalized correlation function (GCC-PHAT). An inverse Fourier transform is performed on the power spectrum operator to obtain the cross-correlation function. The time delay component corresponding to the maximum value of the cross-correlation function is extracted and denoted as the estimated time delay caused by the environmental noise signal.

[0016] As a preferred embodiment of the present invention, the calculated power spectral density, modulation identification feature values, second-order cumulants and fourth-order cumulants, and time delay estimates are obtained to construct an environmental noise feature vector.

[0017] Step 2: Construction of the composite path loss model and reproduction of environmental noise;

[0018] A fading channel model is constructed based on environmental noise feature vectors. A modified free-space formula is developed, and the attenuation value is adjusted in real time. Targeted channel simulations are performed on UAV signal interference based on environmental noise characteristics, and a path loss model is constructed.

[0019] As a preferred embodiment of the present invention, an environmental noise feature vector is obtained, and a dynamic attenuation vector is constructed to dynamically correct the path loss model, so that the path loss model is more in line with the real noise environment of the scene where the UAV is located.

[0020] Data fusion is performed on the path loss model and dynamic attenuation vector. A dynamic correction factor is introduced on the basis of free space to construct a composite path loss model, which yields a loss estimation model for the UAV at any location in the entire working environment. The loss estimate of the UAV at any distance and frequency band is calculated by a preset formula.

[0021] As a preferred embodiment of the present invention, a frequency- and distance-dependent environmental noise simulation channel output model is constructed based on loss estimation:

[0022] The environmental noise simulation channel output model is sent to the signal generator as a control parameter to simulate the environmental noise of the UAV at various distances and frequencies.

[0023] Step 3: Calculation of power distribution network parameters and energy optimization;

[0024] Access the power division network processor to calculate the optimal power allocation ratio for the MIMO transmitter array, obtain the simulated parameters of the UAV for deceptive jamming signals and suppressive jamming signals, realize energy optimization between deceptive jamming and suppressive jamming, improve jamming effectiveness and save transmission power.

[0025] A channel capacity maximization model is established based on the water-filling theorem. The objective function and machine constraints of the channel capacity maximization model are defined, a mathematical model is constructed, and the power allocation ratio of each channel is solved based on the bisection numerical method.

[0026] Step 4: Interference signal synthesis and adaptive modulation;

[0027] The environmental noise simulation channel output model obtained in step two and the power allocation ratio obtained in step three are sent to the signal generator to simulate the superposition of environmental noise signal, deceptive interference signal and suppressive interference signal.

[0028] The total power of the deceptive interference signal is allocated to each channel according to the power allocation ratio of each channel, so as to obtain the power of the deceptive interference signal of each channel.

[0029] The total power of the pre-set suppressive interference signal is allocated to each channel according to the power allocation ratio of each channel, so as to obtain the power of the suppressive interference signal of each channel.

[0030] Deceptive interference signals are generated based on the power division of deceptive interference signals in each channel;

[0031] Suppressive interference signals are generated based on the power division of suppressive interference signals in each channel.

[0032] In a preferred embodiment of the present invention, signal superposition is performed to obtain a signal combination for UAV anti-interference testing, including:

[0033] Combination 1: Environmental noise signal superimposed with deceptive interference signal;

[0034] Combination 2: Environmental noise signal superimposed with suppressive interference signal;

[0035] Combination 3: Environmental noise signal superimposed with deceptive interference signal superimposed with suppressive interference signal.

[0036] The signal generator is instructed to output combined interference signals to the UAV according to combination one, combination two, and combination three.

[0037] Step 5: Communication quality assessment;

[0038] Record the signal communication status of UAVs under various superimposed combinations of environmental noise signals, deceptive jamming signals, and suppressive jamming signals, monitor their communication response delay, positioning deviation, and communication interruption rate, and quantitatively evaluate the communication quality of UAVs under the influence of environmental noise signals and targeted deceptive jamming signals and suppressive jamming signals.

[0039] Record the communication response delay, positioning deviation, and communication interruption rate of the UAV under environmental noise signals, and extract the anti-interference test results of the UAV. Specifically:

[0040] Record the first communication response delay E11, the first positioning deviation E12, and the first communication interruption rate E13 of the UAV under the condition of environmental noise superimposed with deceptive interference signals;

[0041] Record the second communication response delay E21, the second positioning deviation E22, and the second communication interruption rate E23 of the UAV under the condition of environmental noise superimposed with suppressive interference signals;

[0042] Record the third communication response delay E31, the third positioning deviation E32, and the third communication interruption rate E33 of the UAV under the condition of environmental noise signal superimposed with deceptive interference signal and suppressive interference signal.

[0043] As a preferred embodiment of the present invention, the determination is made based on the anti-interference test results of the UAV, specifically as follows:

[0044] If the first communication response delay E11 is greater than the first communication response delay preset threshold E11Max, the first positioning deviation E12 is greater than the first positioning deviation preset threshold E12Max, and the first communication interruption rate E13 is greater than the first communication interruption rate preset threshold E13Max, then the anti-interference capability of the UAV against deceptive interference signals is deemed unqualified.

[0045] If the second communication response delay E21 is greater than the second communication response delay preset threshold E21Max, the second positioning deviation E22 is greater than the second positioning deviation preset threshold E22Max, and the second communication interruption rate E23 is greater than the second communication interruption rate preset threshold E23Max, then the UAV's anti-interference capability against suppressive interference signals is deemed unqualified.

[0046] If the third communication response delay E31 is greater than the third communication response delay preset threshold E31Max, the third positioning deviation E32 is greater than the third positioning deviation preset threshold E32Max, and the third communication interruption rate E33 is greater than the third communication interruption rate preset threshold E33Max, then the anti-interference capability of the UAV against the superimposed signal of deceptive interference signal and suppressive interference signal is deemed unqualified.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] 1. This invention extracts multi-dimensional environmental feature vectors, including power spectral density, modulation characteristics, and signal delay. Based on these vectors, a dynamic attenuation matrix is ​​constructed, and a composite path loss model is established on the basis of a modified free-space propagation model. This effectively simulates interference characteristics in real wireless propagation scenarios, making the test channel more closely resemble the actual deployment environment, thereby improving the reliability and adaptability of interference testing.

[0049] 2. This invention combines the water-filling theorem to construct a mathematical model for maximizing channel capacity. Based on the signal-to-noise ratio and gain conditions of each channel, it calculates the optimal power allocation ratio to achieve differentiated energy regulation for each sub-channel. Simultaneously, it introduces power allocation weight coefficients for deceptive interference and suppressive interference to dynamically adjust the energy allocation ratio between interference methods, taking into account the actual needs of both induced disturbance and brute-force suppression interference strategies. This achieves the optimal balance between interference effectiveness and energy efficiency under total power constraints.

[0050] 3. This invention generates three types of signals—environmental noise, deceptive interference, and suppressive interference—based on optimal power ratios, and these signals can be flexibly combined into three composite interference test scenarios. By combining communication response delay, positioning deviation, and communication interruption rate—a precise anti-interference evaluation system is formed, effectively improving the accuracy of testing the robustness, stability, and fault tolerance of UAV communication systems under different interference environments. Attached Figure Description

[0051] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings:

[0052] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Please see Figure 1 As shown, a UAV interference testing method based on dynamic attenuation and power distribution includes the following steps:

[0055] Step 1: Multi-dimensional environmental signal acquisition and environmental noise signal feature extraction;

[0056] A 4-channel MIMO receiver, set up in the UAV's operating environment, synchronously collects environmental noise signals for UAV communication in various frequency bands, including the GPS-dedicated L1 / L2 communication band, the 5.8GHz image transmission communication band, and the 2.4GHz remote control communication band. IQ data streams from the environmental signals are extracted using Fourier transform and spectrum sensing algorithms, and environmental signal attenuation characteristic data of the UAV's operating environment are extracted, providing fundamental data for the reproduction of environmental noise in the entire UAV operating environment.

[0057] It's important to note that IQ data streams are fundamental to radio frequency (RF) signal processing. Essentially, they decompose the original signal into in-phase (1-component) and quadrature (Q-component) components, using complex numbers to fully describe the signal's amplitude and phase information. IQ data streams can losslessly reconstruct the complete characteristics of RF signals, providing raw material for subsequent modulation / demodulation and spectrum analysis (such as OFDM). Drones need to process three types of signals simultaneously: GPS (positioning and navigation), 2.4GHz remote control (control commands), and 5.8GHz video transmission. Through orthogonal decomposition, IQ data streams can process signal characteristics from different frequency bands in parallel, avoiding inter-band interference. For example, in the 5.8GHz band for drone video transmission, IQ data streams can capture subtle frequency shifts in video signals, supporting decoding of high-definition images.

[0058] Extract the raw data x(t) of the time-series environmental signal, where t is time.

[0059] The power spectral density (PSD) is extracted using Fourier transform and spectrum sensing algorithms, specifically as follows:

[0060] A preset time window length T is used to divide the sampling time of the raw data of the time-series environmental signal into several consecutive time windows, with the starting time of each time window being t0. The power spectral density within the previous time window is then calculated. :

[0061]

[0062] Where j is the imaginary unit and f is the characteristic frequency obtained from the Fourier transform.

[0063] Furthermore, high-order cumulative feature classification is performed based on modulation recognition. The specific process is as follows:

[0064] By preset formula

[0065] Calculate modulation identification feature values, including second-order cumulants. and fourth-order cumulants .

[0066] It should be noted that the second-order cumulant reflects the offset of the ambient noise signal from the program and DC components, while the fourth-order cumulant reflects the kurtosis of the ambient noise signal, that is, the sharpness of the signal distribution.

[0067] Furthermore, the delay estimation calculation for UAV communication is performed based on the generalized correlation function and dual-channel fast Fourier transform. The specific process is as follows:

[0068] Perform a fast Fourier transform on the original signal x(t) using two channels to obtain the two-channel Fourier transform results X(f) and Y(f).

[0069] Using the two-channel Fourier transform results X(f) and Y(f) as calculation parameters, the generalized correlation function GCC-PHAT is preset: Calculate the power spectrum operator Relationship function with time delay Among them The conjugate Fourier transform of the Fourier transform result Y(f), where... The power spectrum operator is obtained by weighting the cross power spectrum using a weighting function. Here, τ represents the time delay component.

[0070] For power spectrum operator Performing the inverse Fourier transform yields the cross-correlation function: .

[0071] Extracting cross-correlation function The time delay component τ corresponding to the maximum value is denoted as the time delay estimate TDE(τ) caused by the environmental noise signal.

[0072] Furthermore, the calculated power spectral density is obtained. Modulation identification feature value and And the time delay estimate TDE(τ). Generate the environmental noise feature vector F=[ , , ,TDE(τ)].

[0073] Step 2: Construction of the composite path loss model and reproduction of environmental noise;

[0074] A fading channel model is constructed based on the environmental noise feature vector, a modified free space formula is developed, the attenuation value is adjusted in real time, and targeted channel simulation is performed on the signal interference of UAVs based on the environmental noise characteristics.

[0075] Construct a path loss model:

[0076] Where d is the distance from the drone to the communication terminal, f is the communication frequency, and c is the speed of light.

[0077] Furthermore, obtain the environmental noise feature vector F=[ , , , A dynamic attenuation vector is constructed to dynamically correct the path loss model, making the path loss model more consistent with the real noise environment of the drone's scenario.

[0078] The dynamic decay vector is specifically:

[0079] γ1, γ2, γ3 and γ4 are all preset influence factors.

[0080] Data fusion is performed on the path loss model and dynamic attenuation vector. A dynamic correction factor is introduced on the free space basis to construct a composite path loss model, resulting in a loss estimation model for the UAV at any location in the entire working environment. The specific process is as follows:

[0081] By preset formula Calculate the loss estimate of a UAV at any distance d and any frequency band f. , where k = 1, 2, 3, 4.

[0082] Furthermore, based on loss estimates Construct a frequency- and distance-dependent environmental noise simulation channel output model:

[0083]

[0084] The environmental noise simulation channel output model is sent to the signal generator as a control parameter to simulate the environmental noise of the UAV at various distances and frequencies.

[0085] Step 3: Calculation of power distribution network parameters and energy optimization;

[0086] Access the power division network processor to calculate the optimal power allocation ratio for the MIMO transmitter array, obtain the simulated parameters of the UAV for deceptive jamming signals and suppressive jamming signals, realize energy optimization between deceptive jamming and suppressive jamming, improve jamming effectiveness and save transmission power.

[0087] Based on the water-filling theorem, a channel capacity maximization model is established. The objective function formula for the channel capacity maximization model is:

[0088]

[0089] Where i is the channel sequence number, N is the total number of channels; SNRi is the signal-to-noise ratio of each channel i; Let P0 be the power allocation ratio for each channel i, and let P0 be the preset reference power spectral density of deceptive interference and suppressive interference signals; where hi is the channel gain of each channel i; and C is the total channel capacity.

[0090] The constraints of the channel capacity maximization model include:

[0091] Maximize this capacity constraint to maximize the total channel capacity C;

[0092] Power allocation ratio constraint: the sum of all power allocation ratios is 1, and any power allocation ratio is greater than or equal to 0 and less than or equal to 1.

[0093] Right now

[0094] It's important to note that the Water-filling Theorem is a classic model in information theory and signal processing used to maximize channel capacity under power-constrained conditions. The principle of the Water-filling Theorem is to distribute a finite total power among the sub-channels in a multi-channel environment to maximize the overall transmission rate. The theorem treats each sub-channel as a container of unequal depth, with the bottom height corresponding to the noise power of that channel. Distributing the total power among these containers, while minimizing the amount of power supplied, maximizes the amount of water (i.e., the information capacity of each channel) in each container.

[0095] Furthermore, the objective function and machine constraints of the channel capacity maximization model are defined, a mathematical model is constructed, and the power allocation ratio Ki of each channel i is solved based on the bisection numerical method.

[0096] Step 4: Interference signal synthesis and adaptive modulation;

[0097] The environmental noise simulation channel output model obtained in step two and the power allocation ratio obtained in step three are sent to the signal generator to simulate the superposition of environmental noise signal, deceptive interference signal and suppressive interference signal.

[0098] The environmental noise signal is specifically as follows:

[0099]

[0100] Based on the power allocation ratio Ki for each channel i, the preset total power of the deceptive interference signal is allocated to each channel, resulting in the power allocation of the deceptive interference signal for each channel. ;

[0101] Based on the power allocation ratio Ki for each channel i, the preset total power of the suppressive interference signal is allocated to each channel to obtain the power allocation of the suppressive interference signal for each channel. ;

[0102] The deceptive interference signals are specifically:

[0103]

[0104] Among them The initial carrier phase of the preset deceptive interference signal; where... A deceptive modulation symbol sequence obtained by copying according to a legitimate protocol.

[0105] The specific suppressive interference signals are:

[0106]

[0107] Among them The initial carrier phase of the preset suppressive interference signal; where The signal substrate is a pre-defined band-limited Gaussian white noise suppression signal.

[0108] Furthermore, the signals are superimposed to obtain a signal combination used for UAV anti-interference testing, including:

[0109] Combination 1: Environmental noise signal superimposed with deceptive interference signal;

[0110] Combination 2: Environmental noise signal superimposed with suppressive interference signal;

[0111] Combination 3: Environmental noise signal superimposed with deceptive interference signal superimposed with suppressive interference signal.

[0112] The signal generator is instructed to output combined interference signals to the UAV according to combination one, combination two, and combination three.

[0113] Step 5: Communication quality assessment;

[0114] Record the signal communication status of UAVs under various superimposed combinations of environmental noise signals, deceptive jamming signals, and suppressive jamming signals, monitor their communication response delay, positioning deviation, and communication interruption rate, and quantitatively evaluate the communication quality of UAVs under the influence of environmental noise signals and targeted deceptive jamming signals and suppressive jamming signals.

[0115] Record the communication response delay, positioning deviation, and communication interruption rate of the UAV under environmental noise signals, and extract the anti-interference test results of the UAV. Specifically:

[0116] Record the first communication response delay E11, the first positioning deviation E12, and the first communication interruption rate E13 of the UAV under the condition of environmental noise superimposed with deceptive interference signals;

[0117] Record the second communication response delay E21, the second positioning deviation E22, and the second communication interruption rate E23 of the UAV under the condition of environmental noise superimposed with suppressive interference signals;

[0118] Record the third communication response delay E31, the third positioning deviation E32, and the third communication interruption rate E33 of the UAV under the condition of environmental noise signal superimposed with deceptive interference signal and suppressive interference signal.

[0119] Furthermore, the determination is made based on the anti-interference test results of the UAV, specifically as follows:

[0120] If the first communication response delay E11 is greater than the preset threshold, the first positioning deviation E12 is greater than the preset threshold, and the first communication interruption rate E13 is greater than the preset threshold, then the anti-interference capability of the UAV against deceptive interference signals is deemed unqualified.

[0121] If the first communication response delay E11 is greater than the first communication response delay preset threshold E11Max, the first positioning deviation E12 is greater than the first positioning deviation preset threshold E12Max, and the first communication interruption rate E13 is greater than the first communication interruption rate preset threshold E13Max, then the anti-interference capability of the UAV against deceptive interference signals is deemed unqualified.

[0122] If the second communication response delay E21 is greater than the second communication response delay preset threshold E21Max, the second positioning deviation E22 is greater than the second positioning deviation preset threshold E22Max, and the second communication interruption rate E23 is greater than the second communication interruption rate preset threshold E23Max, then the UAV's anti-interference capability against suppressive interference signals is deemed unqualified.

[0123] If the third communication response delay E31 is greater than the third communication response delay preset threshold E31Max, the third positioning deviation E32 is greater than the third positioning deviation preset threshold E32Max, and the third communication interruption rate E33 is greater than the third communication interruption rate preset threshold E33Max, then the anti-interference capability of the UAV against the superimposed signal of deceptive interference signal and suppressive interference signal is deemed unqualified.

[0124] It should be understood that the terms “comprising” and “including” used in this disclosure and claims indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0125] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. As used in this disclosure and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this disclosure and claims means any combination and all possible combinations of one or more of the associated listed items, and includes such combinations;

[0126] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for testing UAV interference based on dynamic attenuation and power distribution, characterized in that, Includes the following steps: Step 1: Multi-dimensional environmental signal acquisition and environmental noise signal feature extraction; Access the 4-channel MIMO receiver set in the drone's working environment to synchronously collect environmental noise signals of the drone's communication in various frequency bands, including environmental noise signals of the GPS-dedicated communication band, image transmission communication band, and remote control communication band; extract the IQ data stream from the environmental signals through Fourier transform and spectrum sensing algorithms, extract the environmental signal attenuation characteristic data of the drone's working environment, and provide basic data for the reproduction of environmental noise in the entire drone's working environment. Step 2: Construction of the composite path loss model and reproduction of environmental noise; The original signal is subjected to a fast Fourier transform of the dual-channel signal to obtain the dual-channel Fourier transform result; using the dual-channel Fourier transform result as the calculation parameter, the power spectrum operator and time delay relationship function are calculated through the preset generalized correlation function GCC-PHAT. Perform an inverse Fourier transform on the power spectrum operator to obtain the cross-correlation function; extract the time delay component corresponding to the maximum value of the cross-correlation function, and denote it as the time delay estimate caused by the environmental noise signal; obtain the calculated power spectral density, modulation identification feature value, second-order cumulant and fourth-order cumulant, and time delay estimate to construct the environmental noise feature vector; A fading channel model is constructed based on the environmental noise feature vector, a modified free space formula is constructed, the attenuation value is adjusted in real time, and a targeted channel simulation is performed on the signal interference of UAVs based on the environmental noise characteristics to construct a path loss model. The specific process of constructing the path loss model is as follows: Obtain environmental noise feature vectors and construct dynamic attenuation vectors to dynamically correct the path loss model, making the path loss model more consistent with the real noise environment of the drone's scene. Data fusion is performed on the path loss model and dynamic attenuation vector. A dynamic correction factor is introduced on the basis of free space to construct a composite path loss model. This results in a loss estimation model for the UAV at any location in the entire working environment. The loss estimate of the UAV at any distance and any frequency band is calculated by a preset formula. Based on loss estimation, a frequency- and distance-dependent environmental noise simulation channel output model is constructed: The environmental noise simulation channel output model is sent to the signal generator as a control parameter to simulate the environmental noise of the UAV at various distances and frequencies. Step 3: Calculation of power distribution network parameters and energy optimization; Access the power division network processor to calculate the optimal power allocation ratio for the MIMO transmitter array, obtain the simulated parameters of the UAV for deceptive jamming signals and suppressive jamming signals, realize energy optimization between deceptive jamming and suppressive jamming, improve jamming effectiveness and save transmission power; Step 4: Interference signal synthesis and adaptive modulation; The environmental noise simulation channel output model obtained in step two and the optimal power allocation ratio obtained in step three are sent to the signal generator to simulate the superposition of environmental noise signal, deceptive interference signal and suppressive interference signal. Step 5: Communication quality assessment; Record the signal communication status of UAVs under various superimposed combinations of environmental noise signals, deceptive jamming signals, and suppressive jamming signals, monitor their communication response delay, positioning deviation, and communication interruption rate, and quantitatively evaluate the communication quality of UAVs under the influence of environmental noise signals and targeted deceptive jamming signals and suppressive jamming signals.

2. The UAV interference testing method based on dynamic attenuation and power distribution according to claim 1, characterized in that, The specific process for extracting environmental signal attenuation characteristic data of the UAV's operating environment is as follows: Extract the raw data of the time-series environmental signals; Power spectral density (PSD) is extracted using Fourier transform and spectrum sensing algorithms. High-order cumulative feature classification is performed based on modulation recognition, and modulation recognition feature values ​​are calculated, including second-order and fourth-order cumulative values. Delay estimation calculation for UAV communication based on generalized correlation function and dual-channel fast Fourier transform.

3. The UAV interference testing method based on dynamic attenuation and power distribution according to claim 1, characterized in that, The specific process for calculating the optimal power allocation ratio is as follows: A channel capacity maximization model is established based on the water-filling theorem. The objective function and machine constraints of the channel capacity maximization model are defined, a mathematical model is constructed, and the optimal power allocation ratio of each channel is solved based on the bisection numerical method.

4. The UAV interference testing method based on dynamic attenuation and power distribution according to claim 1, characterized in that, The specific process of simulating the superposition of environmental noise signals, deceptive interference signals, and suppressive interference signals is as follows: The preset total power of the deceptive interference signal is allocated to each channel according to the optimal power allocation ratio of each channel, so as to obtain the power of the deceptive interference signal of each channel. The preset total power of the suppressive interference signal is allocated to each channel according to the optimal power allocation ratio of each channel, so as to obtain the power of the suppressive interference signal of each channel. Deceptive interference signals are generated based on the power division of deceptive interference signals in each channel; Suppressive jamming signals are generated based on the power division of suppressive jamming signals in each channel; The signals are superimposed to obtain a signal combination used for anti-interference testing of UAVs.

5. The UAV interference testing method based on dynamic attenuation and power distribution according to claim 4, characterized in that, The signal combinations used for UAV anti-jamming testing include: Combination 1: Environmental noise signal superimposed with deceptive interference signal; Combination 2: Environmental noise signal superimposed with suppressive interference signal; Combination 3: Environmental noise signal superimposed with deceptive interference signal superimposed with suppressive interference signal; The signal generator is instructed to output combined interference signals to the UAV according to combination one, combination two, and combination three.

6. The UAV interference testing method based on dynamic attenuation and power distribution according to claim 1, characterized in that, The specific process for quantitatively assessing communication quality is as follows: Record the communication response delay, positioning deviation, and communication interruption rate of the UAV under environmental noise signals, and extract the anti-interference test results of the UAV. Specifically: Record the first communication response delay, first positioning deviation, and first communication interruption rate of the UAV under the condition of environmental noise superimposed with deceptive interference signals; Record the second communication response delay, second positioning deviation, and second communication interruption rate of the UAV under the condition of environmental noise superimposed with suppressive interference signals; Record the third communication response delay, third positioning deviation, and third communication interruption rate of the UAV under the condition of environmental noise signal superimposed with deceptive interference signal and suppressive interference signal; The determination is made based on the results of the UAV anti-interference test.

7. The UAV interference testing method based on dynamic attenuation and power distribution according to claim 6, characterized in that, The specific process for determining the effectiveness of the UAV anti-interference test results is as follows: If the first communication response delay is greater than the first communication response delay preset threshold, the first positioning deviation is greater than the first positioning deviation preset threshold, and the first communication interruption rate is greater than the first communication interruption rate preset threshold, then the anti-interference capability of the UAV against deceptive interference signals is deemed unqualified. If the second communication response delay is greater than the second communication response delay preset threshold, the second positioning deviation is greater than the second positioning deviation preset threshold, and the second communication interruption rate is greater than the second communication interruption rate preset threshold, then the UAV's anti-interference capability against suppressive interference signals is deemed unqualified. If the third communication response delay is greater than the third communication response delay preset threshold, the third positioning deviation is greater than the third positioning deviation preset threshold, and the third communication interruption rate is greater than the third communication interruption rate preset threshold, then the anti-interference capability of the UAV against the superimposed signals of deceptive interference signals and suppressive interference signals is deemed unqualified.

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