Signal enhancement method and device based on microwave technology

Abstract

The invention discloses a signal enhancement method and device based on a microwave technology, and relates to the technical field of signal processing. The method comprises the following steps: acquiring an original microwave signal, sampling the original signal to obtain a discrete signal, performing spectral analysis on the obtained discrete signal, and extracting a frequency characteristic parameter of the signal; calculating microwave energy information according to the discrete signals, monitoring the characteristic change condition in real time, setting an adaptive filter, and performing multipath signal enhancement processing by using the adaptive filter according to the extracted frequency characteristic parameters, the energy information and the characteristic change condition monitored in real time; performing inverse fast Fourier transform on the enhanced signal, and converting the enhanced signal from the frequency domain to the time domain to obtain an enhanced time domain signal; and introducing a signal quality evaluation index, and performing quality evaluation on the enhanced time domain signal. According to the invention, the signal purity and intensity can be effectively improved, the bit error rate is reduced, and the accuracy and stability of microwave signal transmission are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microwave signal processing, and in particular to a signal enhancement method and device based on microwave technology. Background Art

[0002] At a time when microwave technology is widely used, signal enhancement is a key link to ensure system performance. In the field of communications, microwave signals undertake massive data transmission tasks. For example, in 5G and even future 6G communications, when signals propagate in complex environments, factors such as building obstruction and atmospheric attenuation weaken the signal strength and reduce the quality, resulting in limited data transmission rate and increased bit error rate, which seriously affects the user experience. In terms of radar detection, weak microwave echo signals need to be accurately enhanced to identify targets at long distances or with low scattering cross-sections. Traditional methods are difficult to meet the detection needs of small targets or targets in complex backgrounds.

[0003] At present, traditional signal enhancement methods have many limitations. Filters with fixed parameters cannot adapt to the dynamic changes of signals. In the face of time-varying noise and multipath effects, it is difficult to effectively suppress interference and enhance useful signals. Simple signal merging strategies will cause signal distortion and information loss, and cannot fully utilize the information contained in multipath signals. In addition, existing technologies lack a comprehensive and adaptive signal quality evaluation and feedback adjustment mechanism, making it difficult to ensure that the enhanced signal continues to meet the needs of complex and changing application scenarios.

[0004] With the rapid development of 5G, 6G communications, intelligent transportation, military and national defense, etc., higher requirements are placed on the quality of microwave signals, and more efficient, intelligent and adaptive signal enhancement technologies are urgently needed. Summary of the invention

[0005] The present invention proposes a signal enhancement method based on microwave technology, comprising: The original microwave signal is obtained by a microwave signal receiving device, the original signal is sampled to obtain a discrete signal, the obtained discrete signal is subjected to spectrum analysis, and the frequency characteristic parameters of the signal are extracted; Calculate microwave energy information based on discrete signals, monitor feature changes in real time, set an adaptive filter, and use the adaptive filter to perform multipath signal enhancement processing based on the extracted frequency feature parameters, energy information, and feature changes monitored in real time; After multipath signal enhancement processing, a preliminary enhanced microwave signal is obtained, and the enhanced signal is subjected to inverse fast Fourier transform to convert it from the frequency domain back to the time domain to obtain an enhanced time domain signal; Signal quality evaluation indicators are introduced to evaluate the quality of the enhanced time domain signal. If the signal quality does not meet expectations, the influencing parameters of the adaptive filter are automatically adjusted until the signal quality meets the requirements.

[0006] The signal enhancement method based on microwave technology as described above, wherein an original microwave signal is obtained by a microwave signal receiving device, the original signal is sampled to obtain a discrete signal, the obtained discrete signal is subjected to spectrum analysis, and the frequency characteristic parameters of the signal are extracted, including the following sub-steps: The original microwave signal is obtained by using a microwave signal receiving device, and the original signal is sampled according to a preset oversampling rate using oversampling technology to obtain discrete signal data, and the discrete signal is subjected to fast Fourier transform to calculate the spectrum amplitude value and phase value to generate a frequency domain feature vector; The energy distribution of the signal is calculated based on the high-order cumulant algorithm, and the energy characteristics after non-Gaussian noise suppression are extracted through fourth-order and higher cumulant operations to identify the statistical characteristics of the nonlinear components in the signal; According to the spectrum amplitude fluctuation range and phase consistency threshold, a signal stability evaluation model is established to screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude amplitude jumps or phase mutation signals caused by noise interference.

[0007] The signal enhancement method based on microwave technology as described above, wherein the multipath signal enhancement processing is performed using an adaptive filter, comprises the following sub-steps: According to the dynamic change trend of the spectrum amplitude and phase values, the frequency response function of the filter is constructed, the envelope curve of the spectrum amplitude is fitted through the interpolation algorithm, and the phase compensation mechanism is introduced to correct the signal distortion caused by the Doppler effect or propagation attenuation; Based on fuzzy logic control rules and combined with the energy distribution characteristics of high-order cumulative output, the fuzzy membership function of gain coefficient and bandwidth parameter is established to dynamically adjust the passband range and stopband attenuation strength of the filter according to the energy concentration. For multipath propagation paths, the time delay estimation and signal coherence analysis methods are used to separate the components of each path. The time delay difference of the signal components is determined by calculating the cross-correlation function between the paths. The effective paths are screened based on the coherence matrix, and the enhanced signal is reconstructed through the weighted combining algorithm.

[0008] As described above, a signal enhancement method based on microwave technology, wherein for a multipath propagation path, a delay estimation and a signal coherence analysis method are used to separate the components of each path, and an enhanced signal is reconstructed by a weighted combining algorithm, comprising the following sub-steps: The multipath delay is calculated by the signal arrival time difference, the main propagation path is identified by combining the signal coherence matrix, the generalized cross-correlation algorithm is used to estimate the delay value of each path component, and a multipath delay distribution histogram is generated; Based on the path energy proportion and phase offset, a weight allocation model for multipath signal components is constructed. The weight coefficient is dynamically adjusted according to the signal-to-noise ratio and phase consistency of the path signal, and the path components with energy proportions higher than the preset threshold are preferentially retained. The maximum ratio combining algorithm is used to superimpose the separated signal components, and the combining weight coefficient is set according to the signal-to-noise ratio of each path component to eliminate the signal distortion caused by multipath interference and restore the time domain waveform integrity of the original signal.

[0009] A signal enhancement method based on microwave technology as described above, wherein, after obtaining the enhanced time domain signal, it also includes performing amplitude normalization processing on the enhanced time domain signal according to the mean square value and high-order cumulant of the time domain signal, including the following sub-steps: The mean square value and high-order cumulant of the time-domain enhanced signal are calculated to generate a dynamic energy adjustment coefficient, and the normalization scaling ratio is determined according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean; According to the preset energy range threshold, the signal amplitude is normalized by using a piecewise linear compression algorithm, multiple amplitude intervals are set and the compression slopes are defined respectively to avoid signal overload or quantization distortion; A signal envelope monitoring mechanism is established to track the peak fluctuation of the normalized signal in real time.

[0010] The signal enhancement method based on microwave technology as described above, wherein a signal quality evaluation index is introduced to perform quality evaluation on the enhanced time domain signal, comprises the following sub-steps: A multi-dimensional quality evaluation index is constructed based on the signal-to-noise ratio, spectrum flatness and phase jitter parameters to calculate the signal-to-noise ratio gain, spectrum ripple fluctuation variance and phase difference variance of the signal in the target frequency band; If the evaluation result does not reach the preset threshold, the gain parameter, frequency response function and multipath combining weight of the filter are adjusted inversely; The oversampling rate and classification screening threshold are recalibrated through an iterative optimization algorithm, the optimal oversampling multiple is determined by the golden section search method, and the screening threshold is updated in combination with the classifier confidence until the output signal meets the quality requirements.

[0011] A signal enhancement method based on microwave technology as described above, wherein the oversampling rate and the classification screening threshold are calibrated twice by an iterative optimization algorithm, comprising the following sub-steps: According to the deviation between the quality assessment result and the target value, a parameter adjustment gradient vector is generated, and the sensitivity coefficient of each processing link parameter is defined to determine the adjustment direction; The fuzzy PID control algorithm is used to incrementally correct the filter parameters. The correction values ​​of the proportional, integral and differential terms are calculated according to the fuzzy membership of the deviation value, and the adjustment step size is limited to prevent overshoot. Record historical adjustment trajectories, predict the optimal parameter combination through machine learning models, use long short-term memory networks to analyze the implicit rules of parameter adjustment sequences, and generate parameter prediction values ​​to accelerate the convergence process.

[0012] The present invention also proposes a signal enhancement device based on microwave technology, comprising: Information acquisition and preprocessing module: obtain the original microwave signal through the microwave signal receiving device, sample the original signal to obtain a discrete signal, perform spectrum analysis on the obtained discrete signal, and extract the frequency characteristic parameters of the signal; Signal enhancement module: Calculate microwave energy information based on discrete signals, monitor feature changes in real time, set an adaptive filter, and use the adaptive filter to perform multipath signal enhancement processing based on the extracted frequency feature parameters, energy information, and feature changes monitored in real time; After multipath signal enhancement processing, a preliminary enhanced microwave signal is obtained, and the enhanced signal is subjected to inverse fast Fourier transform to convert it from the frequency domain back to the time domain to obtain an enhanced time domain signal; Information evaluation module: Introduce signal quality evaluation indicators to evaluate the quality of the enhanced time domain signal. If the signal quality does not meet expectations, the influencing parameters of the adaptive filter are automatically adjusted through feedback until the signal quality meets the requirements.

[0013] The present invention also provides a computer storage medium, comprising: at least one memory and at least one processor; A memory for storing one or more program instructions; A processor is used to run one or more program instructions to execute any of the above-mentioned signal enhancement methods based on microwave technology.

[0014] The beneficial effects achieved by the present invention are as follows: (1) From the perspective of improving signal processing capabilities, through oversampling technology, spectrum analysis and high-order cumulant calculation, signal features can be accurately extracted, Gaussian noise can be effectively suppressed, signals with large interference can be removed, and high-quality signals can be retained, laying a solid foundation for subsequent enhanced processing and greatly improving the purity of the signal.

[0015] (2) In terms of adaptive processing, the adaptive filter is designed by utilizing the real-time monitoring of signal characteristic changes. The frequency response function and gain parameters are dynamically adjusted according to the spectrum amplitude value, phase value, etc., so as to flexibly respond to the dynamic changes of the signal, significantly enhance the signal quality, and ensure stable signal transmission.

[0016] (3) To address the multipath effect, the system uses time delay estimation, signal coherence analysis and weighted combining algorithms to accurately separate and combine signal components of different paths, effectively eliminate signal distortion caused by multipath interference, restore the time domain waveform integrity of the original signal, and improve signal reliability and accuracy.

[0017] (4) In addition, the signal quality evaluation index and feedback adjustment mechanism introduced can comprehensively evaluate the enhanced signal and automatically optimize the processing parameters until the signal quality meets the standard, thus realizing the intelligent and precise signal processing and effectively ensuring the efficient transmission and application of microwave signals in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0019] Figure 1 This is a flow chart of a signal enhancement method based on microwave technology provided in an embodiment of the present application.

[0020] Figure 2 This is a schematic diagram of a signal enhancement device based on microwave technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1 like Figure 1 As shown, the present application embodiment provides a signal enhancement method based on microwave technology, comprising: Step S1, obtaining an original microwave signal through a microwave signal receiving device, sampling the original signal to obtain a discrete signal, performing spectrum analysis on the obtained discrete signal, and extracting frequency characteristic parameters of the signal; Specifically, the original microwave signal is obtained through a microwave signal receiving device, and the original signal is sampled using an oversampling technique to obtain discrete signal data. The obtained discrete signal is subjected to spectrum analysis, the spectrum amplitude value and phase value of the signal are calculated, and the frequency characteristic parameters of the signal are extracted; the high-order cumulative amount of the signal is calculated to reflect the energy size of the signal and suppress the influence of Gaussian noise. According to the energy size and frequency characteristic parameters of the signal, the signal is preliminarily classified and screened, the signal with large noise interference is removed, and the signal with high energy and stable frequency characteristics is retained. A signal characteristic change monitoring mechanism is established to record the changes of the signal characteristic parameters in real time. Specifically, the following sub-steps are included: Step S11, using a microwave signal receiving device to obtain an original microwave signal, using an oversampling technique to sample the original signal according to a preset oversampling rate to obtain discrete signal data, performing a fast Fourier transform on the discrete signal, calculating a spectrum amplitude value and a phase value, and generating a frequency domain feature vector; A microwave signal receiving device is used to capture the original microwave signal. The oversampling technology is used to sample the original signal according to the preset oversampling rate. By increasing the sampling frequency to make it higher than the minimum sampling frequency required by the Nyquist sampling theorem, more signal detail information is captured, so that the discrete signal data can more carefully and accurately reflect the intrinsic characteristics of the original signal. After obtaining the discrete signal data, a fast Fourier transform is performed on it to convert the signal from the time domain to the frequency domain.

[0023] In the frequency domain, the following formula is used to calculate the spectrum amplitude value corresponding to each frequency component:

[0024] in, Indicates The spectrum amplitude value corresponding to the frequency component; It represents the real part function, which acts on the complex number in the brackets and extracts its component on the real axis; Represents the original discrete signal Weighted processing After that, the frequency domain signal obtained by fast Fourier transform (FFT) is The value of each frequency point reflects the frequency domain characteristics of the weighted signal; Indicates the length of the noise filter coefficient, which limits the participation in the current frequency point The range of noise components to be filtered at the noise arrive The noise components at these locations will participate in the noise processing of the current frequency point; represents the noise filter coefficient; Represents the noise component in the discrete frequency domain at the frequency point The value of , which reflects the distribution of noise at different locations in the frequency domain; Represents the imaginary part function, which is used to extract the component of the complex number in brackets on the imaginary axis.

[0025] The phase value corresponding to each frequency component is calculated using the following formula:

[0026] in, Indicates The phase value corresponding to the frequency component; Represents a complex number, which contains the amplitude and phase information of the signal at this frequency point. It is the frequency index in the frequency domain, and its value range is usually from 0 to the number of FFT points. ; It represents the imaginary part function, which is used to extract the component of the complex number in brackets on the imaginary axis; It represents the real part function, which acts on the complex number in the brackets and extracts its component on the real axis.

[0027] Finally, the calculated spectrum amplitude and phase values ​​are combined in a certain order to generate a frequency domain feature vector: ,in, represents the frequency domain feature vector; represents the frequency component; Indicates The spectrum amplitude value corresponding to the frequency component; Indicates The phase value corresponding to the frequency component.

[0028] Step S12, calculating the energy distribution of the signal based on a high-order cumulant algorithm, extracting the energy characteristics after non-Gaussian noise suppression through fourth-order and higher cumulant operations, and identifying the statistical characteristics of the nonlinear components in the signal; By preprocessing the collected discrete data, the offset and scale effects of the signal are eliminated. Then, the fourth-order and higher-order cumulants of the signal are calculated, where the fourth-order cumulants The calculation formula is:

[0029] in, represents the fourth-order cumulant; It is expressed as the length of the signal; The index variable for summation, which indicates the current sample point number, ranges from 1 to ; The signal is The value of the sampling points represents the discrete signal data; Indicates the mean of the signal, indicates the average value of the signal; Step S13: Establish a signal stability evaluation model based on the spectrum amplitude fluctuation range and the phase consistency threshold, screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude amplitude jumps or phase mutation signals caused by noise interference; Comprehensively analyze the signal's spectrum amplitude and phase information to establish a signal stability evaluation model. Calculate the fluctuation range of the spectrum amplitude and determine the mean value of the signal amplitude through statistical analysis methods. and standard deviation , and then set the amplitude fluctuation threshold ,Right now ,in, is the amplitude fluctuation threshold; is the maximum value of the spectrum amplitude; is the minimum value of the spectrum amplitude.

[0030] At the same time, the phase consistency is calculated by the absolute value of the phase difference With the preset phase consistency threshold For comparison, the phase stability of the evaluation signal is calculated using the following formula to calculate the absolute value of the phase difference:

[0031] in, represents the absolute value of the phase difference, Indicates the total number of points in the spectrum; Represents an index variable, ranging from 1 to ; Indicates The phase value at each frequency point.

[0032] If the signal spectrum amplitude fluctuation range Less than the preset threshold , and phase consistency Greater than the preset threshold , then the signal is considered to have good frequency stability. In addition, combined with the signal energy threshold , through the formula: Filter out candidate signals that meet the preset energy and frequency stability, among which, represents the signal energy, Represents the total number of points in the spectrum, the number of points at which the signal is sampled in the frequency domain, Indicates The spectrum amplitude value of the frequency point indicates that the signal is at The amplitude of a frequency point.

[0033] Step S2, calculating microwave energy information according to discrete signals, and monitoring characteristic changes in real time, setting an adaptive filter, and performing multipath signal enhancement processing using the adaptive filter according to the extracted frequency characteristic parameters, energy information, and characteristic changes monitored in real time; Specifically, an adaptive filter is designed to enhance the signal by using the extracted frequency characteristic parameters, energy information and real-time monitored characteristic changes. The frequency response function of the filter is calculated according to the signal's spectrum amplitude value, phase value and characteristic change trend. The gain parameter of the filter is dynamically adjusted by using fuzzy logic control according to the signal's high-order cumulant and mean square value. In view of the multipath effect in the microwave propagation process, the signal components propagated on different paths are estimated and merged. Specifically, the following sub-steps are included: Step S21, constructing a frequency response function of the filter according to the dynamic change trend of the spectrum amplitude value and the phase value, fitting the envelope curve of the spectrum amplitude value through an interpolation algorithm, and introducing a phase compensation mechanism to correct the signal distortion caused by the Doppler effect or propagation attenuation; Step S22: Based on fuzzy logic control rules and in combination with the energy distribution characteristics of the high-order cumulative output, a fuzzy membership function of the gain coefficient and the bandwidth parameter is established, and the passband range and stopband attenuation strength of the filter are dynamically adjusted according to the energy concentration; The signal is processed using the previous high-order cumulant algorithm to obtain the energy distribution characteristics that can highlight the essence of the signal and suppress Gaussian noise. Then, the energy concentration and energy distribution uniformity are defined as fuzzy input variables, and the gain coefficient and bandwidth parameter are defined as fuzzy output variables, and fuzzy membership functions covering multiple fuzzy sets are constructed respectively. Based on signal processing experience and actual needs, a series of fuzzy logic control rules that describe the corresponding relationship between the fuzzy sets of input and output variables are formulated. Then, the membership degree of the output variable in each fuzzy set is determined through fuzzy reasoning, and then it is converted into a specific value through defuzzification. Finally, the passband range and stopband attenuation strength of the filter are dynamically adjusted according to the obtained values ​​to improve the quality and efficiency of microwave signal processing.

[0034] Step S23: for multipath propagation paths, use delay estimation and signal coherence analysis methods to separate the path components, determine the delay difference of the signal components by calculating the cross-correlation function between the paths, select the effective path based on the coherence matrix, and reconstruct the enhanced signal through a weighted combination algorithm; Step S231, calculating the multipath delay by the signal arrival time difference, identifying the main propagation path in combination with the signal coherence matrix, estimating the delay value of each path component by using the generalized cross-correlation algorithm, and generating a multipath delay distribution histogram; The multipath propagation signal is collected by using a high-sensitivity, wide-band receiving antenna array, and then the signal is converted into a digital signal through anti-aliasing low-pass filtering and analog-to-digital conversion, and pre-processed by removing DC offset and normalizing amplitude. The signal arrival time difference is obtained by performing cross-correlation operation on the signals received by the two antennas, searching for the peak position of the cross-correlation function, and calculating the multipath delay by combining the antenna position and the signal propagation speed. For any two antennas, and Received signal and , let the cross-correlation function be ,in is the cross-correlation function; is the observation time of the signal; and Respectively represent the numbers of the two antennas; and Respectively and The two antennas are The received signal; Represents the time delay parameter, whose value determines the time offset between signals when calculating the cross-correlation function. The peak position , you can get the signal from the antenna and The time difference of arrival is based on the spatial relationship of the antennas and the known signal propagation speed. , calculate the multipath delay ,in, is the multipath delay; is the correlation function The peak position of is the signal propagation speed.

[0035] Perform Fourier transform on the signal received by the antenna to obtain the frequency domain signal and , calculate the signal coherence coefficient and construct the coherence matrix, and use the following formula to calculate the signal correlation coefficient:

[0036] in, Indicates antenna and antenna The received signal is at a frequency The coherence coefficient at is used to measure the correlation between the two signals at this frequency, and its value is between 0 and 1. The closer the value is to 1, the closer the two antennas receive signals at the frequency. The closer the value is to 0, the weaker the coherence. and Respectively represent the numbers of the two antennas; and Respectively and Fourier transform of yes The complex conjugate of ; Represents the mathematical expectation operation, which is used to calculate the statistical average of the corresponding signal quantities.

[0037] The analysis matrix identifies the main propagation path. The generalized cross-correlation algorithm is used. The cross-power spectrum is processed with a weighted function and then inverse Fourier transform is performed to obtain the generalized cross-correlation function. The peak position is searched to accurately estimate the delay value of each path component. Finally, all estimated multipath delay values ​​are counted, the delay interval is divided, and the multipath delay distribution histogram is drawn with the delay value as the horizontal axis and the number of occurrences as the vertical axis.

[0038] Step S232: construct a weight distribution model for multipath signal components based on the path energy proportion and the phase offset, dynamically adjust the weight coefficient according to the signal-to-noise ratio and phase consistency of the path signal, and preferentially retain the path components whose energy proportion is higher than a preset threshold; Calculate the energy of each separated path component and the total energy of all path components to obtain the energy proportion of each path, perform Hilbert transform on the path component to obtain the analytical signal, calculate its instantaneous phase and integrate to obtain the phase offset. Construct a weight distribution model that includes energy proportion and phase offset, and dynamically adjust the model coefficient according to the signal-to-noise ratio and phase consistency of the path component. At the same time, set a preset threshold for energy proportion, give priority to retaining path components with energy proportions higher than the threshold, and set a smaller weight coefficient for path components that do not meet the conditions.

[0039] Step S233: Use the maximum ratio combining algorithm to superimpose the separated signal components, set the combining weight coefficient according to the signal-to-noise ratio of each path component, eliminate the signal distortion caused by multipath interference, and restore the time domain waveform integrity of the original signal; All separated path components are superimposed according to the weight coefficient to obtain the reconstructed signal. By reasonably allocating the weight coefficient, the path components with high signal-to-noise ratio contribute more to the reconstructed signal and restore the time domain waveform integrity of the original signal.

[0040] Step S3: After the multipath signal enhancement processing, a preliminary enhanced microwave signal is obtained, and the enhanced signal is subjected to inverse fast Fourier transform to convert it from the frequency domain back to the time domain to obtain an enhanced time domain signal; Specifically, after the adaptive filtering enhancement process, a preliminary enhanced microwave signal is obtained, and the enhanced signal is subjected to inverse fast Fourier transform using the spectrum amplitude value and phase value of the signal, and is converted from the frequency domain back to the time domain to obtain an enhanced time domain signal, and the enhanced time domain signal is subjected to amplitude normalization process according to the mean square value and high-order cumulant of the signal to keep its energy within a reasonable range. Specifically, the following sub-steps are included: Step S31, calculating the mean square value and high-order cumulant of the time domain enhanced signal, generating a dynamic energy adjustment coefficient, and determining a normalized scaling ratio according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean; Considering the characteristics of the signal in different time periods, the mean square value is calculated using the following formula: ,in, represents the mean square value; Indicates the signal length; Represents the time domain enhanced signal.

[0041] The formula for higher-order cumulants is as follows:

[0042] in, Indicates cumulative amount; represents the order of higher-order cumulants; It is expressed as the length of the signal; The index variable for summation, which indicates the current sample point number, ranges from 1 to ; The signal is The value of the sampling points represents the discrete signal data; Indicates the mean of the signal, indicates the average value of the signal; Through these calculations, dynamic energy adjustment coefficients and normalized scaling ratios are generated.

[0043] Step S32: according to the preset energy range threshold, a piecewise linear compression algorithm is used to normalize the signal amplitude, multiple amplitude intervals are set and compression slopes are defined respectively to avoid signal overload or quantization distortion; According to the preset energy range threshold and , use piecewise linear compression algorithm to normalize the signal amplitude. Set multiple amplitude intervals and define the compression slope of each interval separately and offset , the calculation formula is:

[0044] in, Represents the normalized signal amplitude value, which means The normalized signal amplitude of the sampling points; Represents the amplitude value of the original signal, indicating that The original signal amplitude of the sampling points; Indicates the preset lower bound of the amplitude, indicating the minimum value of the signal amplitude; Indicates the preset upper limit of the amplitude, indicating the maximum value of the signal amplitude; represents the compression slope of the first interval, indicating that The slope of the linear transformation when ; The offset of the first interval indicates that The linear transformation offset when ; represents the compression slope of the second interval, indicating that The slope of the linear transformation when ; The offset of the second interval indicates that The linear transformation offset when ; represents the compression slope of the third interval, indicating that when The slope of the linear transformation when ; The offset of the third interval indicates that The linear transformation offset when .

[0045] The piecewise linear compression algorithm is used to avoid signal overload or quantization distortion and ensure that the signal amplitude is within a reasonable range.

[0046] Step S33: Establish a signal envelope monitoring mechanism to track the peak wave of the normalized signal in real time.

[0047] By monitoring the envelope in real time, the peak wave of the normalized signal is tracked. A sliding time window is set to count the maximum value of the envelope and related statistical features in each window. When the peak wave changes abnormally, the corresponding processing mechanism is triggered to adjust the subsequent processing parameters or issue an early warning signal.

[0048] Step S4: introducing a signal quality evaluation index to evaluate the quality of the enhanced time domain signal. If the signal quality does not meet expectations, automatically feedback and adjust the influencing parameters of the adaptive filter until the signal quality meets the requirements. Specifically, a signal quality evaluation index is introduced to evaluate the quality of the enhanced signal. According to the evaluation results, if the signal quality does not meet expectations, the processing parameters in the first two steps are automatically adjusted and secondary processing is performed until the signal quality meets the requirements. Specifically, the following sub-steps are included: Step S41, constructing a multi-dimensional quality assessment index based on the signal-to-noise ratio, spectrum flatness and phase jitter parameters, and calculating the signal-to-noise ratio gain, spectrum ripple fluctuation variance and phase difference variance of the signal in the target frequency band; First, calculate the signal-to-noise ratio gain of the signal , by comparing the signal-to-noise ratio before and after enhancement , evaluate the effect of signal enhancement processing on noise suppression, the formula is as follows: ,in, is the signal-to-noise ratio gain of the signal; and are the signal-to-noise ratio after enhancement and the signal-to-noise ratio before enhancement, respectively.

[0049] Secondly, calculate the spectrum ripple fluctuation variance, and evaluate the stability of the signal spectrum by analyzing the fluctuation of the spectrum flatness. The calculation formula is as follows:

[0050] in, The variance of the spectrum flatness is expressed as the degree of volatility; Indicates the length of the signal; Indicates The spectrum flatness value of each frequency point; Indicates the mean value of spectrum flatness; Indicates the index of the frequency point, ranging from 0 to .

[0051] Finally, the phase differential variance is calculated. By analyzing the changes in the phase jitter parameters, the stability of the signal phase is evaluated. The phase differential variance is calculated using the following formula:

[0052] in, It represents the phase difference variance, which indicates the fluctuation degree of the phase difference value; Indicates the length of the signal; Indicates The phase difference value of each frequency point; represents the mean of the phase differences; Indicates the index of the frequency point, ranging from 0 to .

[0053] Step S42: if the evaluation result does not reach the preset threshold, then reversely adjust the influencing parameters of the filter; The inverse adjustment of the influencing parameters of the filter specifically includes the following sub-steps: Step S421, generating a parameter adjustment gradient vector according to the deviation between the quality assessment result and the target value, and defining the sensitivity coefficient of each processing link parameter to determine the adjustment direction; Step S422, using a fuzzy PID control algorithm to incrementally correct the filter parameters, calculating the correction amounts of the proportional, integral and differential terms according to the fuzzy membership of the deviation, and limiting the adjustment step size to prevent overshoot; Step S423, record the historical adjustment trajectory, predict the optimal parameter combination through the machine learning model, use the long short-term memory network to analyze the implicit rules of the parameter adjustment sequence, and generate parameter prediction values ​​to accelerate the convergence process; LSTM captures the long-term dependencies of time series data and is suitable for processing historical data for parameter adjustment. It accelerates the convergence process of parameter adjustment and improves optimization efficiency through predictions of machine learning models.

[0054] Step S43, recalibrate the oversampling rate and classification screening threshold through an iterative optimization algorithm, use the golden section search method to determine the optimal oversampling multiple, and update the screening threshold in combination with the classifier confidence until the output signal meets the quality requirements; Embodiment 2 like Figure 2 As shown, Embodiment 2 of the present application provides a signal enhancement device based on microwave technology, comprising: Information collection and preprocessing module 21: obtains the original microwave signal through a microwave signal receiving device, samples the original signal to obtain a discrete signal, performs spectrum analysis on the obtained discrete signal, and extracts the frequency characteristic parameters of the signal; the information collection and preprocessing module includes the following submodules: Signal acquisition and preliminary processing submodule 211: using a microwave signal receiving device to obtain the original microwave signal, using oversampling technology to sample the original signal according to a preset oversampling rate to obtain discrete signal data, performing fast Fourier transform on the discrete signal, calculating the spectrum amplitude value and phase value, and generating a frequency domain feature vector; Signal feature extraction and analysis submodule 212: Calculate the energy distribution of the signal based on a high-order cumulant algorithm, extract the energy features after non-Gaussian noise suppression through fourth-order and higher cumulant operations, and identify the statistical characteristics of non-linear components in the signal; extract the energy features after non-Gaussian noise suppression through fourth-order and higher cumulant operations, identify the statistical characteristics of non-linear components in the signal and calculate fourth-order cumulants; Signal classification and screening submodule 213: Based on the spectrum amplitude fluctuation range and phase consistency threshold, a signal stability evaluation model is established to screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude amplitude jump or phase mutation signals caused by noise interference; Signal enhancement module 22: calculate microwave energy information according to discrete signals, and monitor characteristic changes in real time, set an adaptive filter, and use the adaptive filter to perform multipath signal enhancement processing according to the extracted frequency characteristic parameters, energy information and characteristic changes monitored in real time; after the multipath signal enhancement processing, a preliminary enhanced microwave signal is obtained, and the enhanced signal is subjected to inverse fast Fourier transform, and it is converted from the frequency domain back to the time domain to obtain an enhanced time domain signal; it includes the following submodules: Filter frequency response function construction submodule 221: According to the dynamic change trend of the spectrum amplitude value and phase value, the frequency response function of the filter is constructed, the envelope curve of the spectrum amplitude value is fitted by the interpolation algorithm, and the phase compensation mechanism is introduced to correct the signal distortion caused by the Doppler effect or propagation attenuation; the envelope curve of the spectrum amplitude value is fitted by the interpolation algorithm to capture the amplitude change characteristics of the signal. The interpolation algorithm adopts methods such as spline interpolation, linear interpolation or high-order polynomial interpolation to ensure the smoothness and accuracy of the envelope curve. In addition, the phase compensation mechanism is introduced to correct the signal distortion caused by the Doppler effect or propagation attenuation.

[0055] Fuzzy logic control gain parameter adjustment submodule 222: Based on fuzzy logic control rules and combined with the energy distribution characteristics of the high-order cumulative output, a fuzzy membership function of the gain coefficient and the bandwidth parameter is established, and the passband range and stopband attenuation strength of the filter are dynamically adjusted according to the energy concentration; the energy distribution characteristics of the signal are extracted by calculating the high-order cumulative amount of the signal. These characteristics are used to construct a fuzzy membership function to reflect the concentration of signal energy. According to the energy concentration, the passband range and stopband attenuation strength of the filter are dynamically adjusted.

[0056] Multipath signal separation and merging submodule 223: For multipath propagation paths, the delay estimation and signal coherence analysis methods are used to separate the path components, the delay difference of the signal components is determined by calculating the cross-correlation function between the paths, and the effective paths are screened based on the coherence matrix, and the enhanced signal is reconstructed through the weighted merging algorithm.

[0057] Information evaluation module 23: introduces signal quality evaluation indicators to evaluate the quality of the enhanced time domain signal. If the signal quality does not meet expectations, the influencing parameters of the adaptive filter are automatically adjusted through feedback until the signal quality meets the requirements. It includes the following submodules: Signal enhancement submodule 231: calculates the mean square value and high-order cumulant of the time domain enhanced signal, generates a dynamic energy adjustment coefficient, and determines the normalization scaling ratio according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean; Signal normalization submodule 232: According to the preset energy range threshold, the signal amplitude is normalized by using a piecewise linear compression algorithm, multiple amplitude intervals are set and compression slopes are defined respectively to avoid signal overload or quantization distortion; according to the preset energy range threshold and , use piecewise linear compression algorithm to normalize the signal amplitude. Set multiple amplitude intervals and define the compression slope of each interval separately and offset , through the piecewise linear compression algorithm, signal overload or quantization distortion is avoided, ensuring that the signal amplitude is within a reasonable range.

[0058] Signal envelope monitoring submodule 233: establishes a signal envelope monitoring mechanism to track the peak wave of the normalized signal in real time.

[0059] Model parameter fine-tuning submodule 234: constructs a multi-dimensional quality evaluation index based on the signal-to-noise ratio, spectrum flatness and phase jitter parameters, and calculates the signal-to-noise ratio gain, spectrum ripple fluctuation variance and phase difference variance of the signal in the target frequency band; Filter parameter adjustment submodule 235: if the evaluation result does not reach the preset threshold, the filter gain parameter, frequency response function and multipath combining weight are reversely adjusted; Standard scoring sample collection submodule 236: The oversampling rate and classification screening threshold are recalibrated through an iterative optimization algorithm, the optimal oversampling multiple is determined by the golden section search method, and the screening threshold is updated in combination with the classifier confidence until the output signal meets the quality requirements.

[0060] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor; The memory is used to store one or more program instructions; The processor is used for running one or more program instructions to execute a signal enhancement method based on microwave technology.

[0061] Corresponding to the above embodiment, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer storage medium contains one or more program instructions, and the one or more program instructions are used by a processor to execute a signal enhancement method based on microwave technology.

[0062] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned signal enhancement method based on microwave technology.

[0063] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0064] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or a mature storage medium in the art. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0065] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0066] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0067] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus DRAM (DRRAM).

[0068] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0069] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. Storage media can be any available media that can be accessed by general or special-purpose computers.

[0070] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions and improvements made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A signal enhancement method based on microwave technology, characterized in that: include: The original microwave signal is obtained by a microwave signal receiving device, the original signal is sampled to obtain a discrete signal, the obtained discrete signal is subjected to spectrum analysis, and the frequency characteristic parameters of the signal are extracted; Calculate microwave energy information based on discrete signals, monitor feature changes in real time, set an adaptive filter, and use the adaptive filter to perform multipath signal enhancement processing based on the extracted frequency feature parameters, energy information, and feature changes monitored in real time; After multipath signal enhancement processing, a preliminary enhanced microwave signal is obtained, and the enhanced signal is subjected to inverse fast Fourier transform to convert it from the frequency domain back to the time domain to obtain an enhanced time domain signal; Signal quality evaluation indicators are introduced to evaluate the quality of the enhanced time domain signal. If the signal quality does not meet expectations, the influencing parameters of the adaptive filter are automatically adjusted until the signal quality meets the requirements.

2. The signal enhancement method based on microwave technology according to claim 1, characterized in that: The method comprises the following steps: obtaining an original microwave signal through a microwave signal receiving device, sampling the original signal to obtain a discrete signal, performing spectrum analysis on the obtained discrete signal, and extracting the frequency characteristic parameters of the signal. The original microwave signal is obtained by using a microwave signal receiving device, and the original signal is sampled according to a preset oversampling rate using oversampling technology to obtain discrete signal data, and the discrete signal is subjected to fast Fourier transform to calculate the spectrum amplitude value and phase value to generate a frequency domain feature vector; The energy distribution of the signal is calculated based on the high-order cumulant algorithm, and the energy characteristics after non-Gaussian noise suppression are extracted through fourth-order and higher cumulant operations to identify the statistical characteristics of the nonlinear components in the signal; According to the spectrum amplitude fluctuation range and phase consistency threshold, a signal stability evaluation model is established to screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude amplitude jumps or phase mutation signals caused by noise interference.

3. The signal enhancement method based on microwave technology according to claim 1, characterized in that: The multipath signal enhancement processing is performed using an adaptive filter, including the following sub-steps: According to the dynamic change trend of the spectrum amplitude and phase values, the frequency response function of the filter is constructed, the envelope curve of the spectrum amplitude is fitted through the interpolation algorithm, and the phase compensation mechanism is introduced to correct the signal distortion caused by the Doppler effect or propagation attenuation; Based on fuzzy logic control rules and combined with the energy distribution characteristics of high-order cumulative output, the fuzzy membership function of gain coefficient and bandwidth parameter is established to dynamically adjust the passband range and stopband attenuation strength of the filter according to the energy concentration. For multipath propagation paths, the time delay estimation and signal coherence analysis methods are used to separate the components of each path. The time delay difference of the signal components is determined by calculating the cross-correlation function between the paths. The effective paths are screened based on the coherence matrix, and the enhanced signal is reconstructed through the weighted combining algorithm.

4. The signal enhancement method based on microwave technology according to claim 3, characterized in that: For multipath propagation paths, the delay estimation and signal coherence analysis methods are used to separate the path components, and the enhanced signal is reconstructed through a weighted combination algorithm, including the following sub-steps: The multipath delay is calculated by the signal arrival time difference, the main propagation path is identified by combining the signal coherence matrix, the generalized cross-correlation algorithm is used to estimate the delay value of each path component, and a multipath delay distribution histogram is generated; Based on the path energy proportion and phase offset, a weight allocation model for multipath signal components is constructed. The weight coefficient is dynamically adjusted according to the signal-to-noise ratio and phase consistency of the path signal, and the path components with energy proportions higher than the preset threshold are preferentially retained. The maximum ratio combining algorithm is used to superimpose the separated signal components, and the combining weight coefficient is set according to the signal-to-noise ratio of each path component to eliminate the signal distortion caused by multipath interference and restore the time domain waveform integrity of the original signal.

5. The signal enhancement method based on microwave technology according to claim 1, characterized in that: After obtaining the enhanced time domain signal, the method further includes performing amplitude normalization processing on the enhanced time domain signal according to the mean square value and the high-order cumulant of the time domain signal, including the following sub-steps: The mean square value and high-order cumulant of the time-domain enhanced signal are calculated to generate a dynamic energy adjustment coefficient, and the normalization scaling ratio is determined according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean; According to the preset energy range threshold, the signal amplitude is normalized by using a piecewise linear compression algorithm, multiple amplitude intervals are set and the compression slopes are defined respectively to avoid signal overload or quantization distortion; A signal envelope monitoring mechanism is established to track the peak fluctuation of the normalized signal in real time.

6. The signal enhancement method based on microwave technology according to claim 1, characterized in that: The signal quality evaluation index is introduced to evaluate the quality of the enhanced time domain signal, including the following sub-steps: A multi-dimensional quality evaluation index is constructed based on the signal-to-noise ratio, spectrum flatness and phase jitter parameters to calculate the signal-to-noise ratio gain, spectrum ripple fluctuation variance and phase difference variance of the signal in the target frequency band; If the evaluation result does not reach the preset threshold, the gain parameter, frequency response function and multipath combining weight of the filter are adjusted inversely; The oversampling rate and classification screening threshold are recalibrated through an iterative optimization algorithm, the optimal oversampling multiple is determined by the golden section search method, and the screening threshold is updated in combination with the classifier confidence until the output signal meets the quality requirements.

7. The signal enhancement method based on microwave technology according to claim 6, characterized in that: The oversampling rate and classification screening threshold are calibrated again through an iterative optimization algorithm, including the following sub-steps: According to the deviation between the quality assessment result and the target value, a parameter adjustment gradient vector is generated, and the sensitivity coefficient of each processing link parameter is defined to determine the adjustment direction; The fuzzy PID control algorithm is used to incrementally correct the filter parameters. The correction values ​​of the proportional, integral and differential terms are calculated according to the fuzzy membership of the deviation value, and the adjustment step size is limited to prevent overshoot. Record historical adjustment trajectories, predict the optimal parameter combination through machine learning models, use long short-term memory networks to analyze the implicit rules of parameter adjustment sequences, and generate parameter prediction values ​​to accelerate the convergence process.

8. A signal enhancement device based on microwave technology, characterized in that: include: Information acquisition and preprocessing module: obtain the original microwave signal through the microwave signal receiving device, sample the original signal to obtain a discrete signal, perform spectrum analysis on the obtained discrete signal, and extract the frequency characteristic parameters of the signal; Signal enhancement module: Calculate microwave energy information based on discrete signals, monitor feature changes in real time, set an adaptive filter, and use the adaptive filter to perform multipath signal enhancement processing based on the extracted frequency feature parameters, energy information, and feature changes monitored in real time; After multipath signal enhancement processing, a preliminary enhanced microwave signal is obtained, and the enhanced signal is subjected to inverse fast Fourier transform to convert it from the frequency domain back to the time domain to obtain an enhanced time domain signal; Information evaluation module: Introduce signal quality evaluation indicators to evaluate the quality of the enhanced time domain signal. If the signal quality does not meet expectations, the influencing parameters of the adaptive filter are automatically adjusted through feedback until the signal quality meets the requirements.

9. A computer storage medium, characterized in that include: at least one memory and at least one processor; A memory for storing one or more program instructions; A processor, used to run one or more program instructions to execute a signal enhancement method based on microwave technology as described in any one of claims 1-7.

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