Radar measurement compensation method and device

Abstract

The invention provides a radar measurement compensation method and device, and the method comprises the steps: obtaining a collected atmospheric environment data sample and a radar measurement data sample, and carrying out the matching of the atmospheric environment data sample and the radar measurement data sample according to a collected timestamp, so as to obtain radar sequence data and corresponding environment humidity data; preprocessing the radar sequence data and performing normalization processing on the preprocessed radar sequence data to obtain normalized radar data; performing sampling processing on the normalized radar data and the environment humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval; and performing polynomial fitting on the humidity-normalized radar data to obtain a compensation parameter, and compensating a radar result obtained by field real-time measurement according to the compensation parameter. According to the invention, radar measurement errors can be reduced.

Description

Technical Field

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

[0002] Since the wavelengths of electromagnetic waves in different media are different, the propagation speeds are different, which causes the current measurement method based on the delay method to introduce measurement errors, especially when the measurement accuracy is relatively high. At present, the commonly used radar interferometry method is mainly compensated by the meteorological model correction method or the cancellation method: the meteorological model correction method is to derive and calculate the refractive index n of the atmosphere; the cancellation method is to set a fixed point or select a stable point in the scene, cancel other measurements with the selected point, and regard the remaining value as a change value, and then solve the change of the local point.

[0003] However, the meteorological model correction method is a theoretical method, which requires more atmospheric parameters and a more complex model, so the effect is poor in actual use and it is difficult to meet the needs of actual engineering applications. There is no unified rule for the selection of fixed points or stable points in the elimination method, which is more random or difficult to select in some scenes, so the compensation results lack stability. Summary of the invention

[0004] The present invention provides a radar measurement compensation method and device, which are used to solve the problem of large error in radar measurement in the prior art.

[0005] In a first aspect, the present invention provides a radar measurement compensation method, the radar measurement compensation method comprising:

[0006] 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;

[0007] Preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data;

[0008] Sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval;

[0009] 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.

[0010] In one embodiment of the present invention, 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 includes:

[0011] 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.

[0012] In one embodiment of the present invention, the step of preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data includes:

[0013] 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;

[0014] 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;

[0015] Normalize the radar sequence data after removing outliers to obtain normalized radar data

[0016] In one embodiment of the present invention, if the absolute value of the difference between the measured value at the current moment and the measured values ​​at the two previous 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 moments is used to replace the measured value at the current moment to eliminate the abnormal value, which is expressed by the following formula:

[0017]

[0018] Wherein, K represents a preset threshold.

[0019] In one embodiment of the present invention, the radar sequence data with outliers removed is normalized to obtain normalized radar data, which is expressed by the following formula:

[0020]

[0021]

[0022] Among them, r base represents the reference value obtained by averaging, r j Represents r1 to r n one of the.

[0023] In one embodiment of the present invention, 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:

[0024] Performing partition processing on the normalized radar data and the ambient humidity data;

[0025] 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.

[0026] In one embodiment of the present invention, 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:

[0027] For humidity intervals where radar measurement data is not collected, the missing data of the humidity intervals are filled by a Lagrange interpolation algorithm;

[0028] Assuming that the corresponding measured value r under humidity w is missing, the following formula is used to calculate To interpolate:

[0029]

[0030] Among them, l j (w) is the Lagrange polynomial, which is expressed as:

[0031]

[0032] After sampling and interpolation processing, the humidity-normalized radar data is obtained. Wherein h≤m, h is a positive integer.

[0033] In one embodiment of the present invention, the step of performing polynomial fitting on the humidity-normalized radar data to obtain compensation parameters comprises:

[0034] 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:

[0035]

[0036] 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;

[0037] 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 = 1, 2, 3, ..., 100, represents the compensation parameter of atmospheric humidity from 0% to 100%, where:

[0038] n j =y(w=j%);

[0039] The compensation parameter n j Generate a compensation table and save it to the radar memory.

[0040] In one embodiment of the present invention, the step of compensating the radar measurement data according to the compensation parameter includes:

[0041] 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 compensation parameter n under the humidity condition corresponding to the humidity data is obtained by querying the compensation table. j ;

[0042] 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 ;

[0043] Among them, R corr =R / n j .

[0044] In one embodiment of the present invention, the radar measurement compensation method further includes:

[0045] 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.

[0046] In a second aspect, the present invention further provides a radar measurement compensation device, the radar measurement compensation device comprising:

[0047] 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;

[0048] 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;

[0049] 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 obtained by on-site real-time measurement according to the compensation parameters.

[0050] The radar measurement compensation method and device provided by the present invention, because the fluctuation of radar measurement results is related to atmospheric humidity and temperature, the present invention collects atmospheric environment data samples and radar measurement data samples, normalizes the radar data, samples the normalized radar data and environmental data, and then 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a schematic flow chart of the radar measurement compensation method provided by the present invention;

[0053] Figure 2 is a schematic diagram of radar measurement results provided by the present invention;

[0054] Figure 3is a schematic diagram of on-site temperature data provided by the present invention;

[0055] Figure 4 is a schematic diagram of on-site humidity data provided by the present invention;

[0056] Figure 5 It is a schematic diagram of the effects before and after compensation provided by the present invention;

[0057] Figure 6 It is a structural schematic diagram of the radar measurement compensation device provided by the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0060] In order to solve the problem of large errors in radar measurement in the prior art, the present invention provides a radar measurement compensation method and device. Since the fluctuation of radar measurement results is related to atmospheric humidity and temperature, the present invention collects atmospheric environment data samples and radar measurement data samples, normalizes the radar data, samples the normalized radar data and environmental data, and then performs polynomial fitting on the humidity-normalized radar data to obtain compensation parameters. The obtained compensation parameters are then used to compensate for the radar results obtained by on-site real-time measurement, thereby reducing radar measurement errors.

[0061] Combine the following Figure 1-Figure 6 The radar measurement compensation method and apparatus of the present invention are described.

[0062] Please refer to Figure 1 , Figure 1 1 is a flow chart of a radar measurement compensation method provided by the present invention. A radar measurement compensation method, the radar measurement compensation method comprising:

[0063] Step 110, 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 environmental humidity data.

[0064] Step 120: preprocess the radar sequence data and normalize the preprocessed radar sequence data to obtain normalized radar data.

[0065] Step 130 , sampling the normalized radar data and the ambient humidity data to obtain humidity-normalized radar data uniformly distributed in each humidity interval.

[0066] 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.

[0067] The above steps 110 to 140 are described in detail below.

[0068] Please refer to Figure 2 , Figure 3 as well as Figure 4 , Figure 2 is a schematic diagram of the radar measurement result provided by the present invention, Figure 3 is a schematic diagram of the on-site temperature data provided by the present invention, Figure 4 is a schematic diagram of the on-site humidity data provided by the present invention. In the actual radar measurement process, the fluctuation of the radar measurement result is mainly related to the atmospheric humidity and atmospheric temperature. Figure 3 It shows that the fluctuation trend of radar measurement results is inversely proportional to temperature, Figure 4 It shows that the fluctuation trend of radar measurement results is proportional to humidity. Therefore, by collecting radar measurement data for a long time and correcting the radar measurement results with temperature and humidity as parameters, the radar measurement error can be reduced.

[0069] In order to simplify the algorithm model and facilitate quick compensation, the present invention uses humidity data as an example for compensation, mainly because the water vapor content in the air will produce atmospheric refraction and affect the propagation speed of electromagnetic waves, but the present invention is not limited to compensation based on humidity data.

[0070] Exemplarily, before executing the above step 110, the radar measurement compensation method of the present invention further includes:

[0071] First, a radar with high-precision measurement capabilities is installed in the measurement area, and 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.

[0072] Exemplarily, in the above 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 environmental humidity data includes:

[0073] Step 1101, matching the atmospheric environment data samples with the radar measurement data samples according to the timestamp 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.

[0074] It should be noted that the above-mentioned atmospheric environment data samples and radar measurement data samples can be obtained by long-term sample collection according to a preset interval time, so as to correct the radar measurement results by taking humidity as a parameter.

[0075] 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 remove these abnormal values.

[0076] Exemplarily, in the above step 120, the step of preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data includes:

[0077] Step 1201: 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.

[0078] Step 1202: If the absolute value of the difference between the current measurement value and the two previous and next measurement values ​​is greater than a preset threshold, the current measurement value is determined to be an abnormal value, and the average of the two previous and next measurement values ​​is used to replace the current measurement value to eliminate the abnormal value.

[0079] Specifically, it is expressed by the following formula:

[0080]

[0081] Wherein, K represents a preset threshold.

[0082] Step 1203: normalize the radar sequence data after removing the outliers to obtain normalized radar data.

[0083] Specifically, after removing the outliers, it is necessary to normalize the fluctuation 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 reference value, and then the radar measurement results are divided by the mean to obtain normalized radar data, which can be expressed by the following formula:

[0084]

[0085]

[0086] 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 .

[0087] 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.

[0088] If the collected radar sequence data and the corresponding environmental humidity data are unevenly distributed, the analysis of the radar measurement data may be affected. In order to solve this problem, the present invention performs sampling processing on the normalized radar data described in step 120 to achieve uniform distribution of the normalized radar data in each humidity interval.

[0089] Exemplarily, in the above 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:

[0090] Step 1301, performing partition processing on the normalized radar data and the ambient humidity data.

[0091] Before sampling, the normalized radar data and the ambient humidity data need to be partitioned for better sampling. In this process, the data can be divided into multiple intervals, and sufficient data is ensured for analysis in each humidity interval.

[0092] Step 1302: sampling the normalized radar data and the ambient humidity data after the partition processing to obtain m humidity-normalized radar data pairs evenly distributed in each humidity interval. Wherein m≤n, m is a positive integer.

[0093] In some embodiments of the present invention, if radar measurement data is not collected in some humidity intervals, interpolation processing can be used to fill in the humidity intervals where these data are missing. The interpolation processing can be implemented by some mathematical methods, such as Lagrange interpolation method or Newton interpolation method, etc. These methods can infer the value of the missing data based on the trend and characteristics of the existing data, so as to analyze the data more comprehensively.

[0094] 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 To interpolate:

[0095]

[0096] Among them, l j (w) is the Lagrange polynomial, which is expressed as:

[0097]

[0098] After sampling and interpolation, humidity-normalized radar data is obtained. Wherein h≤m, h is a positive integer.

[0099] 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.

[0100] Humidity-normalized radar data Perform polynomial fitting to obtain compensation parameters.

[0101] 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.

[0102] Exemplarily, in the above step 140, the step of performing polynomial fitting on the humidity-normalized radar data to obtain compensation parameters includes:

[0103] Step 1401, the Lagrangian polynomial is fitted using the least square method, and a suitable polynomial order k is selected to obtain the best fitting effect, and the expression is:

[0104]

[0105] 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;

[0106] 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:

[0107] n j =y(w=j%).

[0108] Step 1403: Set the compensation parameter n j Generate a compensation table and save it to the radar memory.

[0109] The generated compensation parameters (such as n0, n1, n2, ..., n 100 ) is organized into a compensation table so that the original measurement data can be compensated by looking up the table in the subsequent data processing process.

[0110] When the radar measures other monitoring points, the radar result is compensated according to the radar result obtained by real-time measurement of the atmospheric humidity sensor and the compensation parameter obtained in step 1403, so as to reduce the radar measurement error.

[0111] Exemplarily, in the above step 140, the step of compensating the radar measurement data according to the compensation parameter includes:

[0112] Step 1404: round w*100 to obtain a positive integer j according to the humidity data w obtained by the real-time measurement of the atmospheric humidity sensor, and obtain the compensation parameter n under the humidity condition corresponding to the humidity data by querying the compensation table. j .

[0113] For example, if the detected humidity data 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 to a positive integer of 53, and the compensation parameter n is also used. 53 .

[0114] Step 1405: 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 ;

[0115] Among them, R corr =R / n i , i is a positive integer.

[0116] In summary, the present invention 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.

[0117] Please refer to Figure 5 , Figure 5 It is a schematic diagram of the effects before and after compensation provided by the present invention. Figure 5 The radar fluctuation before compensation is shown to be around -5mm to +15mm, with a root mean square error of 7.2mm; after compensation, most of the fluctuations are distributed within ±3mm, with a root mean square error of 2.6mm, which greatly reduces the radar measurement error.

[0118] The radar measurement compensation device provided by the present invention is described below. The radar measurement compensation device described below and the radar measurement compensation method described above can be referenced to each other.

[0119] Please refer to Figure 6 , Figure 6 1 is a schematic diagram of the structure of the radar measurement compensation device provided by the present invention. A radar measurement compensation device, the radar measurement compensation device 600 includes a receiving module 610, a processing module 620 and a compensation module 630.

[0120] Exemplarily, the receiving module 610 is used for:

[0121] 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.

[0122] Exemplarily, the processing module 620 is used to:

[0123] 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 uniformly distributed in each humidity interval.

[0124] Exemplarily, the compensation module 630 is used to:

[0125] Polynomial fitting is performed on the humidity-normalized radar data to obtain compensation parameters, and the radar results measured in real time on site are compensated according to the compensation parameters.

[0126] Exemplarily, the receiving module 610 is further configured to:

[0127] 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.

[0128] Exemplarily, the processing module 620 is further configured to:

[0129] 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;

[0130] 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;

[0131] Normalize the radar sequence data after removing outliers to obtain normalized radar data

[0132] For example, it is represented by the following formula:

[0133]

[0134] Wherein, K represents a preset threshold.

[0135] For example, it is represented by the following formula:

[0136]

[0137]

[0138] Among them, r base represents the reference value obtained by averaging, r j Represents r1 to r n one of the.

[0139] Exemplarily, the processing module 620 is further configured to:

[0140] Performing partition processing on the normalized radar data and the ambient humidity data;

[0141] 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.

[0142] Exemplarily, the processing module 620 is further configured to:

[0143] For humidity intervals where radar measurement data is not collected, the missing data of the humidity intervals are filled by a Lagrange interpolation algorithm;

[0144] Assuming that the corresponding measured value r under humidity w is missing, the following formula is used to calculate To interpolate:

[0145]

[0146] Among them, l j (w) is the Lagrange polynomial, which is expressed as:

[0147]

[0148] After sampling and interpolation processing, the humidity-normalized radar data is obtained. Wherein h≤m, h is a positive integer.

[0149] Exemplarily, the compensation module 630 is further configured to:

[0150] 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:

[0151]

[0152] 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;

[0153] 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:

[0154] n j =y(w=j%);

[0155] The compensation parameter n j Generate a compensation table and save it to the radar memory.

[0156] Exemplarily, the compensation module 630 is further configured to:

[0157] 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 compensation parameter n under the humidity condition corresponding to the humidity data is obtained by querying the compensation table. j ;

[0158] 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 ;

[0159] Among them, R corr =R / n j .

[0160] It should be noted here that the above-mentioned radar measurement compensation device provided in an embodiment of the present invention 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 that are the same as those in the method embodiment will not be described in detail here.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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 invention.

Claims

1. A radar measurement compensation method, characterized in that: The radar measurement compensation method comprises: 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, characterized in that: 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, characterized in that: The step of preprocessing the radar sequence data and normalizing the preprocessed radar sequence data to obtain normalized radar data includes: 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, characterized in that: If the absolute value of the difference between the measured value at the current moment and the measured values ​​at the two moments before and after 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 moments before and after is used to replace the measured value at the current moment to eliminate the abnormal value, which is expressed by the following formula: Wherein, K represents a preset threshold.

5. The radar measurement compensation method according to claim 4, characterized in that: The radar sequence data with outliers removed is normalized to obtain normalized radar data, which is expressed by the following formula: Among them, 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, characterized in that: 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, characterized in that: 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 according to claim 7, characterized in that: 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, characterized in that: 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 compensation parameter n under the humidity condition corresponding to the humidity data is obtained by querying the compensation table. 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, characterized in that: 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, characterized in that: The radar measurement compensation device comprises: 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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