A method for processing microwave signals based on a grating sensor composite channel

Abstract

This invention relates to the technical field, specifically to a microwave signal processing method based on a composite channel of a grating sensor. In this invention, a long short-term memory network is used to capture the temporal characteristics of the signal. During spectrum calculation and time-domain conversion, each frequency component in the microwave signal is accurately extracted, avoiding the shortcomings of traditional methods that cannot effectively extract complex signal features. By converting the signal from the time domain to the frequency domain and enhancing the frequency components, a multi-stage recursive correction technique can finely adjust the frequency, amplitude, and phase of the signal in stages. The result of each correction is fed back to the next stage, gradually improving the quality of the microwave signal and significantly reducing signal loss and interference. Through this intelligent optimization and dynamic correction method, unnecessary interference and errors during signal transmission are significantly reduced, improving the efficiency and stability of the signal processing process.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a microwave signal processing method based on a composite channel of a grating sensor. Background Technology

[0002] The field of signal processing technology specifically involves the comprehensive application of mathematics, algorithms, and electronic technology to perform operations such as filtering, denoising, modulation, demodulation, enhancement, and spectrum analysis on analog or digital signals from different sources in order to extract or restore the effective signal content carrying information.

[0003] A method for processing microwave signals by combining signals sensed by a grating sensor with microwave signals is proposed. By constructing a composite channel, the optical sensitivity of the grating sensor is used to sensitively capture and convert changes in microwave signals, thereby achieving high-precision optimization processing of microwave signals. The aim is to improve the transmission accuracy of microwave signals, reduce signal loss and interference, and ensure stable and reliable transmission of signals in complex electromagnetic environments.

[0004] Traditional methods often rely on simple frequency and time domain processing, resulting in a simplistic processing procedure and a lack of fine-tuning mechanisms. In complex electromagnetic environments, signal interference is severe. Furthermore, traditional methods typically use fixed frequency thresholds for processing, which fails to effectively eliminate noise and interference, leading to the loss of important signal components. The lack of a multi-stage recursive correction mechanism also prevents the fine-tuning of signal parameters according to environmental changes during processing, resulting in unstable signal quality, low processing efficiency, and impacting the reliability and stability of the system. Summary of the Invention

[0005] The purpose of this invention is to provide a microwave signal processing method based on a composite channel of a grating sensor to solve the above-mentioned problems.

[0006] This invention is achieved through the following technical solution:

[0007] A microwave signal processing method based on a composite channel of a grating sensor, comprising:

[0008] Step 1: Based on the microwave signal acquired by the grating sensor, the signal is filtered. After low-pass filtering, noise with a frequency higher than the set threshold is removed. Then, the signal amplitude is normalized and the signal is standardized by amplitude correction to generate an effective signal.

[0009] Step 2: Based on the effective signal, the signal spectrum is calculated using a long short-term memory network. At the same time, the signal is converted from the time domain to the frequency domain, each frequency component is extracted, and the main frequency range in the signal is determined. Frequency bands are selected according to the component intensity and noise interference, and the amplitude of the frequency bands is enhanced to generate an enhanced spectrum signal.

[0010] Step 3: Based on the enhanced spectrum signal, adjust the frequency, amplitude and phase of the signal in stages, and perform multiple recursive corrections. Detect the signal effect after each correction and adjust each parameter to further optimize the correction effect at each stage, generating a recursive correction signal.

[0011] Step 4: Based on the recursive correction signal, extract and analyze the time-domain features of the signal, identify the interference source in the signal, and determine the frequency, intensity, and duration characteristics of the interference by comparing the signal change patterns frame by frame, thereby generating the interference identification result.

[0012] Step 5: Based on the interference identification results, select an appropriate frequency adjustment rule, correct the signal gain control and frequency, and optimize the signal according to phase compensation and intensity compensation to generate an optimized signal.

[0013] As a further aspect of the present invention, the specific steps for generating the effective signal are as follows:

[0014] Based on the microwave signal acquired by the grating sensor, low-pass filtering is performed, a frequency threshold is set and data points higher than the threshold are selected, and the amplitude is set to zero to remove high-frequency noise and generate a filtered signal.

[0015] Based on the filtered signal, the signal amplitude is normalized, the extreme amplitude value in the signal is calculated, the amplitude of each data point is divided by the extreme value, and the signal amplitude is adjusted to a uniform range to generate a normalized signal.

[0016] Based on the normalized signal, the signal amplitude is corrected, each signal point is traversed, and compared with a preset standard range. At the same time, the signal amplitude is adjusted to meet the set standard, and a valid signal is generated.

[0017] As a further aspect of the present invention, the specific steps for generating the strong spectrum signal are as follows:

[0018] Based on the effective signal, a long short-term memory network is used. First, the signal is divided into segments according to a set time window. The frequency components in each time window are calculated. A window function is used to weight each time segment. The frequency and amplitude information in each time window are used to generate a frequency domain signal.

[0019] Based on the frequency domain signal, the spectrum is divided into multiple frequency bands according to the frequency range, the frequency bands that affect the transmission quality are selected, the boundary of each frequency band is calculated, and all frequency components within the frequency band are extracted to generate the key frequency band signal.

[0020] Based on the key frequency band signal, the gain of the signal in each selected frequency band is adjusted. First, the gain coefficient of the frequency band is calculated and applied to the amplitude of all frequency points within the frequency band to enhance the signal strength of the frequency band and generate an enhanced spectrum signal.

[0021] As a further aspect of the present invention, the long short-term memory network is configured according to the formula:

[0022] ;

[0023] in: Represents frequency domain signals, It's frequency. It is a time window. It is an index of the time-domain signal. The first time-domain signal represents the second time-domain signal. The amplitude of each sampling point It is an integer index. This represents the weighting coefficients of the window function. It is a complex exponential term. It is the base of the natural logarithm. It is the imaginary unit. Indicates the frequency components in signal analysis. It is an index of the time-domain signal. It represents the total number of sampling points for the signal.

[0024] The key frequency band signal is generated by dividing the frequency domain signal into multiple sub-bands according to a set frequency range. Then, based on the correlation between the power spectral density, energy concentration and transmission performance indicators within the frequency band, the frequency bands that affect the transmission quality are identified and defined as key frequency bands. For key frequency bands, the complex spectral values ​​of the frequency points within the band are extracted, and the spectral components of non-key frequency bands are set to zero, thereby forming a spectral signal containing the frequency components of the key frequency band, which is the key frequency band signal for subsequent gain adjustment or noise suppression processing.

[0025] As a further aspect of the present invention, the specific steps for generating the recursive correction signal are as follows:

[0026] Based on the enhanced spectrum signal, the signal frequency is first detected to obtain the actual frequency value of each frequency band, and compared with the target frequency to calculate the frequency error. Then, the frequency of each frequency band is gradually adjusted to generate a corrected frequency signal.

[0027] Based on the corrected frequency signal, the amplitude of each frequency band is adjusted, the amplitude change of each frequency band is measured, the difference is calculated and the amplitude of the corresponding frequency band is adjusted, the correction amount is applied to each frequency point, the frequency band signal strength is corrected, and a corrected amplitude signal is generated.

[0028] Based on the corrected amplitude signal, the phase of the signal is optimized, the phase deviation is detected, the required phase adjustment amount is obtained, and then the phase of each signal is corrected according to the frequency components. After the phase adjustment is completed, a recursive correction signal is generated.

[0029] Phase correction is performed based on frequency components. The current phase value of the frequency point is extracted and compared with the reference phase and the desired phase to obtain the phase error. The correction factor is calculated based on the error. In the frequency domain, the frequency component is multiplied by a complex exponential factor to achieve phase rotation compensation. At the same time, the consistency of the frequency-phase mapping relationship must be maintained. The phase correction will not destroy the spectral structure of the signal. A phase-aligned frequency domain signal is generated for subsequent time domain reconstruction and control system input.

[0030] As a further aspect of the present invention, the specific steps for generating the interference identification result are as follows:

[0031] Based on the recursive correction signal, time-domain feature extraction is performed. First, the signal is processed by frame segmentation. For each frame, the average amplitude, frequency change, signal peak value and change rate information are extracted. At the same time, the change trend of the signal in each frame is compared to generate a time-domain feature signal.

[0032] Based on the time-domain feature signal, the interference sources in the signal are initially screened, the abnormal fluctuations of the signal in each frame are monitored, a threshold is set and each frame is compared to identify the interference source and generate the interference source candidate signal.

[0033] Based on the candidate interference source signals, the interference source is analyzed, the frequency range, amplitude, and duration of the interference signal are calculated, and the characteristics are analyzed to generate interference identification results.

[0034] The analysis of interference sources extracts the main frequency components of candidate signal segments, calculates the frequency range with dense energy distribution as the frequency range of the interference signal, selects the average power as the interference amplitude index to measure the amplitude value within this range, calculates the duration of the interference signal based on the number of frames and the duration of each frame within the signal duration, and further extracts the feature vector of the interference signal, including frequency stability, amplitude fluctuation rate and modulation characteristics, to provide a basis for subsequent classification and identification and interference suppression strategy generation. The resulting analysis results should include the interference frequency range, maximum amplitude, duration and additional features, and the generated results support the judgment of interference type and system response decision.

[0035] As a further aspect of the present invention, the specific steps for generating the optimized signal are as follows:

[0036] Based on the interference identification results, a gain control rule is selected, the gain requirement for each frequency band is calculated, the signal strength of each frequency band is measured, and then the amplitude of the corresponding frequency band is adjusted according to the required gain value to enhance the target frequency band and generate a gain control signal.

[0037] Based on the gain control signal, frequency correction is performed, the frequency deviation of the signal is measured, and the frequency of each frequency band is adjusted one by one using the correction amount to generate a corrected frequency signal.

[0038] Based on the corrected frequency signal, phase compensation is performed on the signal, the phase deviation of each frequency band is detected, the required adjustment amount is calculated, and the phase compensation is applied to each frequency component. The signal strength is adjusted according to the amplitude correction to generate an optimized signal.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0040] 1. In this invention, the temporal characteristics of the signal are captured by a long short-term memory network, and each frequency component in the microwave signal is accurately extracted during spectrum calculation and time-domain conversion, avoiding the shortcomings of traditional methods that cannot effectively extract complex signal features;

[0041] 2. In this invention, by converting the signal from the time domain to the frequency domain and enhancing the frequency components, the multi-stage recursive correction technique can finely adjust the frequency, amplitude, and phase of the signal in stages. The result of each correction is fed back to the next stage, gradually improving the quality of the microwave signal and significantly reducing signal loss and interference.

[0042] 3. In this invention, through this intelligent optimization and dynamic correction method, the quality of microwave signals during transmission is greatly improved. In an environment with multiple interference sources, it can identify and adapt to interference modes in real time, which significantly reduces unnecessary interference and errors during signal transmission, and improves the efficiency and stability of the signal processing process. Attached Figure Description

[0043] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0044] Figure 1 This is a schematic diagram of the main steps of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and are not intended to limit the invention. It should be noted that this invention is already in the actual research and development stage.

[0046] Example 1

[0047] Please see Figure 1 This invention provides a technical solution: a microwave signal processing method based on a grating sensor composite channel, comprising the following steps:

[0048] Step 1: Based on the microwave signal acquired by the grating sensor, the signal is filtered. After low-pass filtering, noise with a frequency higher than the set threshold is removed. Then, the signal amplitude is normalized and the signal is standardized by amplitude correction to generate an effective signal.

[0049] Step 2: Based on the effective signal, the long short-term memory network is used to calculate the signal spectrum. At the same time, the signal is converted from the time domain to the frequency domain, each frequency component is extracted, and the main frequency range in the signal is determined. Frequency bands are selected according to the component intensity and noise interference, and the amplitude of the frequency bands is enhanced to generate an enhanced spectrum signal.

[0050] Step 3: Based on the enhanced spectrum signal, the frequency, amplitude and phase of the signal are adjusted in stages and recursively corrected multiple times. The effect of the signal after each correction is detected and each parameter is adjusted to further optimize the correction effect of each stage and generate a recursive correction signal.

[0051] Step 4: Based on the recursive correction signal, extract and analyze the time-domain features of the signal, identify the interference source in the signal, and determine the frequency, intensity and duration characteristics of the interference by comparing the signal change pattern frame by frame, and generate interference identification results.

[0052] Step 5: Based on the interference identification results, select the appropriate frequency adjustment rules, correct the signal gain control and frequency, and optimize the signal according to phase compensation and intensity compensation to generate an optimized signal.

[0053] The specific steps for generating a valid signal are as follows:

[0054] Based on the microwave signal acquired by the grating sensor, low-pass filtering is performed, a frequency threshold is set and data points higher than the threshold are selected, and the amplitude is set to zero to remove high-frequency noise and generate a filtered signal.

[0055] Based on the filtered signal, the signal amplitude is normalized, the extreme amplitude value in the signal is calculated, the amplitude of each data point is divided by the extreme value, the signal amplitude is adjusted to a uniform range, and a normalized signal is generated.

[0056] Based on the normalized signal, the signal amplitude is corrected, each signal point is traversed and compared with the preset standard range, and the signal amplitude is adjusted to meet the set standard to generate a valid signal.

[0057] Based on the microwave signal acquired by the grating sensor, a low-pass filtering algorithm is adopted. A frequency threshold is set, and the butter filter in the scipy library of Python is used for low-pass filtering. The frequency threshold is set to 5 GHz, and the filter coefficients are generated by the butter function. The filtfilt function is applied to filter the original signal to remove frequency components higher than 5 GHz and generate a filtered signal. During the filtering process, each signal point in the frequency domain is compared with the set threshold, and the amplitude of all frequency components higher than the threshold is set to zero.

[0058] Based on the filtered signal, a normalization algorithm is adopted. The np.max and np.min functions in the NumPy library are used to calculate the limit amplitude value of the signal. The range of the signal amplitude is obtained by calculating the maximum and minimum values ​​of the signal. The np.divide function is used to divide the amplitude value of each signal data point by the maximum amplitude value of the signal, and the signal amplitude is adjusted to a uniform range of 0 to 1 to generate a normalized signal.

[0059] Based on the normalized signal, an amplitude correction algorithm is adopted. First, a standard amplitude range of 0.2 to 0.8 is set. The if conditional statement of Python is used to traverse each signal data point to determine whether it falls within the set standard range. If it does not, the amplitude is corrected by linear interpolation. The interpolate.interp1d function in the scipy library is used to create an interpolation function to adjust the amplitude value of each data point to the preset range and generate a valid signal.

[0060] The specific steps for generating a strong spectrum signal are as follows:

[0061] Based on the effective signal, a long short-term memory network is used. First, the signal is divided into segments according to a set time window. The frequency components in each time window are calculated. A window function is used to weight each time segment. The frequency and amplitude information in each time window are used to generate a frequency domain signal.

[0062] Based on frequency domain signals, the spectrum is divided into multiple frequency bands according to frequency range, the frequency bands that affect transmission quality are selected, the boundaries of each frequency band are calculated, and all frequency components within the frequency band are extracted to generate key frequency band signals.

[0063] Based on the key frequency band signal, the gain of the signal in each selected frequency band is adjusted. First, the gain coefficient of the frequency band is calculated and applied to the amplitude of all frequency points in the frequency band to enhance the signal strength of the frequency band and generate an enhanced spectrum signal.

[0064] Based on the effective signal, a Long Short-Term Memory (LSTM) network is used. The LSM network model is constructed using the TensorFlow framework and Keras library. The time window length is set to 50ms. The TimeDistributed layer is used to segment the input signal into time series. The frequency components within each time segment are calculated using the LSM network layer. The Hamming window function is used to weight each time segment. The parameters of the window function are set to alpha = 0.54 and beta = 0.46 to generate the frequency domain signal. By performing a Fourier transform on the spectrum of each time window, the frequency and amplitude information within each time segment is extracted to generate the frequency domain data of each time window.

[0065] Based on the frequency domain signal, a frequency segmentation algorithm is adopted, setting the frequency range to 0 Hz to 5 GHz. The fft.fftfreq function of the NumPy library is used to segment the frequency domain signal, setting the boundary of each frequency band to 1 GHz. The 0.5 GHz to 2 GHz frequency bands that affect the transmission quality are selected. The fft.fft function of NumPy is used to extract all frequency components in each frequency band. The signal of each frequency band is filtered according to the set frequency interval to generate key frequency band signals. The frequency components in each frequency band are screened and processed according to their influence to ensure that the key frequency band signals are extracted completely.

[0066] Based on the key frequency band signal, a gain adjustment algorithm is adopted. First, the gain coefficient of the frequency band is calculated. The average amplitude of the selected frequency band is calculated using the np.mean function of NumPy. The gain coefficient is set to 1.5. The np.multiply function of NumPy is used to apply the gain coefficient to the amplitude of each frequency point in the frequency band. The signal.resample function of SciPy library is used to enhance the amplitude of the frequency components, thereby enhancing the signal strength of the frequency band and generating an enhanced spectrum signal.

[0067] Long Short-Term Memory (LSTM) networks, according to the formula:

[0068] ;

[0069] in: Represents frequency domain signals, It's frequency. It is a time window. It is an index of the time-domain signal. The first time-domain signal represents the second time-domain signal. The amplitude of each sampling point is an integer index. This represents the weighting coefficients of the window function. It is a complex exponential term. It is the base of the natural logarithm. It is the imaginary unit. Indicates the frequency components in signal analysis. It is an index of the time-domain signal. It is the total number of sampling points for the signal;

[0070] Execution process: Microwave signals are acquired through a grating sensor to obtain time-domain signals. After window function Weighted calculation of the frequency domain signal This indicates that the signal is in time. and frequency The complex spectrum on the frequency basis function A time-domain to frequency-domain transformation is performed to obtain the frequency components at each time and frequency point. After obtaining the frequency domain signal, the frequency components are then analyzed based on the spectrum. Gain adjustment is applied to the signal; the gain coefficient is... By analyzing and dynamically calculating each frequency band of the signal spectrum, the intensity of the frequency bands is adjusted and an enhanced spectrum is generated. Extract phase information for each frequency band The phase deviation is calculated by comparing it with the target phase, and the phase adjustment amount for each frequency band is also calculated. This information is then added to the original phase value of the frequency band to obtain the corrected phase information. Then, by combining the corrected frequency, amplitude, and phase information, a recursively corrected microwave signal is generated, which improves the stability and accuracy of the signal.

[0071] The key frequency band signal is generated by dividing the frequency domain signal into multiple sub-bands according to a set frequency range. Then, based on the correlation between the power spectral density, energy concentration and transmission performance indicators within the frequency band, the frequency bands that affect the transmission quality are identified and defined as key frequency bands. For key frequency bands, the complex spectral values ​​of the frequency points within the band are extracted, and the spectral components of non-key frequency bands are set to zero, thereby forming a spectral signal containing the frequency components of the key frequency band, which is the key frequency band signal for subsequent gain adjustment or noise suppression processing.

[0072] The specific steps for generating the recursive correction signal are as follows:

[0073] Based on the enhanced spectrum signal, the signal frequency is first detected to obtain the actual frequency value of each frequency band, and compared with the target frequency to calculate the frequency error. Then, the frequency of each frequency band is gradually adjusted to generate a corrected frequency signal.

[0074] Based on the corrected frequency signal, the amplitude of each frequency band is adjusted, the amplitude change of each frequency band is measured, the difference is calculated and the amplitude of the corresponding frequency band is adjusted, the correction amount is applied to each frequency point, the signal strength of the frequency band is corrected, and a corrected amplitude signal is generated.

[0075] Based on the corrected amplitude signal, the phase of the signal is optimized, the phase deviation is detected, the required phase adjustment amount is obtained, and then the phase of each signal is corrected according to the frequency components. After the phase adjustment is completed, a recursive correction signal is generated.

[0076] Based on the enhanced spectrum signal, a frequency detection algorithm is adopted. The actual frequency value of each frequency band is calculated using the `fft.fftfreq` function in the NumPy library. The `signal.find_peaks` function in the SciPy library is used to perform peak detection on the frequency domain signal to obtain the actual frequency of each frequency band. The actual frequency is then compared with the set target frequency to calculate the frequency error. The error value is then calculated by subtracting the target frequency using the `np.subtract` function in the NumPy library. The frequency of each frequency band is gradually adjusted. The `signal.resample` function in the SciPy library is used to adjust the frequency, apply the frequency correction amount, and generate the corrected frequency signal. By adjusting the frequency components of each frequency band, the frequency correction is finally completed.

[0077] Based on the corrected frequency signal, an amplitude adjustment algorithm is adopted. The numpy np.abs function is used to measure the amplitude change of each frequency band, calculate the corrected amplitude difference, and adjust the amplitude of each frequency band using the numpy np.multiply function. The correction amount is set to 1.2 and applied to the amplitude of all frequency points within each frequency band. The scipy library signal.resample function is used to resample the corrected signal to correct the amplitude of each frequency band.

[0078] Based on the corrected amplitude signal, a phase optimization algorithm is adopted. The phase information of each frequency band signal is obtained using the NumPy's `np.angle` function, and the phase deviation of each signal is calculated. The target phase is set to 0. The phase of each frequency band is optimized using the `signal.angle` function from the SciPy library to obtain the required phase adjustment amount. The adjustment amount is applied to the phase of each frequency point using the NumPy's `np.add` function. The corrected phase is sampled using the `signal.resample` function from the SciPy library to complete the phase correction of each signal, so that the phase of the signal meets the target requirements.

[0079] Phase correction is performed based on frequency components. The current phase value of the frequency point is extracted and compared with the reference phase and the desired phase to obtain the phase error. The correction factor is calculated based on the error. In the frequency domain, the frequency component is multiplied by a complex exponential factor to achieve phase rotation compensation. At the same time, the consistency of the frequency-phase mapping relationship must be maintained. The phase correction will not destroy the spectral structure of the signal. A phase-aligned frequency domain signal is generated for subsequent time domain reconstruction and control system input.

[0080] The specific steps for generating interference identification results are as follows:

[0081] Based on the recursive correction signal, time-domain feature extraction is performed. First, the signal is processed by dividing it into frames. For each frame, the average amplitude, frequency change, signal peak value and change rate information are extracted. At the same time, the change trend of the signal in each frame is compared to generate a time-domain feature signal.

[0082] Based on time-domain feature signals, interference sources in the signal are initially screened, abnormal fluctuations in each frame of the signal are monitored, thresholds are set and each frame is compared to identify interference sources and generate candidate interference source signals.

[0083] Based on candidate interference source signals, the interference source is analyzed, the frequency range, amplitude and duration of the interference signal are calculated, and the characteristics are analyzed to generate interference identification results.

[0084] Based on recursive signal correction, a time-domain feature extraction algorithm is adopted. First, the signal is divided into frames using the NumPy's np.split function, with each frame length set to 100ms and an overlap of 50ms between frames. The average amplitude of the signal in each frame is calculated using the NumPy's np.mean function, and the frequency change of the signal within each frame is calculated using the NumPy's np.diff function. The peak values ​​of the signal are detected using the SciPy's signal.find_peaks function, and the rate of change of the signal in each frame is calculated using the NumPy's np.gradient function. The changing trends of the signal in each frame are compared to generate a time-domain feature signal.

[0085] Based on time-domain feature signals, an interference source screening algorithm is adopted. The abnormal fluctuation threshold of each frame signal is set to 3 standard deviations. The standard deviation of each frame signal is calculated using the numpy np.std function and compared with the set threshold. The frames with fluctuations exceeding the threshold are detected by the numpy np.where function, and possible interference sources are identified. The peak fluctuations of each frame signal are detected using the scipy signal.find_peaks function. The interference sources are initially screened based on fluctuation amplitude and frequency characteristics, forming a set of candidate interference source signals.

[0086] Based on the candidate interference signals, an interference source analysis algorithm is adopted. First, the frequency range of each candidate signal is calculated using the NumPy function `np.fft.fft`. Then, the amplitude range of the interference signal is analyzed using the NumPy functions `np.max` and `np.min`. The frequency range of each interference signal is calculated, and the duration of the interference signal is analyzed using the SciPy function `signal.find_peaks`. Combining the amplitude and frequency information, the characteristic differences of each interference signal are calculated using the NumPy functions `np.subtract` and `np.add`. Finally, the key characteristic parameters of each interference source are output.

[0087] The analysis of interference sources extracts the main frequency components of candidate signal segments, calculates the frequency range with dense energy distribution as the frequency range of the interference signal, selects the average power as the interference amplitude index to measure the amplitude value within this range, calculates the duration of the interference signal based on the number of frames and the duration of each frame within the signal duration, and further extracts the feature vector of the interference signal, including frequency stability, amplitude fluctuation rate and modulation characteristics, to provide a basis for subsequent classification and identification and interference suppression strategy generation. The resulting analysis results should include the interference frequency range, maximum amplitude, duration and additional features, and the generated results support the judgment of interference type and system response decision.

[0088] The specific steps for generating the optimized signal are as follows:

[0089] Based on the interference identification results, a gain control rule is selected, the gain requirement for each frequency band is calculated, the signal strength of each frequency band is measured, and then the amplitude of the corresponding frequency band is adjusted according to the required gain value to enhance the target frequency band and generate a gain control signal.

[0090] Based on the gain control signal, frequency correction is performed by measuring the frequency deviation of the signal and adjusting the frequency of each frequency band one by one using the correction amount to generate a corrected frequency signal.

[0091] Based on the corrected frequency signal, phase compensation is performed on the signal, the phase deviation of each frequency band is detected, the required adjustment amount is calculated, and the phase compensation is applied to each frequency component. The signal strength is adjusted according to the amplitude correction to generate an optimized signal.

[0092] Based on the interference identification results, a gain control rule is adopted. First, the np.abs function of the NumPy library is used to measure the signal strength of each frequency band and calculate the amplitude value of each frequency band. The average amplitude of each frequency band is obtained by the np.mean function of NumPy. The gain requirement of each frequency band is calculated by the np.subtract function of NumPy. The target gain value is set to 1.5. According to the required gain value, the gain value is applied to the amplitude of each frequency band by the np.multiply function of NumPy to enhance the signal strength of the target frequency band and generate a gain control signal. This gain value is dynamically adjusted according to the signal strength of each frequency band so that the signal amplitude meets the preset requirements.

[0093] Frequency correction is performed based on the gain control signal. First, the phase information of the signal is measured using the NumPy np.angle function. Then, the frequency deviation of each frequency band is calculated using the NumPy np.diff function. The frequency correction amount is set to 0.05 Hz. The frequency of each frequency band is adjusted one by one using the NumPy np.add function. The correction amount is applied to each frequency band, and the frequency of the frequency band is adjusted to generate the corrected frequency signal. In this process, the frequency deviation is gradually corrected by comparing the target frequency with the actual frequency of each frequency band, so that the signal frequency matches the target value.

[0094] Based on the corrected frequency signal, phase compensation is performed on the signal. First, the phase deviation of each frequency band is detected using the NumPy's np.angle function, and the phase difference value of each frequency band is calculated. The phase correction amount is set to π / 4. The phase compensation amount is applied to the phase of each frequency band using the NumPy's np.add function, and the amplitude of the signal is adjusted using the NumPy's np.multiply function. The amplitude of the signal generated after phase compensation is readjusted to the set value, and finally the optimized signal is obtained.

[0095] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for processing microwave signals based on a composite channel of grating sensors, characterized in that Microwave signal processing for the composite channel of a grating sensor includes the following steps: Step 1: Based on the microwave signal acquired by the grating sensor, the signal is filtered. After low-pass filtering, noise with a frequency higher than the set threshold is removed. Then, the signal amplitude is normalized and the signal is standardized by amplitude correction to generate an effective signal. Step 2: Based on the effective signal, the signal spectrum is calculated using a long short-term memory network. At the same time, the signal is converted from the time domain to the frequency domain, each frequency component is extracted, and the main frequency range in the signal is determined. Frequency bands are selected according to the component intensity and noise interference, and the amplitude of the frequency bands is enhanced to generate an enhanced spectrum signal. Step 3: Based on the enhanced spectrum signal, adjust the frequency, amplitude and phase of the signal in stages, and perform multiple recursive corrections. Detect the signal effect after each correction and adjust each parameter to further optimize the correction effect at each stage, generating a recursive correction signal. Step 4: Based on the recursive correction signal, extract and analyze the time-domain features of the signal, identify the interference source in the signal, and determine the frequency, intensity, and duration characteristics of the interference by comparing the signal change patterns frame by frame, thereby generating the interference identification result. Step 5: Based on the interference identification results, select an appropriate frequency adjustment rule, correct the signal gain control and frequency, and optimize the signal according to phase compensation and intensity compensation to generate an optimized signal.

2. The grating sensor composite channel based microwave signal processing method of claim 1, wherein, The specific steps for generating the effective signal are as follows: Based on the microwave signal acquired by the grating sensor, low-pass filtering is performed, a frequency threshold is set and data points higher than the threshold are selected, and the amplitude is set to zero to remove high-frequency noise and generate a filtered signal. Based on the filtered signal, the signal amplitude is normalized, the extreme amplitude value in the signal is calculated, the amplitude of each data point is divided by the extreme value, and the signal amplitude is adjusted to a uniform range to generate a normalized signal. Based on the normalized signal, the signal amplitude is corrected, each signal point is traversed, and compared with a preset standard range. At the same time, the signal amplitude is adjusted to meet the set standard, and a valid signal is generated.

3. The grating sensor composite channel based microwave signal processing method of claim 1, wherein, The specific steps for generating the strong spectrum signal are as follows: Based on the effective signal, a long short-term memory network is used. First, the signal is divided into segments according to a set time window. The frequency components in each time window are calculated. A window function is used to weight each time segment. The frequency and amplitude information in each time window are used to generate a frequency domain signal. Based on the frequency domain signal, the spectrum is divided into multiple frequency bands according to the frequency range, the frequency bands that affect the transmission quality are selected, the boundary of each frequency band is calculated, and all frequency components within the frequency band are extracted to generate the key frequency band signal. Based on the key frequency band signal, the gain of the signal in each selected frequency band is adjusted. First, the gain coefficient of the frequency band is calculated and applied to the amplitude of all frequency points within the frequency band to enhance the signal strength of the frequency band and generate an enhanced spectrum signal.

4. The grating sensor composite channel based microwave signal processing method of claim 1, wherein, The Long Short-Term Memory (LSTM) network is configured according to the formula: ； in: Represents frequency domain signals, It's frequency. It is a time window. It is an index of the time-domain signal. The first time-domain signal represents the second time-domain signal. The amplitude of each sampling point It is an integer index. This represents the weighting coefficients of the window function. It is a complex exponential term. It is the base of the natural logarithm. It is the imaginary unit. Indicates the frequency components in signal analysis. It is an index of the time-domain signal. It represents the total number of sampling points for the signal.

5. The microwave signal processing method based on the composite channel of the grating sensor according to claim 3, wherein the generation of the key frequency band signal divides the frequency domain signal into multiple sub-frequency bands according to a set frequency range, and then identifies the frequency bands that affect the transmission quality based on the correlation between the power spectral density, energy concentration and transmission performance indicators within the frequency band, defining them as key frequency bands. For key frequency bands, the complex spectral values ​​of the frequency points within the frequency band are extracted, and the spectral components of non-key frequency bands are set to zero, thereby forming a spectral signal containing the frequency components of the key frequency band, which is the key frequency band signal for subsequent gain adjustment or noise suppression processing.

6. The microwave signal processing method based on a grating sensor composite channel according to claim 1, characterized in that, The specific steps for generating the recursive correction signal are as follows: Based on the enhanced spectrum signal, the signal frequency is first detected to obtain the actual frequency value of each frequency band, and compared with the target frequency to calculate the frequency error. Then, the frequency of each frequency band is gradually adjusted to generate a corrected frequency signal. Based on the corrected frequency signal, the amplitude of each frequency band is adjusted, the amplitude change of each frequency band is measured, the difference is calculated and the amplitude of the corresponding frequency band is adjusted, the correction amount is applied to each frequency point, the frequency band signal strength is corrected, and a corrected amplitude signal is generated. Based on the corrected amplitude signal, the phase of the signal is optimized, the phase deviation is detected, the required phase adjustment amount is obtained, and then the phase of each signal is corrected according to the frequency components. After the phase adjustment is completed, a recursive correction signal is generated.

7. The microwave signal processing method based on a grating sensor composite channel according to claim 6, wherein the phase correction according to frequency components is performed by extracting the current phase value of the frequency point according to the frequency components and comparing it with the reference phase and the desired phase to obtain the phase error, calculating the correction factor according to the error, multiplying the frequency component by a complex exponential factor in the frequency domain to achieve phase rotation compensation, while maintaining the consistency of the frequency-phase mapping relationship, and ensuring that the phase correction does not destroy the spectral structure of the signal, generating a phase-aligned frequency domain signal for subsequent time domain reconstruction and control system input.

8. The grating sensor composite channel based microwave signal processing method of claim 1, wherein, The specific steps for generating the interference identification result are as follows: Based on the recursive correction signal, time-domain feature extraction is performed. First, the signal is processed by frame segmentation. For each frame, the average amplitude, frequency change, signal peak value and change rate information are extracted. At the same time, the change trend of the signal in each frame is compared to generate a time-domain feature signal. Based on the time-domain feature signal, the interference sources in the signal are initially screened, the abnormal fluctuations of the signal in each frame are monitored, a threshold is set and each frame is compared to identify the interference source and generate the interference source candidate signal. Based on the candidate interference source signals, the interference source is analyzed, the frequency range, amplitude, and duration of the interference signal are calculated, and the characteristics are analyzed to generate interference identification results.

9. The microwave signal processing method based on a grating sensor composite channel according to claim 8, wherein the analysis of the interference source extracts the main frequency components of the candidate signal segment, calculates the frequency range with dense energy distribution as the frequency range of the interference signal, selects the average power as the interference amplitude index to measure the amplitude value within the range, calculates the duration of the interference signal based on the number of frames and the duration of each frame within the signal duration, and further extracts the feature vector of the interference signal, including frequency stability, amplitude fluctuation rate and modulation characteristics, to provide a basis for subsequent classification and identification and interference suppression strategy generation. The resulting analysis results should include the interference frequency range, maximum amplitude, duration and additional features, and the generated results support interference type judgment and system response decision.

10. The grating sensor composite channel based microwave signal processing method of claim 1, wherein, The specific steps for generating the optimized signal are as follows: Based on the interference identification results, a gain control rule is selected, the gain requirement for each frequency band is calculated, the signal strength of each frequency band is measured, and then the amplitude of the corresponding frequency band is adjusted according to the required gain value to enhance the target frequency band and generate a gain control signal. Based on the gain control signal, frequency correction is performed by measuring the frequency deviation of the signal and adjusting the frequency of each frequency band one by one using the correction amount to generate a corrected frequency signal. Based on the corrected frequency signal, phase compensation is performed on the signal, the phase deviation of each frequency band is detected, the required adjustment amount is calculated, and the phase compensation is applied to each frequency component. The signal strength is adjusted according to the amplitude correction to generate an optimized signal.

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