Statistical property agile blocking signal construction method and system

Abstract

The application relates to the technical field of navigation blocking signal, and discloses a blocking signal construction method with rapidly changed statistical characteristics, which comprises the following steps: constructing a statistical characteristic parameter library; wherein the statistical characteristic parameter library comprises statistical characteristic parameters of different types; dividing the blocking signal emission duration into time periods according to a statistical characteristic switching period to obtain continuous statistical characteristic switching time periods; wherein the first-order statistical characteristic of the blocking signal in each statistical characteristic switching time period is fixed, and the second-order statistical characteristic is changed; selecting statistical characteristic parameters of the next switching time period from the statistical characteristic parameter library after each statistical characteristic switching time period is completed; matching the switching time periods of the statistical characteristic parameters of different types based on a hierarchical switching strategy, and switching the statistical characteristic parameters of the corresponding types in time; and generating K paths of blocking signals in parallel in a baseband digital domain, and superimposing the K paths of blocking signals to generate a non-stationary blocking signal. The application further discloses a blocking signal construction system with rapidly changed statistical characteristics.

Description

Technical Field

[0001] This application relates to the field of navigation blocking signal technology, and for example to a method and system for constructing blocking signals with agile statistical characteristics. Background Technology

[0002] In modern navigation receivers, adaptive zeroing technology uses the joint processing of multi-element receiving antennas and multiple time samples to adaptively estimate the covariance matrix of the blocking signal, form the optimal adaptive filter weights, and effectively cancel out interference. Adaptive zeroing can effectively suppress interference signals with constant statistical characteristics.

[0003] The adaptive zeroing algorithm is based on the assumption that the statistical characteristics of the blocking signal remain stable within the observation window. Traditional methods for generating navigation blocking signals, such as Gaussian white noise and frequency-modulated signals, have relatively constant statistical characteristics. It is difficult to simultaneously change the signal's mean, power spectrum, spectral shape, and other multidimensional statistical characteristics on a millisecond-level time scale, and therefore, it is difficult to make these changes exceed the tracking capability of the adaptive zeroing algorithm. Within a sufficiently long observation window, the covariance matrix of the aforementioned blocking signal can be accurately estimated by the receiver, and the adaptive zeroing receiver can form effective adaptive weights based on this stable covariance matrix.

[0004] While existing time-varying blocking methods attempt to alter the characteristics of the blocking signal, these changes often exhibit periodic or predictable patterns, such as linear frequency modulation or fixed-period power modulation. Receivers can learn and track these patterns, gradually adjusting the parameters of the adaptive algorithm to ultimately achieve effective cancellation. Some studies attempt to introduce non-stationarity in a single dimension, such as changing only the power or only the center frequency of the spectrum. However, the degrees of freedom of adaptive nulling algorithms come from the joint processing of spatial, frequency, and time dimensions. Changes in a single dimension can often be partially compensated for. Therefore, such methods of introducing non-stationarity in a single dimension are insufficient to effectively disrupt the multi-dimensional adaptive mechanism of adaptive nulling algorithms such as STAP (Space-Time Adaptive Processing). Summary of the Invention

[0005] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0006] This disclosure provides a method and system for constructing a blocking signal with agile statistical characteristics to effectively counter the adaptive zeroing anti-interference mechanism of modern navigation receivers such as STAP.

[0007] In some embodiments, the method includes: constructing a statistical characteristic parameter library; wherein the statistical characteristic parameter library includes different types of statistical characteristic parameters; dividing the transmission duration of the blocking signal into time periods according to the statistical characteristic switching cycle to obtain continuous statistical characteristic switching time periods; wherein the first-order statistical characteristic of the blocking signal is fixed and the second-order statistical characteristic changes within each statistical characteristic switching time period; after each statistical characteristic switching time period is completed, selecting the statistical characteristic parameter for the next switching time period from the statistical characteristic parameter library so that the second-order statistical characteristic of adjacent time periods has no periodicity and is statistically unpredictable; matching the switching time periods of different types of statistical characteristic parameters based on a hierarchical switching strategy, and switching the corresponding types of statistical characteristic parameters in a time-sharing manner to achieve staggered and agile changes in the second-order statistical characteristic at different time scales; generating K blocking signals in parallel in the baseband digital domain, and superimposing the K blocking signals to generate a non-stationary blocking signal.

[0008] In some embodiments, after each statistical characteristic switching period is completed, the statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library, including: at the end of the current statistical characteristic switching period, using the current statistical characteristic parameters... Based on the principle of probability proportionality, candidate statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library by probability weighting according to the selection probability proportionality mechanism; an index is generated for each statistical characteristic switching period by a pseudo-random sequence generator; the basic statistical characteristic parameters for the corresponding switching period are obtained by mapping from the candidate statistical characteristic parameters according to the index; wherein, the distance between the statistical characteristic parameters corresponding to adjacent statistical characteristic switching periods in the parameter space is greater than or equal to the distance threshold; the basic statistical characteristic parameters are used as the statistical characteristic parameters for the next switching period.

[0009] In some embodiments, the probability proportionality mechanism satisfies: In the above formula, To select candidate statistical characteristic parameters for the next handover period The conditional probability, Basic statistical characteristic parameters Candidate statistical characteristic parameters for the next handover period The eigenvalue differences.

[0010] In some embodiments, constructing a statistical characteristic parameter library includes: pre-constructing a complex mean library. and power envelope function library Spectrum parameter library Among them, the complex mean library Includes L complex values, power envelope function library Includes M power envelope functions and a spectral parameter library. Includes P vectors of spectral shape parameters; based on the complex mean library and power envelope function library Spectrum parameter library The parameters are combined to generate parameter combinations; the theoretical Gram matrix corresponding to the two parameter combinations in adjacent switching time periods is calculated, and the eigenvalue difference between the two parameter combinations is obtained based on the theoretical Gram matrix; candidate parameter combinations that meet the preset eigenvalue conditions are selected, and the candidate parameter combinations are integrated to construct a statistical characteristic parameter library.

[0011] In some embodiments, the preset feature value conditions include: In the above formula, The difference in eigenvalues ​​for any two parameter combinations This is the threshold for feature differences.

[0012] In some embodiments, a hierarchical switching strategy is used to match the switching periods of different types of statistical characteristic parameters, and the corresponding types of statistical characteristic parameters are switched in a time-sharing manner to achieve staggered and agile changes of second-order statistical characteristics at different time scales. This includes: configuring fast-layer time periods, medium-speed layer time periods, and slow-speed layer time periods according to the statistical characteristic switching cycle; wherein, the fast-layer time period corresponds to the power envelope function, the medium-speed layer time period corresponds to the spectral parameters, and the slow-speed layer time period corresponds to the complex mean; within the fast-layer time period, at the end of the statistical characteristic switching cycle, the power envelope function corresponding to the next switching period is updated; within the medium-speed layer time period, at the end of the statistical characteristic switching cycle, the spectral parameters corresponding to the next switching period are updated; within the slow-speed layer time period, at the end of the statistical characteristic switching cycle, the complex mean corresponding to the next switching period is updated.

[0013] In some embodiments, K-channel blocking signals are generated in parallel in the baseband digital domain, and the K-channel blocking signals are superimposed to generate non-stationary blocking signals, including: superimposing the K-channel blocking signals. Generate the original non-stationary blocking signal For the original non-stationary blocking signal The signal undergoes sequential digital-to-analog conversion, mixing, and power amplification to generate a non-stationary blocking signal.

[0014] In some embodiments, the method further includes: after obtaining continuous statistical characteristic switching periods, and before selecting statistical characteristic parameters for the next switching period from the statistical characteristic parameter library after each statistical characteristic switching period is completed, calculating the power spectral density of the current statistical characteristic switching period using a time window function. Smooth weighted fusion is performed to achieve a smooth transition of the statistical characteristics of the blocking signal.

[0015] In some embodiments, the system includes: a parameter library management module configured to construct a statistical characteristic parameter library; wherein the statistical characteristic parameter library includes different types of statistical characteristic parameters; generating K-channel blocking signals in parallel in the baseband digital domain, and superimposing the K-channel blocking signals to generate a non-stationary blocking signal; a statistical characteristic switching control module configured to divide the transmission duration of the blocking signal into time periods according to the statistical characteristic switching cycle to obtain continuous statistical characteristic switching time periods; wherein the first-order statistical characteristic of the blocking signal is fixed and the second-order statistical characteristic changes within each statistical characteristic switching time period; a non-stationary signal generation module configured to select statistical characteristic parameters for the next switching time period from the statistical characteristic parameter library after each statistical characteristic switching time period is completed, so that the second-order statistical characteristic of adjacent time periods is non-periodic and statistically unpredictable, and to match the switching time periods of different types of statistical characteristic parameters based on a hierarchical switching strategy, and to switch the corresponding types of statistical characteristic parameters in a time-sharing manner to achieve staggered and agile changes of the second-order statistical characteristic at different time scales; a signal synthesis module configured to generate K-channel blocking signals in parallel in the baseband digital domain; and a radio frequency interface module configured to superimpose the K-channel blocking signals to generate a non-stationary blocking signal.

[0016] In some embodiments, the radio frequency interface module includes: a digital-to-analog converter configured to perform digital-to-analog conversion on the original non-stationary blocking signal to obtain an analog non-stationary blocking signal; and a radio frequency front-end link configured to sequentially perform mixing and power amplification on the analog non-stationary blocking signal to generate a statistically agile non-stationary blocking signal and radiate it through a transmitting antenna.

[0017] The statistically agile blocking signal construction method and system provided in this disclosure can achieve the following technical effects:

[0018] By constructing a statistical characteristic parameter library containing different types of statistical characteristic parameters, and dividing the transmission duration of the blocking signal into time periods based on the statistical characteristic switching cycle, the first-order statistical characteristics of the blocking signal remain fixed within each switching period, while the second-order statistical characteristics are dynamically variable. This completely breaks the foundation of the stable statistical characteristics of traditional adaptive zeroing algorithms. Simultaneously, after each statistical characteristic switching period, statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library, effectively avoiding the periodic changes in the second-order statistical characteristics. This makes the statistical evolution of the blocking signal highly random and unpredictable. The receiver cannot fit, learn, or predict interference features through short-term signal sampling and machine learning, preventing the interference features from being deciphered and counteracted, thus ensuring the stability and concealment of the blocking interference.

[0019] Furthermore, a layered switching strategy is used to achieve time-division interleaved agility of different types of statistical characteristic parameters. Compared with single-parameter interference methods, this increases the complexity of the statistical characteristic changes of the blocking signal, putting dynamic tracking pressure on the receiver's multi-dimensional adaptive nulling loop. Based on this, by generating and superimposing K blocking signals in parallel in the baseband digital domain, a non-stationary blocking signal is constructed. This prevents the convergence rate of the receiver's adaptive weights from reaching the agility rate of the statistical characteristics, making it difficult to iteratively form effective nulling weights. Ultimately, this leads to the receiver's anti-interference nulling performance nearing failure, significantly improving the overall suppression capability and interference effectiveness of the adaptive anti-interference receiver.

[0020] This application dynamically adjusts the multidimensional statistical characteristics of the blocking signal within a millisecond timescale, making the eigenvalue evolution rate of the blocking signal exceed the adaptive tracking capability of the receiver, thereby effectively countering the adaptive zeroing anti-interference mechanism of modern navigation receivers such as STAP.

[0021] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0022] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0023] Figure 1 This is a schematic diagram of a method for constructing a blocking signal with statistically agile characteristics provided in an embodiment of this disclosure;

[0024] Figure 2 This is a schematic diagram of another statistically agile blocking signal construction method provided in this embodiment of the disclosure;

[0025] Figure 3 This is a schematic diagram of another statistically agile blocking signal construction method provided in this embodiment of the disclosure;

[0026] Figure 4 This is a schematic diagram of another statistically agile blocking signal construction method provided in this embodiment of the disclosure;

[0027] Figure 5 This is a schematic diagram of another statistically agile blocking signal construction method provided in this embodiment of the disclosure;

[0028] Figure 6 This is a time sequence diagram of the layered switching strategy;

[0029] Figure 7 This is a comparison of the time evolution curves of the Gram matrix eigenvalues ​​between the method in this application and the traditional blocking method;

[0030] Figure 8 This is a comparison chart of the blocking effects of the method in this application and traditional blocking methods on the STAP anti-interference receiver;

[0031] Figure 9 This is a schematic diagram of a statistically agile blocking signal construction system provided in an embodiment of this disclosure. Detailed Implementation

[0032] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure 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 for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0033] Unless otherwise stated, the term "multiple" means two or more.

[0034] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0035] Combination Figure 1 As shown, this disclosure provides a method for constructing a blocking signal with agile statistical characteristics, including:

[0036] S01, the processor constructs a statistical characteristic parameter library. This library includes statistical characteristic parameters of different types.

[0037] S02, the processor divides the transmission duration of the blocking signal into time periods according to the statistical characteristic switching cycle, obtaining continuous statistical characteristic switching time periods. Within each statistical characteristic switching time period, the first-order statistical characteristic of the blocking signal is fixed, while the second-order statistical characteristic changes.

[0038] S03, after each statistical characteristic switching period, the processor selects the statistical characteristic parameters for the next switching period from the statistical characteristic parameter library, so that the second-order statistical characteristics of adjacent periods are not periodic and are statistically unpredictable.

[0039] S04, the processor matches the switching time periods of different types of statistical characteristic parameters based on the hierarchical switching strategy, and switches the corresponding types of statistical characteristic parameters in a time-sharing manner to achieve the interleaved agility of second-order statistical characteristics at different time scales.

[0040] S05, the processor generates K-channel blocking signals in parallel in the baseband digital domain, and performs superposition processing on the K-channel blocking signals to generate non-stationary blocking signals.

[0041] The method for constructing a jamming signal with agile statistical characteristics provided in this disclosure constructs a statistical characteristic parameter library containing different types of statistical characteristic parameters. By dividing the transmission duration of the jamming signal into time periods based on the statistical characteristic switching cycle, the first-order statistical characteristics of the jamming signal remain fixed within each switching period, while the second-order statistical characteristics are dynamically variable. This completely breaks the foundation of the stable statistical characteristics of traditional adaptive zeroing algorithms. Simultaneously, after each statistical characteristic switching period, statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library. This effectively avoids the periodic changes in the second-order statistical characteristics, making the statistical evolution of the jamming signal highly random and unpredictable. The receiver cannot fit, learn, or predict interference features through short-term signal sampling and machine learning, preventing the interference features from being deciphered and counteracted, thus ensuring the stability and concealment of the jamming interference.

[0042] Furthermore, a layered switching strategy is used to achieve time-division interleaved agility of different types of statistical characteristic parameters. Compared with single-parameter interference methods, this increases the complexity of the statistical characteristic changes of the blocking signal, putting dynamic tracking pressure on the receiver's multi-dimensional adaptive nulling loop. Based on this, by generating and superimposing K blocking signals in parallel in the baseband digital domain, a non-stationary blocking signal is constructed. This prevents the convergence rate of the receiver's adaptive weights from reaching the agility rate of the statistical characteristics, making it difficult to iteratively form effective nulling weights. Ultimately, this leads to the receiver's anti-interference nulling performance nearing failure, significantly improving the overall suppression capability and interference effectiveness of the adaptive anti-interference receiver.

[0043] In summary, the embodiments of this disclosure dynamically adjust the multidimensional statistical characteristics of the blocking signal within a millisecond timescale, making the eigenvalue evolution rate of the blocking signal exceed the adaptive tracking capability of the receiver, thereby effectively countering the adaptive zeroing anti-interference mechanism of modern navigation receivers such as STAP.

[0044] Optionally, combined Figure 2 As shown, after each statistical characteristic switching period, the processor selects the statistical characteristic parameters for the next switching period from the statistical characteristic parameter library, including:

[0045] S11, at the end of the current statistical characteristic switching period, the processor uses the current statistical characteristic parameters... Based on this, candidate statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library using a probability-weighted selection mechanism. Among these, .in, These are statistical characteristic parameters. For the complex mean, Let be the power envelope function. These are spectral parameters.

[0046] S12, the processor generates an index for each statistical characteristic switching period through a pseudo-random sequence generator.

[0047] S13, the processor maps the basic statistical characteristic parameters corresponding to the switching time period from the candidate statistical characteristic parameters according to the index. Among them, the distance between the statistical characteristic parameters corresponding to adjacent statistical characteristic switching time periods in the parameter space is greater than or equal to the distance threshold.

[0048] In this step, In the above formula, This represents the distance in the parameter space between statistical characteristics corresponding to adjacent statistical characteristic switching cycles. This is the distance threshold.

[0049] S14, the processor uses the basic statistical characteristic parameters as the statistical characteristic parameters for the next switching period.

[0050] Thus, at the end of the current statistical characteristic switching period, the system does not perform a completely random selection of statistical characteristic parameters, but instead executes a constrained random walk, specifically based on the current statistical characteristic parameters. Based on this, candidate statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library using a probability-weighted selection mechanism proportional to the selection probability. An index is generated for each statistical characteristic switching period using a pseudo-random sequence generator, and the basic statistical characteristic parameters for the corresponding switching period are mapped from the candidate statistical characteristic parameters according to the index. Finally, the basic statistical characteristic parameters are used as the statistical characteristic parameters for the next switching period, ensuring that the distance between the statistical characteristic parameters of adjacent switching periods in the parameter space is greater than or equal to a distance threshold. This makes the second-order statistical characteristics of adjacent periods non-periodic and statistically unpredictable, forcing the receiver to be unable to reliably estimate the current and future statistical characteristics within a limited training sample, even when using RLS (Recursive Least Squares) or Kalman filtering for adaptive tracking. The inherent delay in weight updates leads to a continuous accumulation of tracking errors, thus continuously suppressing the receiver's polarization adaptation capability.

[0051] Furthermore, the combination design of parameter selection mechanism controlled by pseudo-random sequence generator and hundreds of statistical characteristic parameter libraries enables the evolution sequence of blocking characteristics to have extremely long period and high randomness, which cannot be deciphered by the receiver through learning and prediction. It has good general adversarial capability against receivers with different configurations and adaptive algorithms.

[0052] Optionally, the probability-proportional mechanism of selection satisfies: .

[0053] In the above formula, To select candidate statistical characteristic parameters for the next handover period The conditional probability, Basic statistical characteristic parameters Candidate statistical characteristic parameters for the next handover period The eigenvalue differences.

[0054] Thus, the selection probability proportionality mechanism satisfies the selection probability. Proportional to the difference in eigenvalues This mechanism ensures that each switch generates a drastic change in statistical characteristics, making it impossible for the receiver to predict and learn the changing patterns of the blocking signal through Kalman filtering. This directly undermines the assumption of stationarity of the Gram matrix, thus guaranteeing the stability and concealment of the blocking interference.

[0055] It should be noted that the processor randomly selects a statistical characteristic parameter from the statistical characteristic parameter library as the statistical characteristic parameter for the first statistical characteristic switching period.

[0056] Optionally, the processor switches cycles according to statistical characteristics. The duration of the blocking signal transmission is divided into time periods to obtain continuous statistical characteristic switching periods, including:

[0057] In the Statistical characteristic switching period Internal, first-order characteristics of blocking signals The second-order property is fixed and changes in the following manner:

[0058] .

[0059] in, It is the frequency response function. and These are the corresponding indices of the power envelope function and the spectral parameters for the next handover period, obtained through S11 to S14, respectively. , It represents the set of natural numbers.

[0060] Optionally, combined Figure 3 As shown, the processor constructs a statistical characteristic parameter library, including:

[0061] S21, The processor pre-builds a complex mean library. and power envelope function library Spectrum parameter library Among them, the complex mean library include A complex value, power envelope function library include A power envelope function, a spectral parameter library include A vector of spectral shape parameters.

[0062] In this step, the complex mean library Used to control the DC component and initial phase of the blocking signal. The complex values ​​are distributed within the unit circle of the complex plane. For example, 50. Power envelope function library. The instantaneous change in signal power over time is used to control the blocking signal. The power envelope function includes exponential decay, Gaussian impulse, and Rayleigh distribution. For example, 20. Spectrum parameter set library In the vector, the spectral shape parameter includes the center frequency. ,bandwidth and roll-off factor .

[0063] In some alternative embodiments, the complex mean library Represented as: , For the first The complex mean of each statistical characteristic switching period , The number of complex means is used to characterize the non-stationary drift characteristics of the blocking signal over time. .in, Represents the complex field.

[0064] Power envelope function library Represented as: , .in, For the first The fundamental power of a power envelope function. For the first The modulation depth of each power envelope function is used to control the power fluctuation amplitude. For the first The modulation frequency of a power envelope function, No. The initial phase of each power envelope function, the number of power envelope functions is... .

[0065] Spectrum parameter library Represented as: .in, For the first The center frequency of the power spectrum in a given spectral shape. For the first The signal bandwidth of a spectrum shape For the first The sidelobe attenuation rate of each spectral shape, and the number of spectral parameter sets are... .

[0066] Therefore, the above parameter library is generated through discrete combination. The combined configuration of power envelope and spectral shape forms a multi-dimensional statistical characteristic parameter space.

[0067] S22, the processor bases the complex mean library and power envelope function library Spectrum parameter library Combine them to generate parameter combinations.

[0068] S23, the processor calculates the theoretical Gram matrix corresponding to the two sets of parameter combinations in adjacent switching time periods, and obtains the eigenvalue difference between the two sets of parameter combinations based on the theoretical Gram matrix.

[0069] In this step, the difference in eigenvalues ​​between any two parameter combinations is: . , For parameter combination The corresponding theoretical Gram matrix.

[0070] S24, the processor filters out candidate parameter combinations whose feature value differences meet preset feature value conditions, and integrates the candidate parameter combinations to construct a statistical characteristic parameter library.

[0071] Thus, a complex mean library is pre-built. and power envelope function library Spectrum parameter library Based on this, an offline adversarial pre-assessment is performed, calculating the corresponding theoretical Gram matrix and eigenvalue differences for two sets of parameters in adjacent switching periods from the three databases. Candidate parameter combinations that meet preset eigenvalue conditions are selected, and a statistical characteristic parameter database is constructed to ensure that subsequent switching of statistical characteristic parameters can generate sufficient non-stationary shocks.

[0072] Optionally, the preset eigenvalue conditions include: In the above formula, This is the threshold for feature differences.

[0073] In this way, by controlling the difference in eigenvalues ​​between two sets of parameters in adjacent switching periods to be greater than or equal to the eigenvalue difference threshold, the rate of change of eigenvalues ​​can be ensured. Exceeding the critical tracking rate of receiver RLS adaptation ,For example This ensures that subsequent switching of selected statistical characteristic parameters can generate sufficient non-stationary shocks. Among these, The weighting coefficients and , , This is the inherent time constant for the receiver's RLS adaptation.

[0074] In this embodiment, the fast layer controls the dynamic modulation of the power envelope function, generating rapid power fluctuations at the millisecond level. The medium-speed layer controls the periodic switching of spectral parameters, altering the frequency domain properties of the blocking signal. The slow layer controls the change in negative mean, adjusting the DC component of the blocking signal.

[0075] It should be noted that the processor quantifies and controls the non-stationary intensity of the following hierarchical switching strategy using the following formula:

[0076] .

[0077] in, These are the weighting coefficients for each layer, set according to their contribution to the blocking effect of adaptive zeroing. For the first Eigenvalue differences caused by changes in layer statistical properties The statistical characteristic switching cycle for the corresponding layer. For fast layer, It is a medium-speed layer. This is a slow-speed layer.

[0078] Optionally, combined Figure 4 As shown, the processor matches the switching time periods of different types of statistical characteristic parameters based on a hierarchical switching strategy, and switches the corresponding types of statistical characteristic parameters in a time-sharing manner to achieve staggered and agile changes of second-order statistical characteristics at different time scales, including:

[0079] S31, the processor configures the fast layer time period, medium-speed layer time period, and slow layer time period according to the statistical characteristics switching cycle. Among them, the fast layer time period corresponds to the power envelope function, the medium-speed layer time period corresponds to the spectral parameters, and the slow layer time period corresponds to the complex mean.

[0080] S32, during the fast layer time period, at the end of the statistical characteristic switching cycle, the processor updates the power envelope function to the power envelope function corresponding to the next switching period.

[0081] In this step, when the fast layer time period includes multiple statistical characteristic switching cycles, the processor updates the power envelope function to the power envelope function corresponding to the next switching period at the end of the statistical characteristic switching cycle within the fast layer time period. This includes: within the fast layer time period, the processor updates the power envelope function to the power envelope function corresponding to the next switching period at the end of each statistical characteristic switching cycle, so as to perform multiple power envelope function updates within the fast layer time period.

[0082] S33, during the medium-speed layer time period, at the end of the statistical characteristic switching cycle, the processor updates the spectrum parameters to the corresponding spectrum parameters for the next switching period.

[0083] In this step, when the medium-speed layer time period includes multiple statistical characteristic switching cycles, the processor updates the spectrum parameters corresponding to the next switching period at the end of the statistical characteristic switching cycle within the medium-speed layer time period. This includes: within the medium-speed layer time period, the processor updates the spectrum parameters corresponding to the next switching period at the end of each statistical characteristic switching cycle, so as to perform multiple spectrum parameter updates within the medium-speed layer time period.

[0084] S34, during the slow layer time period, the processor updates the value to the complex mean corresponding to the next switching period at the end of the statistical characteristic switching cycle.

[0085] In this step, when the slow layer time period includes multiple statistical characteristic switching cycles, the processor updates the complex mean corresponding to the next switching period at the end of the statistical characteristic switching cycle within the slow layer time period. This includes: within the slow layer time period, the processor updates the complex mean corresponding to the next switching period at the end of each statistical characteristic switching cycle, so as to perform multiple complex mean updates within the slow layer time period.

[0086] In this way, fast-layer, medium-layer, and slow-layer time periods are configured according to the statistical characteristic switching cycle to trigger updates of the power envelope function, spectral parameters, and complex mean, respectively. Specifically, within the fast-layer time period, the power envelope function corresponding to the next switching period is updated at the end of the statistical characteristic switching cycle. Within the medium-layer time period, the spectral parameters corresponding to the next switching period are updated at the end of the statistical characteristic switching cycle. Within the slow-layer time period, the complex mean corresponding to the next switching period is updated at the end of the statistical characteristic switching cycle. This avoids synchronous changes of the power envelope, spectral parameters, and complex mean on the same time scale, ensuring that the three statistical characteristic dimensions have different hold times and frequencies of change, forming a non-stationary statistical characteristic with multiple time scales interleaved.

[0087] In some alternative embodiments, the duration of the fast layer time period is shorter than the duration of the medium-speed layer time period, and the duration of the medium-speed layer time period is shorter than the duration of the slow layer time period.

[0088] As an example, combined Figure 6 As shown, , , These represent the durations corresponding to the fast-paced, medium-paced, and slow-paced time periods, respectively. , , .

[0089] Optionally, the processor generates K blocking signals in parallel in the baseband digital domain, and performs superposition processing on the K blocking signals to generate non-stationary blocking signals, including:

[0090] Processor superimposed K-channel blocking signal Generate the original non-stationary blocking signal .

[0091] Processor for raw non-stationary blocking signals The signal undergoes sequential digital-to-analog conversion, mixing, and power amplification to generate a non-stationary blocking signal.

[0092] in, . For the first Road blockage signal The relative amplitude, For the first Road blockage signal The phase. It should be noted that, and All change dynamically over time.

[0093] In this way, the blocking signals generated independently and in parallel at the baseband end of the K-channel are... The signal is synthesized into a single excitation signal (i.e., the original non-stationary blocking signal) in the time domain, and then subjected to digital-to-analog conversion, mixing, and power amplification to generate a composite signal containing K non-stationary dimensions (i.e., the non-stationary blocking signal), which greatly increases the adaptive processing complexity of the receiver.

[0094] Combination Figure 5 As shown in the embodiments of this disclosure, a method for constructing a blocking signal with agile statistical characteristics is also provided, including:

[0095] S41, the processor constructs a statistical characteristic parameter library. This library includes statistical characteristic parameters of different types.

[0096] S42, the processor divides the transmission duration of the blocking signal into time periods according to the statistical characteristic switching cycle, obtaining continuous statistical characteristic switching time periods. Within each statistical characteristic switching time period, the first-order statistical characteristic of the blocking signal is fixed, while the second-order statistical characteristic changes.

[0097] S43, the processor, within the current statistical characteristic switching period, uses a time window function to calculate the power spectral density for that period. Smooth weighted fusion is performed to achieve a smooth transition of the statistical characteristics of the blocking signal.

[0098] In some alternative embodiments, , This is the time window function. It is a Hanning window or a trapezoidal window. The power spectral density is the result of smooth weighted fusion.

[0099] S44, after each statistical characteristic switching period, the processor selects the statistical characteristic parameters for the next switching period from the statistical characteristic parameter library, so that the second-order statistical characteristics of adjacent periods are not periodic and are statistically unpredictable.

[0100] S45, the processor matches the switching time periods of different types of statistical characteristic parameters based on a hierarchical switching strategy, and switches the corresponding types of statistical characteristic parameters in a time-sharing manner to achieve staggered and agile changes of second-order statistical characteristics at different time scales.

[0101] S46, the processor generates K-channel blocking signals in parallel in the baseband digital domain, and performs superposition processing on the K-channel blocking signals to generate non-stationary blocking signals.

[0102] The statistical characteristic agile blocking signal construction method provided in this disclosure avoids spectral sputtering or being identified by the receiver through transient features due to abrupt changes in statistical characteristic parameters. After obtaining continuous statistical characteristic switching periods and before selecting the statistical characteristic parameters for the next switching period from the statistical characteristic parameter library after each statistical characteristic switching period is completed, the power spectral density of the current statistical characteristic switching period is balanced and weighted by a time window function. This ensures that the second-order statistical characteristics are physically differentiable while maintaining strong non-stationarity, forcing the receiver to always be in a transitional state of lag tracking.

[0103] Combination Figure 9 As shown in the figure, this disclosure also provides a block signal construction system 10 with agile statistical characteristics, including a parameter library management module 101, a statistical characteristic switching control module 102, a non-stationary signal generation module 103, and a signal synthesis module 104.

[0104] The parameter library management module 101 is configured to build a statistical characteristic parameter library. This statistical characteristic parameter library includes different types of statistical characteristic parameters.

[0105] The statistical characteristic switching control module 102 is configured to divide the transmission duration of the blocking signal into time periods according to the statistical characteristic switching cycle, thereby obtaining continuous statistical characteristic switching time periods. Within each statistical characteristic switching time period, the first-order statistical characteristic of the blocking signal is fixed, while the second-order statistical characteristic changes.

[0106] The non-stationary signal generation module 103 is configured to select statistical characteristic parameters for the next switching period from the statistical characteristic parameter library after each statistical characteristic switching period is completed, so that the second-order statistical characteristics of adjacent periods are non-periodic and statistically unpredictable. It also matches the switching periods of different types of statistical characteristic parameters based on a hierarchical switching strategy and switches the corresponding types of statistical characteristic parameters in a time-sharing manner, thereby realizing the staggered and agile changes of second-order statistical characteristics at different time scales.

[0107] The signal synthesis module 104 is configured to generate K-channel blocking signals in parallel in the baseband digital domain.

[0108] The radio frequency interface module 105 is configured to superimpose K-channel blocking signals to generate non-stationary blocking signals and output them.

[0109] Optionally, the non-stationary signal generation module 103 is deployed on an FPGA (Field-Programmable Gate Array).

[0110] Optionally, the RF interface module 105 includes a digital-to-analog converter (DAC) and an RF front-end link. The DAC is configured to perform digital-to-analog conversion on the original non-stationary blocking signal to obtain an analog non-stationary blocking signal. The RF front-end link is configured to sequentially perform mixing and power amplification on the analog non-stationary blocking signal to generate a statistically agile non-stationary blocking signal, which is then radiated through the transmitting antenna. In this way, a millisecond-level statistically agile non-stationary blocking signal is obtained and antenna radiation is achieved.

[0111] In practical applications, Figure 7 (a) shows the eigenvalue tracking of the Gram matrix under the condition of a blocking signal with conventional constant statistical characteristics; where the blue solid line represents the actual eigenvalues ​​of the blocking signal Gram matrix. The red dashed line represents the eigenvalues ​​of the Gram matrix estimated by the receiver's RLS algorithm. .

[0112] like Figure 7 As shown in (a), since the statistical characteristics of traditional blocking signals are relatively constant and the eigenvalues ​​of the Gram matrix change slowly, the receiver's RLS algorithm can quickly converge. The red dashed line and the blue solid line basically coincide, the tracking error is small, and the receiver can effectively eliminate this kind of interference.

[0113] Figure 7 (b) shows the Gram matrix eigenvalue tracking method of this invention. For example... Figure 7 As shown in (b), this application enables the Gram eigenvalue (blue solid line) to exhibit rapid, large-amplitude changes at the millisecond level, while the receiver RLS algorithm (red dashed line) has an inherent time constant. It is unable to keep up with the rate of change of the Gram matrix eigenvalues, and is in a state of continuous tracking followed by loss of lock, which leads to the failure of covariance matrix estimation.

[0114] Figure 7 (c) The red curve represents the rate of change of the eigenvalues ​​of the Gram matrix generated by the present invention, and the black dotted line represents the critical tracking rate of the receiver. ,and .like Figure 7As shown in (c), the peak rate of change of Gram matrix eigenvalues ​​generated by the method of this application significantly exceeds the critical tracking rate, reaching more than 3 times, and the non-stationary intensity has exceeded the receiver's adaptive processing capability.

[0115] Figure 8 This is a comparison chart showing the blocking effects of the proposed method and traditional blocking methods on STAP anti-jamming receivers. (See attached image.) Figure 8 As shown, the STAP anti-jamming receiver successfully converged to the traditional blocking method (grey curve) after approximately 20ms of initial learning and weight iteration. The output SINR (Signal to Interference plus Noise Ratio) rapidly increased from an initial value of -10dB and stabilized at around 25dB, far exceeding the demodulation threshold. This indicates that the receiver successfully formed a null and successfully filtered out the blocking signal. In contrast, the proposed method (red curve) suffers from a situation where the rate of change of the Gram matrix eigenvalues ​​of the blocking signal exceeds the receiver's tracking capability, resulting in the adaptive weights remaining consistently lagging and mismatched. The output SINR oscillated violently between -8dB and 6dB, consistently failing to break through the 10dB demodulation threshold. This indicates that the receiver is in a continuous unlocked state and cannot restore normal navigation functionality.

[0116] This application achieves millisecond-level dynamic agility of the first and second-order statistical characteristics of blocking signals through a synergistic design of a layered switching strategy and a multi-dimensional statistical characteristic parameter library. Based on the time-domain superposition and phase / power dynamic coupling mechanism of K independent non-stationary signals, a complex blocking scenario with multiple time scales and staggered evolution is constructed, violating the stationarity assumption that disrupts adaptive zeroing and avoiding the deficiency of traditional blocking signals with constant statistical characteristics being easily estimated by the covariance matrix. Combining parameter selection based on constrained random walks and an online eigenvalue feedback mechanism, it ensures that the rate of change of the Gram matrix eigenvalues ​​of the blocking signal always exceeds the receiver's adaptive tracking rate by 1.5-3 times. Compared to traditional single-dimensional or periodic blocking schemes, this effectively ensures that the receiver's adaptive weights are always in a tracking-lost state, significantly improving the suppression performance against modern navigation receivers employing adaptive zeroing mechanisms, and demonstrating significant application value.

[0117] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to execute the above-described method for constructing a blocking signal with agile statistical characteristics.

[0118] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0119] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0120] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

Claims

1. A method for constructing a blocking signal with agile statistical characteristics, characterized in that, include: Construct a statistical characteristic parameter library; the statistical characteristic parameter library includes different types of statistical characteristic parameters; The duration of the blocking signal transmission is divided into time periods according to the statistical characteristic switching cycle to obtain continuous statistical characteristic switching time periods; wherein, the first-order statistical characteristic of the blocking signal is fixed and the second-order statistical characteristic changes within each statistical characteristic switching time period. After each statistical characteristic switching period is completed, the statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library so that the second-order statistical characteristics of adjacent periods are not periodic and are statistically unpredictable. Based on the hierarchical switching strategy, the switching time periods of different types of statistical characteristic parameters are matched, and the corresponding types of statistical characteristic parameters are switched in time-sharing manner to achieve the staggered and agile transformation of second-order statistical characteristics at different time scales. K blocking signals are generated in parallel in the baseband digital domain, and the K blocking signals are superimposed to generate non-stationary blocking signals.

2. The method according to claim 1, characterized in that, After each statistical characteristic switching period is completed, the statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library, including: At the end of the current statistical characteristic switching period, using the current statistical characteristic parameters Based on this, candidate statistical characteristic parameters for the next switching period are selected from the statistical characteristic parameter library using a probability-weighted selection mechanism that is proportional to the selection probability. An index is generated for each statistical characteristic switching time period using a pseudo-random sequence generator; The basic statistical characteristic parameters for the corresponding switching time period are obtained by mapping from the candidate statistical characteristic parameters according to the index; wherein, the distance between the statistical characteristic parameters corresponding to adjacent statistical characteristic switching time periods in the parameter space is greater than or equal to the distance threshold. The basic statistical characteristic parameters are used as the statistical characteristic parameters for the next switching period.

3. The method according to claim 2, characterized in that, The selection probability proportionality mechanism satisfies: ; In the above formula, To select candidate statistical characteristic parameters for the next handover period The conditional probability, Basic statistical characteristic parameters Candidate statistical characteristic parameters for the next handover period The eigenvalue differences.

4. The method according to claim 1, characterized in that, Construct a statistical characteristic parameter library, including: Pre-constructed complex mean library and power envelope function library Spectrum parameter library Among them, the complex mean library Includes L complex values, power envelope function library Includes M power envelope functions and a spectral parameter library. Includes P vectors of spectral shape parameters; According to the complex mean library and power envelope function library Spectrum parameter library Combine them to generate parameter combinations; Calculate the theoretical Gram matrix corresponding to the two sets of parameter combinations in adjacent handover periods, and obtain the eigenvalue difference between the two sets of parameter combinations based on the theoretical Gram matrix; Select alternative parameter combinations whose eigenvalue differences meet preset eigenvalue conditions, and integrate these alternative parameter combinations to construct a statistical characteristic parameter library.

5. The method according to claim 4, characterized in that, Preset eigenvalue conditions include: ; In the above formula, The difference in eigenvalues ​​for any two parameter combinations This is the threshold for feature differences.

6. The method according to claim 1, characterized in that, Based on a hierarchical switching strategy, the switching time periods for different types of statistical characteristic parameters are matched, and the corresponding types of statistical characteristic parameters are switched in a time-sharing manner to achieve staggered and agile changes in second-order statistical characteristics at different time scales, including: Based on the statistical characteristics switching cycle, the fast layer time period, medium-speed layer time period, and slow layer time period are configured respectively; among them, the fast layer time period corresponds to the power envelope function, the medium-speed layer time period corresponds to the spectral parameters, and the slow layer time period corresponds to the complex mean. During the fast layer time period, at the end of the statistical characteristic switching cycle, the power envelope function is updated to the power envelope function corresponding to the next switching time period; During the medium-speed layer time period, at the end of the statistical characteristic switching cycle, the spectral parameters are updated to the corresponding spectral parameters for the next switching period. During the slow-speed layer time period, at the end of the statistical characteristic switching cycle, it is updated to the complex mean corresponding to the next switching period.

7. The method according to claim 1, characterized in that, K blocking signals are generated in parallel in the baseband digital domain. These K blocking signals are then superimposed to generate a non-stationary blocking signal, including: Superimposed K-channel blocking signal Generate the original non-stationary blocking signal ; For the original non-stationary blocking signal The signal undergoes sequential digital-to-analog conversion, mixing, and power amplification to generate a non-stationary blocking signal.

8. The method according to any one of claims 1 to 7, characterized in that, Also includes: After obtaining continuous statistical characteristic switching periods, and before selecting the statistical characteristic parameters for the next switching period from the statistical characteristic parameter library after each statistical characteristic switching period is completed, the power spectral density of the current statistical characteristic switching period is obtained by applying a time window function. Smooth weighted fusion is performed to achieve a smooth transition of the statistical characteristics of the blocking signal.

9. A statistically agile blocking signal construction system, comprising: The parameter library management module is configured to build a statistical characteristic parameter library; the statistical characteristic parameter library includes different types of statistical characteristic parameters. K blocking signals are generated in parallel in the baseband digital domain, and the K blocking signals are superimposed to generate non-stationary blocking signals. The statistical characteristic switching control module is configured to divide the transmission duration of the blocking signal into time periods according to the statistical characteristic switching cycle to obtain continuous statistical characteristic switching time periods; wherein, the first-order statistical characteristic of the blocking signal is fixed and the second-order statistical characteristic changes within each statistical characteristic switching time period. The non-stationary signal generation module is configured to select statistical characteristic parameters for the next switching period from the statistical characteristic parameter library after each statistical characteristic switching period is completed, so that the second-order statistical characteristics of adjacent periods are non-periodic and statistically unpredictable. It also matches the switching periods of different types of statistical characteristic parameters based on a hierarchical switching strategy and switches the corresponding types of statistical characteristic parameters in a time-sharing manner, so as to realize the staggered and agile changes of second-order statistical characteristics at different time scales. The signal synthesis module is configured to generate K blocking signals in parallel in the baseband digital domain; The radio frequency interface module is configured to superimpose K-channel blocking signals to generate non-stationary blocking signals.

10. The system according to claim 9, characterized in that, The radio frequency interface module includes: A digital-to-analog converter is configured to perform digital-to-analog conversion on the original non-stationary blocking signal to obtain an analog non-stationary blocking signal; The radio frequency front-end link is configured to sequentially mix and amplify the analog non-stationary blocking signal to generate a statistically agile non-stationary blocking signal, which is then radiated through the transmitting antenna.

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