Radar dynamic anti-interference method and system based on interference source positioning

Abstract

The invention relates to the technical field of information, and discloses a radar dynamic anti-interference method and system based on interference source positioning. The method comprises the following steps: acquiring current signal data and a historical signal sequence, and determining an initial deviation value; extracting a historical deviation sequence according to the initial deviation value to obtain a deviation change trend vector; calculating a trend slope and a fluctuation amplitude, and determining an adjustment trigger signal; extracting feature interaction influence according to the adjustment trigger signal, and determining a weight coefficient update increment; iterative optimization is carried out in combination with the convergence rate parameter, and an optimized convergence rate value is obtained; adjusting deviation compensation model parameters according to the convergence speed value, and outputting a deviation compensation result when the compensation residual error is lower than a preset threshold value; and carrying out positioning calculation according to a deviation compensation result, and determining a corrected positioning coordinate. The model is dynamically optimized through technologies such as a sliding window and gradient descent, the problem of positioning deviation caused by complex environment signal interference is solved, and the positioning precision and the response speed are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, in particular to a radar dynamic anti-interference method and system based on interference source positioning. BACKGROUND

[0002] As a core perception device in navigation, national defense, transportation and other fields, the positioning accuracy of radar directly determines the system performance. However, in complex environments, multipath effect, obstacle shielding, non-line-of-sight propagation and dynamic interference source switching can easily cause unpredictable fluctuations in the strength, frequency and modulation mode of the received signal, leading to positioning deviation and seriously affecting the reliable operation of radar in complex scenarios. Therefore, an efficient dynamic anti-interference technology is needed to solve this core problem.

[0003] The existing radar anti-interference technology has significant defects when dealing with complex dynamic interference. Firstly, most technologies rely on a single static bias compensation model, which cannot adapt to the spatiotemporal dynamic changes of signal characteristics. For example, in urban canyons, high-rise building reflections can cause signal mutations, or in indoor scenarios, wall attenuation can cause changes in bias patterns. The static model has difficulty in adjusting the compensation strategy in real time, resulting in low positioning accuracy. Secondly, the feature interaction processing capability is insufficient. The interaction between signal strength, frequency and modulation mode has a significant impact on bias, but existing technologies lack accurate analysis of such feature interactions, and the weight coefficient update is lagging, which cannot timely reflect the dynamic contribution of each feature to the bias, exacerbating the compensation residual error. Thirdly, the balance between model optimization and real-time performance is difficult. Although some technologies use sliding windows or iterative optimization algorithms, the window size is fixed and cannot match the signal change frequency. Moreover, when dealing with high-dimensional features, algorithms such as gradient descent may have slow convergence or high computational complexity, resulting in response delays when the signal mutates, making it difficult to meet the dual requirements of real-time performance and low residual error. In addition, existing technologies do not establish a cooperative mechanism between interference source positioning and anti-interference, and only compensate for the bias without associating with the dynamics of the interference source, resulting in insufficient anti-interference robustness.

[0004] To address the above-mentioned defects, the present application dynamically adapts to interference changes through the method of "signal data collection-bias trend analysis-triggering dynamic adjustment-weight iterative optimization-model parameter adaptation-positioning coordinate correction", improving the radar anti-interference capability and positioning accuracy. SUMMARY

[0005] The present application relates to the field of information technology and discloses a radar dynamic anti-interference method and system based on interference source positioning. By dynamically optimizing the model using sliding windows and gradient descent techniques, the positioning deviation caused by signal interference in complex environments is addressed, and the positioning accuracy and response speed are improved.

[0006] In a first aspect, the present application provides a radar dynamic anti-interference method based on interference source positioning, comprising: Step S101, obtaining current signal data and historical signal sequence, and extracting signal strength, frequency and modulation mode characteristic parameters, and calculating an initial deviation value according to the signal strength, frequency and modulation mode characteristic parameters; Step S102, extracting a historical deviation sequence from the initial deviation value based on a dynamic sliding window, and performing time series analysis on the historical deviation sequence to generate a deviation change trend vector; Step S103, calculating a trend slope and a fluctuation amplitude using the deviation change trend vector, and determining an adjustment trigger signal according to a relationship between the trend slope and the fluctuation amplitude and a preset threshold size; Step S104, extracting a related feature interaction from the deviation change trend vector according to the adjustment trigger signal, determining a correlation weight between features, and calculating a weight coefficient update increment according to the correlation weight; Step S105, updating the convergence speed parameter according to the weight coefficient update increment, and optimizing the updated convergence speed parameter through a gradient descent iterative optimization algorithm to obtain an optimized convergence speed value; Step S106, adjusting the deviation compensation model parameter according to the optimized convergence speed value, calculating a compensation residual of the adjusted deviation compensation model, and outputting a deviation compensation result if the compensation residual is lower than a preset residual threshold; Step S107, adjusting the input parameter weight of the positioning algorithm according to the deviation compensation result, and calculating the adjusted input parameter through a weighted positioning algorithm to determine a corrected positioning coordinate.

[0007] Optionally, the step S101 comprises: Obtaining current interference signal data and historical signal sequence, comprising: collecting current interference signal data in real time through a radar signal receiving device, and calling historical signal sequence within a preset time period matched with the current radar working mode from a pre-established signal database; Calculating an initial deviation value according to the characteristic parameters of the current interference signal and the historical signal sequence, comprising: calculating the strength deviation component, the frequency deviation component and the modulation mode deviation component of the interference signal and the historical signal respectively; Determining the weight coefficient of the deviation component according to the variance of the historical signal sequence, calculating the initial deviation value through weighted summation, and storing the initial deviation value in association with the calculation time stamp, the radar working mode and the interference scene label.

[0008] Optionally, the step S102 comprises: Applying a sliding window technology to extract a historical deviation sequence from a pre-stored deviation database, wherein the window size of the sliding window technology is dynamically adjusted according to the signal change frequency; performing time series analysis on the historical deviation sequence to generate a deviation change trend vector; performing normalization processing on the deviation change trend vector to obtain a standardized deviation change trend vector.

[0009] Optionally, the step S103 comprises: extracting a time domain linear regression slope component in the deviation change trend vector, and determining the time domain linear regression slope component as a trend slope within a time window; calculating a fluctuation amplitude of the deviation change trend vector by using a standard deviation algorithm on time domain and frequency domain feature components of the deviation change trend vector; if the trend slope exceeds a preset slope threshold and the fluctuation amplitude satisfies an abnormality determination condition, marking a mutation mode and generating an adjustment trigger signal.

[0010] Optionally, the step S104 comprises: extracting an interference scenario label, a timestamp, a trend slope and a fluctuation amplitude according to the adjustment trigger signal; matching a scenario data set in the historical trend database according to the interference scenario label, and extracting feature interaction data from the scenario data set according to the timestamp, the trend slope and the fluctuation amplitude; analyzing a correlation degree between features in the feature interaction data by using a Pearson correlation coefficient matrix, and determining correlation weights of three groups of feature interactions of signal strength-frequency, signal strength-modulation mode and frequency-modulation mode; obtaining a basic weight coefficient of a current deviation compensation model, and calculating a weight coefficient update increment according to the basic weight coefficient, the correlation weights and the dynamic adjustment coefficient.

[0011] Optionally, the step S105 comprises: obtaining a current convergence speed parameter from a parameter buffer area of a radar deviation compensation model, the parameter buffer area containing a convergence speed parameter, an update timestamp and a corresponding interference scenario label; weighting and summing the weight coefficient update increment with an intensity influence coefficient, a frequency influence coefficient and a modulation influence coefficient to calculate an update amount of the convergence speed parameter, and then superimposing the update amount of the convergence speed parameter with a current convergence speed parameter to obtain an updated convergence speed parameter, the intensity influence coefficient, the frequency influence coefficient and the modulation influence coefficient being determined by historical data statistics; iteratively optimizing the updated convergence speed parameter by using a gradient descent method.

[0012] Optionally, the step S106 comprises: calling an initial parameter of a current deviation compensation model, the initial parameter including a deviation compensation coefficient and a compensation offset; Based on the optimized convergence speed value, the deviation compensation coefficient and the compensation offset are adjusted so that the adjusted parameters are adapted to the convergence speed of the deviation compensation model. The characteristic parameters of the current signal are input into the adjusted deviation compensation model. After the moving average filtering process, the real-time compensation value is output. Then, the ideal compensation value that matches the current interference scene and signal characteristics is retrieved. The absolute value error between the real-time compensation value and the ideal compensation value is the compensation residual. If the compensation residual is lower than the residual threshold, the output includes the real-time compensation value after filtering, the new compensation coefficient, the new compensation offset, the timestamp, and the scene label. If the compensation residual is higher than the residual threshold, return to step S105 to iterate and optimize the convergence speed parameters again.

[0013] Optionally, step S107 includes: The deviation compensation result is decomposed into values ​​corresponding to the original radar positioning coordinates. x Directional components and y Directional components; According to the above x Directional components and the y The input parameters of the directional component adjustment positioning algorithm include the distance estimation value corresponding to the signal strength, the phase offset value corresponding to the frequency, and the positioning error correction value corresponding to the modulation method. The weighted positioning method is adopted, which obtains the corrected coordinate components by multiplying each adjusted input parameter by its corresponding adjusted weight and then summing the product results.

[0014] Secondly, this application provides a radar dynamic anti-jamming system based on interference source localization, the system comprising: The signal acquisition module is used to acquire current signal data and historical signal sequences, calculate and determine the initial deviation value, and ensure the real-time performance and accuracy of data acquisition. The deviation analysis module extracts historical deviation sequences based on initial deviation values, processes them to generate a deviation change trend vector, and intuitively reflects the characteristics of deviation changes over time. The trigger judgment module uses the deviation change trend vector to calculate the trend slope and fluctuation amplitude. If the slope exceeds the preset threshold, it immediately determines and generates an adjustment trigger signal to start the subsequent compensation and adjustment process. The weight optimization module extracts the interaction effects of features based on the adjustment trigger signal, analyzes the correlation of features to determine the correlation weight, and calculates the weight coefficient update increment to provide a basis for updating model parameters. The convergence optimization module obtains the current convergence speed parameter, updates the parameter incrementally in conjunction with the weight coefficient, and optimizes it through an iterative optimization algorithm to obtain the optimized convergence speed value, thereby improving the model response speed. The compensation output module adjusts the parameters of the deviation compensation model according to the optimized convergence speed value, calculates the compensation residual, and outputs the deviation compensation result if the residual is lower than the preset threshold for subsequent positioning correction. The positioning correction module integrates the deviation compensation results into the positioning calculation process, adjusts the input parameters of the positioning algorithm, calculates and determines the corrected positioning coordinates through the positioning algorithm, and achieves accurate positioning.

[0015] Thirdly, this application provides a radar dynamic anti-jamming device based on interference source localization. The radar dynamic anti-jamming device based on interference source localization includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the radar dynamic anti-jamming method based on interference source localization.

[0016] This application proposes a radar dynamic anti-jamming method and system based on interference source localization, which is applicable to radar anti-jamming localization scenarios in complex environments. It can solve the problems of poor static model adaptability, insufficient feature interaction processing, imbalance between model optimization and real-time performance, and weak anti-jamming robustness in existing technologies. Compared with existing technologies, the beneficial effects of the technical solution of this application are at least as follows: First, by acquiring current signal data and historical signal sequences to determine the initial deviation value, and combining it with sliding window technology to dynamically extract historical deviation sequences and generate a deviation change trend vector, the limitations of the traditional static deviation compensation model are overcome. The analysis dimension can be adaptively adjusted according to the signal change frequency, adapting to the spatiotemporal dynamic changes of signal characteristics in complex environments, and avoiding the problem of a sharp drop in positioning accuracy when the signal changes abruptly or the deviation mode changes.

[0017] Second, based on the interaction between the signal strength, frequency, and modulation method extracted by adjusting the trigger signal, the update increment of the weight coefficient is determined through feature correlation analysis. This makes up for the shortcomings of the existing technology in the ability to process feature interactions, so that the weight coefficient can reflect the dynamic contribution of each feature to the deviation in real time, reduce the compensation residual caused by the lag in weight update, and improve the accuracy of deviation compensation.

[0018] Third, by combining the weight coefficient update increment and convergence speed parameters, the gradient descent iterative optimization algorithm is used to obtain the optimized convergence speed value. This ensures the scientific nature of the model parameter adjustment and balances computational complexity and convergence efficiency through dynamic optimization. It avoids the problem of slow convergence or response delay in traditional algorithms when dealing with high-dimensional features, thus meeting the dual requirements of radar for real-time positioning and low residuals.

[0019] Fourth, the deviation compensation results are integrated into the positioning calculation process. The corrected positioning coordinates are determined through a weighted positioning algorithm, and a collaborative mechanism for interference source positioning and anti-interference is established. Instead of simply compensating for deviations, the positioning process is optimized by associating the dynamic characteristics of interference sources. This enhances the anti-interference robustness of the radar in complex scenarios such as multiple interference source switching and multipath effects, and ensures the stable and reliable operation of the radar system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a radar dynamic anti-jamming method based on interference source localization according to this application; Figure 2 This is a diagram illustrating a radar dynamic anti-jamming process based on interference source localization, as described in this application. Figure 3 This is a schematic diagram of a radar dynamic anti-jamming system based on interference source localization according to this application; Figure 4 This is a schematic diagram of a radar dynamic anti-jamming device based on interference source localization according to this application. Detailed Implementation

[0022] This application provides a radar dynamic anti-jamming method and system based on interference source localization. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0023] This application relates to the field of information technology and discloses a radar dynamic anti-jamming method and system based on interference source localization. The method includes: acquiring current signal data and historical signal sequences to determine an initial deviation value; extracting historical deviation sequences based on the initial deviation value to obtain a deviation change trend vector; calculating the trend slope and fluctuation amplitude; if the trend slope exceeds a preset threshold, determining an adjustment trigger signal; extracting feature interaction effects based on the adjustment trigger signal to determine the weight coefficient update increment; iteratively optimizing the convergence speed parameter to obtain an optimized convergence speed value; adjusting the deviation compensation model parameters based on the convergence speed value; outputting the deviation compensation result when the compensation residual is lower than a preset threshold; and performing positioning calculations based on the deviation compensation result to determine the corrected positioning coordinates. This application uses techniques such as sliding window and gradient descent to dynamically optimize the model, solving the positioning deviation problem caused by signal interference in complex environments and improving positioning accuracy and response speed.

[0024] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the radar dynamic anti-jamming method based on interference source localization in this application includes: Step S101: Obtain the current signal data and historical signal sequence, and extract the signal strength, frequency and modulation mode characteristic parameters, and calculate the initial deviation value based on the signal strength, frequency and modulation mode characteristic parameters.

[0025] In one specific embodiment, the process of performing step S101 may specifically include the following steps: Acquiring current interference signal data and historical signal sequences includes: real-time acquisition of current interference signal data through radar signal receiving equipment, and retrieval of historical signal sequences within a preset time period that match the current radar operating mode from a pre-established signal database; The initial deviation value is calculated based on the characteristic parameters of the current interference signal and the historical signal sequence, including: calculating the intensity deviation component, frequency deviation component and modulation mode deviation component of the interference signal and the historical signal respectively; The weighting coefficients of the deviation components are determined based on the variance of the historical signal sequence. The initial deviation value is obtained by weighted summation and then stored in association with the calculation timestamp, radar operating mode, and interference scene label.

[0026] Specifically, the signal receiving equipment on the radar system (such as a high-sensitivity antenna module adapted to the radar's operating frequency band, such as X-band or S-band) is activated to sample interference signals in the current environment in real time. The sampling frequency must match the radar's operating frequency (e.g., X-band radars commonly use a 100MHz sampling rate). The collected current interference signal data includes time-domain waveforms (such as voltage amplitude data in a continuous time series) and spectral characteristics (such as the power distribution of the signal at different frequency points), which can be directly used for subsequent feature extraction. Simultaneously, historical signal sequences matching the current radar operating mode (such as search mode or tracking mode) are retrieved from a pre-established signal database. This database stores signal records under different interference scenarios (such as electromagnetic interference, multipath reflection interference, and dynamic interference source switching scenarios). Each record includes a timestamp, signal type label (such as "urban canyon multipath interference" or "airport electromagnetic interference"), and corresponding signal strength, frequency, and modulation parameters. During retrieval, a timestamp filtering mechanism is used to select historical signal sequences from the most recent 30 minutes to build a comparison benchmark, ensuring the timeliness of the historical data.

[0027] Secondly, regarding signal strength, the received signal strength indication is calculated using the power detection module built into the signal receiving device and quantified into dBm units (typically ranging from -40dBm to -100dBm, e.g., the current interference signal strength indication is -68dBm). Regarding frequency, a Fast Fourier Transform algorithm is applied to the time-domain waveform of the current interference signal data to convert it into a frequency-domain signal. A peak detection algorithm is used to identify and extract the dominant frequency components from the frequency spectrum. Digital filtering techniques are employed to eliminate out-of-band noise during extraction, and cubic spline interpolation is used to improve the resolution of the frequency parameters. Regarding modulation, the demodulation circuit of the radar system analyzes the changes in signal amplitude and frequency over time, and a maximum likelihood estimation algorithm is used to identify the modulation mode (e.g., ASK, FSK, QPSK, or FM, AM) used by the interference signal. These three types of extracted feature parameters address the problem of incomplete feature extraction and insufficient accuracy leading to distorted assessments.

[0028] Furthermore, a multi-dimensional difference quantization method is used to calculate the deviation components of the three types of feature parameters: the signal strength deviation component is the algebraic difference between the current signal strength value and the average signal strength under the same interference scenario in the historical signal sequence. For example, if the current strength indication is -68dBm and the average strength indication of the "urban multipath interference" scenario in the historical signal sequence is -72dBm, then the signal strength deviation component is 4dBm; the frequency deviation component is calculated using the Euclidean distance algorithm, that is, the difference between the currently extracted main frequency and the frequency mean of the historical signal sequence. For example, if the current main frequency is 10.03GHz and the frequency mean of the historical signal sequence is 10GHz, then the frequency deviation component is 0.03GHz; the modulation mode deviation component is assigned a qualitative value according to the mode matching degree. If the current modulation mode is consistent with the modulation mode of the historical signal sequence, then this component is 0; if they are inconsistent, a preset qualitative deviation value is assigned. Subsequently, the weighting coefficients of each deviation component are determined based on the statistical variance of the historical signal sequence. If the variance of signal strength in the historical data is large, the weight of the signal strength deviation component is set to 0.5, the weight of the frequency deviation component is set to 0.3, and the weight of the modulation mode deviation component is set to 0.2. The initial deviation value is calculated using the weighted summation formula (initial deviation value = signal strength deviation component × strength weight + frequency deviation component × frequency weight + modulation mode deviation component × modulation weight).

[0029] Finally, the calculated initial deviation values ​​are stored in the radar system's local cache or associated deviation database. The storage format must include the calculation timestamp (e.g., "2028-08-21 15:48:55"), the current radar operating mode (e.g., "tracking mode"), and the interference scene label (e.g., "urban multipath interference"). A circular buffer is used to manage the data during storage, and newly generated initial deviation values ​​automatically overwrite expired data in the database that has exceeded the preset storage time, maintaining storage efficiency. Simultaneously, the database query process employs index optimization techniques (e.g., a composite index based on interference scene labels and timestamps) to accelerate the retrieval of historical deviation sequences using sliding window technology in subsequent steps, addressing the problem of low processing efficiency caused by scattered and disordered data storage.

[0030] Step S102: Extract historical deviation sequences from the initial deviation values ​​based on a dynamic sliding window, and perform time series analysis on the historical deviation sequences to generate a deviation change trend vector.

[0031] In one specific embodiment, the process of performing step S102 may specifically include the following steps: By applying the sliding window technique, historical deviation sequences are extracted from a pre-stored deviation database. The window size of the sliding window technique is dynamically adjusted according to the frequency of signal changes. Time series analysis is performed on the historical deviation sequence to generate a deviation change trend vector; The deviation change trend vector is normalized to obtain the standardized deviation change trend vector.

[0032] Specifically, a sliding window technique is employed. When retrieving the most recent historical deviation sequence from a pre-stored deviation database, the sliding window is initiated by setting a time interval backward from the current time corresponding to the initial deviation value. The initial size of the sliding window is set to 10 seconds of data points, and the number of data points within the window is dynamically adjusted according to the radar signal sampling rate. If the radar's operating sampling rate is 100MHz (i.e., 100×10⁻⁶ data points per second), the window size is adjusted accordingly. 6 If there are 1000 signal data points, then a 10-second window contains 1000 data points. Window boundary processing uses a mirror extension method to avoid data truncation; the extension length is set to 5% of the window size (e.g., a 10-second window extends by 0.5 seconds of data points) to prevent incomplete sequences due to missing boundary data. The signal frequency change rate is calculated. If the change rate exceeds 5% / second (e.g., a 5% change in 1 second when the signal frequency suddenly changes from 10GHz to 10.5GHz), the window is reduced to 5 seconds of data points; if the frequency is stable (change rate less than 0.5% / second), the window is expanded to 20 seconds of data points. The adjustment step size is limited by the system's minimum response time of 1 millisecond to ensure that window adjustment does not lag behind signal changes. Simultaneously, data processing employs a double buffering mechanism: the front buffer stores the original deviation sequence retrieved from the deviation database in real time, while the back buffer temporarily stores sequence segments to be processed, avoiding data read / write conflicts. In addition, the dynamic window adjustment algorithm also monitors the change of signal spectrum entropy. If the entropy value changes abruptly and exceeds the preset threshold, it immediately triggers window reconfiguration. The window size is negatively correlated with the rate of change of entropy (the higher the rate of change of entropy, the smaller the window). This can solve the problems of fixed windows being unable to match the frequency of signal changes, data redundancy during high-frequency interference, and insufficient information during low-frequency stability. It avoids the distortion of historical deviation sequence features caused by traditional fixed windows and ensures that the extracted sequence can accurately reflect the transient and steady-state characteristics of the deviation.

[0033] Secondly, for multi-scale feature extraction of the historical deviation sequence extracted by the sliding window, the instantaneous change of adjacent deviation values ​​is first calculated by the first-order difference algorithm (e.g., the first-order difference result of the sequence [2.009,2.015,2.021,2.018,2.025] is [0.006,0.006,-0.003,0.007]), and then the acceleration of change is calculated by the second-order difference algorithm (e.g., the second-order difference based on the first-order difference result is [0,-0.009,0.01]). Subsequently, the original historical deviation sequence is fitted by the linear regression algorithm to obtain the regression coefficients reflecting the time evolution trend of the deviation (e.g., slope 0.004 / second, intercept 2.005). These coefficients constitute the time-domain basic components of the deviation change trend vector. Simultaneously, Fourier transform is applied to the historical deviation sequence to detect periodic features in the sequence (e.g., identifying interference signals with a period of 0.5 seconds). The amplitude of the dominant frequency component (e.g., 0.005) is normalized and used as the frequency domain feature of the trend vector. The deviation change trend vector is fixed at 6 dimensions. The first three dimensions correspond to time domain features (sequence mean, linear regression slope, and second-order difference curvature, e.g., [2.015, 0.004, 0.01]), and the last three dimensions store frequency domain features (the normalized amplitudes of the first three dominant frequency components, e.g., [0.8, 0.15, 0.05]). The feature extraction stage adopts a parallel computing architecture, with independent computing units allocated to time domain analysis (difference, linear regression) and frequency domain analysis (Fourier transform) to improve processing efficiency. The vector generation algorithm is optimized using fixed-point arithmetic, maintaining a time domain feature calculation accuracy of 0.001 and a frequency domain feature resolution of 0.1Hz. By integrating time-domain and frequency-domain features, the problem of delayed response to periodic interference in simple time-domain analysis is solved, so that the deviation change trend vector can reflect both the instantaneous change speed and direction of the deviation, and capture the frequency characteristics of periodic interference.

[0034] Finally, the deviation trend vector is normalized to obtain a standardized deviation trend vector. The normalization process uses the minimax method. First, the value ranges of each dimension feature are statistically analyzed from the radar system's training dataset (containing deviation trend vector samples under different operating modes and interference scenarios): the time-domain mean is 1.8-2.2, the linear regression slope is -0.01-0.01 / second, the second-order difference curvature is -0.02-0.02, and the normalization amplitude of the first three main frequency components in the frequency domain is 0-1. The standardized value of each dimension is calculated using the formula "Standardized component = (Original component - Minimum value of this dimension) / (Maximum value of this dimension - Minimum value of this dimension)". For example, for the deviation trend vector [2.015, 0.004, 0.01, 0.8, 0.15, 0.05], the standardized time-domain mean is... The standardized slope of the linear regression is The second-order difference curvature after standardization is ( After standardization of the three frequency domain components, the original values ​​are retained, resulting in a standardized deviation trend vector [0.5375, 0.7, 0.75, 0.8, 0.15, 0.05]. The normalization module has a built-in automatic parameter calibration function with a calibration cycle of 1 hour. Each calibration extracts historical trend vector data from the deviation database for the most recent 24 hours, recalculates the value range of each dimension, and updates the normalization parameters to ensure parameter timeliness. Simultaneously, the normalization process eliminates the dimensional differences in deviation features under different radar operating modes (such as search mode and tracking mode), making the standardized deviation trend vector comparable across scenarios and solving the problem of inconsistent feature scales under different modes, leading to weight imbalances in subsequent threshold comparisons.

[0035] Step S103: Calculate the trend slope and fluctuation amplitude using the deviation change trend vector, and then determine the adjustment trigger signal based on the relationship between the trend slope and the fluctuation amplitude and the preset threshold.

[0036] In one specific embodiment, the process of executing step S103 may specifically include the following steps: Extract the time-domain linear regression slope component from the deviation change trend vector and determine it as the trend slope within the time window; The fluctuation amplitude is calculated using the standard deviation algorithm for the time-domain and frequency-domain characteristic components of the deviation change trend vector; If the trend slope exceeds a preset slope threshold and the fluctuation amplitude meets the abnormality determination conditions, it is marked as a mutation mode and an adjustment trigger signal is generated.

[0037] Specifically, when calculating the trend slope within the time window, it is necessary to extract the time-domain linear regression slope component from the deviation change trend vector. This component originates from the least squares fitting result of the historical deviation sequence. For example, the extracted historical deviation sequence is [2.009, 2.015, 2.021, 2.018, 2.025] (corresponding to a 5-second sliding window, with timestamps of [timestamps not specified]). t 1 to t 5) Obtain the fitted straight line equation through linear regression fitting. y =0.004 t +2.005 ( t The unit is seconds. yIf the deviation value is given, then the slope of the straight line, 0.004 / second, is the trend slope. Its value directly reflects the rate of change of the deviation value over time within the time window—a positive slope indicates an upward trend, while a negative slope indicates a downward trend. The larger the absolute value of the slope, the more drastic the change in deviation. The calculation of the trend slope here strictly corresponds to the definition of the "slope value of the deviation change trend vector," and the time window is consistent with the sliding window in step S102 (e.g., 5 seconds) to ensure the continuity of the data's time dimension and avoid distortion of trend judgment due to window mismatch. This processing solves the problem of existing technologies lacking the quantification of the rate of deviation change and being unable to accurately identify signal abrupt changes.

[0038] Furthermore, when calculating the fluctuation amplitude of the deviation change trend vector [0.5375, 0.7, 0.75, 0.8, 0.15, 0.05], its standard deviation is 0.294. This standard deviation value is the fluctuation amplitude, and its magnitude reflects the stability of the deviation change—the smaller the fluctuation amplitude, the more stable the deviation change, and vice versa. The correspondence between the difference sequence and the fluctuation amplitude here ensures that the fluctuation amplitude can accurately quantify the dispersion of the deviation change, solving the problem that existing technologies lack effective assessment of deviation fluctuations and easily misjudge noise as signal abrupt changes.

[0039] Finally, when determining whether the trend slope exceeds a preset threshold and generating an adjustment trigger signal, the preset threshold needs to be determined based on historical interference scenario data of the radar system. Different interference scenarios correspond to different thresholds: for example, in urban multipath interference scenarios, the signal is easily affected by building reflections, leading to frequent deviation changes, so the preset threshold is set to 0.005 / second; in airport electromagnetic interference scenarios, the signal is relatively stable, so the preset threshold is set to 0.008 / second. The calculated trend slope is compared with the preset threshold of the corresponding scenario. If the trend slope exceeds the threshold, the current interference signal is marked as being in a sudden change mode, and an adjustment trigger signal is generated. This signal is a digital level signal, using a high level (logic 1) to indicate triggering and a low level (logic 0) to indicate not triggering. The signal needs to carry a timestamp, the current interference scenario label (such as "urban multipath interference"), and trend slope and fluctuation amplitude data, which is transmitted through the radar system's internal bus to the next step to initiate the subsequent deviation compensation adjustment process. If the trend slope does not exceed the threshold, the sudden change mode is not marked, and no adjustment trigger signal is generated; the system maintains the current deviation compensation model parameters. This threshold judgment mechanism solves the problem that static models cannot adjust compensation strategies in real time when signals change abruptly. By quickly generating adjustment trigger signals, it ensures that the system can start the compensation process in a timely manner, avoiding a sudden drop in positioning accuracy. At the same time, the fluctuation amplitude can be used as an auxiliary judgment basis. If the trend slope slightly exceeds the threshold but the fluctuation amplitude is extremely small, it can be judged as random noise and will not trigger adjustment, further improving the judgment accuracy.

[0040] Step S104: Extract relevant feature interaction effects from the deviation change trend vector according to the adjustment trigger signal, determine the correlation weight between each feature, and calculate the weight coefficient update increment according to the correlation weight.

[0041] In one specific embodiment, the process of executing step S104 may specifically include the following steps: Based on the adjustment trigger signal analysis, the interference scene label, timestamp, trend slope, and fluctuation amplitude are extracted. Based on the interference scene label, match the scene dataset in the historical trend database, and extract feature interaction data from the scene dataset based on the timestamp, the trend slope, and the fluctuation amplitude; The correlation between features in the feature interaction dataset was analyzed using the Pearson correlation coefficient matrix to determine the correlation weights of the three sets of feature interactions: signal strength-frequency, signal strength-modulation mode, and frequency-modulation mode. Obtain the basic weight coefficients of the current deviation compensation model, and calculate the weight coefficient update increment based on the basic weight coefficients, the correlation weights, and the dynamic adjustment coefficients.

[0042] Specifically, when extracting relevant feature interactions from the historical trend database based on the adjustment trigger signal, the adjustment trigger signal carries key retrieval information, including the calculation timestamp (e.g., "2025-08-21 15:48:55"), interference scenario label (e.g., "airport electromagnetic interference"), trend slope (0.004 / second), and fluctuation amplitude (e.g., 0.0060). This information serves as a composite condition for database retrieval. The historical trend database stores feature data under various interference scenarios in the radar's past operations. Each record contains real-time sampled values ​​of signal strength, frequency, and modulation mode, as well as records of interaction relationships between the three types of features. During the extraction process, the interference scenario label is used to match datasets of the same scenario in the database. Records within the most recent hour are filtered by timestamp, and feature interaction data with similar interference change trends are further filtered by combining the trend slope and fluctuation amplitude range. For example, from the 1-hour data of the "airport electromagnetic interference" scenario, 20 sets of signal strength-frequency-modulation mode interaction records that meet the slope and amplitude range are extracted to ensure that the extracted feature interaction effects are highly consistent with the current interference state.

[0043] Furthermore, when analyzing the interaction effects of features to determine the correlation weights between each feature, 20 sets of extracted feature interaction data are used as input to conduct feature correlation analysis on the interaction between signal strength, frequency, and modulation method. This invention uses the Pearson correlation coefficient to calculate the degree of linear correlation between features. For example, when calculating the correlation coefficient between signal strength and frequency, the mean signal strength and mean frequency in the 20 sets of data are first statistically analyzed, and then the correlation coefficient is calculated using the formula... (in The correlation coefficient is... x For signal strength, Average signal strength y For frequency, The correlation coefficient is calculated using the frequency average. This indicates a strong positive correlation between signal strength and frequency, with frequency having a significant impact on strength. When calculating the correlation coefficient between signal strength and modulation scheme, the modulation scheme is first quantized to obtain the correlation coefficient. This indicates a weak correlation between signal strength and modulation method, with modulation method switching having little impact on signal strength; the correlation coefficient between frequency and modulation method was calculated. This indicates a moderate correlation between frequency and modulation scheme, with some modulation scheme switching accompanied by minor frequency fluctuations. Correlation weights are assigned based on the absolute value of the correlation coefficient; the larger the absolute value of the correlation coefficient, the more significant the impact of the corresponding feature interaction on the deviation. Therefore, the correlation weight for signal strength-frequency interaction is set to 0.55, for signal strength-modulation scheme interaction to 0.2, and for frequency-modulation scheme interaction to 0.25. This process addresses the problems of existing technologies being unable to quantify the contribution of feature interactions to the deviation and the subjective nature of weight allocation.

[0044] The specific calculation of the weight coefficient update increment based on the correlation weight is as follows: The basic weight coefficients of the current radar deviation compensation model are retrieved (e.g., signal strength basic weight 0.4, frequency weight 0.3, modulation mode weight 0.3). A weighted average method is used to calculate the weight coefficient update increment, specifically: Weight coefficient update increment = (Signal strength - Frequency interaction weight × Signal strength basic weight + Signal strength - Modulation mode interaction weight × Modulation mode basic weight + Frequency - Modulation mode interaction weight × Frequency basic weight) × Dynamic adjustment coefficient. The dynamic adjustment coefficient is determined based on the fluctuation amplitude; the greater the fluctuation, the larger the adjustment coefficient, ensuring the increment matches the severity of interference changes. Assuming the adjustment coefficient is set to 0.12 when the fluctuation amplitude is 0.294, the weight coefficient update increment is (0.55 × 0.4 + 0.2 × 0.3 + 0.25 × 0.3) × 0.12 = 0.0426. The update increments are further allocated to the weights of each feature: signal strength weight increment = 0.55 × 0.0426 ≈ 0.0234, frequency weight increment = 0.25 × 0.0426 ≈ 0.0107, modulation scheme weight increment = 0.2 × 0.0426 ≈ 0.0085, finally obtaining the weight coefficient update increment vector [0.0234, 0.0107, 0.0085]. The calculation results are stored in the system weight cache and associated with the timestamp of the adjustment trigger signal and the interference scene label, solving the problem of lagging weight coefficient updates and the inability to reflect the dynamic contribution of feature interaction to the deviation in real time.

[0045] Step S105: Update the convergence speed parameter incrementally according to the weight coefficient, and optimize the updated convergence speed parameter by gradient descent iterative optimization algorithm to obtain the optimized convergence speed value.

[0046] In one specific embodiment, the process of executing step S105 may specifically include the following steps: The current convergence speed parameters are obtained from the parameter buffer of the radar bias compensation model. The parameter buffer includes the convergence speed parameters, update timestamps, and corresponding interference scene labels. The update amount of the convergence speed parameter is calculated by weighting and summing the update increment of the weight coefficient with the intensity influence coefficient, frequency influence coefficient and modulation influence coefficient respectively. Then, the update amount of the convergence speed parameter is superimposed with the current convergence speed parameter to obtain the updated convergence speed parameter. The intensity influence coefficient, frequency influence coefficient and modulation influence coefficient are determined by statistical analysis of historical data. The updated convergence speed parameters are iteratively optimized using the gradient descent method.

[0047] Specifically, when obtaining the current convergence speed parameter, the parameter is retrieved from the parameter cache of the radar bias compensation model. The parameter cache stores real-time parameters categorized by radar operating mode (e.g., tracking mode, search mode). Each record includes the convergence speed parameter, update timestamp, and corresponding interference scenario label (e.g., "urban multipath interference" or "airport electromagnetic interference"). The convergence speed parameter reflects the response speed of the bias compensation model, with a value range of 0.1-0.8. The larger the value, the faster the model responds to changes in bias. For example, in tracking mode, due to the need to quickly adapt to changes in interference caused by target movement, the initial value of the current convergence speed parameter is set to 0.3; in search mode, the signal coverage is wide and the interference is relatively stable, so the initial value of the parameter is set to 0.2. During retrieval, the parameter is accurately matched using the current radar operating mode label, and the parameter update timestamp is verified to ensure the timeliness of the parameter.

[0048] When updating the convergence speed parameters based on the weight coefficient update increment, the weight coefficient update increment generated in step S104 (such as the vector [0.0234, 0.0107, 0.0085], corresponding to the weight increments of signal strength, frequency, and modulation method, respectively) is used as input. The parameter update amount is calculated using a weighted summation formula: Parameter update amount = (Signal strength weight increment × Strength influence coefficient + Frequency weight increment × Frequency influence coefficient + Modulation method weight increment × Modulation influence coefficient). Among them, the influence coefficient is determined based on historical data statistics to have the most significant impact on the model response speed, and the strength influence coefficient is set to 0.5; frequency is second, set to 0.3; and modulation method has a smaller impact, set to 0.2. Substituting into the weight coefficient update increment calculation: Parameter update amount = (0.0234 × 0.5 + 0.0107 × 0.3 + 0.0085 × 0.2) = 0.01661. The updated value is then added to the current convergence speed parameter to obtain the updated convergence speed parameter: 0.3 + 0.01661 = 0.31661 in tracking mode and 0.2 + 0.01661 = 0.21661 in search mode. During the update process, the maximum value of the parameter should be limited to no more than 0.8 and the minimum value should not be lower than 0.1 to avoid abnormal parameters causing the model to respond too quickly and oscillate, or too slowly and lag.

[0049] The updated convergence speed parameters are optimized using gradient descent. Initially, the learning rate is set to 0.01, the maximum number of iterations is set to 100, and the convergence threshold is set to 0.001. The iteration process uses the sum of squared residuals as the loss function, which is obtained by calculating the difference between the current model output and the target value in real time. In each iteration, the gradient of the loss function with respect to the convergence speed parameters is calculated using numerical differentiation, with a differentiation step size of 0.001. Parameter updates are performed in the opposite direction of the gradient, and the update step size is determined by the product of the learning rate and the gradient value. The iteration terminates when the loss function value falls below 0.001 or the maximum number of iterations is reached; the convergence speed parameter value obtained at this point is the optimized convergence speed value. Gradient descent iterative optimization addresses the problems of slow convergence, high computational complexity, and response delays during signal abrupt changes in traditional iterative algorithms, ensuring that the optimized convergence speed value can quickly adapt to current disturbance changes.

[0050] Step S106: Adjust the parameters of the deviation compensation model according to the optimized convergence speed value, calculate the compensation residual of the adjusted deviation compensation model, and output the deviation compensation result if the compensation residual is lower than the preset residual threshold.

[0051] In one specific embodiment, the process of executing step S106 may specifically include the following steps: Retrieve the initial parameters of the current deviation compensation model, including the deviation compensation coefficient and the compensation offset; Based on the optimized convergence speed value, the deviation compensation coefficient and the compensation offset are adjusted so that the adjusted parameters are adapted to the convergence speed of the deviation compensation model. The characteristic parameters of the current signal are input into the adjusted deviation compensation model. After the moving average filtering process, the real-time compensation value is output. Then, the ideal compensation value that matches the current interference scene and signal characteristics is retrieved. The absolute value error between the real-time compensation value and the ideal compensation value is the compensation residual. If the compensation residual is lower than the residual threshold, the output includes the real-time compensation value after filtering, the new compensation coefficient, the new compensation offset, the timestamp, and the scene label. If the compensation residual is higher than the residual threshold, return to step S105 to iterate and optimize the convergence speed parameters again.

[0052] Specifically, when adjusting the parameters of the deviation compensation model based on the optimized convergence speed value, the current deviation compensation coefficient and compensation offset are first retrieved from the parameter storage unit of the deviation compensation model. The deviation compensation coefficient is used to quantify the compensation weight of signal characteristics on deviation (such as the deviation compensation amount corresponding to each 1dBm change in signal strength), and the compensation offset is used to correct the system deviation caused by fixed interference. The initial values ​​of both need to match the radar operating mode. For example, in tracking mode, the initial deviation compensation coefficient is 1.2 (high response requirement) and the compensation offset is 0.05 (small fixed deviation); in search mode, the initial coefficient is 1.0 (stable requirement) and the offset is 0.03. The optimized convergence speed value (such as 0.31661 obtained in step S105, tracking mode) is used as the basis for adjustment. The correlation logic between parameters and convergence speed value is established: the deviation compensation coefficient is adjusted with positive correlation, and the formula is "new compensation coefficient = original compensation coefficient × (1 + optimized convergence speed value × 0.1)". Substituting the data, the new compensation coefficient is 1.2 × (1 + 0.31661 × 0.1) ≈ 1.238, ensuring that the faster the convergence speed, the larger the coefficient is to enhance the compensation sensitivity; the compensation offset is adjusted incrementally, and the formula is "new compensation offset = original compensation offset + optimized convergence speed value × 0.02". Substituting the data, the new compensation offset is 0.05 + 0.31661 × 0.02 ≈ 0.0563, so that the offset adapts to the dynamic changes of interference synchronously with the convergence speed.

[0053] Furthermore, when calculating the compensation residual of the adjusted deviation compensation model, the characteristic parameters of the current interference signal (signal strength -68dBm, frequency 10.03GHz, modulation mode FM) are used as input and substituted into the adjusted deviation compensation model (including the new compensation coefficient 1.238 and the new compensation offset 0.0563). The compensation value is output through model calculation—the model calculation logic is "compensation value = (signal strength deviation component × compensation coefficient) + (frequency deviation component × compensation coefficient × 0.8) + compensation offset" (the frequency component weight of 0.8 is derived from the historical feature correlation analysis). If the signal strength deviation component is 4dBm and the frequency deviation component is 0.03GHz, then the compensation value = (4 × 1.238) + (0.03 × 1.238 × 0.8) + 0.0563 ≈ 5.038. Subsequently, a compensation target value was determined. This value was retrieved from a historical trend database, and an ideal compensation value (e.g., 5.036) matching the current interference scenario (e.g., "urban multipath interference") and signal characteristics was selected. The compensation residual was calculated using the formula: "Compensation residual = |Adjusted model output compensation value - Compensation target value|". Substituting the data, the compensation residual was found to be |5.038 - 5.036| = 0.002. During the calculation, digital filtering techniques (e.g., moving average filtering, with a window of 3 data points) were used to eliminate output fluctuations caused by interference signal noise, ensuring the accuracy of the residual calculation and resolving the problem that residual calculation was affected by noise and could not accurately reflect the compensation effect.

[0054] Finally, if the compensation residual is lower than the preset threshold, the preset threshold needs to be set in conjunction with the radar operating mode and interference scenario when outputting the deviation compensation result: in tracking mode, the threshold is set to 0.005 due to the need for high-precision positioning; in search mode, the threshold is set to 0.01 due to the wide signal coverage. The calculated compensation residual of 0.002 is compared with the tracking mode threshold of 0.005. If the residual is determined to be lower than the threshold, the deviation compensation result is generated. The result includes the adjusted model output compensation value of 5.038, the new deviation compensation coefficient of 1.238, the new compensation offset of 0.0563, the calculation timestamp (e.g., "2025-08-21 15:48:55"), and the interference scenario label "urban multipath interference". This result is stored in the system compensation result cache and simultaneously transmitted to the next process via the internal data bus. If the compensation residual is higher than the threshold, the process returns to step S105 for iterative optimization until the residual meets the standard. This threshold judgment and result output mechanism solves the problem of positioning deviation accumulation caused by putting compensation results into use without verification, and ensures that the output deviation compensation results can provide reliable support for subsequent positioning correction.

[0055] Step S107: Adjust the weights of the input parameters of the positioning algorithm according to the deviation compensation result, and perform weighted calculation on the adjusted input parameters through a weighted positioning algorithm to determine the corrected positioning coordinates.

[0056] In one specific embodiment, the process of executing step S107 may specifically include the following steps: The deviation compensation result is decomposed into values ​​corresponding to the original radar positioning coordinates. x Directional components and y Directional components; According to the above x Directional components and the y The input parameters of the directional component adjustment positioning algorithm include the distance estimation value corresponding to the signal strength, the phase offset value corresponding to the frequency, and the positioning error correction value corresponding to the modulation method. The weighted positioning method is adopted, which obtains the corrected coordinate components by multiplying each adjusted input parameter by its corresponding adjusted weight and then summing the product results.

[0057] Specifically, when integrating the deviation compensation result into the positioning calculation process, the deviation compensation result must include the adjusted compensation value, the deviation compensation coefficient, the compensation offset, and the associated interference scene label (such as urban multipath interference, airport electromagnetic interference) and calculation timestamp. The positioning calculation process originally includes an original coordinate generation step; at this point, a coordinate correction step based on the deviation compensation result needs to be inserted, decomposing the compensation value into components corresponding to the original positioning coordinates (such as the original...). x Coordinate correspondence x Directional compensation component, original y Coordinate correspondence y (Direction compensation component), ensuring a one-to-one correspondence between compensation data and coordinate components, such as the original... x When the coordinate is 50 meters, if the compensation value is... x If the direction compensation component is 0.0965 meters, then it is directly associated with the original coordinate component for subsequent correction to avoid correction errors caused by data misalignment.

[0058] Furthermore, when adjusting the input parameters of the positioning algorithm based on the deviation compensation results, the input parameters of the positioning algorithm include the distance estimation value corresponding to the signal strength, the phase offset value corresponding to the frequency, and the positioning error correction value corresponding to the modulation method. The initial weights of these parameters need to be adjusted in conjunction with the weight coefficient update increment in the deviation compensation results. For example, if the signal strength weight increment in the deviation compensation results is 0.0234, the frequency weight increment is 0.0107, and the modulation method weight increment is 0.0085, and the initial weights of the signal strength, frequency, and modulation method in the positioning algorithm are 0.5, 0.3, and 0.2, then the adjusted signal strength weight is 0.5 + 0.0234 = 0.5234, the frequency weight is 0.3 + 0.0107 = 0.3107, and the modulation method weight is 0.2 + 0.0085 = 0.2085. During this adjustment process, the weight change of each input parameter is directly related to the corresponding weight increment in the deviation compensation result, ensuring that the input parameter weight can reflect the dynamic contribution of each signal feature to the positioning under the current interference in real time, and solving the problem of compensation residual caused by insufficient feature interaction processing capability and lagging weight coefficient update in the existing technology.

[0059] Finally, when calculating the corrected positioning coordinates using the weighted positioning method, each adjusted input parameter (such as the weighted signal strength distance estimate, frequency phase offset, and modulation error correction value) is first multiplied by its corresponding adjusted weight, and then the results of each multiplication are summed to obtain the corrected coordinate components. x Taking directional coordinate calculation as an example, if the estimated distance for the adjusted signal strength is 30 meters, multiplying it by the adjusted weight of 0.5234 yields 15.702; the corresponding frequency phase shift... x The component is 15 meters, multiplied by the adjusted weight of 0.3107, resulting in 4.0065; the modulation method corresponds to... x The direction error correction value is 5 meters, multiplied by the adjusted weight of 0.2085, resulting in 1.0425; summing the three values, 15.702 + 4.0065 + 4.0065 = 20.751 meters, gives the result. x Coordinates after direction correction. y The direction coordinate calculation logic is the same. If the estimated signal strength distance after adjustment is 20 meters, multiplying it by 0.5234 gives 10.468; the frequency phase offset corresponds to... y The component is 10 meters, multiplied by 0.3107, resulting in 3.107; the modulation method corresponds to... y The direction error correction value is 3 meters, multiplied by 0.2085, we get 0.6255; summing, we get 10.468 + 3.107 + 0.6255 = 14.2005 meters, which is... yThe coordinates after direction correction. In this weighted calculation process, the input parameters, adjusted weights, and coordinate components form a clear data correspondence. The algorithm prioritizes signal features that have a significant impact under the current interference through weight allocation, solving the problem that static models cannot adapt to the dynamic changes of signals in complex environments and the sharp drop in positioning accuracy. At the same time, it associates interference scene labels to achieve the synergy between interference source localization and anti-interference, enhancing the anti-interference robustness under multipath effects and dynamic interference source switching scenarios.

[0060] The radar dynamic anti-jamming method based on interference source localization in the embodiments of this application has been described above. The radar dynamic anti-jamming processing procedure 200 based on interference source localization in the embodiments of this application will be described below. Please refer to... Figure 2 The processing procedure 200 revolves around a closed-loop operation of radar dynamic anti-interference based on interference source localization. It consists of seven processes: signal acquisition 201, deviation analysis 202, slope and threshold judgment 203, weight optimization 204, convergence optimization 205, compensation output 206, and positioning correction output 207. These processes work together to achieve signal processing and positioning correction functions. The specific process is as follows: In the signal acquisition process 201, the signal receiving device samples the current interference signal in real time according to the radar operating frequency. At the same time, it retrieves historical sequences from the database storing historical signals of the same operating mode for the past 30 minutes by dynamically matching the signal frequency. After sampling, it extracts features such as signal strength, frequency (extracted by fast Fourier transform + peak detection), and modulation mode (identified by demodulation circuit + maximum likelihood estimation). The deviation is calculated by multi-dimensional difference quantification (the strength deviation is the difference between the current and historical mean, the frequency deviation is Euclidean distance, and the modulation mode deviation is qualitatively assigned and weighted according to the historical variance).

[0061] In the deviation analysis process 202, an initial 10-second sliding window (containing 1000 data points) is used. Based on the signal frequency change rate (more than 5% / second, shortened to 5 seconds; less than 0.5% / second, extended to 20 seconds; double buffering mechanism ensures no read / write conflicts), frequency domain analysis (Fourier transform to extract frequency domain components) and time domain analysis (first-order / second-order difference to calculate change and acceleration, linear regression fitting to obtain time domain components) are performed on the signal to obtain regression slope, principal amplitude, etc. After normalization by the maximum-minimum method (based on the statistical range of the training dataset and 1-hour calibration parameters), a 6-dimensional (3 time domain + 3 frequency domain) trend vector is generated.

[0062] The slope and threshold judgment process 203 first extracts the time-domain linear regression slope based on the trend vector and calculates the vector standard deviation for deviation analysis; it then judges whether the slope exceeds the preset scene threshold (urban multipath interference 0.005 / second, airport electromagnetic interference 0.008 / second). If it does, a high-level trigger signal with timestamp and scene label is generated; if it does not exceed, it further judges whether the fluctuation amplitude exceeds the threshold. If it does, an adjustment trigger signal is generated; if it does not exceed, the deviation analysis process is re-executed.

[0063] The weight optimization process 204 is driven by the adjustment trigger signal. Based on the timestamp, scene label, and slope / amplitude of the trigger signal, 20 sets of similar interaction data are extracted from the historical database. The correlation degree between each pair of features (such as intensity-frequency, with a weight of 0.55 when R∈(0.6,1)) is calculated using the Pearson correlation coefficient and normalized to obtain the correlation weight. The weight increment is calculated as "base weight + (interaction weight × base weight) × dynamic adjustment coefficient (determined according to the fluctuation amplitude, such as 0.12 when the amplitude is 0.294)" and output to the convergence optimization process.

[0064] During the convergence optimization process, step 205 receives the weight update increment and retrieves the current convergence speed parameters from the parameter buffer (tracking mode 0.3, search mode 0.2, verification timestamp). It then adds the current parameters according to the formula "parameter update amount = intensity increment × 0.5 + frequency increment × 0.3 + modulation increment × 0.2" (limited to 0.1-0.8). It iterative optimization is performed using gradient descent (learning rate 0.01, 100 iterations, convergence threshold 0.001, residual sum of squares as the loss function). If the convergence condition is met, the optimized speed value is output; otherwise, iteration continues.

[0065] The compensation output process involves inputting 206 to optimize the convergence speed. The compensation model parameters are adjusted according to "compensation coefficient = original coefficient × (1 + convergence speed × 0.1), offset = original offset + convergence speed × 0.02" (e.g., compensation coefficient for tracking mode 1.2 → 1.238). The residual between the model output compensation value and the ideal value in the historical database is calculated (e.g., 5.038 - 5.036 = 0.002, after moving average filtering for noise reduction). It is then determined whether the residual is lower than the scene threshold (0.005 for tracking mode, 0.01 for search mode). If yes, the compensation result with parameters, timestamp, and scene label is output; otherwise, the process returns to the convergence optimization process for readjustment.

[0066] The positioning correction output process 207 input compensation result is broken down into... x / y The coordinate component insertion positioning process correction steps include: adjusting the weights of the positioning algorithm input parameters according to the weight increment (e.g., intensity weight 0.5 → 0.5234); and using the weighted positioning method (summing the adjusted parameters × corresponding weights, e.g., ...). xThe coordinates are calculated as 15.702 + 4.0065 + 1.0425 = 20.751 meters. After merging the coordinates, the corrected coordinates are output, completing the entire process from signal acquisition to precise positioning correction.

[0067] The radar dynamic anti-jamming method based on interference source localization in the embodiments of this application has been described above. The radar dynamic anti-jamming system based on interference source localization in the embodiments of this application is described below. Please refer to [link / reference]. Figure 3 One embodiment of the radar dynamic anti-jamming system based on interference source localization in this application includes: The signal acquisition module 301 is used to acquire current signal data and historical signal sequences, calculate and determine the initial deviation value, and ensure the real-time performance and accuracy of data acquisition.

[0068] The deviation analysis module 302 extracts the historical deviation sequence based on the initial deviation value, and generates a deviation change trend vector after processing, which intuitively reflects the characteristics of deviation change over time.

[0069] The trigger judgment module 303 uses the deviation change trend vector to calculate the trend slope and fluctuation amplitude. If the slope exceeds the preset threshold, it immediately determines and generates an adjustment trigger signal to start the subsequent compensation adjustment process.

[0070] The weight optimization module 304 extracts the interaction effects of features based on the adjustment trigger signal, analyzes the correlation of features to determine the correlation weight, and calculates the weight coefficient update increment to provide a basis for updating model parameters.

[0071] The convergence optimization module 305 obtains the current convergence speed parameter, updates the parameter incrementally in combination with the weight coefficient, and optimizes it through an iterative optimization algorithm to obtain the optimized convergence speed value, thereby improving the model response speed.

[0072] The compensation output module 306 adjusts the parameters of the deviation compensation model according to the optimized convergence speed value, calculates the compensation residual, and outputs the deviation compensation result if the residual is lower than the preset threshold for subsequent positioning correction.

[0073] The positioning correction module 307 integrates the deviation compensation result into the positioning calculation process, adjusts the input parameters of the positioning algorithm, calculates and determines the corrected positioning coordinates through the positioning algorithm, and achieves accurate positioning.

[0074] above Figure 3 The radar dynamic anti-jamming system based on interference source localization in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The radar dynamic anti-jamming device based on interference source localization in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0075] Reference Figure 4This invention also provides a radar dynamic anti-jamming device 400 based on interference source localization. This radar dynamic anti-jamming device based on interference source localization can be a server, and its internal structure can be as follows: Figure 4 As shown. The radar dynamic anti-jamming device 400 based on interference source location includes a processor 402, a memory 403, a display screen 404, an input device 405, a network interface 406, and a database 407 connected via a system bus 401. The processor 402, designed as a computer, provides computing and control capabilities. The memory 403 of the radar dynamic anti-jamming device based on interference source location includes a non-volatile storage medium 4031 and internal memory 4032. The non-volatile storage medium 4031 stores the operating system and computer programs. The internal memory 4032 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database 407 of the radar dynamic anti-jamming device based on interference source location stores the corresponding data in this embodiment. The network interface 406 of the radar dynamic anti-jamming device based on interference source location is used for communication with external terminals via a network connection. The computer program, executed by the processor, can implement the above-described method.

[0076] Those skilled in the art will understand that Figure 4 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the radar dynamic anti-jamming device based on interference source localization applied thereto. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0077] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A radar dynamic anti-jamming method based on interference source localization, characterized in that, The method includes: Step S101: Obtain current signal data and historical signal sequences, and extract signal strength, frequency and modulation mode characteristic parameters, and calculate the initial deviation value based on the signal strength, frequency and modulation mode characteristic parameters; Step S102: Extract historical deviation sequences from the initial deviation values ​​based on a dynamic sliding window, and perform time series analysis on the historical deviation sequences to generate a deviation change trend vector; Step S103: Calculate the trend slope and fluctuation amplitude using the deviation change trend vector, and then determine the adjustment trigger signal based on the relationship between the trend slope and the fluctuation amplitude and the preset threshold. Step S104: Extract relevant feature interaction effects from the deviation change trend vector according to the adjustment trigger signal, determine the correlation weight between each feature, and calculate the weight coefficient update increment according to the correlation weight. Step S105: Update the incremental convergence speed parameter according to the weight coefficient, and optimize the updated convergence speed parameter by gradient descent iterative optimization algorithm to obtain the optimized convergence speed value. Step S106: Adjust the parameters of the deviation compensation model according to the optimized convergence speed value, calculate the compensation residual of the adjusted deviation compensation model, and output the deviation compensation result if the compensation residual is lower than the preset residual threshold. Step S107: Adjust the weights of the input parameters of the positioning algorithm according to the deviation compensation result, and perform weighted calculation on the adjusted input parameters through a weighted positioning algorithm to determine the corrected positioning coordinates.

2. The radar dynamic anti-jamming method based on interference source localization according to claim 1, characterized in that, Step S101 includes: Acquiring current interference signal data and historical signal sequences includes: real-time acquisition of current interference signal data through radar signal receiving equipment, and retrieval of historical signal sequences within a preset time period that match the current radar operating mode from a pre-established signal database; The initial deviation value is calculated based on the characteristic parameters of the current interference signal and the historical signal sequence, including: calculating the intensity deviation component, frequency deviation component and modulation mode deviation component of the interference signal and the historical signal respectively; The weighting coefficients of the deviation components are determined based on the variance of the historical signal sequence. The initial deviation value is obtained by weighted summation and then stored in association with the calculation timestamp, radar operating mode, and interference scene label.

3. The radar dynamic anti-jamming method based on interference source localization according to claim 2, characterized in that, Step S102 includes: By applying the sliding window technique, historical deviation sequences are extracted from a pre-stored deviation database. The window size of the sliding window technique is dynamically adjusted according to the frequency of signal changes. Time series analysis is performed on the historical deviation sequence to generate a deviation change trend vector; The deviation change trend vector is normalized to obtain the standardized deviation change trend vector.

4. The radar dynamic anti-jamming method based on interference source localization according to claim 1, characterized in that, Step S103 includes: Extract the time-domain linear regression slope component from the deviation change trend vector and determine it as the trend slope within the time window; The fluctuation amplitude is calculated using the standard deviation algorithm for the time-domain and frequency-domain characteristic components of the deviation change trend vector; If the trend slope exceeds a preset slope threshold and the fluctuation amplitude meets the abnormality determination conditions, it is marked as a mutation mode and an adjustment trigger signal is generated.

5. The radar dynamic anti-jamming method based on interference source localization according to claim 3, characterized in that, Step S104 includes: Based on the adjustment trigger signal analysis, the interference scene label, timestamp, trend slope, and fluctuation amplitude are extracted. Based on the interference scene label, match the scene dataset in the historical trend database, and extract feature interaction data from the scene dataset based on the timestamp, the trend slope, and the fluctuation amplitude; The correlation between features in the feature interaction dataset was analyzed using the Pearson correlation coefficient matrix to determine the correlation weights of the three sets of feature interactions: signal strength-frequency, signal strength-modulation mode, and frequency-modulation mode. Obtain the basic weight coefficients of the current deviation compensation model, and calculate the weight coefficient update increment based on the basic weight coefficients, the correlation weights, and the dynamic adjustment coefficients.

6. The radar dynamic anti-jamming method based on interference source localization according to claim 1, characterized in that, Step S105 includes: The current convergence speed parameters are obtained from the parameter buffer of the radar bias compensation model. The parameter buffer includes the convergence speed parameters, update timestamps, and corresponding interference scene labels. The update amount of the convergence speed parameter is calculated by weighting and summing the update increment of the weight coefficient with the intensity influence coefficient, frequency influence coefficient and modulation influence coefficient respectively. Then, the update amount of the convergence speed parameter is superimposed with the current convergence speed parameter to obtain the updated convergence speed parameter. The intensity influence coefficient, frequency influence coefficient and modulation influence coefficient are determined by statistical analysis of historical data. The updated convergence speed parameters are iteratively optimized using the gradient descent method.

7. The radar dynamic anti-jamming method based on interference source localization according to claim 1, characterized in that, Step S106 includes: Retrieve the initial parameters of the current deviation compensation model, including the deviation compensation coefficient and the compensation offset; Based on the optimized convergence speed value, the deviation compensation coefficient and the compensation offset are adjusted so that the adjusted parameters are adapted to the convergence speed of the deviation compensation model. The characteristic parameters of the current signal are input into the adjusted deviation compensation model. After the moving average filtering process, the real-time compensation value is output. Then, the ideal compensation value that matches the current interference scene and signal characteristics is retrieved. The absolute value error between the real-time compensation value and the ideal compensation value is the compensation residual. If the compensation residual is lower than the residual threshold, the output includes the real-time compensation value after filtering, the new compensation coefficient, the new compensation offset, the timestamp, and the scene label. If the compensation residual is higher than the residual threshold, return to step S105 to iterate and optimize the convergence speed parameters again.

8. The radar dynamic anti-jamming method based on interference source localization according to claim 7, characterized in that, Step S107 includes: The deviation compensation result is decomposed into values ​​corresponding to the original radar positioning coordinates. x Directional components and y Directional components; According to the above x Directional components and the y The input parameters of the directional component adjustment positioning algorithm include the distance estimation value corresponding to the signal strength, the phase offset value corresponding to the frequency, and the positioning error correction value corresponding to the modulation method. The weighted positioning method is adopted, which obtains the corrected coordinate components by multiplying each adjusted input parameter by its corresponding adjusted weight and then summing the product results.

9. A radar dynamic anti-jamming system based on interference source localization, characterized in that, For implementing the radar dynamic anti-jamming method based on interference source localization as described in any one of claims 1-8, the radar dynamic anti-jamming system based on interference source localization comprises: The signal acquisition module is used to acquire current signal data and historical signal sequences, calculate and determine the initial deviation value, and ensure the real-time performance and accuracy of data acquisition. The deviation analysis module extracts historical deviation sequences based on initial deviation values, processes them to generate a deviation change trend vector, and intuitively reflects the characteristics of deviation changes over time. The trigger judgment module uses the deviation change trend vector to calculate the trend slope and fluctuation amplitude. If the slope exceeds the preset threshold, it immediately determines and generates an adjustment trigger signal to start the subsequent compensation and adjustment process. The weight optimization module extracts the interaction effects of features based on the adjustment trigger signal, analyzes the correlation of features to determine the correlation weight, and calculates the weight coefficient update increment to provide a basis for updating model parameters. The convergence optimization module obtains the current convergence speed parameter, updates the parameter incrementally in conjunction with the weight coefficient, and optimizes it through an iterative optimization algorithm to obtain the optimized convergence speed value, thereby improving the model response speed. The compensation output module adjusts the parameters of the deviation compensation model according to the optimized convergence speed value, calculates the compensation residual, and outputs the deviation compensation result if the residual is lower than the preset threshold for subsequent positioning correction. The positioning correction module integrates the deviation compensation results into the positioning calculation process, adjusts the input parameters of the positioning algorithm, calculates and determines the corrected positioning coordinates through the positioning algorithm, and achieves accurate positioning.

10. A radar dynamic anti-jamming device based on interference source localization, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the radar dynamic anti-jamming method based on interference source localization as described in any one of claims 1 to 8.

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