Processing method and system for large-data-volume radar data

Abstract

The invention discloses a processing method and system for large-data-volume radar data, and relates to the technical field of radar data processing, real-time data: setting radar working parameters, establishing a data set for storing real-time data, and performing data cleaning; on the basis of the target feature data, locking of the same target by all radars is completed; processing the variable quantity of the target trace point data by adopting a second method to obtain the variable quantity of the target relative to the target trace point data, and performing trace point prediction on the target to realize tracking; historical data: establishing a database for storing all data sets and performing data cleaning; and processing the targets of the same type by adopting a third method to obtain the variation of the target trace point data of the targets of the same type in different environments, and performing trace point prediction on the targets of the same type to realize tracking. According to the method, the processing of massive radar real-time data and historical data can be realized at the same time, the application efficiency is improved, and the improvement of the target tracking precision and stability is finally realized.

Description

Technical Field

[0001] The present invention relates to the field of radar data processing technology, and in particular to a method and system for processing large amount of radar data. Background Art

[0002] Radar data processing, referred to as RDP, is the post-processing process of signal processing. The input of radar data processing is the target point track formed by radar signal processing. The point track information includes the target's distance, azimuth and pitch value. Radar data processing obtains the target track by correlating the measurement sets obtained by multi-circle scanning. After successfully starting the target track, the data processing can correct the radar's measurement errors of the target position and speed through filtering algorithms, and accurately estimate the target's true information. Through continuous observation of the target, the system can provide information such as the target's position, speed, acceleration and landing point.

[0003] It is necessary not only to process the real-time radar data, but also to perform secondary processing on the stored radar data. By studying the previous radar data, the regular characteristics can be discovered to improve the efficiency and level of radar data application. In general, radar data processing is to understand the detection target more deeply and systematically, capture the target more efficiently, and measure the target more accurately. However, in order to improve the accuracy and stability of target tracking, it is no longer sufficient to locate the target through a single radar, but to integrate the observation data of multiple radars to improve the accuracy and stability of target tracking. As a result, the real-time detection data and historical detection data of space targets obtained by radar are increasing in both quantity and type, and the application efficiency and timeliness of radar data cannot be guaranteed. Therefore, a processing method and system for large-volume radar data is urgently needed. Summary of the invention

[0004] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides a method for processing large amount of radar data, the method comprising: Setting working parameters of multiple radars, so that all radars work based on the set working parameters; acquiring real-time data of all radars, and establishing a data set for storing the real-time data for each radar, wherein the real-time data includes target point data, target feature data, BIT information data and environmental parameter data; The data set described in the first method is used for data cleaning, specifically: obtaining BIT information data in any radar real-time data as first BIT information data, judging whether the first BIT information data are all within a preset range, if not, cleaning the target point data, target feature data and environmental parameter data corresponding to the first BIT information data in the data set, if yes, taking no action; Based on the data set after data cleaning, target feature data in the real-time data of any radar is obtained and recorded as first target feature data, and target point trace data corresponding to the first target feature data is recorded as first target point trace data; based on the first target feature data, a first target is determined, and the corresponding target feature data is matched in the real-time data of the remaining radars to obtain a matching result, and based on the matching result, the target point trace data corresponding to the first target is locked in the remaining radars; Calculate a first change in the first target point trace data within a preset time, and a second change in all target point trace data corresponding to the first target in the remaining radars within the preset time; based on the first change and all second changes, use a second method to obtain a first reference change in the target point trace data corresponding to the first target; perform point trace prediction on the first target based on the first reference change to achieve tracking of the first target; Establishing a database for storing all data sets, and using the first method to clean the database; Based on the database after data cleaning, target feature data of the same type of target is obtained and recorded as second target feature data, target point trace data corresponding to all second target feature data is recorded as second target point trace data, and environmental parameter data corresponding to all second target feature data is recorded as second environmental parameter data; Based on all the second target point trace data and all the second environmental parameter data, a third method is used to obtain the second benchmark variation of the second target point trace data of the same type of target in different environments; based on the second benchmark variation, point trace prediction is performed on the same type of target to achieve tracking of the same type of target.

[0005] The present invention is implemented by the following technical solution: This solution provides a method for processing large amounts of real-time data and historical data, specifically: Real-time data processing of large amounts of data: Set the working parameters of several radars. All radars work based on the set working parameters (such as clock parameters, scanning direction, etc.). Then obtain the real-time data of each radar and establish a data set to store the real-time data. The real-time data includes target point data, target feature data, BIT information data and environmental parameter data. The first method is used to clean the data set. BIT technology can perform self-detection on the radar system to identify and isolate faults. The BIT information data is obtained in turn to determine whether the radar has failed. Specifically, the BIT information data in the real-time data of any radar is obtained, recorded as the first BIT information data, and whether the first BIT information data is within the preset range is determined. If so, it means that the radar is working normally. If not, it means that the radar has failed. Therefore, it is difficult to ensure the accuracy of the acquired real-time data, and this part of the data needs to be cleaned. Based on the data set after data cleaning, target feature data in the real-time data of any radar is obtained and recorded as the first target feature data, and target point trace data corresponding to the first target feature data is recorded as the first target point trace data. The first target is locked through the first target feature data, and then the corresponding target feature data is matched in the real-time data of the remaining radars, so as to complete the locking of the first target in the remaining radars; The first change amount of the first target point trace data within the preset time and the second change amount of the target point trace data corresponding to the first target in the remaining radars within the preset time are calculated respectively, and there are multiple second change amounts. Then, the second method is used to process the first change amount and all the second change amounts (to improve the accuracy of the prediction) to obtain the first benchmark change amount of the target point trace data corresponding to the first target. Finally, the first benchmark change amount is used to predict the subsequent point trace changes within the same preset time to complete the tracking of the first target.

[0006] Large volume of historical data processing: Establish a database for storing all data sets, and clean the database using the first method; Based on the database after data cleaning, target feature data of the same type of target is obtained and recorded as second target feature data, target point data corresponding to the second target feature data is recorded as second target point data, and corresponding environmental parameter data is recorded as second environmental parameter data; Based on all the second target point trace data and all the second environmental parameter data, the third method is used to obtain the second baseline change amount of the target point trace data of the same type of target in different environments. When a radar detects the same type of target again in the future, the baseline change amount of the target point trace data of this type of target can be matched through the corresponding environmental parameter data, and then the baseline change amount can be used to track the same type of target.

[0007] As an optional technical solution, based on the first target feature data, determining the first target and matching the corresponding target feature data in the real-time data of the remaining radars includes: Based on the real-time data of the remaining radars, all target feature data in each radar are obtained, and the similarity between each target feature data in each radar and the first target feature data is calculated in sequence using the first formula to obtain a first calculation result; Based on the first calculation result, locking the first target and corresponding real-time data in the remaining radars; The first formula is specifically: is the similarity between the first target feature data and the remaining target feature data in the remaining radars, is a constant, is the characteristic value corresponding to the first target characteristic data, is the characteristic value corresponding to the remaining target characteristic data in the remaining radar.

[0008] As an optional technical solution, the characteristic value corresponding to the target characteristic data in any radar is calculated by the second formula, including: is the characteristic value corresponding to the target characteristic data in any radar, is a positive integer greater than or equal to 1, is the sub-feature value corresponding to the target feature data in any radar, The number of sub-feature values ​​contained in the target feature data of any radar.

[0009] As an optional technical solution, the method further includes assigning sub-feature values ​​corresponding to target feature data in any radar: Obtaining target characteristic data from any radar as third target characteristic data; Acquire all target sub-feature data included in the third target feature data, and acquire historical data of all target sub-feature data; Based on the historical data, dividing the target sub-feature data included in the third target feature data into data intervals, and assigning a value to each data interval; Based on the assigned data interval, the corresponding target sub-feature data is assigned to obtain the corresponding sub-feature value.

[0010] As an optional technical solution, the second method includes: Put the first variation and all second variations into a preset set; Calculate the mean of all changes in the preset set to obtain the mean change; Sequentially calculating the differences between all the changes in the preset set and the mean change to obtain a second calculation result; Based on the second calculation result, a change amount having the smallest difference from the mean change amount is selected from the preset set, and recorded as a first initial reference change amount; All remaining changes in the preset set except the first initial reference change are sequentially convolved with the first initial reference change, and the first initial reference change is corrected multiple times to obtain a first reference change.

[0011] As an optional technical solution, the third method includes: Obtaining any type of target is recorded as the second type of target; Acquire all second target point trace data and all second environmental parameter data of the second type of targets in different time periods to obtain acquisition results; Based on the obtained result, the second method is used to calculate the second initial baseline change amount of the second type of target in each time period, and the fourth method is used to process the second environmental parameter data of the corresponding time period to obtain a baseline environmental parameter value; The second initial reference change amount and the reference environmental parameter value are combined to obtain a second reference change amount.

[0012] As an optional technical solution, each second environmental parameter data includes a plurality of environmental parameter sub-data; Constructing a curve graph with time as the horizontal axis and the single environmental parameter sub-data as the vertical axis, and processing the curve graph using a preset software to obtain a mean value corresponding to the single environmental parameter sub-data in the first time period, and recording it as the environmental parameter sub-value; Using the third formula to calculate all the environmental parameter sub-data included in each second environmental parameter data, to obtain a third calculation result; Based on the third calculation result, the reference environmental parameter value is obtained.

[0013] As an optional technical solution, the third formula includes: is the reference environmental parameter value, is the environmental parameter sub-value in a single second environmental parameter data, is the coefficient for each sub-value of the environmental parameter, It is the number of environmental parameter sub-data contained in a single second environmental parameter data.

[0014] As an optional technical solution, the preset software is MathSword numerical calculation software.

[0015] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides a system for processing large amount of radar data, the system comprising: A data set unit is used to set the working parameters of multiple radars, and all radars work based on the set working parameters; obtain the real-time data of all radars, and establish a data set for each radar to store the real-time data, wherein the real-time data includes target point data, target feature data, BIT information data and environmental parameter data; A data cleaning unit is used to clean the data set using the first method, specifically: obtaining BIT information data in any radar real-time data as first BIT information data, judging whether the first BIT information data are all within a preset range, if not, cleaning the data set including the first BIT information data and the corresponding target point data, target feature data and environmental parameter data, if yes, taking no action; The first target point trace unit is used to obtain target feature data in the real-time data of any radar based on the data set after data cleaning, record it as the first target feature data, and record the target point trace data corresponding to the first target feature data as the first target point trace data; based on the first target feature data, determine the first target, and match the corresponding target feature data in the real-time data of the remaining radars to obtain a matching result, and based on the matching result, lock the target point trace data corresponding to the first target in the remaining radars; A first tracking unit is used to calculate a first change amount of the first target point trace data within a preset time, and a second change amount of all target point trace data corresponding to the first target in the remaining radar within the preset time; based on the first change amount and all the second change amounts, a second method is used to process and obtain a first reference change amount of the target point trace data corresponding to the first target; based on the first reference change amount, a point trace prediction is performed on the first target to achieve tracking of the first target; A database unit, establishing a database for storing all data sets, and using the first method to perform data cleaning on the database; A second target point trace unit is used to obtain target feature data of the same type of target based on the database after data cleaning and record it as second target feature data, record all target point trace data corresponding to the second target feature data as second target point trace data, and record all environmental parameter data corresponding to the second target feature data as second environmental parameter data; The second tracking unit is used to obtain the second benchmark variation of the second target point trace data of the same type of target in different environments based on all the second target point trace data and all the second environmental parameter data by using the third method; based on the second benchmark variation, perform point trace prediction on the same type of target to achieve tracking of the same type of target.

[0016] One or more technical solutions provided by the present invention have at least the following technical effects or advantages: The invention discloses a processing method for large-volume radar data. The processing method is provided for the massive real-time data and historical data of the radar. For the real-time data: by setting the radar working parameters, a data set for storing the real-time data is established, and based on the acquired BIT information data, data cleaning is performed on each data set to ensure the accuracy of real-time data acquisition; based on the target feature data, all radars are locked on the same target; the second method is used to process the change amount of the target point trace data corresponding to the same target in the same time, and the benchmark change amount of the target point trace data of the target is obtained. Through the benchmark change amount, the target is predicted to track the target; for the historical data: a database for storing all data sets is established and data cleaning is performed; based on the target feature data, it is determined to be the same type of target, and the third method is used to process the target point trace data and environmental parameter data corresponding to the same type of target, and the benchmark change amount of the target point trace data of the same type of target under different environments is obtained. Through the benchmark change amount, the target of the same type is predicted to track the target. The invention can not only realize the processing of massive radar real-time data and historical data at the same time, but also improve the application efficiency, and finally realize the improvement of target tracking accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation on the embodiments of the present invention; Figure 1 It is a flow chart of a method for processing large amount of radar data in the present invention; Figure 2 The present invention is a schematic diagram of the composition of a system for processing large amounts of radar data. DETAILED DESCRIPTION

[0018] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those within the scope of this description. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0020] Embodiment 1 Please refer to Figure 1 , Figure 1The present invention is a flowchart of a method for processing large amounts of radar data, the method comprising: Setting working parameters of multiple radars, so that all radars work based on the set working parameters; acquiring real-time data of all radars, and establishing a data set for storing the real-time data for each radar, wherein the real-time data includes target point data, target feature data, BIT information data and environmental parameter data; The data set described in the first method is used for data cleaning, specifically: obtaining BIT information data in any radar real-time data as first BIT information data, judging whether the first BIT information data are all within a preset range, if not, cleaning the target point data, target feature data and environmental parameter data corresponding to the first BIT information data in the data set, if yes, taking no action; Based on the data set after data cleaning, target feature data in the real-time data of any radar is obtained and recorded as first target feature data, and target point trace data corresponding to the first target feature data is recorded as first target point trace data; based on the first target feature data, a first target is determined, and the corresponding target feature data is matched in the real-time data of the remaining radars to obtain a matching result, and based on the matching result, the target point trace data corresponding to the first target is locked in the remaining radars; Calculate a first change in the first target point trace data within a preset time, and a second change in all target point trace data corresponding to the first target in the remaining radars within the preset time; based on the first change and all second changes, use a second method to obtain a first reference change in the target point trace data corresponding to the first target; perform point trace prediction on the first target based on the first reference change to achieve tracking of the first target; Establishing a database for storing all data sets, and using the first method to clean the database; Based on the database after data cleaning, target feature data of the same type of target is obtained and recorded as second target feature data, target point trace data corresponding to all second target feature data is recorded as second target point trace data, and environmental parameter data corresponding to all second target feature data is recorded as second environmental parameter data; Based on all the second target point trace data and all the second environmental parameter data, a third method is used to obtain the second benchmark variation of the second target point trace data of the same type of target in different environments; based on the second benchmark variation, point trace prediction is performed on the same type of target to achieve tracking of the same type of target.

[0021] The specific embodiments of the present invention are as follows: Radar real-time data processing: Set the working parameters of multiple radars (such as clock parameters, scanning direction, etc.), and all radars will work based on the set working parameters; Acquire the real-time data of all radars, and establish a data set for each radar to store real-time data, including target point data, target feature data, BIT information data and environmental parameter data; The first method is used to clean the data set, specifically: obtaining BIT information data in any radar real-time data as first BIT information data, judging whether the first BIT information data are all within a preset range, if not, cleaning the data set including the first BIT information data and the corresponding target point data, target feature data and environmental parameter data, if yes, taking no action; Among them, the BIT technology in the radar can perform self-detection on the radar system to identify and isolate faults. Therefore, by obtaining BIT information data, it is determined whether the radar has a fault. If so, the acquired real-time data needs to be cleaned to ensure the accuracy of the acquired data; Based on the data set after data cleaning, target feature data in the real-time data of any radar is obtained and recorded as first target feature data, and target point trace data corresponding to the first target feature data is recorded as first target point trace data; based on the first target feature data, a first target is determined, and the corresponding target feature data is matched in the real-time data of the remaining radars to obtain a matching result, and based on the matching result, the target point trace data corresponding to the first target is locked in the remaining radars; This embodiment provides a method for locking the same target in all radars, specifically: Based on the real-time data of the remaining radars, all target feature data in each radar are obtained, and the similarity between each target feature data in each radar and the first target feature data is calculated in sequence using the first formula to obtain a first calculation result; Based on the first calculation result, locking the first target and corresponding real-time data in the remaining radars; The first formula is specifically: is the similarity between the first target feature data and the remaining target feature data in the remaining radars, is a constant, is the characteristic value corresponding to the first target characteristic data, is the characteristic value corresponding to the characteristic data of the remaining targets in the remaining radars; The characteristic value corresponding to the target characteristic data in any radar is calculated by the second formula, including: is the characteristic value corresponding to the target characteristic data in any radar, is a positive integer greater than or equal to 1, is the sub-feature value corresponding to the target feature data in any radar, The number of sub-feature values ​​contained in the target feature data of any radar; This implementation also includes assigning sub-feature values ​​corresponding to target feature data in any radar: Obtaining target characteristic data from any radar as third target characteristic data; Acquire all target sub-feature data included in the third target feature data, and acquire historical data of all target sub-feature data; Based on the historical data, dividing the target sub-feature data included in the third target feature data into data intervals, and assigning a value to each data interval; Based on the assigned data interval, the corresponding target sub-feature data is assigned to obtain the corresponding sub-feature value.

[0022] Among them, this embodiment uses the first formula to accurately calculate the similarity between the target feature data, and determines whether they are the same target through the similarity. For example, a threshold value f is set, and when the similarity exceeds f, it is determined to be the same target; and the first formula essentially calculates the similarity by calculating the difference between the feature values ​​corresponding to the target feature data. Therefore, this embodiment uses the second formula to accurately calculate the feature value corresponding to the target feature data. Therefore, the feature value corresponding to each target feature data also includes multiple sub-feature values. Therefore, this embodiment also provides a method for assigning sub-feature values, specifically: obtaining all target sub-feature data contained in the target feature data, and obtaining historical data of all target sub-feature data, based on the historical data, dividing the target sub-feature data, and assigning values ​​to each data interval. When the target sub-feature data corresponding to any target feature data is obtained, it is only necessary to obtain the corresponding data interval to obtain the assignment of the target sub-feature data.

[0023] Calculate a first change in the first target point trace data within a preset time, and a second change in all target point trace data corresponding to the first target in the remaining radars within the preset time; based on the first change and all second changes, use a second method to obtain a first reference change in the target point trace data corresponding to the first target; perform point trace prediction on the first target based on the first reference change to achieve tracking of the first target; The second method comprises: Put the first variation and all second variations into a preset set; Calculate the mean of all changes in the preset set to obtain the mean change; Sequentially calculating the differences between all the changes in the preset set and the mean change to obtain a second calculation result; Based on the second calculation result, a change amount having the smallest difference from the mean change amount is selected from the preset set, and recorded as a first initial reference change amount; All remaining changes in the preset set except the first initial reference change are sequentially convolved with the first initial reference change, and the first initial reference change is corrected multiple times to obtain a first reference change.

[0024] Among them, this embodiment puts the first change and all the second changes into a preset set, calculates the mean of all the changes in the preset set, and obtains the mean change; calculates the difference between the preset set and the mean change in turn, and screens out the change with the smallest difference, which is recorded as the first initial baseline change; then uses all the changes in the preset set except the first initial baseline change to perform convolution with the first initial baseline change, and corrects the first initial baseline change multiple times to obtain the first baseline change, thereby ensuring the accuracy of the first baseline change.

[0025] This embodiment uses the above technical solution to process a large amount of real-time radar data of the same target and ensure the accuracy and stability of tracking the same target.

[0026] Radar historical data processing: Establishing a database for storing all data sets, and using the first method to clean the database; Based on the database after data cleaning, target feature data of the same type of target is obtained and recorded as second target feature data, target point trace data corresponding to all second target feature data is recorded as second target point trace data, and environmental parameter data corresponding to all second target feature data is recorded as second environmental parameter data; Based on all the second target point trace data and all the second environmental parameter data, a third method is used to obtain the second benchmark variation of the second target point trace data of the same type of target in different environments; based on the second benchmark variation, point trace prediction is performed on the same type of target to achieve tracking of the same type of target.

[0027] The third method comprises: Obtaining any type of target is recorded as the second type of target; Acquire all second target point trace data and all second environmental parameter data of the second type of targets in different time periods to obtain acquisition results; Based on the obtained result, the second method is used to calculate the second initial baseline change amount of the second type of target in each time period, and the fourth method is used to process the second environmental parameter data of the corresponding time period to obtain a baseline environmental parameter value; The second initial reference change amount and the reference environmental parameter value are combined to obtain a second reference change amount.

[0028] The fourth method comprises: Obtain any time period as a first time period, and obtain all second environmental parameter data within the first time period, each second environmental parameter data including a plurality of environmental parameter sub-data; Constructing a curve graph with time as the horizontal axis and the single environmental parameter sub-data as the vertical axis, and processing the curve graph using a preset software to obtain a mean value corresponding to the single environmental parameter sub-data in the first time period, and recording it as the environmental parameter sub-value; Using the third formula to calculate all the environmental parameter sub-data included in each second environmental parameter data, to obtain a third calculation result; Based on the third calculation result, the reference environmental parameter value is obtained.

[0029] The third formula includes: is the reference environmental parameter value, is the environmental parameter sub-value in a single second environmental parameter data, is the coefficient for each sub-value of the environmental parameter, It is the number of environmental parameter sub-data contained in a single second environmental parameter data.

[0030] Among them, the preset software is MathSword numerical calculation software.

[0031] Among them, this embodiment first calculates the second initial baseline variation of the same type of target in each time period by the second method. In order to obtain the baseline variation of the target point trace data of the same type of target in different environments, it is also necessary to combine the second initial baseline variation with the corresponding environmental parameters. Therefore, this embodiment processes the environmental parameter data by the fourth method to obtain the baseline environmental parameter value, specifically: by constructing a curve chart with time as the horizontal axis and environmental parameter sub-data as the vertical axis, and using preset software to process the curve chart, the environmental parameter sub-value is obtained; then the third formula is used to calculate all environmental parameter sub-data to obtain the calculation result, and the baseline environmental parameter value is obtained based on the third result. Finally, the second initial baseline variation and the corresponding baseline environmental parameter value are combined. When the subsequent radar detects the same type of target, the corresponding environmental parameter data is obtained and converted to obtain the environmental parameter value. The corresponding baseline variation is obtained through the converted environmental parameter value. Based on the baseline variation, the point trace prediction and tracking of this type of target are realized.

[0032] This embodiment uses the above technical solution to process a large amount of radar historical data of the same type of target, and can ensure the accuracy and stability of tracking the same type of target.

[0033] Furthermore, when a certain type of target already exists in the database, when the subsequent radar tracks this type of target, a first baseline change and a second baseline change will be obtained. Therefore, the present embodiment also provides processing of the first baseline change and the second baseline change, including: calculating the difference between the first baseline change and the second baseline change. If the difference is within a preset range, the average of the first baseline change and the second baseline change is used as the new baseline change to achieve point trajectory prediction and tracking of the target. If the difference is not within the preset range, the first baseline change is used to predict and track the target.

[0034] Embodiment 2 Please refer to Figure 2 , Figure 2 The present invention is a schematic diagram of a system for processing large amounts of radar data, the system comprising: A data set unit is used to set the working parameters of multiple radars, and all radars work based on the set working parameters; obtain the real-time data of all radars, and establish a data set for each radar to store the real-time data, wherein the real-time data includes target point data, target feature data, BIT information data and environmental parameter data; A data cleaning unit is used to clean the data set using the first method, specifically: obtaining BIT information data in any radar real-time data as first BIT information data, judging whether the first BIT information data are all within a preset range, if not, cleaning the data set including the first BIT information data and the corresponding target point data, target feature data and environmental parameter data, if yes, taking no action; The first target point trace unit is used to obtain target feature data in the real-time data of any radar based on the data set after data cleaning, record it as the first target feature data, and record the target point trace data corresponding to the first target feature data as the first target point trace data; based on the first target feature data, determine the first target, and match the corresponding target feature data in the real-time data of the remaining radars to obtain a matching result, and based on the matching result, lock the target point trace data corresponding to the first target in the remaining radars; A first tracking unit is used to calculate a first change amount of the first target point trace data within a preset time, and a second change amount of all target point trace data corresponding to the first target in the remaining radar within the preset time; based on the first change amount and all the second change amounts, a second method is used to process and obtain a first reference change amount of the target point trace data corresponding to the first target; based on the first reference change amount, a point trace prediction is performed on the first target to achieve tracking of the first target; A database unit, establishing a database for storing all data sets, and using the first method to perform data cleaning on the database; A second target point trace unit is used to obtain target feature data of the same type of target based on the database after data cleaning and record it as second target feature data, record all target point trace data corresponding to the second target feature data as second target point trace data, and record all environmental parameter data corresponding to the second target feature data as second environmental parameter data; The second tracking unit is used to obtain the second benchmark variation of the second target point trace data of the same type of target in different environments based on all the second target point trace data and all the second environmental parameter data by using the third method; based on the second benchmark variation, perform point trace prediction on the same type of target to achieve tracking of the same type of target.

[0035] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0036] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for processing large amounts of radar data, characterized in that: The method comprises: Setting working parameters of multiple radars, so that all radars work based on the set working parameters; acquiring real-time data of all radars, and establishing a data set for storing the real-time data for each radar, wherein the real-time data includes target point data, target feature data, BIT information data and environmental parameter data; The data set described in the first method is used for data cleaning, specifically: obtaining BIT information data in any radar real-time data as first BIT information data, judging whether the first BIT information data are all within a preset range, if not, cleaning the target point data, target feature data and environmental parameter data corresponding to the first BIT information data in the data set, if yes, taking no action; Based on the data set after data cleaning, target feature data in the real-time data of any radar is obtained and recorded as first target feature data, and target point trace data corresponding to the first target feature data is recorded as first target point trace data; based on the first target feature data, a first target is determined, and the corresponding target feature data is matched in the real-time data of the remaining radars to obtain a matching result, and based on the matching result, the target point trace data corresponding to the first target is locked in the remaining radars; Calculate a first change in the first target point trace data within a preset time, and a second change in all target point trace data corresponding to the first target in the remaining radars within the preset time; based on the first change and all second changes, use a second method to obtain a first reference change in the target point trace data corresponding to the first target; perform point trace prediction on the first target based on the first reference change to achieve tracking of the first target; Establishing a database for storing all data sets, and using the first method to clean the database; Based on the database after data cleaning, target feature data of the same type of target is obtained and recorded as second target feature data, target point trace data corresponding to all second target feature data is recorded as second target point trace data, and environmental parameter data corresponding to all second target feature data is recorded as second environmental parameter data; Based on all the second target point trace data and all the second environmental parameter data, a third method is used to obtain the second benchmark variation of the second target point trace data of the same type of target in different environments; based on the second benchmark variation, point trace prediction is performed on the same type of target to achieve tracking of the same type of target.

2. A method for processing large amounts of radar data according to claim 1, characterized in that: Determining the first target based on the first target feature data, and matching the corresponding target feature data in the real-time data of the remaining radars includes: Based on the real-time data of the remaining radars, all target feature data in each radar are obtained, and the similarity between each target feature data in each radar and the first target feature data is calculated in sequence using the first formula to obtain a first calculation result; Based on the first calculation result, locking the first target and corresponding real-time data in the remaining radars; The first formula is specifically: is the similarity between the first target feature data and the remaining target feature data in the remaining radars, is a constant, is the characteristic value corresponding to the first target characteristic data, is the characteristic value corresponding to the remaining target characteristic data in the remaining radar.

3. A method for processing large amounts of radar data according to claim 2, characterized in that: The characteristic value corresponding to the target characteristic data in any radar is calculated by the second formula, including: is the characteristic value corresponding to the target characteristic data in any radar, is a positive integer greater than or equal to 1, is the sub-eigenvalue corresponding to the target feature data in any radar, The number of sub-feature values ​​contained in the target feature data of any radar.

4. The method for processing large amount of radar data according to claim 3, characterized in that: The method further includes assigning sub-feature values ​​corresponding to target feature data in any radar: Obtaining target characteristic data from any radar as third target characteristic data; Acquire all target sub-feature data included in the third target feature data, and acquire historical data of all target sub-feature data; Based on the historical data, dividing the target sub-feature data included in the third target feature data into data intervals, and assigning a value to each data interval; Based on the assigned data interval, the corresponding target sub-feature data is assigned to obtain the corresponding sub-feature value.

5. The method for processing large amount of radar data according to claim 1, characterized in that: The second method comprises: Put the first variation and all second variations into a preset set; Calculate the mean of all changes in the preset set to obtain the mean change; Sequentially calculating the differences between all the changes in the preset set and the mean change to obtain a second calculation result; Based on the second calculation result, a change amount having the smallest difference from the mean change amount is selected from the preset set, and recorded as a first initial reference change amount; All remaining changes in the preset set except the first initial reference change are sequentially convolved with the first initial reference change, and the first initial reference change is corrected multiple times to obtain a first reference change.

6. The method for processing large amount of radar data according to claim 5, characterized in that: The third method comprises: Obtaining any type of target is recorded as the second type of target; Acquire all second target point trace data and all second environmental parameter data of the second type of targets in different time periods to obtain acquisition results; Based on the obtained result, the second method is used to calculate the second initial baseline change amount of the second type of target in each time period, and the fourth method is used to process the second environmental parameter data of the corresponding time period to obtain a baseline environmental parameter value; The second initial reference change amount and the reference environmental parameter value are combined to obtain a second reference change amount.

7. The method for processing large amount of radar data according to claim 6, characterized in that: The fourth method comprises: Obtain any time period as a first time period, and obtain all second environmental parameter data within the first time period, each second environmental parameter data including a plurality of environmental parameter sub-data; Constructing a curve graph with time as the horizontal axis and the single environmental parameter sub-data as the vertical axis, and processing the curve graph using a preset software to obtain a mean value corresponding to the single environmental parameter sub-data in the first time period, and recording it as the environmental parameter sub-value; Using the third formula to calculate all the environmental parameter sub-data included in each second environmental parameter data, to obtain a third calculation result; Based on the third calculation result, the reference environmental parameter value is obtained.

8. The method for processing large amount of radar data according to claim 7, characterized in that: The third formula includes: is the reference environmental parameter value, is the environmental parameter sub-value in a single second environmental parameter data, is the coefficient of each environmental parameter sub-value, It is the number of environmental parameter sub-data contained in a single second environmental parameter data.

9. The method for processing large amount of radar data according to claim 7, characterized in that: The preset software is MathSword numerical calculation software.

10. A system for processing large amounts of radar data, characterized in that: The system comprises: A data set unit is used to set the working parameters of multiple radars, and all radars work based on the set working parameters; obtain the real-time data of all radars, and establish a data set for each radar to store the real-time data, wherein the real-time data includes target point data, target feature data, BIT information data and environmental parameter data; A data cleaning unit is used to clean the data set using the first method, specifically: obtaining BIT information data in any radar real-time data as first BIT information data, judging whether the first BIT information data are all within a preset range, if not, cleaning the data set including the first BIT information data and the corresponding target point data, target feature data and environmental parameter data, if yes, taking no action; The first target point trace unit is used to obtain target feature data in the real-time data of any radar based on the data set after data cleaning, record it as the first target feature data, and record the target point trace data corresponding to the first target feature data as the first target point trace data; based on the first target feature data, determine the first target, and match the corresponding target feature data in the real-time data of the remaining radars to obtain a matching result, and based on the matching result, lock the target point trace data corresponding to the first target in the remaining radars; A first tracking unit is used to calculate a first change amount of the first target point trace data within a preset time, and a second change amount of all target point trace data corresponding to the first target in the remaining radar within the preset time; based on the first change amount and all the second change amounts, a second method is used to process and obtain a first reference change amount of the target point trace data corresponding to the first target; based on the first reference change amount, a point trace prediction is performed on the first target to achieve tracking of the first target; A database unit, establishing a database for storing all data sets, and using the first method to perform data cleaning on the database; A second target point trace unit is used to obtain target feature data of the same type of target based on the database after data cleaning and record it as second target feature data, record all target point trace data corresponding to the second target feature data as second target point trace data, and record all environmental parameter data corresponding to the second target feature data as second environmental parameter data; The second tracking unit is used to obtain the second benchmark variation of the second target point trace data of the same type of target in different environments based on all the second target point trace data and all the second environmental parameter data by using the third method; based on the second benchmark variation, perform point trace prediction on the same type of target to achieve tracking of the same type of target.

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