Methods, devices and equipment for assessing the data quality of high-frequency ground wave radar

Abstract

This invention provides a method, apparatus, and device for evaluating the quality of high-frequency ground wave radar data. The method includes: constructing an evaluation model; acquiring radar data to be evaluated for each field; inputting the radar data to be evaluated for each field into the evaluation model to obtain a comprehensive score corresponding to each field of radar data. The high-frequency ground wave radar data quality evaluation method based on deep learning provided by this invention achieves automated quality evaluation of large amounts of high-frequency ground wave radar data by constructing an evaluation model, and the evaluation efficiency is high.

Description

Technical Field

[0001] This invention relates to the field of high-frequency ground wave radar data quality assessment technology, and in particular to a method, apparatus, and device for high-frequency ground wave radar data quality assessment. Background Technology

[0002] High-frequency ground-wave radar utilizes the first-order and second-order scattering mechanisms of high-frequency electromagnetic waves from the ocean surface to extract sea state information such as wind, wave, and current fields from radar echoes, enabling large-scale, high-precision, and all-weather real-time monitoring of the marine environment. However, due to various factors, the quality of radar data varies greatly. Directly using it for the inversion of ocean dynamic parameters can lead to misinterpretations. Furthermore, high-frequency ground-wave radar data is voluminous, and there is a lack of automated methods for quality assessment of large volumes of such data specifically designed for the marine environment. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a high-frequency ground wave radar data quality assessment method based on deep learning.

[0004] A method for assessing the data quality of high-frequency ground wave radar includes:

[0005] Develop an evaluation model;

[0006] Acquire radar data for each field to be evaluated;

[0007] Each radar data point to be evaluated is input into the evaluation model to obtain a comprehensive score for each radar data point.

[0008] Furthermore, in the high-frequency ground wave radar data quality assessment method described above, the construction of the assessment model includes:

[0009] Acquire the quality assessment indicators corresponding to the radar data; the quality assessment indicators include: first-order Bragg peak correlation index, radio frequency interference correlation index, and ionospheric clutter correlation index.

[0010] Based on the aforementioned quality assessment indicators, determine the sub-quality assessment indicators corresponding to each quality assessment indicator;

[0011] The score value corresponding to each sub-quality assessment index in each radar data field is obtained according to the sub-quality assessment index;

[0012] Based on the score value corresponding to each sub-quality assessment index in each radar data session, an assessment information table is generated for each radar data session; the assessment information table includes: quality assessment index, sub-quality assessment index corresponding to the quality assessment index, and score value corresponding to each sub-quality assessment index;

[0013] An evaluation matrix is ​​generated based on the evaluation information table corresponding to each radar data session; the evaluation matrix includes the radar data from the 12 sessions.

[0014] The scores of each sub-quality assessment indicator in the evaluation matrix are standardized to obtain the standardized score x for each sub-quality assessment indicator. ij ;x ij Let represent the standardized score of the j-th sub-quality assessment indicator for the i-th data field;

[0015] Based on the standardized score x corresponding to each sub-quality assessment indicator ij Determine the entropy value H corresponding to each sub-quality assessment index. j , in Let be the weight value of the i-th field data under the j-th sub-quality assessment index, and m be the number of fields included in each group of assessment data;

[0016] Based on the entropy value H corresponding to each sub-quality evaluation index j Determine the weight w corresponding to each sub-quality assessment indicator. j , n represents the number of sub-quality assessment indicators;

[0017] Based on the weight value w corresponding to each sub-quality assessment indicator j and the standardized score x ij An evaluation model is constructed, wherein the evaluation model is as follows:

[0018]

[0019] Furthermore, in the high-frequency ground wave radar data quality assessment method described above, the first-order Bragg peak correlation index includes four sub-quality assessment indices: echo completeness coefficient B1, near-range signal-to-noise ratio B2, long-range signal-to-noise ratio B3, and maximum detection range B4.

[0020] The radio frequency interference-related indicators include four sub-quality assessment indicators: ocean echo influence factor R1, interference-to-noise ratio R2, distance influence factor R3, and Doppler influence factor R4.

[0021] The ionospheric clutter related indicators include four sub-quality assessment indicators: ocean echo influence factor I1, interference-to-noise ratio I2, distance influence factor I3, and Doppler influence factor I4, totaling 12 quality assessment indicators.

[0022] Furthermore, in the high-frequency ground wave radar data quality assessment method described above, when the quality assessment index is a first-order Bragg peak correlation index, the standardized score x corresponding to its sub-quality assessment index is... ijThe following formula is used to obtain:

[0023]

[0024] When the quality assessment index is a radio frequency interference-related index or an ionospheric clutter-related index, the standardized score x corresponding to its sub-quality assessment index is... ij The following formula is used to obtain:

[0025]

[0026] Where, r ij For the i-th data, the score of the j-th sub-quality assessment index.

[0027] Furthermore, the high-frequency ground wave radar data quality assessment method described above updates the weight w corresponding to each sub-quality assessment index in real time, using one hour as the update unit. j The evaluation model is then readjusted with the updated weight values ​​corresponding to each sub-quality evaluation index.

[0028] Furthermore, in the high-frequency ground wave radar data quality assessment method described above, the step of acquiring radar data to be assessed for each field includes:

[0029] Acquire real-time radar echo data for each field to be evaluated;

[0030] The real-time radar echo data is input into the radar echo detection network to obtain the detection results. The detection results include: [Flag, Xcenter, Ycenter, Width, High], where Flag represents the detection flag, 0 represents the first-order Bragg peak correlation index, 1 represents the radio frequency interference correlation index, and 2 represents the ionospheric clutter correlation index; Xcenter represents the X-axis proportion of the detection frame center point; Ycenter represents the Y-axis proportion of the detection frame center point; Width represents the width proportion of the detection frame; and High represents the height proportion of the detection frame.

[0031] Based on the detection results, the radar data to be evaluated for each event is determined, and the radar data to be evaluated for each event is used as the input to the evaluation model.

[0032] Furthermore, in the high-frequency ground wave radar data quality assessment method described above, determining the radar data to be assessed for each field based on the detection results includes:

[0033] The relevant indicators of the first-order Bragg peak are determined based on the Flag indicating the detection flag position and the detection frame height ratio High: echo completeness coefficient B1, near-range signal-to-noise ratio B2, far-range signal-to-noise ratio B3, and maximum detection distance B4.

[0034] Echo completeness coefficient B1:

[0035]

[0036] Where N0 is the number of flag bits 0 in the detection result, i.e. the number of Bragg peaks detected; N is the theoretical number of Bragg peaks;

[0037] Near-range signal-to-noise ratio B2:

[0038]

[0039] in, The average power is the noise floor. This represents the peak power of near-range ocean echoes, which is 20% of the radar's ocean echo detection range. Where LRange is the effective detection range of the radar;

[0040] Long-distance signal-to-noise ratio B3:

[0041]

[0042] in, The average power is the noise floor. This represents the peak power of ocean echoes at long distances, which is 80% of the radar's ocean echo detection range. Where LRange is the effective detection range of the radar;

[0043] Maximum detection range B4:

[0044]

[0045] The following radio frequency interference-related indicators are determined based on the detection frame width ratio (Width) and detection frame height ratio (High): ocean echo influence factor R1, interference-to-noise ratio (R2), distance influence factor (R3), and Doppler influence factor (R4).

[0046] Ocean echo influence factor R1:

[0047]

[0048] Where S = High × Width is the normalized area of ​​the detection box;

[0049] Interference-to-noise ratio R2:

[0050]

[0051] in, To detect the average power of radio frequency interference within the frame, Average power at the noise floor;

[0052] Distance influence factor R3:

[0053]

[0054] That is: the sum of the height ratios of the detection frames corresponding to radio frequency interference. If multiple radio frequency interferences are detected, causing R3 to be greater than 1, R3 is set to 1.

[0055] Doppler impact factor R4:

[0056]

[0057] That is: the sum of the proportions of the detection frame width corresponding to radio frequency interference. When multiple radio frequency interferences are detected, resulting in R4 being greater than 1, R4 is 1.

[0058] Based on the detection frame width ratio (Width) and detection frame height ratio (High), the ionospheric clutter related indicators are determined as follows: ocean echo influence factor I1, interference-to-noise ratio I2, distance influence factor I3, and Doppler influence factor I4.

[0059] Ocean echo influence factor I1:

[0060]

[0061] Where S = High × Width is the normalized area of ​​the detection box;

[0062] Interference-to-noise ratio I2:

[0063]

[0064] in, To detect the average power of ionospheric clutter within the frame, Average power at the noise floor;

[0065] Distance Influence Factor I3:

[0066]

[0067] That is: the sum of the height ratios of the detection frame corresponding to ionospheric clutter. If multiple ionospheric clutters are detected, resulting in I3 being greater than 1, I3 is set to 1.

[0068] Doppler impact factor I4:

[0069]

[0070] That is: the sum of the proportions of the detection frame width corresponding to ionospheric clutter. If multiple ionospheric clutters are detected, resulting in I4 being greater than 1, I4 is set to 1.

[0071] The radar data to be evaluated for each field is determined by the first-order Bragg peak correlation index, radio frequency interference correlation index, and ionospheric clutter correlation index. The radar data to be evaluated for each field is an index array: [B1,B2,B3,B4,R1,R2,R3,R4,I1,I2,I3,I4].

[0072] A high-frequency ground wave radar data quality assessment device, comprising:

[0073] Building blocks are used to construct evaluation models;

[0074] The acquisition unit is used to acquire radar data to be evaluated for each field.

[0075] The evaluation unit is used to input the radar data to be evaluated for each radar event into the evaluation model to obtain a comprehensive score for each radar event.

[0076] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the deep learning-based high-frequency ground wave radar data quality assessment method as described in any of the preceding claims.

[0077] A non-transitory computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the deep learning-based high-frequency ground wave radar data quality assessment method as described above.

[0078] The high-frequency ground wave radar data quality assessment method based on deep learning provided by this invention achieves the purpose of automated quality assessment of a large amount of high-frequency ground wave radar data by constructing an assessment model, and the assessment efficiency is high. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating the method for constructing a high-frequency ground wave radar data quality assessment model provided by the present invention.

[0080] Figure 2 This is a flowchart illustrating the generation process of the radar echo detection network of the present invention.

[0081] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0083] Figure 1 This is a flowchart illustrating the high-frequency ground wave radar data quality assessment model construction method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0084] Step 101: Construct the evaluation model;

[0085] Step 102: Acquire radar data for each field to be evaluated;

[0086] Step 103: Input the radar data to be evaluated for each test into the evaluation model to obtain the comprehensive score corresponding to each radar data test.

[0087] The present invention provides a high-frequency ground wave radar data quality assessment method based on deep learning. By constructing an assessment model based on deep learning, it achieves the purpose of automated quality assessment of a large amount of high-frequency ground wave radar data, and the assessment efficiency is high.

[0088] Furthermore, the construction of the evaluation model includes:

[0089] Step 201: Obtain the quality assessment indicators corresponding to the radar data; the quality assessment indicators include: first-order Bragg peak correlation index, radio frequency interference correlation index, and ionospheric clutter correlation index.

[0090] Step 202: Determine the sub-quality assessment indicators corresponding to each quality assessment indicator based on the quality assessment indicators;

[0091] Specifically, radar data quality assessment indicators are divided into three categories: first-order Bragg peak correlation indicators, radio frequency interference correlation indicators, and ionospheric clutter correlation indicators. Each category contains four sub-quality assessment indicators, and the detailed explanations of each sub-quality assessment indicator are as follows:

[0092] (1) Correlation index of first-order Bragg peak

[0093] Echo integrity factor B1: Measures the integrity of radar received echo signals. It is quantitatively described by the number of first-order Bragg peaks. Generally, transmitter, receiver or antenna failures will cause echo signal loss.

[0094] Near-range signal-to-noise ratio (SNR) B2: measures the strength of ocean echo signals. Near-range is defined as 20% of the radar ocean echo detection range, and SNR B2 is the peak SNR of the ocean echo at that range.

[0095] Long-range signal-to-noise ratio B3: measures the strength of ocean echo signals. Long-range is defined as 80% of the radar ocean echo detection range. The signal-to-noise ratio B3 is the peak signal-to-noise ratio of the ocean echo at that range.

[0096] Maximum detection range B4: Measures the maximum detection range of ocean echoes and is related to the radar's operating frequency.

[0097] (2) Radio frequency interference related indicators

[0098] Ocean echo impact factor R1: measures the degree of influence of radio frequency interference on ocean echoes, and is quantitatively described by the cross-exchange ratio of the radio frequency interference detection frame and the ocean echo detection frame;

[0099] Interference-to-noise ratio R2: measures the strength of radio frequency interference;

[0100] Distance impact factor R3: measures the extent to which radio frequency interference extends along the distance axis;

[0101] Doppler influence factor R4: measures the extent to which radio frequency interference extends along the Doppler axis.

[0102] (3) Ionospheric clutter related indicators

[0103] Ocean echo influence factor I1: measures the degree of influence of ionospheric clutter on ocean echoes, and is quantitatively described by the cross-union ratio of the ionospheric clutter detection frame and the ocean echo detection frame;

[0104] Interference-to-noise ratio (I2): measures the strength of ionospheric clutter;

[0105] Distance Influence Factor I3: Measures the extent to which ionospheric clutter extends along the distance axis;

[0106] Doppler influence factor I4: measures the extent to which ionospheric clutter extends along the Doppler axis.

[0107] The high-frequency ground wave radar data quality assessment method based on deep learning provided by this invention divides each quality assessment index into sub-quality assessment indices and constructs an assessment model using these sub-quality assessment indices. This achieves a comprehensive and objective assessment of radar data quality, improves assessment efficiency, and the assessment values ​​of each sub-quality assessment index can also provide guidance for equipment operation status analysis, radar frequency selection, interference suppression, and other tasks.

[0108] Step 203: Obtain the score value corresponding to each sub-quality assessment index in each radar data based on the sub-quality assessment index corresponding to each quality assessment index;

[0109] Specifically, the following section provides a detailed explanation of how to obtain the score value corresponding to each sub-quality assessment indicator:

[0110] First, the evaluation values ​​of each sub-quality assessment indicator are divided into five levels:

[0111] (1) Correlation index of first-order Bragg peak

[0112] Echo completeness coefficient B1: 8, 6, 4, 2, 0. Since the antenna array is 2 transmit and 4 receive and is a dual-station network, each echo data distance Doppler should contain 8 Bragg peaks.

[0113] Near-range signal-to-noise ratio B2: >25, 20~25, 15~20, 10~15, <10;

[0114] Long-distance signal-to-noise ratio B3: >20, 15~20, 10~15, 5~10, <5;

[0115] Maximum detection range B4: Since the radar detection range is related to the operating frequency, for dual-frequency operating mode, the two frequencies need to be classified separately. High frequency mode (around 12MHz): >25, 20~25, 15~20, 10~15, <10; Low frequency mode (around 8MHz): >30, 25~30, 20~25, 15~20, <15;

[0116] (2) Radio frequency interference related indicators

[0117] Ocean echo influence factors R1: 0.8–1, 0.6–0.8, 0.4–0.6, 0.2–0.4, 0–0.2;

[0118] Interference-to-noise ratio R2: >25, 20~25, 15~20, 10~15, <10;

[0119] Distance influence factor R3: 0.8~1, 0.6~0.8, 0.4~0.6, 0.2~0.4, 0~0.2;

[0120] Doppler influence factor R4: 0.8–1, 0.6–0.8, 0.4–0.6, 0.2–0.4, 0–0.2;

[0121] (3) Ionospheric clutter related indicators

[0122] Ocean echo influence factors I1: 0.8–1, 0.6–0.8, 0.4–0.6, 0.2–0.4, 0–0.2;

[0123] Interference-to-noise ratio I2: >25, 20~25, 15~20, 10~15, <10;

[0124] Distance influence factor I3: 0.8–1, 0.6–0.8, 0.4–0.6, 0.2–0.4, 0–0.2;

[0125] Doppler influence factor I4: 0.8–1, 0.6–0.8, 0.4–0.6, 0.2–0.4, 0–0.2;

[0126] The above classification is the result of statistical analysis of batch data and can be adjusted for different application scenarios. The five levels correspond to five scores: 100, 80, 60, 40, and 0. After obtaining the evaluation value of each sub-quality indicator for each data point, it is matched with the classification level of each indicator to obtain the score value of each indicator.

[0127] Step 204: Generate an evaluation information table for each radar data session based on the score value corresponding to each sub-quality evaluation index in each radar data session; the evaluation information table includes: quality evaluation index, sub-quality evaluation index corresponding to the quality evaluation index, and score value corresponding to each sub-quality evaluation index;

[0128] Specifically, data from one hour is grouped together, and weight values ​​are updated in real time. Since the radar coherence accumulation time is five minutes, each group includes 12 data points, resulting in a 12×12 dimension for the evaluation matrix. The specific presentation format of the evaluation information table is shown in Table 1:

[0129] Table 1 Assessment Information Sheet

[0130]

[0131] Step 205: Generate an evaluation matrix based on the evaluation information table corresponding to each radar data session; the evaluation matrix includes the radar data from the 12 sessions.

[0132] Specifically, the evaluation matrix is ​​represented as follows:

[0133]

[0134] Step 206: Standardize the score value of each sub-quality assessment indicator in the evaluation matrix to obtain the standardized score x corresponding to each sub-quality assessment indicator. ij ;

[0135] Specifically, since the correlation index of the first-order Bragg peak is a positive index, its standardized formula is: Radio frequency interference-related indicators and ionospheric clutter-related indicators are negative indicators, and their standardized formulas are as follows:

[0136] Where i represents the number of data fields being evaluated, j represents the sub-quality evaluation index, and rij For the i-th data point, the score of the j-th sub-quality assessment indicator, x ij This corresponds to the standardized score.

[0137] The high-frequency ground wave radar data quality assessment method based on deep learning provided by this invention calculates standardized scores by using different standardized formulas for the positive index first-order Bragg peak correlation index and the negative index radio frequency interference correlation index and ionospheric clutter correlation index. This changes the nature of the negative index and makes all indices have the same direction of influence on the evaluation system, which helps to simplify the evaluation model.

[0138] Step 207: Based on the standardized score x corresponding to each sub-quality assessment index. ij Determine the entropy value H corresponding to each sub-quality assessment index. j , in Let be the weight value of the i-th field data under the j-th sub-quality assessment index, and m be the number of fields included in each group of assessment data;

[0139] Step 208: Based on the entropy value H corresponding to each sub-quality evaluation index j Determine the weight w corresponding to each sub-quality assessment indicator. j , n represents the number of sub-quality assessment indicators;

[0140] Step 209: Based on the weight value w corresponding to each sub-quality evaluation index j and the standardized score x corresponding to each sub-quality assessment indicator. ij An evaluation model is constructed, and a comprehensive score for the i-th data point is output based on the evaluation model. The evaluation model is as follows:

[0141]

[0142] Specifically, the comprehensive score for the i-th data is finally output based on the evaluation model: The overall score rating is as follows: Excellent (≥90 points); Good (≥80 points, <90 points); Average (≥60 points, <80 points); Poor (<60 points).

[0143] The present invention provides a deep learning-based method for evaluating the quality of high-frequency ground wave radar data. This method uses a constructed evaluation model to automatically evaluate the quality of high-frequency ground wave radar data. The evaluation model can be customized and flexibly set to achieve automation and intelligence. It can effectively improve efficiency when the data volume is large and provide quantitative indicators for data improvement and application.

[0144] Furthermore, the weight w corresponding to each sub-quality evaluation indicator is updated in real time, with an update unit of one hour. j The evaluation model is then readjusted with the updated weight values ​​corresponding to each sub-quality evaluation index.

[0145] The present invention provides a high-frequency ground wave radar data quality assessment method based on deep learning, which updates the weight w corresponding to each sub-quality assessment index in real time. j The evaluation model is adjusted to make the quality assessment of high-frequency ground wave radar data more accurate.

[0146] Furthermore, the acquisition of radar data to be evaluated for each field includes:

[0147] Real-time radar echo data for each field to be evaluated is acquired.

[0148] Specifically, the radar real-time echo data is stored in the form of a matrix with dimensions P×Q, where Q is the number of frequency sweep cycles and P is the number of sampling points in one frequency sweep cycle.

[0149] The real-time radar echo data is input into the radar echo detection network to obtain the detection result. The detection result includes: [Flag, Xcenter, Ycenter, Width, High], where Flag represents the detection flag bit, 0 represents the first-order Bragg peak correlation index, 1 represents the radio frequency interference correlation index, and 2 represents the ionospheric clutter correlation index; Xcenter represents the X-axis proportion of the detection frame center point; Ycenter represents the Y-axis proportion of the detection frame center point; Width represents the width proportion of the detection frame; and High represents the height proportion of the detection frame.

[0150] Based on the detection results, the radar data to be evaluated for each event is determined, and the radar data to be evaluated for each event is used as the input to the evaluation model.

[0151] The following is a detailed explanation of how to determine the implementation of each radar data to be evaluated based on the detection results:

[0152] The first-order Bragg peak correlation indices are determined based on the Flag (representing the detection flag) and the proportion of the detection frame height (High), namely: echo completeness coefficient B1, near-range signal-to-noise ratio B2, far-range signal-to-noise ratio B3, and maximum detection range B4; the radio frequency interference correlation indices are determined based on the proportion of the detection frame width (Width) and the proportion of the detection frame height (High), namely: ocean echo influence factor R1, interference-to-noise ratio R2, range influence factor R3, and Doppler influence factor R4; the ionospheric clutter correlation indices are determined based on the proportion of the detection frame width (Width) and the proportion of the detection frame height (High), namely: ocean echo influence factor I1, interference-to-noise ratio I2, range influence factor I3, and Doppler influence factor I4.

[0153] Echo completeness coefficient B1:

[0154]

[0155] Where N0 is the number of flag bits 0 in the detection result, that is, the number of Bragg peaks detected; N is the theoretical number of Bragg peaks. The radar will get two Bragg peaks for each signal it transmits, that is, positive and negative Bragg peaks.

[0156] Near-range signal-to-noise ratio B2:

[0157]

[0158] in, This refers to the peak power of near-range ocean echoes, which is defined as 20% of the radar's ocean echo detection range. Where LRange is the effective detection range of the radar; The noise floor average power is the average power of all spectral points outside the detection range of the Bragg peak.

[0159] Long-distance signal-to-noise ratio B3:

[0160]

[0161] in, The average power is the noise floor. The peak power of ocean echoes at long distances is defined as 80% of the radar's ocean echo detection range. Where LRange is the effective detection range of the radar;

[0162] Maximum detection range B4:

[0163]

[0164] Ocean echo influence factor R1:

[0165]

[0166] Where S = High × Width is the normalized area of ​​the detection box;

[0167] Interference-to-noise ratio R2:

[0168]

[0169] in, This represents the average power of radio frequency interference within the detection frame, specifically the average power of all spectral points within the detection frame where Flag is 1. Average power at the noise floor;

[0170] Distance influence factor R3:

[0171]

[0172] That is, the sum of the height ratios of the detection frames corresponding to radio frequency interference. When multiple radio frequency interferences are detected, resulting in R3 being greater than 1, R3 is 1.

[0173] Doppler impact factor R4:

[0174]

[0175] That is, the sum of the proportions of the detection frame width corresponding to the radio frequency interference. When multiple radio frequency interferences are detected, resulting in R4 being greater than 1, R4 is 1.

[0176] Ocean echo influence factor I1:

[0177]

[0178] Where S = High × Width is the normalized area of ​​the detection box;

[0179] Interference-to-noise ratio I2:

[0180]

[0181] in, The average power of ionospheric clutter within the detection frame is calculated as the average power of all spectral points within the detection frame with a Flag of 2. Average power at the noise floor;

[0182] Distance Influence Factor I3:

[0183]

[0184] That is, the sum of the height ratios of the detection frame corresponding to the ionospheric clutter. When multiple ionospheric clutters are detected, resulting in I3 being greater than 1, I3 is 1.

[0185] Doppler impact factor I4:

[0186]

[0187] That is, the sum of the proportions of the detection frame width corresponding to ionospheric clutter. When multiple ionospheric clutters are detected, resulting in I4 being greater than 1, I4 is 1.

[0188] The radar data to be evaluated for each field is determined using the first-order Bragg peak correlation index, radio frequency interference correlation index, and ionospheric clutter correlation index. Each radar data field to be evaluated is defined as an index array.

[0189] [B1,B2,B3,B4,R1,R2,R3,R4,I1,I2,I3,I4]

[0190] Finally, the array of indicators is input into the quality assessment model to obtain the comprehensive score for each data point.

[0191] The construction of the radar echo detection network includes: acquiring raw radar echo data; performing two FFTs on the raw radar echo data to obtain the range Doppler spectrum; using the range Doppler spectrum as a training set, and labeling the range Doppler spectrum with the labeling software LabelImg, marking it as ocean echo, radio frequency interference, and ionospheric clutter; and inputting the labeled training samples into the YOLOv5 model to obtain the radar echo detection network.

[0192] Specifically, the collected raw radar echo data comes from dual-station dual-frequency networked radar data, and the range Doppler spectrum is obtained by performing two FFTs on the raw radar echo data. Figure 2 Here is a flowchart of the generation process for the radar echo detection network, as follows: Figure 2 As shown, the obtained range-Doppler spectrum is used as the training set. The range-Doppler spectrum is labeled using the labeling software LabelImg, and is categorized into three types: ocean echo, radio frequency interference, and ionospheric clutter. The labeled training samples are then input into the YOLOv5 model to obtain the radar echo detection network.

[0193] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a high-frequency ground wave radar data quality assessment method, which includes:

[0194] Develop an evaluation model;

[0195] Acquire radar data for each field to be evaluated;

[0196] Each radar data point to be evaluated is input into the evaluation model to obtain a comprehensive score for each radar data point.

[0197] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0198] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the deep learning-based high-frequency ground wave radar data quality assessment method as described above.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating data quality of a high-frequency ground wave radar, characterized by, The method comprises the following steps: constructing an evaluation model; wherein the construction of the evaluation model comprises: obtaining quality evaluation indicators corresponding to radar data; the quality evaluation indicators comprise: first-order Bragg peak related indicators, radio frequency interference related indicators, ionospheric clutter related indicators; determining sub-quality evaluation indicators corresponding to each quality evaluation indicator according to the quality evaluation indicators; obtaining score values corresponding to each sub-quality evaluation indicator in each field of radar data according to the sub-quality evaluation indicators; the first-order Bragg peak related indicators comprise four sub-quality evaluation indicators, which are respectively: echo completeness coefficient B1, near distance signal-to-noise ratio B2, far distance signal-to-noise ratio B3, and maximum detection distance B4; the radio frequency interference related indicators comprise four sub-quality evaluation indicators, which are respectively: ocean echo influence factor R1, interference noise ratio R2, distance influence factor R3, and Doppler influence factor R4; the ionospheric clutter related indicators comprise four sub-quality evaluation indicators, which are respectively: ocean echo influence factor I1, interference noise ratio I2, distance influence factor I3, and Doppler influence factor I4; determining the first-order Bragg peak related indicators: echo completeness coefficient B1, near distance signal-to-noise ratio B2, far distance signal-to-noise ratio B3, and maximum detection distance B4 according to Flag representing a detection flag bit and detection frame height proportion High; echo completeness coefficient B1: wherein N0 is the number of flag bits 0 in the detection result, i.e. the number of detected Bragg peaks; N is the number of Bragg peaks in theory; near distance signal-to-noise ratio B2: wherein, is the noise floor average power, is the peak power of the close-in sea return, the close-in being 20% of the radar sea return detection range, i.e. where LRange is the effective detection range of the radar. far distance signal-to-noise ratio B3: wherein, is the noise floor average power, is the long range ocean echo peak power, long range being 80% of the radar ocean echo detection range, i.e. where LRange is the effective detection range of the radar; maximum detection distance B4: obtaining each field of radar data to be evaluated; inputting each field of radar data to be evaluated into the evaluation model respectively to obtain comprehensive scores corresponding to each field of radar data respectively.

2. The HF ground wave radar data quality evaluation method according to claim 1, characterized in that, The construction of the evaluation model further comprises: generating an evaluation information table corresponding to each field of radar data according to the score values corresponding to each sub-quality evaluation indicator in each field of radar data; the evaluation information table comprises: quality evaluation indicators, sub-quality evaluation indicators corresponding to the quality evaluation indicators, and score values corresponding to each sub-quality evaluation indicator; generating an evaluation matrix according to the evaluation information table corresponding to each field of radar data; the evaluation matrix comprises 12 fields of radar data; The score value of each sub-quality evaluation index in the evaluation matrix is standardized to obtain a standardized score x corresponding to each sub-quality evaluation index ij ; x ij represents the standardized score of the i-th field data and the j-th sub-quality evaluation index According to the standardization score x corresponding to each sub-quality evaluation index ij , determine the entropy value H corresponding to each sub-quality evaluation index j , wherein is the weight value of the i th field data under the j th sub-quality evaluation index, and m is the number of fields contained in each set of evaluation data; According to the entropy value H corresponding to each sub-quality evaluation index j Determine the weight w corresponding to each sub-quality evaluation index j , n is the number of sub-quality evaluation indexes According to the weight value w corresponding to each sub-quality evaluation index j And the standardized score x ij , an evaluation model is constructed, which is:

3. The HF ground wave radar data quality evaluation method according to claim 2, characterized in that, In the case where the quality evaluation index is a first-order Bragg peak related index, the standardized score x of the corresponding sub-quality evaluation index corresponds to ij The following formula is used: In the case that the quality evaluation index is a radio frequency interference related index or an ionospheric clutter related index, the corresponding standardized score x of the corresponding sub-quality evaluation index is ij The following formula is used: wherein r ij is the score of the ith field data, the jth sub-quality evaluation index.

4. The HF ground wave radar data quality evaluation method according to claim 3, characterized in that, updating the weight w corresponding to each sub-quality evaluation index in real time with an update unit of one hour j re-adjusting the evaluation model with the updated weight value corresponding to each sub-quality evaluation index.

5. The HF ground wave radar data quality evaluation method according to claim 4, characterized in that, the step of obtaining each field of radar data to be evaluated comprises: obtaining each field of radar real-time echo data to be evaluated; inputting the radar real-time echo data into a radar echo detection network to obtain a detection result; the detection result comprises: [Flag, Xcenter, Ycenter, Width, High], wherein Flag represents a detection flag bit, 0 represents first-order Bragg peak related indicators, 1 represents radio frequency interference related indicators, and 2 represents ionospheric clutter related indicators; Xcenter represents the X-axis direction proportion of the center point of the detection frame; Ycenter represents the Y-axis direction proportion of the center point of the detection frame; Width represents the width proportion of the detection frame; and High represents the height proportion of the detection frame; determining each field of radar data to be evaluated according to the detection result, and taking each field of radar data to be evaluated as the input of the evaluation model.

6. The HF ground wave radar data quality evaluation method according to claim 5, characterized in that The method comprises the following steps: According to the detection frame width proportion Width and the detection frame height proportion High, the ionospheric scintillation related indexes, i.e., sea echo influence factor I1, interference noise ratio I2, distance influence factor I3, and Doppler influence factor I4, are determined. Sea echo influence factor R1: Wherein, S=HighxWidth is the normalized area of the detection frame. Interference noise ratio R2: wherein, is the average power of the in-band radio frequency interference, is the average power of the noise floor. Distance influence factor R3: That is, the sum of the detection frame height proportions corresponding to the radio frequency interference. In the case of detecting multiple radio frequency interferences, leading to R3 greater than 1, R3 is set to 1. Doppler influence factor R4: That is, the sum of the detection frame width proportions corresponding to the radio frequency interference. In the case of detecting multiple radio frequency interferences, leading to R4 greater than 1, R4 is set to 1. According to the detection frame width proportion Width and the detection frame height proportion High, the ionospheric scintillation related indexes, i.e., sea echo influence factor I1, interference noise ratio I2, distance influence factor I3, and Doppler influence factor I4, are determined. Sea echo influence factor I1: Wherein, S=HighxWidth is the normalized area of the detection frame. Interference noise ratio I2: wherein, to detect the average power of the ionospheric irregularities in the box, is the noise floor average power; Distance influence factor I3: That is, the sum of the detection frame height proportions corresponding to the ionospheric scintillation. In the case of detecting multiple ionospheric scintillations, leading to I3 greater than 1, I3 is set to 1. Doppler influence factor I4: That is, the sum of the detection frame width proportions corresponding to the ionospheric scintillation. In the case of detecting multiple ionospheric scintillations, leading to I4 greater than 1, I4 is set to 1. The first-order Bragg peak related indexes, the radio frequency interference related indexes, and the ionospheric scintillation related indexes are used to determine each field of radar data to be evaluated, and the each field of radar data to be evaluated is an index array: [B1, B2, B3, B4, R1, R2, R3, R4, I1, I2, I3, I4].

7. The apparatus for high-frequency ground wave radar data quality evaluation method according to any one of claims 1 to 6, characterized in that, The method comprises the following steps: The processor executes the program to realize the high-frequency ground wave radar data quality evaluation method according to any one of claims 1 to 6. The computer program is executed by the processor to realize the high-frequency ground wave radar data quality evaluation method according to any one of claims 1 to 6. ​ 8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, ​ 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, ​

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