Radar measurement compensation method and device

Abstract

Disclosed in the present application are a radar measurement compensation method and a device. The method comprises: matching atmospheric environment data samples and radar measurement data samples with respect to timestamps, so as to obtain radar sequence data and environment humidity data; processing the radar sequence data to obtain normalized radar data; performing sampling processing to obtain humidity-normalized radar data; and obtaining a compensation parameter by means of polynomial fitting, and using same for compensating for a radar result, so as to reduce measurement error.

Description

Radar measurement compensation method and device Technical Field

[0001] The present application relates to the field of radar measurement technology, and in particular to a radar measurement compensation method and device. Background Art

[0002] Because electromagnetic waves travel at different wavelengths in different media, their propagation speeds vary. Current measurement methods based on the time-delay method introduce measurement errors, particularly when requiring high precision. Currently, radar interferometry is commonly used to compensate for these errors through either meteorological model correction or cancellation methods. The meteorological model correction method uses a model to derive and calculate the atmospheric refractive index n. The cancellation method uses a fixed point or a selected stable point within the scene, canceling other measurements with the selected point. The remaining value is considered the variable, allowing the local point's changes to be resolved.

[0003] However, the meteorological model correction method is a theoretical approach that requires numerous atmospheric parameters and a complex model. Consequently, its effectiveness in practice is poor and it struggles to meet the demands of practical engineering applications. Furthermore, the elimination method lacks a unified set of rules for selecting fixed or stable points, leading to high randomness and difficulty in selecting them in certain scenarios. Consequently, the compensation results lack stability.

[0004] Summary of the Invention

[0005] The purpose of this application is to provide a radar measurement compensation method and device to solve the problem of large errors in radar measurement in the prior art.

[0006] To achieve the above objectives, the present application provides a radar measurement compensation method, comprising:

[0007] Acquire the collected atmospheric environment data samples and radar measurement data samples, and match the atmospheric environment data samples and the radar measurement data samples according to the timestamps of the acquisition to obtain radar sequence data and corresponding ambient humidity data;

[0008] preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data;

[0009] Sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data that is evenly distributed in each humidity interval;

[0010] Polynomial fitting is performed on the humidity-normalized radar data to obtain compensation parameters, and radar results obtained by on-site real-time measurement are compensated according to the compensation parameters.

[0011] Optionally, the step of matching the atmospheric environment data and the radar measurement data according to the acquired timestamp to obtain radar sequence data and corresponding ambient humidity data includes:

[0012] The atmospheric environment data samples and the radar measurement data samples are matched according to the timestamps recorded by the sensor to obtain n radar sequence data r1, r2, r3, ..., r n and the corresponding environmental humidity data w1,w2,w3,…,w n , where n is a positive integer.

[0013] Optionally, the step of preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data includes:

[0014] The radar sequence data r1, r2, r3, ..., r n The measured value r i and the measured values ​​r at the two moments before and after i-1 ,r i+1 Compare, where i = 1, 2, ..., n-1;

[0015] If the absolute value of the difference between the current measurement value and the measurement values ​​at the two previous and next moments is greater than a preset threshold, the current measurement value is determined to be an abnormal value, and the average of the measurement values ​​at the two previous and next moments is used to replace the current measurement value to eliminate the abnormal value;

[0016] Normalize the radar sequence data after removing outliers to obtain normalized radar data

[0017] Optionally, if the absolute value of the difference between the measured value at the current moment and the measured values ​​at the two previous and next moments is greater than a preset threshold, the measured value at the current moment is determined to be an abnormal value, and the average of the measured values ​​at the two previous and next moments is used to replace the measured value at the current moment to eliminate the abnormal value, which is expressed by the following formula:

[0018] Wherein, K represents a preset threshold.

[0019] Optionally, the radar sequence data after removing outliers is normalized to obtain normalized radar data, which is expressed by the following formula:

[0020] Among them, r base represents the reference value obtained by averaging, r j Represents r1 to rn one of the.

[0021] Optionally, the step of sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval includes:

[0022] Performing partition processing on the normalized radar data and the ambient humidity data;

[0023] The normalized radar data and the ambient humidity data after the interval processing are sampled to obtain m humidity-normalized radar data pairs evenly distributed in each humidity interval. Where m≤n, m is a positive integer.

[0024] Optionally, the step of sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval includes:

[0025] For humidity intervals where no radar measurement data is collected, the missing data in the humidity intervals are filled using a Lagrange interpolation algorithm;

[0026] Assuming that the corresponding measurement value r under humidity w is missing, the following formula is used to calculate Perform interpolation:

[0027] Among them, l j (w) is the Lagrange polynomial, which is expressed as:

[0028] After sampling and interpolation processing, the humidity-normalized radar data is obtained Where h≤m, h is a positive integer.

[0029] Optionally, the step of performing polynomial fitting on the humidity-normalized radar data to obtain compensation parameters includes:

[0030] The Lagrangian polynomial is fitted using the least squares method, and an appropriate polynomial order k is selected to obtain the best fitting effect. The expression is:

[0031] Among them, i represents the number of the data sample 1,2,3,…,h, h is a positive integer, a0,a1,…,a k Indicates the parameters to be fitted, r i represents the normalized radar data, w i Indicates ambient humidity data, L min Indicates that the target value obtained by fitting is the minimum value;

[0032] Get the fitting curve y(w)=a0+a1w i +a2w i 2 +…+a k w i k After the order parameters in are obtained, the corresponding compensation parameter n under relative humidity is extracted from the polynomial coefficients of the fitting curve. j Expressed as n0,n1,n2,…,n 100 , j=1,2,3,…,100, represents the compensation parameter of atmospheric humidity from 0% to 100%, where: n j =y(w=j%);

[0033] The compensation parameter n j Generate a compensation table and save it to the radar memory.

[0034] Optionally, the step of compensating the radar measurement data according to the compensation parameter includes:

[0035] According to the humidity data w measured in real time by the atmospheric humidity sensor, w*100 is rounded to obtain a positive integer j, and the compensation parameter n under the humidity conditions corresponding to the humidity data is obtained by querying the compensation table. j ;

[0036] The compensation parameter n i Apply to the radar result R obtained by on-site real-time measurement to obtain the compensated radar measurement result R corr ;

[0037] Among them, R corr =R / n j .

[0038] Optionally, the radar measurement compensation method further includes:

[0039] A radar is installed in the measurement area, and a transmitting point is installed at the monitoring point so that the radar can detect the target. An atmospheric humidity sensor and a temperature sensor are installed at the monitoring point so that the on-site atmospheric environment data and radar measurement data can be continuously collected.

[0040] The present application also provides a radar measurement compensation device, including:

[0041] A receiving module is used to obtain the collected atmospheric environment data samples and radar measurement data samples, and match the atmospheric environment data samples and the radar measurement data samples according to the timestamps of the collection to obtain radar sequence data and corresponding environmental humidity data;

[0042] a processing module, configured to preprocess the radar sequence data and normalize the preprocessed radar sequence data to obtain normalized radar data; and sample the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval;

[0043] The compensation module is used to perform polynomial fitting on the humidity-normalized radar data to obtain compensation parameters, and compensate the radar results obtained by on-site real-time measurement according to the compensation parameters.

[0044] In an embodiment of the present application, since the fluctuation of the radar measurement results is related to the atmospheric humidity and temperature, the present application reduces the radar measurement error by collecting atmospheric environment data samples and radar measurement data samples, normalizing the radar data, sampling the normalized radar data and environmental data, and then performing polynomial fitting on the humidity-normalized radar data to obtain compensation parameters. The obtained compensation parameters are then used to compensate the radar results obtained by on-site real-time measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The following detailed description of the specific embodiments of the present application in conjunction with the accompanying drawings will make the technical solutions and other beneficial effects of the present application apparent.

[0046] FIG1 is a flow chart of a radar measurement compensation method provided by the present application;

[0047] FIG2 is a schematic diagram of radar measurement results provided by this application;

[0048] FIG3 is a schematic diagram of field temperature data provided by this application;

[0049] FIG4 is a schematic diagram of on-site humidity data provided by this application;

[0050] FIG5 is a schematic diagram of the effects before and after compensation provided by the present application;

[0051] FIG6 is a schematic structural diagram of the radar measurement compensation device provided in this application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0053] Please refer to Figure 1, which is a flow chart of a radar measurement compensation method provided by this application. A radar measurement compensation method, the radar measurement compensation method comprising:

[0054] Step 110: Acquire the collected atmospheric environment data samples and radar measurement data samples, and match the atmospheric environment data samples and the radar measurement data samples according to the collected timestamps to obtain radar sequence data and corresponding environmental humidity data.

[0055] Step 120 : preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data.

[0056] Step 130 : Sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data that is evenly distributed in each humidity interval.

[0057] Step 140: Perform polynomial fitting on the humidity-normalized radar data to obtain compensation parameters, and compensate the radar results obtained by on-site real-time measurement according to the compensation parameters.

[0058] The above steps 110 to 140 are described in detail below.

[0059] Please refer to Figures 2, 3, and 4. Figure 2 is a schematic diagram of the radar measurement results provided by this application, Figure 3 is a schematic diagram of the field temperature data provided by this application, and Figure 4 is a schematic diagram of the field humidity data provided by this application. In the actual radar measurement process, the fluctuation of the radar measurement results is mainly related to the atmospheric humidity and atmospheric temperature. Figure 3 shows that the fluctuation trend of the radar measurement results is inversely proportional to the temperature, and Figure 4 shows that the fluctuation trend of the radar measurement results is directly proportional to the humidity. Therefore, by collecting radar measurement data over a long period of time and correcting the radar measurement results using temperature and humidity as parameters, radar measurement errors can be reduced.

[0060] In order to simplify the algorithm model and facilitate quick compensation, this application uses humidity data as an example for compensation. This is mainly because the water vapor content in the air will produce atmospheric refraction and affect the propagation speed of electromagnetic waves. However, this application is not limited to compensation based on humidity data.

[0061] Optionally, before executing step 110, the radar measurement compensation method of the present application further includes:

[0062] First, a radar with high-precision measurement capabilities is installed in the measurement area. Then, reflection points are installed in the area to be measured (such as a monitoring point) so that the radar can detect the target. Finally, an atmospheric humidity sensor and a temperature sensor are installed at the monitoring point to continuously collect atmospheric environment data and radar measurement data obtained by real-time measurement on site, thereby obtaining corresponding radar measurement results under different humidity conditions.

[0063] Optionally, in step 110, the step of acquiring the collected atmospheric environment data samples and radar measurement data samples, and matching the atmospheric environment data samples and the radar measurement data samples according to the collected timestamps to obtain radar sequence data and corresponding ambient humidity data includes:

[0064] Step 1101: Match the atmospheric environment data samples and the radar measurement data samples according to the timestamps recorded by the atmospheric humidity sensor to obtain n radar sequence data r1, r2, r3, ..., r n and the corresponding environmental humidity data w1,w2,w3,…,w n , where n is a positive integer.

[0065] It should be noted that the above-mentioned atmospheric environment data samples and radar measurement data samples can be obtained by collecting samples over a long period of time according to a preset interval, so as to correct the radar measurement results by taking humidity as a parameter.

[0066] When performing radar measurements, due to interference from various factors, some abnormal values ​​may be collected, which may have a negative impact on the subsequent radar measurement data analysis. Therefore, measures can be taken to eliminate these abnormal values.

[0067] Optionally, in step 120, the step of preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data includes:

[0068] Step 1201: The radar sequence data r1, r2, r3, ..., r n The measured value r i and the measured values ​​r at the two moments before and after i-1 ,r i+1 Compare, where i = 1, 2,…, n-1.

[0069] Step 1202: If the absolute value of the difference between the measurement value at the current moment and the measurement values ​​at the two previous and next moments is greater than a preset threshold, the measurement value at the current moment is determined to be an abnormal value, and the average of the measurement values ​​at the two previous and next moments is used to replace the measurement value at the current moment to eliminate the abnormal value.

[0070] Specifically, it is expressed by the following formula:

[0071] Wherein, K represents a preset threshold.

[0072] Step 1203: normalize the radar sequence data after removing the outliers to obtain normalized radar data.

[0073] Specifically, after removing outliers, it is necessary to normalize the fluctuations of the above radar measurement results. A section of radar measurement data with relatively flat radar measurement results can be selected to calculate its mean as the benchmark value, and then the radar measurement results are divided by the mean to obtain normalized radar data, which can be expressed as follows:

[0074] Among them, r base represents the reference value obtained by averaging, r j Represents r1 to r n One of the j Can represent r1 to r n Any number in .

[0075] It should be noted that in the above step 1202, eliminating outliers in the radar measurement data and normalizing the fluctuation of the radar measurement results are relatively important steps, which can improve the accuracy and reliability of data analysis.

[0076] If the collected radar sequence data and the corresponding ambient humidity data are unevenly distributed, the analysis of the radar measurement data may be affected. To solve this problem, the application performs sampling processing on the normalized radar data described in step 120 to achieve a uniform distribution of the normalized radar data in each humidity range.

[0077] Optionally, in step 130, the step of sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval includes:

[0078] Step 1301: perform partition processing on the normalized radar data and the ambient humidity data.

[0079] Before sampling, the normalized radar data and ambient humidity data need to be partitioned to facilitate better sampling. In this process, the data can be divided into multiple intervals, ensuring that there is sufficient data for analysis in each humidity interval.

[0080] Step 1302: Sampling the normalized radar data and the ambient humidity data after the partitioning process to obtain m humidity-normalized radar data pairs evenly distributed in each humidity interval. Where m≤n, m is a positive integer.

[0081] In some embodiments of the present application, if radar measurement data is not collected in some humidity intervals, interpolation can be used to fill in the missing humidity intervals. Interpolation can be implemented using mathematical methods such as Lagrange interpolation or Newton interpolation. These methods can infer the values ​​of the missing data based on the trends and characteristics of the existing data, thereby providing a more comprehensive data analysis.

[0082] Taking the Lagrange interpolation algorithm as an example, assuming that the corresponding measurement value r under humidity w is missing in the collected radar measurement data, the following formula is used to calculate the humidity: Perform interpolation:

[0083]

[0084] After sampling and interpolation, humidity-normalized radar data is obtained Where h≤m, h is a positive integer.

[0085] It should be noted that in the above step 130, if the distribution of the normalized radar data and the ambient humidity data meets the preset requirements, that is, no sampling or interpolation processing is required, then step 140 can be directly executed on the normalized radar data and ambient humidity data after partition processing.

[0086] Humidity-normalized radar data Perform polynomial fitting to obtain compensation parameters.

[0087] Polynomial fitting is a method widely used in data fitting and curve fitting. This method approximates humidity-normalized radar data based on a polynomial function to obtain a fitting curve y(x) so that the error between the curve and the original data is minimized.

[0088] Optionally, in step 140, the step of performing polynomial fitting on the humidity-normalized radar data to obtain compensation parameters includes:

[0089] Step 1401: Fit the Lagrangian polynomial using the least squares method, and select a suitable polynomial order k to obtain the best fitting effect. The expression is:

[0090] Among them, i represents the number of the data sample 1,2,3,…,h, h is a positive integer, a0,a1,…,ak Indicates the parameters to be fitted, r i represents the normalized radar data, w i Indicates ambient humidity data, L min Indicates that the target value obtained by fitting is the minimum value;

[0091] Get the fitting curve y(w)=a0+a1w i +a2w i 2 +…+a k w i k After the order parameters in are obtained, the corresponding compensation parameter n under relative humidity is extracted from the polynomial coefficients of the fitting curve. j Expressed as n0,n1,n2,…,n 100 , j=0,1,2,…,100, represents the compensation parameter of atmospheric humidity from 0% to 100%, where: n j =y(w=j% ) .

[0092] Step 1403: Compensation parameter n j Generate a compensation table and save it to the radar memory.

[0093] The generated compensation parameters (such as n0, n1, n2, ..., n 100 ) into a compensation table so that the original measurement data can be compensated by looking up the table in the subsequent data processing process.

[0094] When the radar measures other monitoring points, the radar result obtained by the real-time measurement of the atmospheric humidity sensor is used to compensate the radar result using the compensation parameter obtained in step 1403, which can reduce the radar measurement error.

[0095] Optionally, in step 140, the step of compensating the radar measurement data according to the compensation parameter includes:

[0096] Step 1404: Based on the humidity data w measured in real time by the atmospheric humidity sensor, round w*100 to obtain a positive integer j, and obtain the compensation parameter n under the humidity conditions corresponding to the humidity data by querying the compensation table. j .

[0097] For example, if the humidity data detected is 53%, then the compensation parameter n corresponding to the humidity data is selected by querying the compensation table. 53 If the detected humidity data is 53.1%, then 53.1 (53.1% * 100) is rounded off to get a positive integer 53, and the compensation parameter n is also used. 53 .

[0098] Step 1405: The compensation parameter n i Apply to the radar result R obtained by on-site real-time measurement to obtain the compensated radar measurement result R corr ;

[0099] Among them, R corr =R / n i , i is a positive integer.

[0100] To summarize, the present application collects atmospheric environment data samples and radar measurement data samples, normalizes the radar data, samples the normalized radar data and environmental data, performs polynomial fitting on the humidity-normalized radar data to obtain compensation parameters, and then uses the obtained compensation parameters to compensate for the radar results obtained by on-site real-time measurement, thereby reducing radar measurement errors.

[0101] Please refer to Figure 5, which shows the results before and after compensation provided by this application. Figure 5 shows that the radar fluctuation before compensation is approximately -5mm to +15mm, with a root mean square error of 7.2mm. After compensation, most of the fluctuation is distributed within ±3mm, with a root mean square error of 2.6mm, significantly reducing radar measurement errors.

[0102] The radar measurement compensation device provided in the present application is described below. The radar measurement compensation device described below and the radar measurement compensation method described above can be referenced to each other.

[0103] Please refer to FIG6 , which is a schematic diagram of the structure of a radar measurement compensation device provided by the present application. A radar measurement compensation device 600 includes a receiving module 610 , a processing module 620 , and a compensation module 630 .

[0104] Optionally, the receiving module 610 is configured to:

[0105] Acquire the collected atmospheric environment data samples and radar measurement data samples, and match the atmospheric environment data samples and the radar measurement data samples according to the collected timestamps to obtain radar sequence data and corresponding ambient humidity data.

[0106] Optionally, the processing module 620 is configured to:

[0107] The radar sequence data is preprocessed and the preprocessed radar sequence data is normalized to obtain normalized radar data; and the normalized radar data and the ambient humidity data are sampled to obtain humidity-normalized radar data that is evenly distributed in each humidity interval.

[0108] Optionally, the compensation module 630 is configured to:

[0109] Polynomial fitting is performed on the humidity-normalized radar data to obtain compensation parameters, and radar results measured on-site in real time are compensated according to the compensation parameters.

[0110] Optionally, the receiving module 610 is further configured to:

[0111] The atmospheric environment data samples and the radar measurement data samples are matched according to the timestamps recorded by the sensor to obtain n radar sequence data r1, r2, r3, ..., r n and the corresponding environmental humidity data w1,w2,w3,…,w n , where n is a positive integer.

[0112] Optionally, the processing module 620 is further configured to:

[0113] The radar sequence data r1, r2, r3, ..., r n The measured value r i and the measured values ​​r at the two moments before and after i-1 ,r i+1 Compare, where i = 1, 2, ..., n-1;

[0114] If the absolute value of the difference between the current measurement value and the measurement values ​​at the two previous and next moments is greater than a preset threshold, the current measurement value is determined to be an abnormal value, and the average of the measurement values ​​at the two previous and next moments is used to replace the current measurement value to eliminate the abnormal value;

[0115] Normalize the radar sequence data after removing outliers to obtain normalized radar data

[0116] Alternatively, it is represented by the following formula:

[0117] Wherein, K represents a preset threshold.

[0118] Alternatively, it is represented by the following formula:

[0119] Among them, r base represents the reference value obtained by averaging, r j Represents r1 to r n one of the.

[0120] Optionally, the processing module 620 is further configured to:

[0121] Performing partition processing on the normalized radar data and the ambient humidity data;

[0122] The normalized radar data and the ambient humidity data after the interval processing are sampled to obtain m humidity-normalized radar data pairs evenly distributed in each humidity interval. Where m≤n, m is a positive integer.

[0123] Optionally, the processing module 620 is further configured to:

[0124] For humidity intervals where no radar measurement data is collected, the missing data in the humidity intervals are filled using a Lagrange interpolation algorithm;

[0125] Assuming that the corresponding measurement value r under humidity w is missing, the following formula is used to calculate Perform interpolation:

[0126] Among them, l j (w) is the Lagrange polynomial, which is expressed as:

[0127] After sampling and interpolation processing, the humidity-normalized radar data is obtained Where h≤m, h is a positive integer.

[0128] Optionally, the compensation module 630 is further configured to:

[0129] The Lagrangian polynomial is fitted using the least squares method, and an appropriate polynomial order k is selected to obtain the best fitting effect. The expression is:

[0130] Among them, i represents the number of the data sample 1,2,3,…,h, h is a positive integer, a0,a1,…,a k Indicates the parameters to be fitted, r i represents the normalized radar data, w i Indicates ambient humidity data, L min Indicates that the target value obtained by fitting is the minimum value;

[0131] Get the fitting curve y(w)=a0+a1w i +a2w i 2 +…+a k w i k After the order parameters in are obtained, the corresponding compensation parameter n under relative humidity is extracted from the polynomial coefficients of the fitting curve. j Expressed as n0,n1,n2,…,n 100 , j=0,1,2,…,100, represents the compensation parameter of atmospheric humidity from 0% to 100%, where: n j=y(w=j% ) ;

[0132] The compensation parameter n j Generate a compensation table and save it to the radar memory.

[0133] Optionally, the compensation module 630 is further configured to:

[0134] According to the humidity data w measured in real time by the atmospheric humidity sensor, w*100 is rounded to obtain a positive integer j, and the compensation parameter n under the humidity conditions corresponding to the humidity data is obtained by querying the compensation table. j ;

[0135] The compensation parameter n i Apply to the radar result R obtained by on-site real-time measurement to obtain the compensated radar measurement result R corr ;

[0136] Among them, R corr =R / n j .

[0137] It should be noted here that the above-mentioned radar measurement compensation device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A radar measurement compensation method, comprising: Acquire the collected atmospheric environment data samples and radar measurement data samples, and match the atmospheric environment data samples and the radar measurement data samples according to the collected timestamps to obtain radar sequence data and corresponding environmental humidity data; Preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data; Sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval; Polynomial fitting is performed on the humidity-normalized radar data to obtain compensation parameters, and the radar results obtained by on-site real-time measurement are compensated according to the compensation parameters.

2. The radar measurement compensation method according to claim 1, wherein the step of matching the atmospheric environment data with the radar measurement data according to the acquired timestamp to obtain radar sequence data and corresponding environmental humidity data comprises: The atmospheric environment data samples and the radar measurement data samples are matched according to the timestamps recorded by the sensor to obtain n radar sequence data r1, r2, r3, ..., r n and the corresponding environmental humidity data w1,w2,w3,…,w n , where n is a positive integer.

3. The radar measurement compensation method according to claim 2, wherein the step of preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data comprises: The radar sequence data r1, r2, r3, ..., r n The measured value r i The measured values ​​r at two moments before and after i-1 ,r i+1 Compare, where i = 1, 2, ..., n-1; If the absolute value of the difference between the current measurement value and the measurement values ​​at the previous and next two moments is greater than a preset threshold, the current measurement value is determined to be an abnormal value, and the average of the measurement values ​​at the previous and next two moments is used to replace the current measurement value to eliminate the abnormal value; Normalize the radar sequence data after removing outliers to obtain normalized radar data 4. The radar measurement compensation method according to claim 3, wherein if the absolute value of the difference between the measurement value at the current moment and the measurement values ​​at the two previous and next moments is greater than a preset threshold, the measurement value at the current moment is determined to be an abnormal value, and the average of the measurement values ​​at the two previous and next moments is used to replace the measurement value at the current moment to eliminate the abnormal value, which is expressed by the following formula: in, K represents a preset threshold.

5. The radar measurement compensation method according to claim 4, wherein the radar sequence data with outliers removed is normalized to obtain normalized radar data, which is expressed by the following formula: in, r base represents the reference value obtained by averaging, r j Represents r1 to r n one of the.

6. The radar measurement compensation method according to claim 3, wherein the step of sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval comprises: Performing partition processing on the normalized radar data and the ambient humidity data; The normalized radar data and the ambient humidity data after the partition processing are sampled to obtain m humidity-normalized radar data pairs evenly distributed in each humidity interval. Wherein m≤n, m is a positive integer.

7. The radar measurement compensation method according to claim 6, wherein the step of sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval comprises: For humidity intervals where radar measurement data is not collected, the missing data of the humidity intervals are filled by a Lagrange interpolation algorithm; Assuming that the corresponding measured value r under humidity w is missing, the following formula is used to calculate To interpolate: Among them, l j (w) is the Lagrange polynomial, which is expressed as: After sampling and interpolation processing, the humidity-normalized radar data is obtained. Wherein h≤m, h is a positive integer.

8. The radar measurement compensation method of claim 7, wherein the step of performing polynomial fitting on the humidity-normalized radar data to obtain compensation parameters comprises: The Lagrangian polynomial is fitted using the least squares method, and a suitable polynomial order k is selected to obtain the best fitting effect, and the expression is: Where i represents the number of the data sample 1, 2, 3, ..., h, h is a positive integer, a0, a1, ..., a k represents the parameters to be fitted, r i represents the normalized radar data, w i Indicates ambient humidity data, L min Indicates that the target value obtained by fitting is the minimum value; Get the fitting curve y(w)=a0+a1w i +a2w i 2 +…+a k w i k After the various order parameters in are obtained, the corresponding compensation parameter n under relative humidity is extracted from the polynomial coefficients of the fitting curve. j Represented as n0,n1,n2,…,n 100 , j = 0, 1, 2, ..., 100, represents the compensation parameter of atmospheric humidity from 0% to 100%, where: n j =y(w=j %); The compensation parameter n j Generate a compensation table and save it to the radar memory.

9. The radar measurement compensation method according to claim 8, wherein the step of compensating the radar measurement data according to the compensation parameter comprises: According to the humidity data w obtained by real-time measurement by the atmospheric humidity sensor, w*100 is rounded to obtain a positive integer j, and the humidity data corresponding to the humidity condition is obtained by querying the compensation table. The compensation parameter n j ; The compensation parameter n i Applied to the radar result R obtained by real-time measurement on site to obtain the compensated radar measurement result R corr ; Among them, R corr =R / n j .

10. The radar measurement compensation method according to claim 1, wherein the radar measurement compensation method further comprises: A radar is installed in the measurement area, and a transmitting point is installed at the monitoring point so that the radar can detect the target, and an atmospheric humidity sensor and a temperature sensor are installed at the monitoring point so as to continuously collect on-site atmospheric environment data and radar measurement data.

11. A radar measurement compensation device, comprising: A receiving module, used to obtain the collected atmospheric environment data samples and radar measurement data samples, and match the atmospheric environment data samples and the radar measurement data samples according to the collected timestamps to obtain radar sequence data and corresponding environmental humidity data; A processing module, used for preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data; and sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval; The compensation module is used to perform polynomial fitting on the humidity-normalized radar data to obtain compensation parameters, and to compensate the radar results measured in real time on site according to the compensation parameters.

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