A method for testing a drone countermeasure system

Abstract

The application discloses a kind of unmanned plane countermeasure system test methods, belong to unmanned plane technical field, including starting unmanned plane countermeasure system, to fixed sampling period acquisition launch end signal data;The launch end signal data is preprocessed;According to the carrier frequency offset in the preprocessed launch end signal data Generation frequency stability score;According to the adjacent channel leakage ratio and receiving sensitivity in the preprocessed launch end signal data Generation signal integrity degradation index;According to signal integrity degradation index, establish failure probability prediction model and generate failure probability;According to failure probability and frequency stability score Generation countermeasure system stability comprehensive index;According to countermeasure system stability comprehensive index, judge the stability of unmanned plane countermeasure system;The method is aimed at through real-time monitoring and quantitative evaluation improves the long-term operation stability of unmanned plane countermeasure system in complex electromagnetic environment, provides efficient, reliable solution for the operation and maintenance management of unmanned plane countermeasure system.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, and in particular relates to a test method for a UAV countermeasure system. Background Technology

[0002] In recent years, the rapid popularization of drone technology has brought a series of security risks. Unauthorized drone intrusions have become a significant threat to sensitive areas such as airports, military facilities, and large-scale event venues. To address these risks, drone countermeasure systems use technologies such as electromagnetic interference and navigation signal deception to suppress or force drones to land. However, the long-term operational stability of these systems in complex electromagnetic environments faces severe challenges.

[0003] Signal parameter drift problem: Key parameters such as carrier frequency offset, adjacent channel leakage ratio and receiver sensitivity at the transmitter of the countermeasure system are easily affected by ambient temperature, device aging and external interference, resulting in signal quality degradation;

[0004] Lack of fault prediction capability: Existing testing methods mostly rely on single static index detection (such as spectrum analysis), lacking quantitative assessment of the dynamic degradation of signal integrity, making it difficult to predict potential system faults;

[0005] The comprehensive evaluation system is inadequate: traditional methods only set threshold alarms for a single parameter and do not establish a correlation model between frequency stability, signal degradation and system failure probability, thus failing to fully reflect system reliability.

[0006] Currently, the industry generally uses manual periodic testing or offline calibration methods, which suffer from low efficiency, poor real-time performance, and inability to predict systemic risks. Therefore, there is an urgent need for a testing method that can monitor multi-dimensional signal parameters in real time, quantitatively assess system stability, and predict failure probabilities to improve the operational efficiency and reliability of UAV countermeasure systems. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a testing method for unmanned aerial vehicle (UAV) countermeasure systems, thus solving the aforementioned problems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for testing a drone countermeasure system, comprising the following steps:

[0009] S1: Activate the UAV countermeasure system to acquire transmitter signal data at a fixed sampling period; the transmitter signal data includes carrier frequency offset, adjacent channel leakage ratio, and receiver sensitivity.

[0010] S2: Preprocess the signal data at the transmitting end;

[0011] S3: Generate a frequency stability score based on the carrier frequency offset in the preprocessed transmitter signal data;

[0012] S4: Generate a signal integrity degradation index based on the adjacent channel leakage ratio and receiver sensitivity in the preprocessed transmitter signal data;

[0013] S5: Based on the signal integrity degradation index, establish a fault probability prediction model and generate fault probabilities;

[0014] S6: Generate a comprehensive stability index for the countermeasure system based on the failure probability and frequency stability scores;

[0015] S7: Determine the stability of the UAV countermeasure system based on the comprehensive stability index of the countermeasure system.

[0016] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0017] Further technical solution: The method of preprocessing the transmitter signal data specifically includes:

[0018] S2.1: Remove outliers from the transmitted signal data;

[0019] S2.2: Smooth the transmitter data after removing outliers.

[0020] Further technical solution: The smoothing process for the transmitter data after removing outliers specifically includes:

[0021] Through the formula:

[0022]

[0023] Generate preprocessed transmitter signal data Y;

[0024] In the formula, n represents the number of samples, Y i This represents the transmitter signal data from the i-th sample.

[0025] Further technical solution: The method for generating the frequency stability score specifically includes:

[0026] Through the formula:

[0027] ;

[0028] Generation frequency stability score S f ;

[0029] In the formula, maxS f This represents the theoretical maximum score value, K represents the penalty coefficient, Δf represents the carrier frequency offset, and f0 represents the offset rate threshold.

[0030] Further technical solution: The method for generating the signal integrity degradation index specifically includes:

[0031] Through the formula:

[0032]

[0033] Generate signal integrity degradation index D rf ;

[0034] In the formula, ACLR represents the adjacent channel leakage ratio, ACLR0 represents the initial value of the adjacent channel leakage ratio, maxΔACLR represents the threshold value for the change in the adjacent channel leakage ratio, and P... min This indicates the receiver sensitivity, P. 0 min This represents the receiver sensitivity, maxΔP min This represents the threshold for changes in receiver sensitivity, where α and β are weighting coefficients, and α+β=1.

[0035] A further technical solution: The expression for the fault probability prediction model is specifically as follows:

[0036]

[0037] Among them, P fail This represents the failure probability, max|Δf| represents the carrier frequency offset change threshold, Δf represents the carrier frequency offset, and γ is the signal integrity degradation index D. rf The regression coefficients are denoted by ℇ, which represents the regression coefficient of the carrier frequency offset Δf, b is the model bias term, and exp is the exponential function.

[0038] Further technical solution: The specific method for generating the comprehensive stability index of the countermeasure system is as follows:

[0039] Through the formula:

[0040]

[0041] Generate the Comprehensive Stability Index (SSI) for the countermeasure system;

[0042] In the formula, S f This represents the frequency stability score, P. fail This represents the probability of failure. a1 and a2 are both weighting coefficients, and a1+a2=1.

[0043] Further technical solutions: The methods for determining the stability of the UAV countermeasure system specifically include:

[0044] S7.1: Obtain historical data of the comprehensive stability index of the countermeasure system and generate the stability decline rate;

[0045] S7.2: Determine the stability of the UAV countermeasure system based on the current comprehensive stability index and stability decline rate of the countermeasure system.

[0046] A further technical solution: The method for generating the stability degradation rate is as follows:

[0047] Through the formula:

[0048]

[0049] The rate of decrease in stability, P, is generated.

[0050] In the formula, SSI t-1 This represents historical data for the Comprehensive Stability Index of the Countermeasures System (SSI). t This represents the overall stability index of the current countermeasures system.

[0051] This invention provides a method for testing a drone countermeasure system, which has the following advantages compared with the prior art:

[0052] 1. This invention acquires transmitter signal data in real time and dynamically generates stability scores, degradation indices and failure probabilities. It combines historical data to quantitatively evaluate system stability, solving the problem that existing technologies cannot capture the dynamic change trend of signal parameters and predict systemic risks. It has the advantages of real-time monitoring of multi-dimensional signal parameters, dynamic quantitative evaluation of system stability and accurate prediction of failure probabilities. Attached Figure Description

[0053] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] In existing technologies, UAV countermeasure systems often experience signal parameter drift at the transmitter due to changes in ambient temperature, component aging, or external interference during long-term operation in complex electromagnetic environments. Traditional testing methods rely on periodic manual inspections or offline calibration, judging system status based solely on single static indicators, and cannot monitor dynamic changes in signal parameters in real time. For example, existing technologies measure carrier frequency offset using spectrum analyzers, but lack a correlation model between parameter drift and system stability, making it difficult to predict potential failure risks. Furthermore, the lack of collaborative analysis of multi-dimensional parameters such as adjacent channel leakage ratio and receiver sensitivity results in an incomplete comprehensive evaluation system and delayed operation and maintenance response.

[0056] To address the aforementioned issues, it is first necessary to clarify that the root cause of the system stability degradation lies in the ineffective quantification of the dynamic drift of signal parameters. Traditional single-point detection cannot capture the continuous changing trends of parameters; therefore, it is considered to collect multi-dimensional signal data at fixed periods. Secondly, signal integrity degradation involves the interaction of multiple parameters, necessitating the design of a comprehensive index to reflect the degree of degradation. By analyzing the impact of carrier frequency offset on the core performance of the system, a frequency stability scoring mechanism is proposed. Simultaneously, combining the dynamic relationship between adjacent channel leakage ratio and receiver sensitivity, a signal integrity degradation index is constructed. Finally, the above indexes are integrated with a fault probability prediction model to form a comprehensive stability assessment system, achieving a closed loop from data acquisition to decision-making.

[0057] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0058] Please see Figure 1 The present invention provides a method for testing a drone countermeasure system, comprising the following steps:

[0059] S1: Activate the UAV countermeasure system to acquire transmitter signal data at a fixed sampling period; the transmitter signal data includes carrier frequency offset, adjacent channel leakage ratio, and receiver sensitivity.

[0060] S2: Preprocess the signal data at the transmitting end;

[0061] S3: Generate a frequency stability score based on the carrier frequency offset in the preprocessed transmitter signal data;

[0062] S4: Generate a signal integrity degradation index based on the adjacent channel leakage ratio and receiver sensitivity in the preprocessed transmitter signal data;

[0063] S5: Based on the signal integrity degradation index, establish a fault probability prediction model and generate fault probabilities;

[0064] S6: Generate a comprehensive stability index for the countermeasure system based on the failure probability and frequency stability scores;

[0065] S7: Determine the stability of the UAV countermeasure system based on the comprehensive stability index of the countermeasure system.

[0066] The fixed sampling period refers to collecting signal parameters at fixed time intervals, such as collecting data every 5 seconds, and capturing the dynamic trend of parameter changes through periodic monitoring. Preprocessing refers to outlier removal and smoothing of the raw data, such as using a moving average algorithm to eliminate random noise interference. Frequency stability scoring assesses signal source stability by quantifying carrier frequency offset, for example, by comparing the offset with a threshold to generate a score. Signal integrity degradation index considers the changes in adjacent channel leakage ratio and receiver sensitivity, for example, by using weighted calculations to reflect the degree of signal quality degradation. Fault probability prediction model converts the degradation index into the probability of fault occurrence, for example, by using a logistic regression model to establish a mapping relationship. The comprehensive countermeasure system stability index integrates the evaluation results of frequency stability and fault probability, for example, by generating a comprehensive score through linear weighting.

[0067] Specifically, after system startup, carrier frequency offset, adjacent channel leakage ratio, and receiver sensitivity data are continuously collected at fixed intervals. In the preprocessing stage, a threshold method is used to identify and remove outliers, followed by smoothing the data using a sliding window averaging algorithm. Based on the processed carrier frequency offset, its deviation ratio from a preset threshold is calculated, and a frequency stability score is generated by combining this with a penalty coefficient. Simultaneously, based on the offset of the adjacent channel leakage ratio relative to its initial value and the degree of change in receiver sensitivity, a signal integrity degradation index is generated through weighted summation. This degradation index is input into a logistic regression model, which outputs a predicted fault probability value. Finally, the frequency stability score and fault probability are fused according to preset weights to generate a comprehensive index. When the comprehensive index falls below a set threshold or the rate of decline exceeds a critical value, a system stability alarm is triggered.

[0068] Compared to existing technologies, current methods rely solely on single measurements to determine parameter limits, failing to reflect dynamic trends. This solution establishes a continuous monitoring mechanism through periodic data acquisition and preprocessing to capture parameter drift. Traditional techniques lack multi-parameter correlation analysis; this solution quantifies the impact of signal integrity degradation on system reliability by constructing a degradation index and failure probability model. Furthermore, existing evaluation systems depend on manual experience to set single thresholds; this solution integrates multi-dimensional indicators through a comprehensive index to achieve an objective quantitative assessment of system stability.

[0069] Through the above technical solutions, this application achieves real-time dynamic monitoring of the operational status of the UAV countermeasure system, solving the problem of stability degradation caused by parameter drift. By establishing a signal integrity degradation index and fault probability prediction model, discrete parameter changes are transformed into system-level risk assessments, improving fault early warning capabilities. A comprehensive index integrates frequency stability and fault risk, constructing a multi-dimensional evaluation system to provide quantitative basis for operation and maintenance decisions and reduce manual inspection costs.

[0070] Preferably, the method for preprocessing the transmitter signal data specifically includes:

[0071] S2.1: Remove outliers from the transmitted signal data;

[0072] S2.2: Smooth the transmitter data after removing outliers.

[0073] Outlier removal refers to identifying and removing signal parameter values ​​that deviate from the normal range through a data filtering mechanism. This can be achieved using the quartile method or the standard deviation threshold method, to eliminate abnormal data points caused by sudden changes in the electromagnetic environment or momentary equipment failures. Smoothing refers to noise suppression of continuously sampled data, which can be achieved using the moving average method or low-pass filtering algorithms, to eliminate the interference of high-frequency random noise on the dynamic changing trends of signal parameters.

[0074] Specifically, in the outlier removal process, for example, by setting a reasonable fluctuation range for the carrier frequency offset, sampling points exceeding this range are marked as invalid data and removed, thus avoiding the distortion effect of extreme offsets on the frequency stability score. In the smoothing stage, for example, a moving average algorithm with a window length of 5 is used to process the adjacent channel leakage ratio data, suppressing instantaneous jump noise and preserving the continuous variation characteristics of signal parameters. Therefore, the preprocessed data not only eliminates abnormal fluctuations caused by occasional interference but also retains the true trend of signal degradation, providing a reliable data foundation for subsequent quantitative evaluation.

[0075] Compared to existing technologies, traditional methods only filter outomas using fixed thresholds, failing to consider the dynamic changes in signal parameters and lacking effective means to suppress random noise. This solution employs a phased processing mechanism, first eliminating sudden outlier interference and then suppressing persistent noise, achieving multi-dimensional optimization of signal quality and resolving the data distortion or trend masking problems caused by single processing methods.

[0076] Through the above technical solution, this application effectively solves the problem of insufficient accuracy of subsequent analysis caused by outliers and noise interference in the transmitter signal data, so that the dynamic change characteristics of key parameters such as carrier frequency offset and adjacent channel leakage ratio can be accurately extracted, providing highly reliable data support for system stability assessment and fault prediction.

[0077] Preferably, the smoothing process for the transmitter data after removing outliers specifically includes:

[0078] Through the formula:

[0079]

[0080] Generate preprocessed transmitter signal data Y;

[0081] In the formula, n represents the number of samples, Y i This represents the transmitter signal data from the i-th sample.

[0082] Smoothing refers to noise reduction of discrete signal data, which can be achieved using an absolute value averaging algorithm. This eliminates the canceling effect of positive and negative fluctuations, preserving the true trend of signal amplitude changes. The sampling number n refers to the total number of samples used for data aggregation, which can be set to 10 to 50 consecutive sampling values. This suppresses random errors from single sampling by statistically analyzing multiple periods of data. Absolute value operation converts the original signal data into non-negative values, which can be achieved through modulo operations to avoid numerical cancellation caused by alternating positive and negative fluctuations during arithmetic averaging.

[0083] Specifically, in the data sequence after outlier removal, residual random noise may still manifest as high-frequency, small-amplitude fluctuations. By taking the absolute value of Y_i at each sampling point and then performing an arithmetic average, the instantaneous interference in the positive and negative directions cannot cancel each other out during superposition, thus fully preserving the actual disturbance level of the signal amplitude. For example, when environmental electromagnetic interference causes fluctuations of +0.5dBm and -0.3dBm between adjacent sampling values, the traditional arithmetic average would weaken it to +0.1dBm, while the absolute value average would convert it to 0.4dBm, more accurately reflecting the interference intensity. This processing method allows the subsequently generated frequency stability score to be calculated based on stable signal characteristics, avoiding oscillations in the evaluation results caused by residual noise.

[0084] Compared to existing technologies, traditional signal smoothing methods often employ moving averages or low-pass filtering, which are prone to waveform distortion when dealing with alternating positive and negative noise. For example, the sliding window averaging method underestimates the actual interference intensity when encountering alternating positive and negative noise due to numerical cancellation. This solution introduces absolute value preprocessing, effectively preserving the absolute value information of signal fluctuations. This ensures that the smoothed data not only eliminates high-frequency noise but also accurately reflects the true characteristics of signal amplitude changes, providing a reliable data foundation for subsequent stability assessments.

[0085] Through the above technical solution, this application can effectively suppress the random noise interference remaining after outlier removal, and solve the signal fluctuation problem caused by environmental interference or device aging. The signal data after absolute value averaging can accurately reflect the actual working state of the transmitter, avoid the evaluation error caused by the mutual cancellation of positive and negative fluctuations, thereby improving the accuracy of frequency stability scoring and signal integrity degradation index calculation, and providing reliable data support for system fault prediction.

[0086] Preferably, the method for generating the frequency stability score specifically includes:

[0087] Through the formula:

[0088] ;

[0089] Generation frequency stability score S f ;

[0090] In the formula, maxS f This represents the theoretical maximum score value, K represents the penalty coefficient, Δf represents the carrier frequency offset, and f0 represents the offset rate threshold.

[0091] The theoretical maximum score refers to the highest score benchmark that the system can achieve under ideal conditions. This can be achieved using a preset value under standard test conditions with no offset, serving as a benchmark for the scoring system. The penalty coefficient is an adjustment parameter used to amplify the impact of frequency offset. This can be achieved using empirical data or values ​​optimized through machine learning, strengthening the dynamic adjustment effect of offset on the scoring results. The carrier frequency offset refers to the deviation between the actual detected transmitted signal frequency and the nominal value. This can be measured in real-time using a spectrum analyzer or digital signal processing algorithms, quantifying the degree of abnormal fluctuation in signal frequency. The offset rate threshold is the critical value used to determine whether the frequency offset exceeds the normal range. This can be achieved using the allowable error range in the equipment specifications or statistical values ​​from historical operating data, defining the acceptable frequency fluctuation range.

[0092] Specifically, this technical solution dynamically calculates the ratio of carrier frequency offset to a preset threshold, and weights the offset amplitude using a penalty coefficient, enabling the scoring result to reflect the frequency stability status in real time. When the carrier frequency offset is within the threshold range, the score value decreases slowly to indicate potential risks; when the offset exceeds the threshold, the penalty coefficient significantly reduces the score value to trigger an early warning. For example, during the operation of a drone countermeasure system, if a carrier frequency offset of 2kHz is detected and the offset rate threshold is set to 1kHz, the specific value of the frequency stability score can be calculated using a formula, and this value will be dynamically updated as the offset changes. This quantitative evaluation mechanism enables continuous monitoring of the transmitter signal quality, providing data support for subsequent system stability assessments.

[0093] Compared to existing technologies, traditional methods rely solely on threshold alarms to determine if frequency offset exceeds limits, failing to quantify the impact of offset on system stability. This proposed solution, however, constructs a mathematical scoring model that not only identifies offset exceeding limits but also reflects the cumulative effect of offset through dynamic scoring. For example, even small, persistent offsets that haven't reached the threshold during long-term operation can trigger early warnings through changes in scoring trends. Furthermore, the introduction of a penalty coefficient enhances the system's sensitivity to critical offset states, addressing the overly rigid threshold boundary judgments of traditional methods.

[0094] Through the above technical solution, this application achieves dynamic quantitative evaluation of the frequency stability of the UAV countermeasure system, effectively identifying abnormal carrier frequency offsets caused by environmental interference or component aging. This scoring mechanism can reflect the degree of signal quality degradation in real time, providing key input parameters for fault prediction models, thereby avoiding systemic failures caused by frequency instability. Specifically, when the carrier frequency fluctuates, the system can immediately generate a corresponding stability score, allowing maintenance personnel to take timely maintenance measures based on the score's changing trend, significantly improving the reliability of system operation.

[0095] Preferably, the method for generating the signal integrity degradation index specifically includes:

[0096] Through the formula:

[0097]

[0098] Generate signal integrity degradation index D rf ;

[0099] In the formula, ACLR represents the adjacent channel leakage ratio, ACLR0 represents the initial value of the adjacent channel leakage ratio, maxΔACLR represents the threshold value for the change in the adjacent channel leakage ratio, and P... min This indicates the receiver sensitivity, P. 0 min This represents the receiver sensitivity, maxΔP min This represents the threshold for changes in receiver sensitivity, where α and β are weighting coefficients, and α+β=1.

[0100] The Adjacent Channel Leakage Ratio (ACLR) refers to the power leakage of a signal in adjacent channels. It is specifically measured using a spectrum analyzer to measure the power ratio of the main channel to the adjacent channel, reflecting the interference level of the transmitted signal in the frequency domain. The initial ACLR value (ACLR0) is the baseline measurement of the system under non-degradation conditions. It can be obtained through factory calibration or historical stable data, and is used to quantify the deviation of the current leakage level from the ideal state. Receiver sensitivity P minThis refers to the minimum signal power that the system can recognize. It can be measured using a receiver sensitivity tester under specific bit error rate conditions, and is used to characterize the degree of degradation in receiver performance. Initial value of receiver sensitivity. This refers to the baseline sensitivity value of the system under non-degradation conditions, which can be determined through calibration tests or historical data, and is used to calculate the sensitivity offset. The adjacent channel leakage ratio change threshold maxΔACLR and the receiver sensitivity change threshold are also relevant. This refers to the maximum allowable parameter offset range, which can be set according to system design specifications or experimental data, and is used to normalize parameter variations. The weighting coefficients α and β refer to the proportion of contribution of the two types of parameters to the degradation index, and can be determined using expert experience or a data-driven model. The constraint α + β = 1 ensures the balance of the evaluation results.

[0101] Specifically, the deviation of the adjacent channel leakage ratio from its initial value relative to its change threshold is used to reflect the degree of signal leakage degradation in the frequency domain, while the deviation of the receiver sensitivity from its initial value relative to its change threshold is used to reflect the degree of receiver performance degradation. By multiplying the two types of parameters by weighting coefficients and then linearly superimposing them, the differentiated impact of different parameters on system stability is preserved, while dimensional differences are eliminated through normalization. The constraints on the weighting coefficients allow the evaluation model to adjust the parameter weights according to the needs of the actual scenario, such as increasing the weight of the adjacent channel leakage ratio in electromagnetic interference-sensitive scenarios, or increasing the weight of the receiver sensitivity in receiver performance-sensitive scenarios. This technique transforms the originally isolated static parameter detection into a multi-dimensional dynamic degradation assessment, providing quantitative input for the fault probability prediction model.

[0102] Compared to existing technologies, traditional methods rely solely on single static detections to determine whether parameters exceed thresholds, failing to quantify the dynamic degradation trend of signal integrity over time. This proposed solution addresses the limitations of single-parameter detection by establishing a normalized degradation index model to track the relative changes in adjacent channel leakage ratio and receiver sensitivity in real time, dynamically adjusting the evaluation focus using weighting coefficients. Furthermore, existing technologies lack comprehensive assessments of multi-parameter collaborative degradation, while this solution achieves a unified quantitative characterization of cross-dimensional signal degradation through a linear superposition model.

[0103] Through the above technical solution, this application can monitor the dynamic changing trends of adjacent channel leakage ratio and receiver sensitivity in real time, and generate a comprehensive degradation index through normalization processing and weight allocation, providing a quantitative basis for fault prediction. This solution solves the problem that traditional testing methods cannot assess the dynamic degradation of signal integrity, enabling system maintenance strategies to be optimized based on degradation trends rather than single threshold alarms, thereby improving the accuracy of fault prediction and operational efficiency.

[0104] Preferably, the expression for the fault probability prediction model is as follows:

[0105]

[0106] Among them, P fail This represents the failure probability, max|Δf| represents the carrier frequency offset change threshold, Δf represents the carrier frequency offset, and γ is the signal integrity degradation index D. rf The regression coefficients are denoted by ℇ, which represents the regression coefficient of the carrier frequency offset Δf, b is the model bias term, and exp is the exponential function.

[0107] Among them, the signal integrity degradation index The signal integrity degradation index (SCI) is a quantitative indicator generated by combining the dynamic changes of adjacent channel leakage ratio and receiver sensitivity. It can be implemented using a weighted summation method and is used to characterize the degree of signal quality degradation. The carrier frequency offset Δf is the deviation of the transmitter's carrier frequency from its nominal value. It can be measured using a spectrum analyzer or phase-locked loop circuit and is used to reflect frequency stability. The regression coefficients γ and ε are the weighting parameters for the signal integrity degradation index and frequency offset. They can be determined through machine learning model training and are used to adjust the contribution of different parameters to the failure probability. The model bias term b is the baseline adjustment term of the logistic regression function. It can be optimized by minimizing the prediction error and is used to balance the model's output range. The exponential function exp refers to the natural exponential operation, which can be implemented using a mathematical calculation library and is used to map the linear combination result to a probability value in the range of 0-1.

[0108] Specifically, the fault probability prediction model calculates the signal integrity degradation index by fusing it with the normalized carrier frequency offset using a logistic regression function. Signal integrity degradation index It is generated by a weighted average of the dynamic changes in adjacent channel leakage ratio and receiver sensitivity, directly reflecting the trend of signal quality degradation; carrier frequency offset. By its change threshold The ratios are normalized to eliminate dimensional differences and characterize the degree of frequency stability degradation. Regression coefficients γ and ε adjust the weights of the two types of parameters on the failure probability, respectively, while the bias term b adjusts the model's baseline value. An exponential function converts the linear combination results into probability values, ensuring the failure probability remains within a reasonable range of 0-1. During computation, the model dynamically integrates signal integrity and frequency stability parameters, capturing the correlation between different degradation modes and system failures through parameter weight allocation. Simultaneously, the nonlinear characteristics of the logistic function enhance sensitivity to abnormal parameter changes.

[0109] Compared with existing technologies, traditional methods judge system status based on only a single parameter threshold, failing to consider the interactive effects of signal integrity degradation and frequency offset, and lacking a dynamic quantitative evaluation mechanism. This solution constructs a multi-parameter fusion prediction model, incorporating the dynamic changes of adjacent channel leakage ratio, receiver sensitivity, and carrier frequency offset into the calculation framework. It utilizes regression coefficients to achieve differentiated parameter weight allocation and uses logical functions to achieve a reasonable mapping of probability values, thereby more comprehensively reflecting system degradation trends and failure risks.

[0110] Through the above technical solution, this application solves the problem that existing testing methods cannot dynamically quantify the combined effects of signal integrity degradation and carrier frequency offset, and achieves accurate prediction of the failure probability of UAV countermeasure systems. By fusing the signal quality degradation index and normalized frequency offset, combined with a parameter weight adjustment mechanism, the system degradation characteristics under the coupling effect of multiple parameters can be accurately captured. By using a logistic regression model to convert the linear combination results into probability values, it not only conforms to the physical meaning of failure probability, but also enhances the response sensitivity to extreme parameter changes, thereby providing a reliable quantitative basis for system stability assessment.

[0111] Preferably, the method for generating the comprehensive stability index of the countermeasure system is as follows:

[0112] Through the formula:

[0113]

[0114] Generate the Comprehensive Stability Index (SSI) for the countermeasure system;

[0115] In the formula, S f This represents the frequency stability score, P. fail This represents the probability of failure. a1 and a2 are both weighting coefficients, and a1+a2=1.

[0116] Among them, the System Stability Index (SSI) is a composite index used to quantitatively evaluate the overall stability of a UAV countermeasure system. Specifically, it can be calculated by fusing frequency stability scores and a fault probability inverse index using a linear weighting method. This index can simultaneously reflect both short-term performance fluctuations and long-term fault trends of the system. Frequency stability score This refers to the quantified value of parameter stability calculated based on carrier frequency offset. Specifically, it can be achieved by subtracting the offset penalty term from the theoretical maximum score value. This parameter characterizes the stability of the transmitter's signal frequency. (Fault probability) This refers to the numerical value of the system failure probability predicted by the logistic regression model. Specifically, it can be achieved by jointly modeling the signal integrity degradation index and frequency offset. This parameter is used to quantify the potential failure risk of the system. The weighting coefficients a1 and a2 are proportional factors used to adjust the contribution of frequency stability and failure risk in the comprehensive evaluation. They can be implemented by empirical assignment or dynamic adjustment algorithms. This design allows the evaluation model to adapt to the stability evaluation needs of different application scenarios.

[0117] Specifically, a comprehensive evaluation index is constructed by weighting and summing the frequency stability score with an inverse fault probability index. The frequency stability score directly reflects the impact of the transmitter carrier frequency offset on system stability, while (1- This approach transforms failure probability into a reliability metric, reflecting the correlation between signal integrity degradation and potential failures. Normalization constraints on the weighting coefficients ensure consistent scale in the evaluation results. For example, in scenarios with high electromagnetic interference, a1 can be set to 0.6 to strengthen the weighting of frequency stability evaluation, while in environments with significant device aging, a2 can be set to 0.7 to emphasize fault risk monitoring. This linear combination method retains the independent influence of parameters while enabling flexible configuration of evaluation dimensions through dynamic weight allocation.

[0118] Compared to existing technologies, traditional methods typically employ single-parameter threshold alarm mechanisms, such as monitoring only whether frequency offset exceeds a preset threshold or simply counting the number of failures. This proposed solution, however, establishes a correlation model between frequency stability and failure probability, normalizes and dynamically weights two types of heterogeneous parameters, forming a composite index that characterizes multidimensional degradation features. This evaluation approach not only covers the temporal dimension of system stability changes but also quantifies the coupled effects of different failure modes.

[0119] Through the above technical solution, this application solves the problem of insufficient comprehensive evaluation caused by the lack of multi-parameter correlation models in traditional testing methods. By integrating frequency stability scores and inverse fault probability indicators, it achieves dynamic quantitative evaluation of system reliability, enabling maintenance personnel to simultaneously grasp short-term performance fluctuation trends and long-term fault risk changes, providing multi-dimensional data support for preventive maintenance decisions. This comprehensive index can effectively identify early signs of system stability degradation; for example, when the SSI drops beyond a preset warning value for three consecutive sampling periods, it can trigger the system self-test program to troubleshoot the fault.

[0120] Preferably, the method for determining the stability of the UAV countermeasure system specifically includes:

[0121] S7.1: Obtain historical data of the comprehensive stability index of the countermeasure system and generate the stability decline rate;

[0122] S7.2: Determine the stability of the UAV countermeasure system based on the current comprehensive stability index and stability decline rate of the countermeasure system.

[0123] Among them, the historical data of the comprehensive stability index of the countermeasure system refers to the continuous monitoring data stored in the time series database. Specifically, a distributed storage system can be used to implement periodic recording, which is used to establish a quantitative benchmark for the change of system performance over time.

[0124] Among them, the stability degradation rate refers to the calculation result of the rate of change of the comprehensive index at adjacent time points. Specifically, it can be dynamically updated using the sliding window algorithm to capture the gradual degradation trend of system performance.

[0125] Specifically, the system periodically collects a comprehensive stability index and stores it as a historical dataset. It then uses time-series analysis to calculate the rate of change of the index within adjacent time windows, generating a stability degradation rate parameter. This parameter, along with the current real-time index, forms a dual evaluation input. When the degradation rate exceeds a preset threshold, an early warning mechanism is triggered even if the current index has not reached the alarm threshold. For example, if the degradation rate continues to increase within three consecutive sampling periods and the current index deviates from its initial value by more than a set range, the system is deemed to have a stability risk. This dynamic trend-based evaluation mechanism can identify slow performance degradation problems that traditional static detection methods cannot detect.

[0126] Compared to existing technologies, traditional methods rely on comparing single detection results with fixed thresholds, failing to distinguish between sudden interference and continuous degradation. This solution, by introducing a time-dimensional parameter and establishing a correlation model between historical and real-time data, can differentiate between instantaneous fluctuations and trend-based degradation. For example, existing technologies may trigger false alarms due to single temperature fluctuations, while this solution, by analyzing the trend of the degradation rate, can accurately identify the linear degradation process caused by device aging.

[0127] Through the above technical solution, this application solves the problem that existing testing methods cannot predict the trend of stability degradation, and realizes dynamic monitoring of the system's health status. For example, in complex electromagnetic environments, when the receiving sensitivity slowly decreases due to device aging, the system can provide early warning through the continuous increase of the stability degradation rate, avoiding the risk missed by traditional methods due to single detection values ​​not exceeding the threshold, and providing trend prediction support for maintenance decisions.

[0128] Preferably, the stability degradation rate is generated in the following manner:

[0129] Through the formula:

[0130]

[0131] The rate of decrease in stability, P, is generated.

[0132] In the formula, SSI t-1 This represents historical data for the Comprehensive Stability Index of the Countermeasures System (SSI). t This represents the overall stability index of the current countermeasures system.

[0133] The historical data for the comprehensive stability index of the countermeasure system refers to the comprehensive evaluation value obtained by weighting frequency stability scores and failure probabilities at historical time points. Specifically, it can be implemented using historical SSI records stored in a time-series database, reflecting the continuous trajectory of system performance changes. The current comprehensive stability index of the countermeasure system refers to the quantitative indicator of system stability at the current moment, obtained by real-time acquisition of signal parameters. Specifically, it can be obtained through real-time calculation using an embedded processor, characterizing the current operating state of the system. Time interval. It refers to the time difference between two calculations of the comprehensive stability index. Specifically, it can be achieved by using the difference in evaluation cycles recorded by the system clock, which is used to eliminate the influence of the time dimension on trend analysis.

[0134] Specifically, by dividing the difference in the comprehensive stability index between adjacent time points by the time interval, the discrete stability assessment is transformed into a continuous trend rate of change. When system performance slowly deteriorates, this difference will show a continuously negative value; the rate of deterioration can be accurately quantified by standardizing the time interval. For example, when... When the time frame is set to 24 hours, if the SSI decreases by 0.1, the stability degradation rate is approximately -0.1 / 24 ≈ -0.0042 / hour, indicating the degree of system stability decay per hour. This time-series-based dynamic monitoring method can capture gradual performance degradation that traditional single-detection methods cannot detect, providing a quantitative basis for predicting systemic risks.

[0135] Compared to existing technologies, traditional methods rely solely on threshold alarms to determine system failure, failing to reflect the gradual process of stability changes. This solution, however, establishes a rate-of-change model over time, enabling early detection of system performance degradation trends. For example, existing technologies might trigger an alarm when the SSI falls below 0.5, but this solution can identify abnormal states earlier by calculating the rate of decline as the SSI continuously decreases from 0.8 to 0.7, detecting potential failure risks 20-30% earlier than static threshold detection.

[0136] Through the above technical solution, this application can effectively identify the continuous degradation process of the stability of the UAV countermeasure system and issue an early warning before the system completely fails. When the stability degradation rate exceeds a set threshold, a maintenance reminder can be automatically triggered to avoid the failure of the countermeasure function due to sudden failure. For example, in airport security scenarios, when the stability degradation rate is detected to exceed -0.005 / hour for three consecutive days, the system can automatically generate a maintenance work order to ensure that equipment maintenance is completed before the SSI drops to a critical value, thus maintaining the protection capability of critical infrastructure.

[0137] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for testing a drone countermeasure system, characterized in that, Includes the following steps: S1: Activate the UAV countermeasure system to acquire transmitter signal data at a fixed sampling period; the transmitter signal data includes carrier frequency offset, adjacent channel leakage ratio, and receiver sensitivity. S2: Preprocess the signal data at the transmitting end; S3: Generate a frequency stability score based on the carrier frequency offset in the preprocessed transmitter signal data; S4: Generate a signal integrity degradation index based on the adjacent channel leakage ratio and receiver sensitivity in the preprocessed transmitter signal data; S5: Based on the signal integrity degradation index, establish a fault probability prediction model and generate fault probabilities; S6: Generate a comprehensive stability index for the countermeasure system based on the failure probability and frequency stability scores; S7: Determine the stability of the UAV countermeasure system based on the comprehensive stability index of the countermeasure system; The specific expression for the failure probability prediction model is as follows: ； Among them, P fail This represents the failure probability, max|Δf| represents the carrier frequency offset change threshold, Δf represents the carrier frequency offset, and γ is the signal integrity degradation index D. rf The regression coefficients, represents the regression coefficient of carrier frequency offset Δf, b is the model bias term, and exp is the exponential function.

2. The method for testing a UAV countermeasure system according to claim 1, characterized in that, The specific methods for preprocessing the transmitted signal data include: S2.1: Remove outliers from the transmitted signal data; S2.2: Smooth the transmitter data after removing outliers.

3. The method for testing a UAV countermeasure system according to claim 2, characterized in that, The smoothing process for the transmitter data after removing outliers specifically includes: Through the formula: ； Generate preprocessed transmitter signal data Y; In the formula, n represents the number of samples, Y i This represents the transmitter signal data from the i-th sample.

4. The method for testing a UAV countermeasure system according to claim 1, characterized in that, The method for generating the frequency stability score specifically includes: Through the formula: ； Generation frequency stability score S f ; In the formula, maxS f This represents the theoretical maximum score value, K represents the penalty coefficient, Δf represents the carrier frequency offset, and f0 represents the offset rate threshold.

5. The method for testing a UAV countermeasure system according to claim 1, characterized in that, The method for generating the signal integrity degradation index specifically includes: Through the formula: ； Generate signal integrity degradation index D rf ; In the formula, ACLR represents the adjacent channel leakage ratio, ACLR0 represents the initial value of the adjacent channel leakage ratio, maxΔACLR represents the threshold value for the change in the adjacent channel leakage ratio, and P... min This indicates the receiver sensitivity. This represents the initial value of the receiver sensitivity, maxΔP. min This represents the threshold for changes in receiver sensitivity, where α and β are weighting coefficients, and α+β=1.

6. The method for testing a UAV countermeasure system according to claim 1, characterized in that, The specific method for generating the comprehensive stability index of the countermeasure system is as follows: Through the formula: ； Generate the Comprehensive Stability Index (SSI) for the countermeasure system; In the formula, S f This represents the frequency stability score, P. fail This represents the probability of failure. a1 and a2 are both weighting coefficients, and a1+a2=1.

7. The method for testing a UAV countermeasure system according to claim 1, characterized in that, The methods for determining the stability of a drone countermeasure system specifically include: S7.1: Obtain historical data of the comprehensive stability index of the countermeasure system and generate the stability decline rate; S7.2: Determine the stability of the UAV countermeasure system based on the current comprehensive stability index and stability decline rate of the countermeasure system.

8. A test method for a UAV countermeasure system according to claim 7, characterized in that, The stability degradation rate is generated in the following specific way: Through the formula: ； The rate of decrease in stability, P, is generated. In the formula, SSI t-1 This represents historical data for the Comprehensive Stability Index of the Countermeasures System (SSI). t This represents the overall stability index of the current countermeasures system. This represents the time interval, which is the time difference between two calculations of the stability composite index.

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