A signal enhancement method and device based on microwave technology

Abstract

The present invention discloses a signal enhancement method and device based on microwave technology, which relates to the technical field of signal processing. The method includes: acquiring an original microwave signal, sampling the original signal to obtain a discrete signal, performing spectral analysis on the obtained discrete signal, and extracting the frequency characteristic parameters of the signal; calculating microwave energy information according to the discrete signal, and real-time monitoring the characteristic change situation, setting an adaptive filter, and performing multipath signal enhancement processing by using the adaptive filter according to the extracted frequency characteristic parameters, energy information, and the real-time monitored characteristic change situation; performing inverse fast Fourier transform on the enhanced signal to convert it from the frequency domain back to the time domain to obtain an enhanced time-domain signal; introducing a signal quality evaluation index to evaluate the quality of the enhanced time-domain signal. The present invention can effectively improve the signal purity and intensity, reduce the bit error rate, and significantly improve the accuracy and stability of microwave signal transmission.

Description

Technical Field

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

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

[0003] Currently, traditional signal enhancement methods have many limitations. Filters with fixed parameters cannot adapt to the dynamic changes of signals. Facing 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 make full use of the information contained in multipath signals. Moreover, existing technologies lack a comprehensive and adaptive signal quality evaluation and feedback adjustment mechanism, and it is difficult to ensure that the enhanced signals continuously meet the requirements of complex and changeable application scenarios.

[0004] With the rapid development of fields such as 5G, 6G communications, intelligent transportation, and military defense, higher requirements are put forward for the quality of microwave signals, and there is an urgent need for more efficient, intelligent, and adaptive signal enhancement technologies. Summary of the Invention

[0005] The present invention proposes a signal enhancement method based on microwave technology, including:

[0006] Obtain the original microwave signal through a 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;

[0007] Calculate the microwave energy information according to the discrete signal, and monitor the change of characteristics in real time. Set an adaptive filter, and perform multipath signal enhancement processing using the adaptive filter according to the extracted frequency characteristic parameters, energy information, and the change of characteristics monitored in real time;

[0008] After the multipath signal enhancement processing, obtain a preliminarily enhanced microwave signal, perform an inverse fast Fourier transform on the enhanced signal to convert it from the frequency domain back to the time domain, and obtain the enhanced time-domain signal;

[0009] Introduce a signal quality evaluation index to evaluate the quality of the enhanced time-domain signal. If the signal quality does not meet the expectation, automatically feedback and adjust the influence parameters of the adaptive filter until the signal quality meets the requirements.

[0010] A signal enhancement method based on microwave technology as described above, wherein, if the evaluation result does not reach the preset threshold, reverse-adjust the influence parameters of the filter, including the following sub-steps:

[0011] Use a microwave signal receiving device to obtain the original microwave signal, apply the oversampling technique, sample the original signal according to the preset oversampling rate to obtain discrete signal data, perform a fast Fourier transform on the discrete signal, calculate the spectral amplitude value and phase value, and generate a frequency-domain feature vector;

[0012] Calculate the energy distribution of the signal based on the high-order cumulant algorithm, extract the energy characteristics after non-Gaussian noise suppression through fourth-order and higher-order cumulant operations, and identify the statistical characteristics of the non-linear components in the signal;

[0013] According to the spectral amplitude fluctuation range and phase consistency threshold, establish a signal stability evaluation model, screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude signals with amplitude jumps or phase mutations caused by noise interference.

[0014] A signal enhancement method based on microwave technology as described above, wherein an adaptive filter is used for multipath signal enhancement processing, including the following sub-steps:

[0015] According to the dynamic change trend of the spectral amplitude value and phase value, construct the frequency response function of the filter, fit the envelope curve of the spectral amplitude value through the interpolation algorithm, and introduce a phase compensation mechanism to correct the signal distortion caused by the Doppler effect or propagation attenuation;

[0016] Based on the fuzzy logic control rule, combine the energy distribution characteristics output by the high-order cumulant, establish a fuzzy membership function of the gain coefficient and bandwidth parameter, and dynamically adjust the passband range and stopband attenuation intensity of the filter according to the energy concentration;

[0017] For the multipath propagation path, use the time delay estimation and signal coherence analysis method to separate each path component, determine the time delay difference of the signal components by calculating the cross-correlation function between paths, and screen out the effective paths based on the coherence matrix, and reconstruct the enhanced signal through the weighted merging algorithm.

[0018] A signal enhancement method based on microwave technology as described above, wherein for the multipath propagation path, use the time delay estimation and signal coherence analysis method to separate each path component, and reconstruct the enhanced signal through the weighted merging algorithm, including the following sub-steps:

[0019] Calculate the multipath time delay through the time difference of arrival of signals, identify the main propagation paths by combining the signal coherence matrix, estimate the time delay values of each path component using the generalized cross-correlation algorithm, and generate a multipath time delay distribution histogram;

[0020] Based on the path energy ratio and phase offset, construct a weight allocation model for multipath signal components, dynamically adjust the weight coefficients according to the signal-to-noise ratio and phase consistency of the path signals, and preferentially retain the path components with an energy ratio higher than the preset threshold;

[0021] Use the maximum ratio combining algorithm to superimpose the separated signal components, set the combining weight coefficients according to the signal-to-noise ratio of each path component, eliminate the signal distortion caused by multipath interference, and restore the integrity of the time-domain waveform of the original signal.

[0022] A signal enhancement method based on microwave technology as described above, wherein, after obtaining the enhanced time-domain signal, it further includes amplitude normalization processing of the enhanced time-domain signal according to the mean square value and high-order cumulants of the time-domain signal, including the following sub-steps:

[0023] Calculate the mean square value and high-order cumulants of the time-domain enhanced signal, generate a dynamic energy adjustment coefficient, and determine the normalization scaling ratio according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean;

[0024] According to the preset energy range threshold, use the piecewise linear compression algorithm to normalize the signal amplitude, set multiple amplitude intervals and define the compression slopes respectively to avoid signal overload or quantization distortion;

[0025] Establish a signal envelope monitoring mechanism to track the peak value fluctuation of the normalized signal in real time.

[0026] A signal enhancement method based on microwave technology as described above, wherein a signal quality evaluation index is introduced to evaluate the quality of the enhanced time-domain signal, including the following sub-steps:

[0027] Construct a multi-dimensional quality evaluation index based on the signal-to-noise ratio, spectral flatness, and phase jitter parameters, and calculate the signal-to-noise ratio gain, spectral ripple fluctuation variance, and phase difference variance of the signal within the target frequency band;

[0028] If the evaluation result does not reach the preset threshold, reversely adjust the gain parameter, frequency response function, and multipath combining weight of the filter;

[0029] Perform secondary calibration on 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.

[0030] A signal enhancement method based on microwave technology as described above, wherein if the evaluation result does not reach the preset threshold, the influence parameters of the filter are adjusted reversely, including the following sub-steps:

[0031] Generate a parameter adjustment gradient vector according to the deviation between the quality evaluation result and the target value, and define the sensitivity coefficients of the parameters of each processing link to determine the adjustment direction;

[0032] Use the fuzzy PID control algorithm to perform incremental correction on the filter parameters, calculate the correction amounts of the proportional, integral, and differential terms according to the fuzzy membership degree of the deviation, and limit the adjustment step size to prevent overshoot;

[0033] Record the historical adjustment trajectory, predict the optimal parameter combination through a machine learning model, analyze the implicit rules of the parameter adjustment sequence using a long short-term memory network, and generate parameter prediction values to accelerate the convergence process.

[0034] The present invention also proposes a signal enhancement device based on microwave technology, including:

[0035] Information acquisition and preprocessing module: Obtain the original microwave signal through a 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;

[0036] Signal enhancement module: Calculate the microwave energy information according to the discrete signal, and monitor the characteristic change situation 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 the real-time monitored characteristic change situation; After multipath signal enhancement processing, obtain a preliminarily enhanced microwave signal, perform inverse fast Fourier transform on the enhanced signal to convert it from the frequency domain back to the time domain, and obtain the enhanced time-domain signal;

[0037] Information evaluation module: Introduce a signal quality evaluation index to evaluate the quality of the enhanced time-domain signal. If the signal quality does not meet the expectation, automatically feedback and adjust the influence parameters of the adaptive filter until the signal quality meets the requirements.

[0038] The present invention also proposes a computer storage medium, including: at least one memory and at least one processor;

[0039] The memory is used to store one or more program instructions;

[0040] The processor is used to run one or more program instructions to execute a signal enhancement method based on microwave technology as described in any one of the above.

[0041] The beneficial effects achieved by the present invention are as follows:

[0042] (1)In terms of improving signal processing capabilities, through oversampling technology, spectral 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 enhancement processing and greatly improving the purity of the signals.

[0043] (2)In terms of adaptive processing, by using the changes in signal features monitored in real time, an adaptive filter is designed, and the frequency response function and gain parameters are dynamically adjusted according to spectral amplitude values, phase values, etc., to flexibly respond to signal dynamic changes, significantly enhancing signal quality and ensuring stable signal transmission.

[0044] (3)Regarding multipath effects, by using time delay estimation, signal coherence analysis, and weighted merging algorithms, signal components on different paths can be accurately separated and merged, effectively eliminating signal distortion caused by multipath interference, restoring the integrity of the time-domain waveform of the original signal, and improving the reliability and accuracy of the signal.

[0045] (4)In addition, the introduced signal quality evaluation index and feedback adjustment mechanism comprehensively evaluate the enhanced signal and automatically optimize the processing parameters until the signal quality meets the standard, realizing the intelligence and precision of signal processing, and effectively ensuring the efficient transmission and application of microwave signals in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0047] Figure 1 is a flowchart of a signal enhancement method based on microwave technology provided by an embodiment of the present application.

[0048] Figure 2 is a schematic diagram of a signal enhancement device based on microwave technology provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0050] Embodiment 1

[0051] As Figure 1As shown in the figure, an embodiment of the present application provides a signal enhancement method based on microwave technology, including:

[0052] Step S1: Obtain the original microwave signal through a microwave signal receiving device, sample the original signal to obtain a discrete signal, perform spectral analysis on the obtained discrete signal, and extract the frequency characteristic parameters of the signal.

[0053] Specifically, obtain the original microwave signal through a microwave signal receiving device, use oversampling technology to sample the original signal to obtain discrete signal data, perform spectral analysis on the obtained discrete signal, calculate the spectral amplitude value and phase value of the signal, and extract the frequency characteristic parameters of the signal; calculate the high-order cumulant of the signal to reflect the energy size of the signal, suppress the influence of Gaussian noise, and based on the energy size and frequency characteristic parameters of the signal, perform preliminary classification and screening on the signal, remove signals with large noise interference, retain signals with high energy and stable frequency characteristics, establish a signal characteristic change monitoring mechanism, and record the change of signal characteristic parameters in real time, which specifically includes the following sub-steps:

[0054] Step S11: Use a microwave signal receiving device to obtain the original microwave signal, use oversampling technology, sample the original signal at a preset oversampling rate to obtain discrete signal data, perform a fast Fourier transform on the discrete signal, calculate the spectral amplitude value and phase value, and generate a frequency domain feature vector.

[0055] Use a microwave signal receiving device to capture the original microwave signal, use oversampling technology, sample the original signal at a preset oversampling rate, by increasing the sampling frequency to be higher than the minimum sampling frequency required by the Nyquist sampling theorem, capture more signal detail information, so that the discrete signal data can more accurately reflect the internal characteristics of the original signal. After obtaining the discrete signal data, perform a fast Fourier transform on it to convert the signal from the time domain to the frequency domain.

[0056] In the frequency domain, use the following formula to calculate the spectral amplitude value corresponding to each frequency component:

[0057] Where represents the spectral amplitude value corresponding to the th frequency component; represents the real part function, which acts on the complex number in the brackets and extracts its component on the real axis; represents the weighted processing of the original discrete signal and then the value of the frequency domain signal obtained by the fast Fourier transform (FFT) at the th frequency point, which reflects the frequency domain characteristics of the weighted signal; th frequency point; Indicates the length of the noise filtering coefficient, which defines the range of noise components involved in filtering the noise at the current frequency point At this point, the noise components within the range will participate in the noise processing for the current frequency point; To The noise components at these positions will participate in the noise processing for the current frequency point; Represents the noise filtering coefficient; Indicates the value of the noise component in the discrete frequency domain at the frequency point It reflects the distribution of noise at different positions in the frequency domain; Represents the imaginary part function, used to extract the component of the complex number within the parentheses on the imaginary axis.

[0058] The following formula is used to calculate the phase value corresponding to each frequency component:

[0059] Where, Represents the phase value corresponding to the th frequency component; Represents a complex number, containing the amplitude and phase information of the signal at this frequency point, Is the frequency index in the frequency domain, and its value range is usually from 0 to the number of FFT points ; Represents the imaginary part function, used to extract the component of the complex number within the parentheses on the imaginary axis; Represents the real part function, which acts on the complex number within the parentheses to extract its component on the real axis.

[0060] Finally, the calculated spectral amplitude values and phase values are combined in a certain order to generate a frequency domain feature vector: , where, Represents the frequency domain feature vector; Represents the frequency component; Represents the th spectral amplitude value corresponding to the frequency component; Represents the th phase value corresponding to the frequency component.

[0061] Step S12: Calculate the energy distribution of the signal based on the high-order cumulant algorithm, extract the energy characteristics after suppressing non-Gaussian noise through fourth-order and higher-order cumulant operations, and identify the statistical characteristics of the non-linear components in the signal;

[0062] By preprocessing the collected discrete data, the offset and scale effects of the signal are eliminated. Then, calculate the fourth-order and higher-order high-order cumulants of the signal, where the fourth-order cumulant The calculation formula is:

[0063] Where, Represents the fourth-order cumulant; denotes the length of the signal; The index variable for summation, representing the serial number of the current sample point, with a value range from 1 to ; The value of the signal at the th sampling point, representing the discrete signal data; denotes the mean value of the signal, representing the average value of the signal;

[0064] Step S13: Based on the spectral amplitude fluctuation range and the phase consistency threshold, establish a signal stability evaluation model, screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude signals with amplitude jumps or phase mutations caused by noise interference;

[0065] Conduct a comprehensive analysis of the spectral amplitude and phase information of the signal to establish a signal stability evaluation model. Calculate the fluctuation range of the spectral amplitude, and determine the mean value and standard deviation of the signal amplitude through statistical analysis methods, and then set the amplitude fluctuation threshold , that is, , where is the amplitude fluctuation threshold; is the maximum value of the spectral amplitude; is the minimum value of the spectral amplitude.

[0066] Meanwhile, calculate the phase consistency. Compare the absolute value of the phase difference with the preset phase consistency threshold to evaluate the phase stability of the signal. The absolute value of the phase difference is calculated using the following formula:

[0067] where represents the absolute value of the phase difference, represents the total number of points in the spectrum; represents an index variable, with a value range from 1 to ; represents the phase value of the th frequency point.

[0068] If the spectral amplitude fluctuation range of the signal is less than the preset threshold , and the phase consistency is greater than the preset threshold , then the signal is considered to have good frequency stability. In addition, combined with the energy threshold of the signal, through the formula: screen out candidate signals that meet the preset energy and frequency stability, where represents the signal energy, represents the total number of points in the spectrum, i.e., the number of points at which the signal is sampled in the frequency domain, represents the spectrum amplitude value at the th frequency point, representing the amplitude of the signal at the

[0069] Step S2: Calculate the microwave energy information based on the discrete signal, and monitor the change of characteristics in real time. Set an adaptive filter, and perform multipath signal enhancement processing using the adaptive filter according to the extracted frequency characteristic parameters, energy information, and the change of characteristics monitored in real time;

[0070] Specifically, using the extracted frequency characteristic parameters, energy information, and the change of characteristics monitored in real time, design an adaptive filter to enhance the signal. Calculate the frequency response function of the filter according to the spectrum amplitude value, phase value, and characteristic change trend of the signal. According to the higher-order cumulant and mean square value of the signal, use fuzzy logic control to dynamically adjust the gain parameter of the filter. For the multipath effect in the microwave propagation process, estimate and combine the signal components propagating along different paths, which specifically includes the following sub-steps:

[0071] Step S21: Construct the frequency response function of the filter according to the dynamic change trend of the spectrum amplitude value and phase value. Fit the envelope curve of the spectrum amplitude value through the interpolation algorithm, and introduce a phase compensation mechanism to correct the signal distortion caused by the Doppler effect or propagation attenuation;

[0072] Step S22: Based on the fuzzy logic control rules, combine the energy distribution characteristics output by the higher-order cumulant to establish the fuzzy membership function of the gain coefficient and bandwidth parameter, and dynamically adjust the passband range and stopband attenuation intensity of the filter according to the energy concentration;

[0073] Use the previous higher-order cumulant algorithm to process the signal to obtain the energy distribution characteristics that can highlight the essence of the signal and suppress Gaussian noise. Then define the energy concentration and energy distribution uniformity as fuzzy input variables, and the gain coefficient and bandwidth parameter as fuzzy output variables, and construct the fuzzy membership function covering multiple fuzzy sets respectively. According to the signal processing experience and actual requirements, formulate a series of fuzzy logic control rules describing the corresponding relationship of the fuzzy sets of the input and output variables. Then, through fuzzy reasoning, determine the membership degree of the output variable in each fuzzy set, and then convert it into a specific value through defuzzification. Finally, dynamically adjust the passband range and stopband attenuation intensity of the filter according to the obtained value to improve the quality and efficiency of microwave signal processing.

[0074] Step S23: For multipath propagation paths, use time-delay estimation and signal coherence analysis methods to separate each path component. Determine the time-delay difference of signal components by calculating the cross-correlation function between paths, and screen out effective paths based on the coherence matrix. Reconstruct and enhance the signal through a weighted merging algorithm.

[0075] Step S231: Calculate the multipath time delay through the time difference of arrival of signals, identify the main propagation paths in combination with the signal coherence matrix, use the generalized cross-correlation algorithm to estimate the time-delay values of each path component, and generate a multipath time-delay distribution histogram.

[0076] Collect multipath propagation signals using a high-sensitivity, wide-band receiving antenna array, perform anti-aliasing low-pass filtering and analog-to-digital conversion to digital signals, and perform preprocessing such as removing DC offset and normalizing amplitude. By performing a cross-correlation operation on the signals received by two antennas, search for the peak position of their cross-correlation function to obtain the time difference of arrival of the signals, and calculate the multipath time delay in combination with the antenna positions and the signal propagation speed. For any two antennas and the signals received and , let the cross-correlation function be , where is the cross-correlation function; is the observation duration of the signal; and represent the numbers of the two antennas respectively; and represent respectively and the signals received by the two antennas at time ; represents the time-delay parameter, and its value determines the time offset between signals when calculating the cross-correlation function. By searching for the peak position of , the time difference of arrival of the signal from antennas and can be obtained. According to the spatial position relationship of the antennas and the known signal propagation speed , calculate the multipath time delay , where is the multipath time delay; is the peak position of the cross-correlation function ; is the signal propagation speed.

[0077] Perform a Fourier transform on the signals received by the antennas to obtain the frequency-domain signals and , calculate the signal coherence coefficient and construct a coherence matrix. Use the following formula to calculate the signal correlation coefficient:

[0078] Among them, represents the antenna and the antenna The coherence coefficient of the received signals at frequency is used to measure the correlation degree of these two signals at this frequency, and its value ranges from 0 to 1. The closer the value is to 1, it indicates that the received signals of the two antennas at frequency have stronger coherence; the closer the value is to 0, the weaker the coherence; and respectively represent the numbers of the two antennas; and respectively represent and of the Fourier transform; is the conjugate complex number of represents the mathematical expectation operation, which is used to calculate the statistical average of the corresponding signal quantity.

[0079] The analysis matrix is used to identify the main propagation paths. The generalized cross-correlation algorithm is adopted. After processing the cross-power spectrum with a weighting function, the inverse Fourier transform is performed to obtain the generalized cross-correlation function, and the peak position is searched to accurately estimate the delay values of each path component. Finally, all the estimated multipath delay values are statistically analyzed, the delay intervals are divided, and a multipath delay distribution histogram is drawn with the delay value as the abscissa and the number of occurrences as the ordinate.

[0080] Step S232: Based on the path energy ratio and phase offset, construct a weight allocation model for multipath signal components, dynamically adjust the weight coefficients according to the signal-to-noise ratio and phase consistency of the path signals, and preferentially retain the path components with an energy ratio higher than the preset threshold;

[0081] Calculate the energy of each separated path component and the total energy of all path components to obtain the energy ratio of each path. Perform the Hilbert transform on the path components to obtain the analytic signal, calculate its instantaneous phase and integrate to obtain the phase offset. Construct a weight allocation model including the energy ratio and phase offset, and dynamically adjust the model coefficients according to the signal-to-noise ratio and phase consistency of the path components. At the same time, set the preset threshold of the energy ratio, preferentially retain the path components with an energy ratio higher than this threshold, and set a smaller weight coefficient for the path components that do not meet the conditions.

[0082] Step S233: Use the maximum ratio combining algorithm to superimpose the separated signal components, set the combining weight coefficients according to the signal-to-noise ratio of each path component, eliminate the signal distortion caused by multipath interference, and restore the integrity of the time-domain waveform of the original signal;

[0083] All the separated path components are superimposed according to this weight coefficient to obtain a reconstructed signal. By reasonably allocating the weight coefficient, the path components with high signal-to-noise ratio contribute more to the reconstructed signal, and the integrity of the time-domain waveform of the original signal is restored.

[0084] Step S3: After the multipath signal enhancement process, a preliminarily enhanced microwave signal is obtained. The enhanced signal is subjected to an inverse fast Fourier transform to convert it from the frequency domain back to the time domain, and an enhanced time-domain signal is obtained.

[0085] Specifically, after the adaptive filtering enhancement process, a preliminarily enhanced microwave signal is obtained. Using the spectral amplitude value and phase value of the signal, the enhanced signal is subjected to an inverse fast Fourier transform to convert it from the frequency domain back to the time domain, and an enhanced time-domain signal is obtained. According to the mean square value and high-order cumulant of the signal, the amplitude of the enhanced time-domain signal is normalized to keep its energy within a reasonable range. It specifically includes the following sub-steps:

[0086] Step S31: Calculate the mean square value and high-order cumulant of the time-domain enhanced signal to generate a dynamic energy adjustment coefficient, and determine the normalization scaling ratio according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean value.

[0087] Considering the characteristics of the signal in different time periods, the following formula is used to calculate the mean square value: , where represents the mean square value; represents the signal length; represents the time-domain enhanced signal.

[0088] The formula for the high-order cumulant is as follows:

[0089] where represents the cumulant; represents the order of the high-order cumulant; represents the length of the signal; represents the summation index variable, representing the current sample point number, and the value range is from 1 to ; The value of the signal at the th sampling point, representing the discrete signal data; represents the mean value of the signal, representing the average value of the signal;

[0090] Through these calculations, a dynamic energy adjustment coefficient and a normalization scaling ratio are generated.

[0091] Step S32: According to the preset energy range threshold, the amplitude of the signal is normalized using a piecewise linear compression algorithm, and multiple amplitude intervals are set and the compression slopes are defined respectively to avoid signal overload or quantization distortion.

[0092] According to the preset energy range threshold and , the piecewise linear compression algorithm is used to normalize the signal amplitude. Multiple amplitude intervals are set, and the compression slope and offset of each interval are defined respectively and offset , and the calculation formula is:

[0093] where represents the normalized signal amplitude value, and represents the normalized signal amplitude at the th sampling point; represents the amplitude value of the original signal, and represents the original signal amplitude at the th sampling point; represents the preset lower amplitude bound, which is the minimum value of the signal amplitude; represents the preset upper amplitude bound, which is the maximum value of the signal amplitude; represents the compression slope of the first interval, which is the linear transformation slope when ; The offset of the first interval, which is the linear transformation offset when ; represents the compression slope of the second interval, which is the linear transformation slope when ; The offset of the second interval, which is the linear transformation offset when ; represents the compression slope of the third interval, which is the linear transformation slope when ; The offset of the third interval, which is the linear transformation offset when .

[0094] Through the piecewise linear compression algorithm, signal overload or quantization distortion is avoided, and the signal amplitude is ensured to be within a reasonable range.

[0095] Step S33: Establish a signal envelope monitoring mechanism to track the peak wave of the normalized signal in real time.

[0096] By monitoring the envelope in real time, the peak wave of the normalized signal is tracked. A sliding time window is set, and the maximum value of the envelope and related statistical features are statistically analyzed within each window. When the peak wave shows abnormal changes, the corresponding processing mechanism is triggered to adjust the subsequent processing parameters or send out a warning signal.

[0097] Step S4: Introduce a signal quality evaluation index to evaluate the quality of the enhanced time-domain signal. If the signal quality does not meet the expectation, the influence parameters of the adaptive filter are automatically feedback-adjusted until the signal quality meets the requirements;

[0098] 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 the expectations, the processing parameters in the previous two steps are automatically feedback-adjusted for secondary processing until the signal quality meets the requirements. It specifically includes the following sub-steps:

[0099] Step S41: Construct a multi-dimensional quality evaluation index based on the signal-to-noise ratio, spectral flatness, and phase jitter parameters, and calculate the signal-to-noise ratio gain, spectral ripple variance, and phase difference variance within the target frequency band of the signal;

[0100] First, calculate the signal-to-noise ratio gain , 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: , where 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.

[0101] Secondly, calculate the spectral ripple variance. By analyzing the fluctuation of spectral flatness, evaluate the stability of the signal spectrum. The calculation formula is as follows:

[0102] where represents the variance of spectral flatness, indicating the fluctuation degree of spectral flatness ; represents the length of the signal; represents the spectral flatness value at the th frequency point; represents the mean value of spectral flatness; represents the index of the frequency point, and the value range is from 0 to .

[0103] Finally, calculate the phase difference variance. By analyzing the change of the phase jitter parameter, evaluate the stability of the signal phase. The phase difference variance is calculated using the following formula:

[0104] where represents the phase difference variance, indicating the fluctuation degree of the phase difference value; represents the length of the signal; represents the phase difference value at the th frequency point; represents the mean value of the phase difference; represents the index of the frequency point, and the value range is from 0 to .

[0105] Step S42: If the evaluation result does not reach the preset threshold, inversely adjust the influence parameters of the filter;

[0106] Among them, inversely adjusting the influence parameters of the filter specifically includes the following sub-steps:

[0107] Step S421: Generate a parameter adjustment gradient vector according to the deviation between the quality evaluation result and the target value, and define the sensitivity coefficients of the parameters of each processing link to determine the adjustment direction;

[0108] Step S422: Use the fuzzy PID control algorithm to perform incremental correction on the filter parameters, calculate the correction amounts of the proportional, integral, and differential terms according to the fuzzy membership degree of the deviation, and limit the adjustment step size to prevent overshoot;

[0109] Step S423: Record the historical adjustment trajectory, predict the optimal parameter combination through a machine learning model, use a 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;

[0110] LSTM captures the long-term dependence relationship of time series data, is suitable for processing historical data of parameter adjustment, and through the prediction of the machine learning model, accelerates the convergence process of parameter adjustment and improves the optimization efficiency.

[0111] Step S43: Perform secondary calibration on 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;

[0112] Embodiment 2

[0113] As Figure 2 shown, Embodiment 2 of the present application provides a signal enhancement device based on microwave technology, including:

[0114] Information acquisition and preprocessing module 21: Obtain the original microwave signal through a 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; The information acquisition and preprocessing module includes the following sub-modules:

[0115] Signal acquisition and preliminary processing sub-module 211: Use a microwave signal receiving device to obtain the original microwave signal, apply oversampling technology to sample the original signal at a preset oversampling rate to obtain discrete signal data, perform a fast Fourier transform on the discrete signal, calculate the spectrum amplitude value and phase value, and generate a frequency domain feature vector;

[0116] Signal Feature Extraction and Analysis Sub-module 212: Calculate the energy distribution of the signal based on the high-order cumulant algorithm, extract the energy features after non-Gaussian noise suppression through the cumulant operation of the fourth order and above, and identify the statistical characteristics of the non-linear components in the signal; extract the energy features after non-Gaussian noise suppression through the cumulant operation of the fourth order and above, identify the statistical characteristics of the non-linear components in the signal, and calculate the fourth-order cumulant;

[0117] Signal Classification and Screening Sub-module 213: Establish a signal stability evaluation model according to the spectral amplitude fluctuation range and the phase consistency threshold, screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude signals with amplitude jumps or phase mutations caused by noise interference;

[0118] Signal Enhancement Module 22: Calculate the microwave energy information based on the discrete signal, and monitor the change of the characteristics in real time. Set an adaptive filter. According to the extracted frequency characteristic parameters, energy information and the change of the characteristics monitored in real time, use the adaptive filter to perform multipath signal enhancement processing; after the multipath signal enhancement processing, obtain a preliminarily enhanced microwave signal, perform an inverse fast Fourier transform on the enhanced signal, convert it from the frequency domain back to the time domain, and obtain the enhanced time-domain signal; including the following sub-modules:

[0119] Filter Frequency Response Function Construction Sub-module 221: Construct the frequency response function of the filter according to the dynamic change trend of the spectral amplitude value and the phase value, fit the envelope curve of the spectral amplitude value through the interpolation algorithm, and introduce a phase compensation mechanism to correct the signal distortion caused by the Doppler effect or propagation attenuation; fit the envelope curve of the spectral amplitude value through 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, a phase compensation mechanism is introduced to correct the signal distortion caused by the Doppler effect or propagation attenuation.

[0120] Fuzzy Logic Control Gain Parameter Adjustment Sub-module 222: Based on the fuzzy logic control rules, combined with the energy distribution characteristics output by the high-order cumulant, establish a fuzzy membership function of the gain coefficient and the bandwidth parameter, and dynamically adjust the passband range and stopband attenuation intensity of the filter according to the energy concentration; calculate the high-order cumulant of the signal to extract the energy distribution characteristics of the signal. These characteristics are used to construct a fuzzy membership function to reflect the energy concentration of the signal. According to the energy concentration, dynamically adjust the passband range and stopband attenuation intensity of the filter.

[0121] Multipath Signal Separation and Merging Sub-module 223: For the multipath propagation path, adopt the time delay estimation and signal coherence analysis methods to separate each path component, determine the time delay difference of the signal components by calculating the cross-correlation function between paths, and screen out the effective paths based on the coherence matrix, and reconstruct the enhanced signal through the weighted merging algorithm.

[0122] Information evaluation module 23: Introduce signal quality evaluation indicators to evaluate the quality of the enhanced time-domain signal. If the signal quality does not meet the expectation, automatically feedback and adjust the influence parameters of the adaptive filter until the signal quality meets the requirements. It includes the following sub-modules:

[0123] Signal enhancement sub-module 231: Calculate the mean square value and high-order cumulant of the time-domain enhanced signal, generate a dynamic energy adjustment coefficient, and determine the normalization scaling ratio according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean value;

[0124] Signal normalization sub-module 232: Normalize the signal amplitude using a piecewise linear compression algorithm according to the preset energy range threshold, set multiple amplitude intervals and define the compression slope for each interval respectively to avoid signal overload or quantization distortion; according to the preset energy range threshold and , normalize the signal amplitude using a piecewise linear compression algorithm. Set multiple amplitude intervals and define the compression slope for each interval respectively and offset , and avoid signal overload or quantization distortion through the piecewise linear compression algorithm to ensure that the signal amplitude is within a reasonable range.

[0125] Signal envelope monitoring sub-module 233: Establish a signal envelope monitoring mechanism to track the peak wave of the normalized signal in real time.

[0126] Model parameter fine-tuning sub-module 234: Construct a multi-dimensional quality evaluation index based on signal-to-noise ratio, spectral flatness, and phase jitter parameters, and calculate the signal-to-noise ratio gain, spectral ripple fluctuation variance, and phase difference variance of the signal in the target frequency band;

[0127] Filter parameter adjustment sub-module 235: If the evaluation result does not reach the preset threshold, inversely adjust the gain parameter, frequency response function, and multipath combination weight of the filter;

[0128] Standard scoring sample collection sub-module 236: Quadratically calibrate the oversampling rate and classification screening threshold through an iterative optimization algorithm, determine the optimal oversampling multiple using the golden section search method, and update the screening threshold in combination with the classifier confidence until the output signal meets the quality requirements.

[0129] Corresponding to the above embodiments, an embodiment of the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0130] The memory is used to store one or more program instructions;

[0131] The processor is used to run one or more program instructions to execute a signal enhancement method based on microwave technology.

[0132] Corresponding to the above embodiments, an embodiment of the present invention provides a computer-readable storage medium. The computer storage medium contains one or more program instructions, and the one or more program instructions are used to be executed by a processor to perform a signal enhancement method based on microwave technology.

[0133] An embodiment disclosed by the present invention provides a computer-readable storage medium. Computer program instructions are stored in the computer-readable storage medium. When the computer program instructions run on a computer, the computer is enabled to execute the above-mentioned signal enhancement method based on microwave technology.

[0134] In an embodiment of the present invention, the processor may be an integrated circuit chip with signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0135] It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present invention may be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, a mature storage medium in the art. The processor reads the information in the storage medium and combines its hardware to complete the steps of the above method.

[0136] The storage medium may be a memory, for example, it may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories.

[0137] Among them, the non-volatile memory may be a read-only memory (ROM for short), a programmable read-only memory (PROM for short), an erasable programmable read-only memory (EPROM for short), an electrically erasable programmable read-only memory (EEPROM for short), or a flash memory.

[0138] The volatile memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0139] 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 memories.

[0140] Those skilled in the art should be aware that, in one or more of the above examples, the functions described in the present invention can be implemented by a combination of hardware and software. When applying software, 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. The computer-readable medium includes computer storage media and communication media, where the communication media includes any medium that facilitates the transfer of a computer program from one place to another. The storage media can be any available medium accessible by a general-purpose or special-purpose computer.

[0141] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, and improvements made on the basis of the technical solutions of the present invention shall be included within the protection scope of the present invention.

Claims

1. A signal enhancement method based on microwave technology, characterized in that Including: Obtain the original microwave signal through a 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; Calculate the microwave energy information based on the discrete signal, and real-time monitor the change of the frequency characteristic parameters of the signal. Set an adaptive filter, and perform multipath signal enhancement processing using the adaptive filter according to the extracted frequency characteristic parameters, energy information, and the real-time monitored characteristic change; After the multipath signal enhancement processing, obtain a preliminarily enhanced microwave signal, perform inverse fast Fourier transform on the enhanced signal to convert it from the frequency domain back to the time domain, and obtain the enhanced time-domain signal; Introduce a signal quality evaluation index to evaluate the quality of the enhanced time-domain signal. If the signal quality does not meet the expectation, automatically feedback and adjust the influence parameters of the adaptive filter until the signal quality meets the requirements.

2. The signal enhancement method based on microwave technology according to claim 1, wherein Obtain the original microwave signal through a 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, including the following sub-steps: Use a microwave signal receiving device to obtain the original microwave signal, apply oversampling technology, sample the original signal according to a preset oversampling rate to obtain discrete signal data, perform fast Fourier transform on the discrete signal, calculate the spectrum amplitude value and phase value, and generate a frequency-domain feature vector; Calculate the energy distribution of the signal based on the high-order cumulant algorithm, extract the energy characteristics after non-Gaussian noise suppression through fourth-order and above cumulant operations, and identify the statistical characteristics of the non-linear components in the signal; According to the spectrum amplitude fluctuation range and phase consistency threshold, establish a signal stability evaluation model, screen out candidate signals that meet the preset energy threshold and frequency stability, and exclude signals with amplitude jumps or phase mutations caused by noise interference.

3. A signal enhancement method based on microwave technology according to claim 1, wherein Perform multipath signal enhancement processing using an adaptive filter, including the following sub-steps: According to the dynamic change trend of the spectrum amplitude value and phase value, construct the frequency response function of the filter, fit the envelope curve of the spectrum amplitude value through an interpolation algorithm, and introduce a phase compensation mechanism to correct the signal distortion caused by the Doppler effect or propagation attenuation; Based on the fuzzy logic control rule, combine the energy distribution characteristics output by the high-order cumulant to establish a fuzzy membership function of the gain coefficient and bandwidth parameter, and dynamically adjust the passband range and stopband attenuation intensity of the filter according to the energy concentration; For the multipath propagation path, use the time delay estimation and signal coherence analysis method to separate each path component, determine the time delay difference of the signal component by calculating the cross-correlation function between paths, and screen out the effective paths based on the coherence matrix, and reconstruct the enhanced signal through the weighted merging algorithm.

4. A signal enhancement method based on microwave technology according to claim 3, characterized in that, For the multipath propagation path, use the time delay estimation and signal coherence analysis method to separate each path component, and reconstruct the enhanced signal through the weighted merging algorithm, including the following sub-steps: Calculate the multipath time delay through the time difference of arrival of the signal, combine the signal coherence matrix to identify the main propagation path, use the generalized cross-correlation algorithm to estimate the time delay value of each path component, and generate a multipath time delay distribution histogram; Based on the path energy ratio and phase offset, a weight allocation model for multipath signal components is constructed, and the weight coefficient is dynamically adjusted according to the signal-to-noise ratio and phase consistency of the path signals, and the path components with an energy ratio 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 integrity of the time-domain waveform of the original signal.

5. A signal enhancement method based on microwave technology according to claim 1, characterized in that After obtaining the enhanced time-domain signal, it also includes amplitude normalization processing of 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: Calculate the mean square value and high-order cumulant of the time-domain enhanced signal to generate a dynamic energy adjustment coefficient, and determine the normalization scaling ratio according to the deviation between the instantaneous amplitude of the signal and the long-term statistical mean value; According to the preset energy range threshold, the piecewise linear compression algorithm is used to normalize the signal amplitude, and multiple amplitude intervals are set and the compression slopes are defined respectively to avoid signal overload or quantization distortion; Establish a signal envelope monitoring mechanism 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, wherein Introduce a signal quality evaluation index to evaluate the quality of the enhanced time-domain signal, including the following sub-steps: Construct a multi-dimensional quality evaluation index based on the signal-to-noise ratio, spectral flatness and phase jitter parameters, and calculate the signal-to-noise ratio gain, spectral ripple fluctuation variance and phase difference variance within 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 reversely; The oversampling rate and classification screening threshold are calibrated twice through an iterative optimization algorithm, the golden section search method is used to determine the optimal oversampling multiple, and the screening threshold is updated in combination with the classifier confidence until the output signal meets the quality requirements.

7. A signal enhancement method based on microwave technology according to claim 6, characterized in that If the evaluation result does not reach the preset threshold, the influence parameters of the filter are adjusted reversely, including the following sub-steps: Generate a parameter adjustment gradient vector according to the deviation between the quality evaluation result and the target value, and define the sensitivity coefficient of the parameters in each processing link to determine the adjustment direction; Use the fuzzy PID control algorithm to incrementally correct the filter parameters, calculate the correction amounts of the proportional, integral and differential terms according to the fuzzy membership degree of the deviation, and limit the adjustment step size to prevent overshoot; Record the historical adjustment trajectory, predict the optimal parameter combination through a machine learning model, use the long short-term memory network to analyze the implicit law of the parameter adjustment sequence, 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 a microwave signal receiving device, sample the original signal to obtain a discrete signal, perform spectral 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, and monitor the changes in the frequency characteristic parameters of the signals in real time. Set an adaptive filter, and perform multipath signal enhancement processing using the adaptive filter according to the extracted frequency characteristic parameters, energy information, and the real-time monitored characteristic changes. After the multipath signal enhancement processing, obtain a preliminarily enhanced microwave signal, perform an inverse fast Fourier transform on the enhanced signal to convert it from the frequency domain back to the time domain, and obtain the 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 the expectations, automatically feedback and adjust the influence parameters of the adaptive filter until the signal quality meets the requirements.

9. A computer storage medium, characterized in that, Comprising: At least one memory and at least one processor; The memory is used to store one or more program instructions; The processor is 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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