CN113126038B - Method, system, storage medium and application for optimizing operating frequency of high-frequency ground wave radar - Google Patents

Abstract

The invention belongs to the technical field of radars, and discloses a high-frequency ground wave radar working frequency optimization method, a system, a storage medium and application thereof, wherein the frequency spectrum data is acquired, and a two-dimensional OS algorithm and a double threshold algorithm are adopted to process the frequency spectrum data so as to obtain frequency spectrum information of anti-impact interference processing; constructing an LSTM network, and performing model training and prediction deviation evaluation; a conic trapezoidal window is selected, the window function is slid on the whole frequency spectrum to extract the average power spectrum and fluctuation information in the window, the total score of the sliding window is calculated, and then the optimal working frequency is selected according to the score. The improvement of the frequency spectrum prediction scheme in the invention enhances the instantaneity and accuracy of frequency selection; the LSTM model predicts the time sequence, trains and learns the frequency spectrum information at the current moment instead of simple data frequency shift, so that the prediction is closer to a true value, the reliability is greatly enhanced, sliding window frequency selection is carried out through a conic trapezoid window, and an interference frequency band can be effectively avoided.

Description

High-frequency ground wave radar operating frequency optimization method, system, storage medium and application

Technical Field

The present invention belongs to the field of radar technology, and in particular relates to a method, system, storage medium and application for optimizing the working frequency of a high-frequency ground wave radar.

Background Art

At present: High-frequency ground wave radar is used for sea monitoring because of its low cost, easy maintenance, over-the-horizon and all-weather advantages. However, the electromagnetic environment of its working frequency band (3-30MHz) is complex, and there are a large number of short-wave communication services, radio interference, impact interference and noise, which affect its working performance. Finding a "quiet frequency" suitable for its work can effectively improve the performance of the radar, so a method is sought to recommend a better working frequency for high-frequency ground wave radar. Long short-term memory network LSTM (Long Short-Term Memory) is a type of feedback neural network. Due to the introduction of feedback mechanism, the recurrent neural network (RNN) has preliminary memory ability, but because it needs to retain all state information, the length of information that can be processed is limited, and the gradient disappearance and gradient explosion problems will occur when back-propagating and updating weights, and the long-term memory capacity is insufficient. The long short-term memory network (LSTM) with input gate, output gate and forget gate introduces memory units, generates a path where the gradient continues to flow for a long time, and the weight of the self-loop is also updated in each iteration. Such a design can avoid the information loss caused by lateral depth, and also solves the problem of gradient disappearance that is easy to occur when the RNN model updates weights. The forget gate controls the preservation of information by deleting some historical information in the memory unit; the input gate filters the output state of the current moment and the previous moment to adjust the useful information entering the LSTM cell. This working mode makes the algorithm based on the long short-term memory network model good at processing long sequence data and can realize time series prediction.

In actual engineering applications, radars often use the current shift prediction model as the spectrum prediction scheme. This model uses the current moment data shifted as the spectrum data for the next moment. However, the working environment of high-frequency ground wave radars is complex. Although the anti-impact interference algorithm is used to eliminate the interference from the sky, there is still a lot of noise and interference. Using this scheme to directly shift the spectrum will ignore the complex and changeable electromagnetic environment, and the real-time monitoring effect cannot be achieved, which affects the accuracy of frequency selection.

The "multiple thresholds-average power scheme" actually adopted by the sliding window frequency selection scheme is to select three gradually decreasing thresholds for rough frequency selection, and the final selected frequency band is in the relatively low interference range. The use of three thresholds improves the accuracy to a certain extent, but it requires multiple experimental tests to obtain the appropriate threshold parameters. In a complex and changeable electromagnetic environment, this method lacks certain flexibility.

Through the above analysis, the problems and defects of the prior art are as follows:

(1) Existing spectrum prediction schemes ignore the complex and changeable electromagnetic environment and cannot achieve the effect of real-time monitoring, which affects the accuracy of frequency selection.

(2) The existing sliding window frequency selection scheme requires multiple experimental tests to obtain appropriate threshold parameters, and has low flexibility.

The difficulty of solving the above problems and defects is: due to the complex and changeable electromagnetic environment, if the "quiet frequency" selected from the currently monitored spectrum is applied to the radar, when the spectrum changes significantly, the "quiet frequency" cannot achieve a good effect. Therefore, a solution that can effectively predict the spectrum at the next moment based on the existing frequency information is necessary. The sliding window frequency selection is scored according to certain criteria, and the selected criteria are of reference significance as frequency selection elements and are less affected by small interference. In summary, it is necessary to find a solution that can effectively predict the spectrum at the next moment based on the current spectrum information and can screen out the optimal frequency according to certain frequency selection principles.

The significance of solving the above problems and defects is: effectively predicting the spectrum in a complex electromagnetic environment provides relatively accurate data information for the subsequent search for the optimal operating frequency, which can approximately achieve the effect of real-time frequency selection. The appropriate sliding window frequency selection scheme can efficiently use the characteristic information of the spectrum to find a better frequency, and the performance of the radar working on this preferred frequency is improved.

Summary of the invention

In view of the problems existing in the prior art, the present invention provides a method, system, storage medium and application for optimizing the working frequency of a high-frequency ground wave radar.

The present invention is implemented as follows: a method, system, storage medium and application for optimizing the working frequency of a high-frequency ground wave radar include:

Step 1: Obtain spectrum data: The frequency monitoring subsystem uses multiple receiving channels to process the received signals in parallel, performs Fourier analysis on each channel, and then merges the analysis results of each channel to obtain complete spectrum data, which is used as spectrum information for anti-shock processing and provides raw data for subsequent processing;

Step 2: Anti-impact interference processing: The spectrum data is processed using a two-dimensional OS algorithm and a double threshold algorithm to obtain spectrum information processed with anti-impact interference, eliminating the impact of the impact interference raising the full-band spectrum and providing more realistic spectrum information for spectrum prediction;

Step 3: Predict spectrum using long short-term memory model: Build LSTM network, and perform model training and prediction deviation evaluation. The LSTM gating structure enables it to balance the short-term and long-term dependencies on the time dimension of the sequence. It has higher prediction accuracy than the current prediction model and provides spectrum information with high prediction accuracy for the sliding window frequency selection module.

Step 4, Average Power Spectrum-Variance Frequency Selection Scheme Sliding Window Frequency Selection: Select a quadratic trapezoidal window, slide this window function across the entire spectrum to extract the average power spectrum and fluctuation information within the window, calculate the total score of the sliding window, and then select the best operating frequency based on the score. Considering the electromagnetic information on the frequency band and both sides of it, it can avoid interference frequency bands and provide preferred frequencies for radar selection in the case of a changeable electromagnetic environment.

Furthermore, in step 1, each receiving channel has a different carrier frequency, thereby covering the entire frequency band.

Further, in step 2, the anti-impact interference processing specifically includes:

(1) Using the two-dimensional OS algorithm, first sort each point in the frequency dimension by amplitude to find the noise floor for each time batch data, and then use the OS algorithm in the time dimension to find the noise floor of the time-frequency diagram;

(2) A double threshold algorithm is used to set the amplitude threshold and pulse width threshold to identify interference pulses, eliminate the frequency monitoring data affected by impact interference or replace it with adjacent non-interference batch data, and then splice and integrate the remaining valid spectrum data to obtain spectrum information with anti-impact interference processing.

Further, in step three, the long short-term memory model predicts the spectrum including:

(1) Data preparation: read spectrum data in batches, divide the spectrum data into training set and test set, and then normalize the data;

(2) Build an LSTM network, set the number of nodes in each layer, and use the default activation function as the sigmoid function and the tanh function;

(3) Initialize network parameters, set the forgetting rate, learning rate, number of neurons, and maximum number of iterations;

(4) Model training and evaluation indicators: The Adam algorithm is used for model training, and the root mean square error is used as the evaluation indicator to measure the prediction deviation of the model. The root mean square error calculation formula is:

代表模型的预测值，x_i代表序列的真实值，n代表预测值个数。in

represents the predicted value of the model, _xi represents the true value of the sequence, and n represents the number of predicted values.

Furthermore, the calculation formula of the long short-term memory model is:

i _t =sigmoid(W _i [x _t ,h _t-1 ]+b _i );

f _t =sigmoid(W _f [x _t ,h _t-1 ]+b _f );

o _t =sigmoid(W _o [x _t ,h _t-1 ]+b _o );

h _t = o _t ·tanh(c _t );

和h_t使用tanh函数作为激活函数。In each time step t, _xt is the input vector, _ct is the cell state vector, _ht is the hidden state vector output according to _ct , where W _* represents the weight matrix, b _* is the bias vector, *∈{i,f,o,c}; the sigmoid function is used as the activation function of the input gate _it , the forget gate _f and the output gate _o , with a value range of [0,1] to control the proportion of information output by each gate;

and h _t uses the tanh function as the activation function.

Furthermore, the sliding window frequency selection of the average power spectrum-variance frequency selection scheme specifically includes:

Firstly, the width of the quadratic trapezoidal window is determined according to the bandwidth of the radar receiving channel and the demodulated signal bandwidth. The designed sliding window is slid across the entire spectrum data, and the sliding window is scored using the scoring formula to select the optimal frequency point in the working frequency band.

Furthermore, the width and shape of the sliding window are selected to match the bandwidth of the radar receiving channel and the bandwidth of the demodulated signal, and the average power and variance are used as frequency selection elements to represent the average amplitude and deviation degree of information within a certain cumulative time window.

Furthermore, the scoring formula is:

x ₁ =n _F1 x _F1 +n _F2 x _F2 ;

In the formula, m _F1 , m _F2 are proportional coefficients, m _F1 = 20, m _F2 = 5; C _MIN , V _MIN represent the minimum mean square value; Faverage represents the average value of the sliding window power in the x _F1 formula, and represents the variance value of the sliding window in the x _F2 formula; x ₁ is the total score of the sliding window; x _F1 is the average power score of the sliding window; x _F2 is the variance score of the sliding window; n _F1 is the proportional coefficient of the average power score of the sliding window, which is 0.7; n _F2 is the proportional coefficient of the variance score of the sliding window, which is 0.3.

Another object of the present invention is to provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

Obtaining spectrum data: The frequency monitoring subsystem uses multiple receiving channels to process the received signals in parallel, performs Fourier analysis on each channel, and combines the analysis results of each channel to obtain complete spectrum data;

Anti-impact interference processing: The spectrum data is processed using a two-dimensional OS algorithm and a double threshold algorithm to obtain spectrum information for anti-impact interference processing;

Long short-term memory model prediction spectrum: build LSTM network, and perform model training and prediction deviation evaluation;

Average power spectrum-variance frequency selection scheme Sliding window frequency selection: Select a quadratic curve trapezoidal window, slide this window function across the entire spectrum to extract the average power spectrum and fluctuation information within the window, calculate the total score of the sliding window, and then select the best operating frequency based on the score.

Another object of the present invention is to provide a high-frequency ground wave radar operating frequency optimization system for the high-frequency ground wave radar operating frequency optimization method, the high-frequency ground wave radar operating frequency optimization system comprising:

The spectrum data acquisition module is used to process the received signals in parallel using multiple receiving channels, perform Fourier analysis on each channel respectively, and then combine the analysis results of each channel to obtain complete spectrum data;

An anti-impact interference processing module is used to process the spectrum data to obtain spectrum information processed by anti-impact interference;

LSTM network model prediction spectrum module, used to build LSTM network, and perform model training and prediction deviation evaluation;

The sliding window frequency selection module is used to select a suitable window function, slide this window function across the entire spectrum to extract the average power spectrum and fluctuation information within the window, calculate the total sliding window score, and then select the best operating frequency based on the score.

Another object of the present invention is to provide a high-frequency ground wave radar, which is equipped with the high-frequency ground wave radar working frequency optimization system.

Combining all the above technical solutions, the advantages and positive effects of the present invention are as follows: The improvement of the spectrum prediction scheme in the present invention enhances the real-time and accuracy of frequency selection. The LSTM model is used to predict time series, and the spectrum information at the current moment is trained and learned instead of simple data frequency shift. By comparing the RMSE of the LSTM prediction and the current translation prediction model on the full frequency band, it is found that compared with the traditional prediction method, the spectrum prediction based on LSTM makes the prediction closer to the true value, which greatly enhances the reliability.

The quadratic trapezoidal window in the present invention performs sliding window frequency selection based on the "average power spectrum-variance frequency selection scheme", selects average power and variance as evaluation indicators, and more comprehensively reflects the spectrum information. The quadratic trapezoidal window can also effectively avoid interference frequency bands.

Comparing the frequency selection simulation results of rectangular window, trapezoidal window and quadratic curve trapezoidal window, it is found that when the rectangular window is scored, there are frequency peaks on both sides of the boundary, and the electromagnetic environment is changeable. These peak frequencies may affect the detection power of the radar. The trapezoidal window and the quadratic curve trapezoidal window can not only reflect the data information within the frequency band, but also take into account the electromagnetic environment information on both sides. Compared with the trapezoidal window, the quadratic curve trapezoidal window is more effective in distinguishing and avoiding small interference.

The present invention provides a comparison diagram of the root mean square error between LSTM and the current translation prediction results to verify the superiority of the LSTM model prediction scheme; in addition, by comparing the results of sliding window frequency selection using a rectangular window and a quadratic curve trapezoidal window, it is found that the quadratic curve trapezoidal window takes into account information on both sides of the frequency band and has higher adaptability in complex electromagnetic environments. The error in the schematic diagram of the long short-term memory model is corrected.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG1 is a flow chart of a method for optimizing the operating frequency of a high-frequency ground wave radar provided by an embodiment of the present invention.

FIG2 is a schematic diagram of a long short-term memory model provided by an embodiment of the present invention.

FIG3 is a schematic diagram of sliding window frequency selection provided by an embodiment of the present invention.

FIG. 4 is a diagram of spectrum data containing impulse interference obtained according to an embodiment of the present invention.

FIG5 is a frequency spectrum diagram after removing impact data using an anti-impact interference algorithm provided by an embodiment of the present invention.

FIG. 6 is a diagram of a “time-frequency-power spectrum” of measured data obtained in a practical application provided by an embodiment of the present invention.

FIG. 7 is a diagram showing a root mean square error of the prediction results of the long short-term memory model provided by an embodiment of the present invention.

FIG8 is a diagram of the root mean square error of the prediction results of the current translation model provided by an embodiment of the present invention.

FIG9 is a comparison diagram of the root mean square error of the prediction results of the long short-term memory model and the current translation model provided by an embodiment of the present invention.

FIG. 10 is a function structure diagram of a quadratic trapezoidal window provided in an embodiment of the present invention.

FIG. 11 is a function structure diagram of a rectangular window provided in an embodiment of the present invention.

FIG12 is a schematic diagram of rectangular window frequency selection provided in an embodiment of the present invention.

FIG13 is a schematic diagram of a quadratic curve trapezoidal window frequency selection provided in an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

In view of the problems existing in the prior art, the present invention provides a method, system, storage medium and application for optimizing the working frequency of a high-frequency ground wave radar. The present invention is described in detail below with reference to the accompanying drawings.

As shown in FIG1 , the method for optimizing the working frequency of a high-frequency ground wave radar provided in an embodiment of the present invention includes:

S101, obtaining spectrum data: the frequency monitoring subsystem uses multiple receiving channels to process the received signals in parallel, performs Fourier analysis on each channel respectively, and then combines the analysis results of each channel to obtain complete spectrum data;

S102, anti-impact interference processing: using a two-dimensional OS algorithm and a double threshold algorithm to process the spectrum data to obtain spectrum information processed with anti-impact interference;

S103, long short-term memory model prediction spectrum: build LSTM network, and perform model training and prediction deviation evaluation;

S104, average power spectrum-variance frequency selection scheme sliding window frequency selection: slide a quadratic curve trapezoidal window on the entire spectrum, extract the average power spectrum and fluctuation information in the window, and select the best operating frequency according to the scoring scheme.

The high-frequency ground wave radar operating frequency optimization method provided by the present invention can also be implemented by ordinary technicians in the industry using other steps. The high-frequency ground wave radar operating frequency optimization method provided by the present invention in FIG. 1 is only a specific embodiment.

The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

和h_t通常使用双曲正切tanh函数作为激活函数。下列公式为LSTM的计算公式：1. As shown in Figure 2, the principle framework diagram of the long short-term memory model (LSTM) introduces input gates, output gates, and forget gates to control the memory and forgetting of time series information. In each time step t, _xt is the input vector, _ct is the cell state vector, and _ht is the hidden state vector output according to _ct , where W _* represents the weight matrix, b _* is the bias vector, *∈{i,f,o,c}. The sigmoid function is used as the activation function of the input gate i _t , the forget gate f _t, and the output gate o _t , with a value range of [0,1], to control the proportion of information output by each gate;

and h _t usually use the hyperbolic tangent tanh function as the activation function. The following formula is the calculation formula of LSTM:

(1)i _t =sigmoid(W _i [x _t ,h _t-1 ]+b _i );

(2)f _t =sigmoid(W _f [x _t ,h _t-1 ]+b _f );

(3)o _t =sigmoid(W _o [x _t ,h _t-1 ]+b _o );

(4)

(5)

(6)h _t = o _t ·tanh(c _t );

The present invention establishes an LSTM network, sets the number of nodes in each layer, the forgetting rate, the learning rate, the number of neurons, and the maximum number of iterations, inputs a part of the spectrum information after anti-impact interference processing into the LSTM network as a training set for learning, and uses the other part as a prediction set to predict the spectrum.

2. Average power spectrum-variance frequency selection scheme frequency selection

Figure 3 is a schematic diagram of sliding window frequency selection. The principle of sliding window frequency selection is to select a suitable window function, score it according to certain criteria, and select the best working frequency based on the score. The width and shape of the sliding window are selected to match the bandwidth of the radar receiving channel and the bandwidth of the demodulated signal. The average power and variance are used as frequency selection elements to represent the average amplitude and deviation degree of information within a certain cumulative time window. In the formula, m _F1 and m _F2 are proportional coefficients, and m _F1 = 20 and m _F2 = 5 are temporarily taken; C _MIN and V _MIN represent the minimum mean square value; Faverage represents the average value of the sliding window power in formula (7) and the variance value of the sliding window in formula (8); x ₁ is the total score of the sliding window; x _F1 is the average power score of the sliding window; x _F2 is the variance score of the sliding window; n _F1 is the proportional coefficient of the average power score of the sliding window, which is taken as 0.7; n _F2 is the proportional coefficient of the variance score of the sliding window, which is taken as 0.3. The formula is as follows:

(7)

(8)

(9)x ₁ =n _F1 x _F1 +n _F2 x _F2 ;

First, the width of the quadratic trapezoidal window is determined according to the radar receiving channel bandwidth and the demodulated signal bandwidth. The designed sliding window is slid across the entire spectrum data, and the sliding window is scored using the above scoring formula to select the optimal frequency point within the working frequency band. Since there are frequency peaks on both sides of the boundary when the rectangular window is scored, and the electromagnetic environment is changeable, these peak frequencies may affect the detection power of the radar, so a quadratic trapezoidal window is used for frequency selection. It can not only reflect the data information within the frequency band, but also take into account the electromagnetic environment information on both sides.

The technical effects of the present invention are described in detail below in conjunction with simulation experiments.

1. Obtain spectrum data: The frequency monitoring subsystem uses multiple receiving channels to process the received signal in parallel. Each receiving channel has a different carrier frequency, thus covering the entire frequency band. Fourier analysis is performed on each channel separately, and then the analysis results of each channel are combined to obtain complete spectrum data.

2. Anti-impact interference processing

I. Use the two-dimensional OS (order statistics) algorithm, that is, the two-dimensional ordered statistics algorithm, to sort each time batch data by amplitude in the frequency dimension, take 1/4 of the sorted samples as the estimated threshold of the noise floor, and use the OS algorithm to base the obtained floor sequence to obtain the noise floor of the time-frequency diagram.

II. Using a double threshold algorithm, setting the amplitude threshold and pulse width threshold to identify interference pulses, remove the frequency monitoring data affected by impact interference or replace it with adjacent non-interference batch data, and then splice and integrate the remaining valid spectrum data to obtain spectrum information with anti-impact interference processing.

Figure 4 and Figure 5 are the frequency spectrum diagrams before and after anti-shock interference.

3. Long short-term memory model predicts spectrum

I. Data preparation: Read the spectrum data in batches, divide the spectrum data into training set and test set, and then normalize the data.

II. Build an LSTM network. Set the number of nodes in each layer, and the activation function defaults to sigmoid function and tanh function.

III. Initialize network parameters. Set the forgetting rate, learning rate, number of neurons, and maximum number of iterations.

IV. Model training and evaluation indicators. The Adam algorithm is used for model training. This experiment uses the root mean square error (RMSE) as an evaluation indicator to measure the prediction deviation of the model.

The frequency band for this admission is 4.3MHz to 4.9MHz, with a frequency interval of 0.5kHz, totaling 1200 frequency points. The time average interval is set to 5 minutes, and each frequency point has a total of 700 time batch points. According to the 90% of each 700 time batches as training sets and 10% as test sets, the results of 70 points are predicted. The forgetting rate is set to 0.1, the learning rate is set to 0.005, the number of neurons is set to 200, and the maximum number of iterations is 250 times. Figure 6 shows the read spectrum information, Figure 7 shows the root mean square error value of the LSTM prediction model, Figure 8 shows the root mean square error value of the current translation prediction model, and Figure 9 shows the root mean square error comparison between the LSTM and current translation prediction results.

4. Average power spectrum-variance frequency selection scheme sliding window frequency selection

Select a quadratic trapezoidal window (Figure 10), slide this window function across the entire spectrum to extract the average power spectrum and fluctuation information within the window, calculate the total score of the sliding window, and then select the best operating frequency based on the score. Figure 11 is a rectangular window structure diagram, and Figures 12 and 13 are schematic diagrams of rectangular window and quadratic trapezoidal window frequency selection, respectively.

It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. A person of ordinary skill in the art will appreciate that the above-mentioned devices and methods can be implemented using computer executable instructions and/or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims (9)

1. a kind of high frequency ground wave radar operating frequency optimal method is characterized in that, described high frequency ground wave radar operating frequency optimal method comprises: Obtain spectrum data: The frequency monitoring subsystem uses multiple receiving channels to process the received signals in parallel, performs Fourier analysis on each channel separately, and combines the analysis results of each channel to obtain complete spectrum data; Anti-shock interference processing: use two-dimensional OS algorithm and double-threshold algorithm to process spectrum data, and obtain spectrum information for anti-shock interference processing; Long-short-term memory model prediction spectrum: construct LSTM network, and perform model training and prediction deviation evaluation; Average power spectrum-variance frequency selection scheme Sliding window frequency selection: slide the quadratic trapezoidal window on the entire spectrum, extract the average power spectrum and fluctuation information in the window, and select the best operating frequency according to the scoring scheme; The anti-shock interference treatment specifically includes: (1) Using the two-dimensional OS algorithm, that is, the two-dimensional ordered statistics algorithm, sort the data of each time batch in the frequency dimension according to the magnitude, and take 1/4 of the sorted samples as the estimated threshold of the noise floor. The base sequence of the OS algorithm base is used to obtain the noise base of the time-frequency graph; (2) Using the double threshold algorithm, set the amplitude threshold and pulse width threshold to identify the interference pulse, eliminate the frequency monitoring data affected by the impact interference or replace it with adjacent non-interference batch data, splice and integrate the remaining effective spectrum data, and obtain Spectrum information for anti-shock interference processing. 2. The method for optimizing the working frequency of high-frequency ground wave radar as claimed in claim 1, wherein each receiving channel has a different carrier frequency and covers the entire frequency band. 3. the preferred method for high-frequency ground wave radar operating frequency as claimed in claim 1, is characterized in that, described long-short-term memory model prediction frequency spectrum comprises: (1) Data preparation, read the spectrum data in batches, divide the spectrum data into training set and test set at the same time, and then normalize the data; (2) Build an LSTM network, set the number of nodes in each layer, and the activation function defaults to the sigmoid function and the tanh function; (3) Initialize network parameters, set forgetting rate, learning rate, number of neurons, and maximum number of iterations; (4) Model training and evaluation indicators, using the Adam algorithm for model training, using the root mean square error as the evaluation index to measure the prediction deviation of the model, the root mean square error calculation formula:

in

Represents the predicted value of the model, _xi represents the real value of the sequence, and n represents the number of predicted values. 4. the preferred method for high frequency ground wave radar operating frequency as claimed in claim 1, is characterized in that, the computing formula of described long short-term memory model: i _t = sigmoid(W _i [x _t ,h _t-1 ]+ _bi ); f _t = sigmoid(W _f [x _t ,h _t-1 ]+b _f ); o _t = sigmoid(W _o [x _t ,h _t-1 ]+b _o );

h _t = o _t ·tanh(c _t ); In each time step t, x _t is the input vector, c _t is the cell state vector, h _t is the hidden state vector output according to c _t , where W _* represents the weight matrix, b _* is the bias vector, *∈{ i,f,o,c}; the sigmoid function is used as the activation function of the input gate _it , the forget gate f _t and the output gate o _t , and the value range is between [0,1], which is used to control the output of each gate proportion of information;

and h _t use the tanh function as the activation function. 5. The method for optimizing the operating frequency of high-frequency ground wave radar as claimed in claim 1, wherein said average power spectrum-variance frequency selection scheme sliding window frequency selection specifically comprises: first according to the radar receiving channel bandwidth and demodulation signal The bandwidth determines the width of the quadratic trapezoidal window, slides the designed sliding window on the entire spectrum data, and scores the sliding window by the scoring formula to select the optimal frequency point in the working frequency band; The width and shape of the sliding window are selected to match the bandwidth of the radar receiving channel and the bandwidth of the demodulated signal, and the average power and variance are used as frequency selection elements to represent the average amplitude and deviation degree of information within a certain cumulative time window. 6. the preferred method for high frequency ground wave radar operating frequency as claimed in claim 5, is characterized in that, described scoring formula is:

x ₁ =n _F1 x _F1 +n _F2 x _F2 ; In the formula, m _F1 and m _F2 are proportional coefficients, m _F1 = 20, m _F2 = 5; C _MIN and V _MIN represent the minimum mean square value; Faverage represents the average value of sliding window power in the x _F1 formula, and in x _F2 The formula is expressed as the variance value of the sliding window; x ₁ is the total score of the sliding window; x _F1 is the average power score of the sliding window; x _F2 is the variance score of the sliding window; n _F1 is the ratio of the average power score of the sliding window Coefficient, take 0.7; n _F2 is the proportional coefficient of sliding window variance score, take 0.3. 7. A computer-readable storage medium, storing a computer program, when the computer program is executed by a processor, the processor is made to perform the following steps: Obtain spectrum data: The frequency monitoring subsystem uses multiple receiving channels to process the received signals in parallel, performs Fourier analysis on each channel separately, and combines the analysis results of each channel to obtain complete spectrum data; Anti-shock interference processing: use the two-dimensional OS algorithm to calculate the noise floor, and use the double-threshold algorithm to identify and eliminate shock pulses to obtain the spectrum information for anti-shock interference processing; Long-short-term memory model prediction spectrum: construct LSTM network, and perform model training and prediction deviation evaluation; Average power spectrum-variance frequency selection scheme Sliding window frequency selection: select the quadratic curve trapezoidal window, slide this window function on the entire spectrum to extract the average power spectrum and fluctuation information in the window, calculate the total score of the sliding window, and then select according to the score Optimum working frequency; The anti-shock interference treatment specifically includes: (1) Using the two-dimensional OS algorithm, that is, the two-dimensional ordered statistics algorithm, sort the data of each time batch in the frequency dimension according to the magnitude, and take 1/4 of the sorted samples as the estimated threshold of the noise floor. The base sequence of the OS algorithm base is used to obtain the noise base of the time-frequency graph; (2) Using the double threshold algorithm, set the amplitude threshold and pulse width threshold to identify the interference pulse, eliminate the frequency monitoring data affected by the impact interference or replace it with adjacent non-interference batch data, splice and integrate the remaining effective spectrum data, and obtain Spectrum information for anti-shock interference processing. 8. A high-frequency ground-wave radar operating frequency optimization system for the high-frequency ground-wave radar operating frequency optimization method described in any one of claims 1 to 6, wherein the high-frequency ground-wave radar operating frequency Preferred systems include: The spectrum data acquisition module is used to process the received signals in parallel by using multiple receiving channels, perform Fourier analysis on each channel respectively, and then combine the analysis results of each channel to obtain complete spectrum data; The anti-shock interference processing module is used to process the spectrum data to obtain the spectrum information for anti-shock interference processing; The LSTM network model predicts the spectrum module, which is used to construct the LSTM network, and conduct model training and evaluation of prediction deviation; The sliding window frequency selection module is used to select a suitable window function, slide the window function on the entire spectrum to extract the average power spectrum and fluctuation information in the window, calculate the total score of the sliding window, and then select the best operating frequency according to the score; The anti-shock interference treatment specifically includes: (1) Using the two-dimensional OS algorithm, that is, the two-dimensional ordered statistics algorithm, sort the data of each time batch in the frequency dimension according to the magnitude, and take 1/4 of the sorted samples as the estimated threshold of the noise floor. The base sequence of the OS algorithm base is used to obtain the noise base of the time-frequency graph; (2) Using the double threshold algorithm, set the amplitude threshold and pulse width threshold to identify the interference pulse, eliminate the frequency monitoring data affected by the impact interference or replace it with adjacent non-interference batch data, splice and integrate the remaining effective spectrum data, and obtain Spectrum information for anti-shock interference processing. 9. A high-frequency ground-wave radar, characterized in that the high-frequency ground-wave radar is equipped with the high-frequency ground-wave radar operating frequency optimization system according to claim 8.

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