Method and system for processing 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.The method comprises the steps that multiple pieces of real-time data used for a target tracking radar are obtained, a data set is constructed and stored, and then BIT information data is used for cleaning the data stored in the data set; locking a tracking target by using the target feature data of any radar, and synchronously obtaining corresponding first target trace point data; after the tracking target is locked, traversing in the data set of the remaining radars to obtain second target trace point data corresponding to the tracking target; calculating the first trace point data, a first variable quantity of the first trace point data in a preset time and second variable quantities of all the second trace point data; and the first variable quantity and all the second variable quantities are processed to obtain a reference variable quantity, so that the tracking of the tracking target is realized. According to the invention, efficient processing of mass radar real-time data can be realized, and improvement of target tracking precision and stability can be 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 amounts of radar data. Background Art

[0002] Radar data processing, or RDP for short, is a post-processing process of signal processing. The input of the radar data processing process is the target track formed by radar signal processing. The track information includes the target's range, azimuth, and pitch values. Radar data processing obtains the target track by correlating the measurement sets obtained from multiple scans. After successfully initiating the target track, the data processing can correct the radar's measurement errors of the target position and velocity 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, velocity, acceleration, and landing point.

[0003] Not only does real-time radar data need to be processed, but stored radar data also needs to be processed again. By studying past radar data, regular characteristics can be discovered, and the efficiency and level of radar data application can be improved. In general, radar data processing aims to gain a deeper and more systematic understanding of the detected target, more efficiently capture the target, and more accurately measure the target. 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. Instead, the observation data of multiple radars is integrated to improve the accuracy and stability of target tracking. As a result, the amount and variety of real-time space target detection data acquired by radar are increasing, and the efficiency and timeliness of radar data application cannot be guaranteed. Therefore, a method and system for processing large amounts of radar data is urgently needed. Summary of the Invention

[0004] In view of this, the present application provides a method and system for processing large amounts of radar data to address the deficiencies in the prior art.

[0005] The first aspect of the present application provides a method for processing large amounts of radar data, comprising: Acquire real-time data from N target tracking radars and establish a data set for each radar for storing real-time data, wherein the real-time data includes target feature data, target point data, BIT information data, and environmental parameter data, where N>1; Based on the BIT information data, cleaning the remaining real-time data in the data set; Based on the data set after data cleaning, target feature data in any radar real-time data 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 characteristic data, the tracking target is locked, and the second target characteristic data corresponding to the tracking target is matched in the real-time data of the remaining radars, and the corresponding second target point track data is synchronously obtained; Calculate the first change of the first target point trace data within a preset time, and the second change of all second target point trace data within the preset time; based on the first change and all second changes, obtain the first baseline change of the target point trace data corresponding to the tracking target, and based on the first baseline change, realize tracking of the tracking target.

[0006] In a possible implementation of the first aspect, the method further includes: Establish a database to store all data sets; Based on the database, target feature data of the same target is obtained and recorded as third target feature data, and target point trace data corresponding to all third target feature data are simultaneously obtained and recorded as third target point trace data, and corresponding environmental parameter data are recorded as first environmental parameter data; Based on all the third target point trace data and all the first environmental parameter data, a first preset method is used to process and obtain a second baseline variation of the third target point trace data of the same target under different environments; Based on the second benchmark change, tracking of the same target is achieved.

[0007] In a possible implementation of the first aspect, the first preset method includes: Obtain multiple sample targets of the same target; Based on the database, obtaining the third target point trace data and the first environmental parameter data of each sample target in different time periods; Processing the first environmental parameter data corresponding to each sample target in different time periods to obtain the baseline environmental parameters; Under the same reference environmental parameters, calculate the average of the first reference variation of the third target trace data corresponding to all sample targets to obtain the second reference variation of the third target trace data of the same target in the same environment; All reference environmental parameters are combined to calculate the second reference variation of the third target point trace data under different environments for the same target.

[0008] In a possible implementation of the first aspect, processing the first environmental parameter data corresponding to each sample target in different time periods to obtain the baseline environmental parameter includes: Obtain any time period as a first time period, and obtain the first environmental parameter data within the first time period; The first environmental parameter data includes a plurality of environmental parameter sub-data, and an influence coefficient is preset for each environmental parameter sub-data; sequentially calculating the mean of each environmental parameter sub-data corresponding to the first environmental parameter data within the first time period to obtain a third calculation result; Based on the third calculation result, the sum of the mean value of each environmental parameter sub-data in the first time period multiplied by the corresponding influence coefficient is calculated as the reference environmental parameter in the first time period.

[0009] In a possible implementation of the first aspect, locking a tracked target based on the first target characteristic data, and matching second target characteristic data corresponding to the tracked target in real-time data of remaining radars includes: Acquire the first target feature data, and perform normalization processing on the data under a preset data protocol to obtain a corresponding feature value recorded as a first feature value; Acquire target feature data of different targets from the real-time data of the remaining radars, and perform normalization processing under the preset data protocol to obtain feature values ​​of the target feature data corresponding to the different targets; Calculating the similarity between the first characteristic value and characteristic values ​​of target characteristic data corresponding to different targets to obtain a first calculation result; Based on the first target characteristic data, a tracking target is locked, and based on the first calculation result, second target characteristic data corresponding to the tracking target is matched in the real-time data of the remaining radars.

[0010] In a possible implementation of the first aspect, processing to obtain a first benchmark variation of target point trace data corresponding to the tracked target includes: Storing the first variation and all second variations in a preset set; Calculating 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 with the smallest difference from the mean change is selected from the preset set, and recorded as a first initial benchmark change; All remaining variations in the preset set except the first initial reference variation are sequentially convolved with the first initial reference variation, and the first initial reference variation is corrected multiple times to obtain a first reference variation.

[0011] In a possible implementation of the first aspect, based on the BIT information data, cleaning the remaining real-time data in the data set includes: Obtaining BIT information data from any radar real-time data as first BIT information data; Determine whether the first BIT information data is within a first preset range. If so, do nothing. If not, clean the corresponding target point data, target feature data, and environmental parameter data in the data set.

[0012] In a possible implementation of the first aspect, the method further includes: Obtain any tracking target as a first tracking target, and obtain a first reference change of the first tracking target with respect to target point trace data; The database is traversed to determine whether there is a target identical to the first tracking target; if not, the first tracking target is tracked based on a first benchmark change of the first tracking target with respect to the target point trace data; if so, the first tracking target is tracked using a second preset method.

[0013] In a possible implementation manner of the first aspect, the second preset method further includes: Acquire first environmental parameter data corresponding to the first tracking target, and simultaneously acquire, under the first environmental parameter data, a second baseline variation of target point trace data corresponding to the same target as the first tracking target in the database; Calculating a difference between a first baseline variation of the first tracking target with respect to the target point trace data and a second baseline variation of the target point trace data corresponding to the same target, determining whether the difference is within a second preset range, and if so, calculating an average of the first baseline variation of the first tracking target with respect to the target point trace data and the second baseline variation of the target point trace data corresponding to the same target as the baseline variation of the target point trace data of the first tracking target under the first environmental parameter data, thereby tracking the first tracking target; If not, tracking the first tracking target is achieved based on a first reference variation of the first tracking target with respect to the target point trace data.

[0014] A second aspect of the present application provides a system for processing large amounts of radar data, comprising: A data set unit is used to obtain real-time data of N target tracking radars and establish a data set for each radar for storing real-time data, wherein the real-time data includes target feature data, target point data, BIT information data and environmental parameter data, where N>1; a data cleaning unit, configured to clean the remaining real-time data in the data set based on the BIT information data; A first target point trace unit is configured to obtain target feature data in any radar real-time data based on the data set after data cleaning and record the data as first target feature data, and record target point trace data corresponding to the first target feature data as first target point trace data; A second target point tracking unit is configured to lock and track a target based on the first target feature data, match the second target feature data corresponding to the tracked target in the real-time data of the remaining radars, and synchronously obtain the corresponding second target point tracking data; The tracking unit is used to calculate the first change of the first target point trace data within a preset time, and the second change of all second target point trace data within the preset time; based on the first change and all second change, the first baseline change of the target point trace data corresponding to the tracking target is obtained by processing, and based on the first baseline change, the tracking of the tracking target is achieved.

[0015] Its beneficial effects are as follows: the present invention discloses a method and system for processing large amounts of radar data, which obtains real-time data from multiple target tracking radars and constructs a data set for storage, and then uses BIT information data to clean the data stored in the data set to ensure the rationality of data acquisition; uses the target feature data of any radar to lock the tracking target, and synchronously obtains the first target point trace data corresponding to the target feature data; after locking the tracking target, uses the target feature data of any radar to traverse the data sets of the remaining radars to obtain the second target point trace data corresponding to the tracking target; calculates the first change of the first point trace data at a preset time, and the second change of all the second point trace data at a preset time; then processes the first change and all the second change to obtain the baseline change of the tracking target with respect to the target point trace, thereby achieving tracking of the tracking target. The present invention not only realizes the processing of massive radar real-time data, but also improves application efficiency and can achieve improved target tracking accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in 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 merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0017] Figure 1 This is a flow chart of a method for processing large amounts of radar data provided by an embodiment of the present application; Figure 2 This is a schematic diagram of the composition of a system for processing large amounts of radar data provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] In this application, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0020] Example 1 In existing technologies, in order to improve the accuracy and stability of target tracking, it is no longer sufficient to perform target positioning through a single radar. Instead, the observation data of multiple radars are integrated to improve the accuracy and stability of target tracking. As a result, the number and types of real-time and historical detection data of space targets obtained by radar are increasing, and the application efficiency and timeliness of radar data cannot be guaranteed.

[0021] Therefore, this application provides a method for processing large amounts of radar data, such as Figure 1 As shown, including: Acquire real-time data from N target tracking radars and establish a data set for each radar for storing real-time data, wherein the real-time data includes target feature data, target point data, BIT information data, and environmental parameter data, where N>1; Based on the BIT information data, cleaning the remaining real-time data in the data set; Based on the data set after data cleaning, target feature data in any radar real-time data 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 characteristic data, the tracking target is locked, and the second target characteristic data corresponding to the tracking target is matched in the real-time data of the remaining radars, and the corresponding second target point track data is synchronously obtained; Calculate the first change of the first target point trace data within a preset time, and the second change of all second target point trace data within the preset time; based on the first change and all second changes, obtain the first baseline change of the target point trace data corresponding to the tracking target, and based on the first baseline change, realize tracking of the tracking target.

[0022] Among them, N real-time data for tracking radars are obtained and a data set for storing real-time data is established for each radar. The purpose is to be able to distinguish the specific data source when the radar data is subsequently processed. The number N is greater than 1 and can be obtained according to actual conditions. This embodiment does not make any specific limitations.

[0023] Real-time data includes target feature data, target point trace data, BIT information data, and environmental parameter data. It's important to note that these data types are familiar in the radar field. Target feature data in radar real-time data refers to key information extracted from radar echo signals that describes the target's physical properties and motion state. It serves as the core basis for radar identification, classification, tracking, and threat assessment. It can be divided into two categories: motion features and physical features. Motion features reflect the target's motion state and serve as the basis for radar tracking and predicting motion trends. Physical features reflect the target's inherent properties and are used to distinguish target types. Target point trace data is the estimated target position and motion parameters obtained by radar by transmitting electromagnetic waves and receiving target echoes. It serves as the basis for radar tracking and identification. BIT information data refers to key data collected in real time by the radar system's built-in self-test function to monitor the device's operating status and faults. It is the core output of the radar system's self-diagnosis, ensuring the proper operation of radar hardware, software, and subsystems, promptly detecting and locating faults, and ensuring the reliability of detection and tracking missions. Environmental parameter data refers to various types of quantitative information about the external environment when collecting radar data. It directly affects the propagation of radar signals, the reception quality of target echoes, and the accuracy of target detection and tracking. It mainly covers meteorological environmental parameters, terrain and land object environmental parameters, and electromagnetic environmental parameters.

[0024] Among them, the BIT information data is used to clean the real-time data stored in the data set. Specifically, the corresponding BIT information data is obtained and it is determined whether it is within the first preset range. That is, when the corresponding BIT information data exceeds the first preset range, it indicates that there is a fault in the working state of the radar itself. At this time, the real-time data collected is distorted. Therefore, this part of the data is cleaned to ensure the rationality of the collected data.

[0025] Among them, based on the data set after data cleaning, the target feature data in the real-time data of any radar is selected from N radars and recorded as the first target feature data, and the target point track data corresponding to the first target feature data is recorded as the first target point track data; first, the first target feature data is used to lock the tracking target, and then, based on the first target feature data, the target feature data corresponding to the tracking target is matched in the real-time data of the remaining radars. The specific implementation logic is as follows: Acquire first target feature data and perform normalization under a preset data protocol to obtain a corresponding eigenvalue, which is recorded as the first eigenvalue. For example, all data related to the physical characteristics of the first target feature data are mapped to a unified range by partitioning the data into intervals and assigning values. The eigenvalues ​​of the mapped data are then summed to obtain the first eigenvalue. The significance of normalizing the data is to eliminate magnitude bias and distribution differences, providing a more reliable basis for subsequent data analysis, modeling, or decision-making. Repeat the above steps to acquire target feature data for different targets from the real-time data of the remaining radars and perform normalization under the same preset data protocol to obtain eigenvalues ​​corresponding to the target feature data for different targets. By calculating the similarity between the first eigenvalue and the eigenvalues ​​corresponding to the target feature data of different targets from the remaining radars, the first target feature data is used to identify the target being tracked. The similarity calculation result (if the similarity exceeds a set value, it is determined to be the same target being tracked) is used to match the second target feature data corresponding to the target being tracked in the real-time data of the remaining radars.

[0026] For calculating the similarity between the first eigenvalue and the eigenvalues ​​of target feature data corresponding to different targets in the remaining radars, the following formula is used: is the similarity between the first eigenvalue and the eigenvalues ​​of target feature data corresponding to different targets in the remaining radars, is a constant, is the first eigenvalue, are the characteristic values ​​of target characteristic data corresponding to different targets in the remaining radars; For any target, the characteristic value of the target characteristic data is calculated using the following formula: 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, The number of sub-eigenvalues ​​included in the target feature data of any radar; Assign the sub-feature values ​​corresponding to the target feature data in any radar, that is, map all the data of the target feature data about the physical features to a unified range. The mapping method adopts the method of interval division and assignment of data, specifically: Acquire target feature data from any radar, and simultaneously acquire all target sub-feature data (about physical features) and historical data contained in the target feature data; Based on historical data, the data range of each target sub-feature data is determined, and then each target sub-feature data is divided into the same number of data intervals, and each data interval is assigned a corresponding value according to the numerical size. For example, a single target sub-feature data is divided into 5 data intervals and assigned values ​​1-5 in sequence. Through the above steps, the target sub-feature data is unified, which facilitates subsequent analysis and processing of the target feature data.

[0027] Among them, after locking the tracking target, the first change amount of the first target point trace data within the preset time and the second change amount of all the second target point trace data within the preset time are calculated. It should be noted that there are multiple second change amounts, and the number is N-1; then the first change amount and all the second change amounts are used to process the first baseline change amount of the tracking target with respect to the target point trace data. Finally, based on the first baseline change amount, the tracking target is achieved. The processing logic is specifically as follows: The first change and all second changes are stored in a preset set, and the mean of all changes in the preset set is calculated to obtain the mean change; then the differences between all changes in the preset set and the mean change are calculated in turn, and according to the multiple differences obtained by calculation, the change corresponding to the smallest difference is selected in the preset set and recorded as the first initial reference change, indicating that the closer the change of the corresponding radar about the target point trace data is to the mean change, and then the change of the target point trace data corresponding to the remaining radars is used to perform convolution sum on the first initial reference change in turn, to achieve multiple corrections to the first initial reference change, and obtain the first reference change; the implementation logic of the convolution sum is to smooth the change of each radar through convolution (suppress noise), and then perform weighted fusion based on the radar accuracy. The core idea is to use weighted fusion to calculate the weighted average of the change of the target point trace data corresponding to different radars.

[0028] It should be noted that in the field of radar target track data processing, the use of convolution is a relatively common method, including: first performing data preprocessing, calculating a time series based on the target track data for each radar; determining the weight of each radar based on measurement accuracy; convolving the changes in the target track data of each radar, and then weighted fusion and smoothing the changes. This embodiment, based on the above technical means, uses a first initial baseline change as an adjustment target, then performs multiple convolutions with the changes in the target track data of the remaining radars and this adjustment target (i.e., convolution followed by weighted fusion), completing multiple corrections to the first initial baseline change to obtain the first baseline change.

[0029] Furthermore, in addition to utilizing radar accuracy for weighted fusion, this embodiment also provides the following weighted fusion method: a different weighting coefficient is set for each change in the preset set except for the first initial baseline change. The logic for setting the weighting coefficient can be allocated based on the difference between each change in the preset set and the mean change. That is, the closer the change in the preset set is to the mean change, the higher its weight coefficient. In this way, the change after weighting coefficient allocation has a higher accuracy. Then, the weighted average corresponding to all remaining changes in the preset set is calculated. That is, after allocating weighting coefficients to all remaining changes in the preset set, the sum is calculated with the first initial baseline change and then the average is calculated. The final weighted average is used as the first baseline change of the target point data corresponding to the tracking target. That is, the motion trajectory of the tracking target is predicted through the first baseline change, thereby achieving tracking of the tracking target.

[0030] The above embodiments achieve efficient processing of massive amounts of real-time radar data and achieve high-precision target tracking. However, during target tracking by radar, massive amounts of historical data are also generated. Studying this massive amount of historical data and discovering its regular characteristics are highly valuable for improving radar data application efficiency and achieving high-precision target tracking. Therefore, in addition to achieving efficient processing of massive amounts of real-time radar data, this embodiment also improves the processing methods for massive amounts of historical radar data, specifically: A database for storing all data sets is established, and all target feature data of the same target in the database are analyzed to obtain the second baseline variation of the target point trace data of the same target in different environments; first, all target feature data of the same target are obtained, and all corresponding target point trace data and corresponding environmental parameter data are simultaneously obtained; all target feature data of the same target are used to lock the same tracking target, and the target point trace data and corresponding environmental parameter data are used to determine the second baseline variation of the target point trace data of the same target in different environments, and finally, the second baseline variation is used to achieve tracking of the same target.

[0031] Among them, regarding how to process and obtain the second baseline change amount of the target point trace data of the same target in different environments, this embodiment provides the following method: obtain multiple sample targets of the same target, and based on the database, obtain the target point trace data and corresponding environmental parameter data of each sample target in different time periods; use the baseline environmental parameter method to classify different environments, and then calculate the mean of the first baseline change amount of the target point trace data corresponding to all sample targets under the same baseline environmental parameters, and obtain the second baseline change amount of the target point trace data of the same target in the same environment. By combining all the baseline environmental parameters, the second baseline transformation amount of the target point trace data of the same target in different environments can be obtained. It should be noted that the first baseline change amount is the same as the first baseline change amount in the radar real-time data processing, and the method of obtaining it will not be explained again.

[0032] Among them, the processing of baseline environmental parameters includes: obtaining any time period and obtaining the environmental parameter data within the first time period. Since the environmental parameter data contains multiple environmental parameter sub-data (such as meteorological environmental parameters, terrain and land object environmental parameters and electromagnetic environmental parameters, meteorological environmental parameters include precipitation data, atmospheric data and wind field data, terrain and land object environmental parameters include terrain feature data and land object type data, and electromagnetic environmental parameters include electromagnetic interference data, background noise data and propagation loss data), then preset the influence coefficient for each environmental parameter sub-data. The influence coefficient setting method can be achieved by analyzing historical data to obtain the influence weight for allocation; then, within the first time period, calculate the mean of each environmental parameter sub-data and multiply it with the corresponding influence coefficient, and then calculate the sum as the baseline environmental parameter within the time period.

[0033] Furthermore, for calculating the mean of each environmental parameter sub-data in the first time period, in addition to the conventional mean calculation method, the following method can also be used for processing, specifically: constructing a curve graph with time as the coordinate and the individual environmental parameter sub-data as the vertical coordinate, and using preset software (such as MathSword numerical calculation software) to process the curve graph, obtaining the mean of the individual environmental parameter sub-data in the first time period as the environmental parameter sub-value, and calculating the mean of each environmental parameter sub-data and multiplying it with the corresponding influence coefficient, and then calculating the corresponding sum value. The specific formula is: is the baseline environmental parameter, is the mean value of a single environmental sub-data (environmental parameter sub-value), is the influence coefficient corresponding to the sub-value of the environmental parameter, The environmental parameter data contains the number of environmental parameter sub-data.

[0034] Among them, this embodiment provides a means for processing massive amounts of radar real-time data and historical data, and first obtains a first baseline change amount of the tracking target (obtained through real-time data processing), then traverses the database to query whether there is a target identical to the tracking target. If so, a second baseline change amount of the same target as the tracking target is calculated (obtained through historical data processing). If not, the first baseline change amount of the tracking target is directly used to track the tracking target.

[0035] Among them, if there is a target identical to the tracking target in the database, first obtain the environmental parameter data of the tracking target through real-time data, and use the environmental parameter data to match the second benchmark change amount of the target point track data of the target identical to the tracking target in the same environment in the database. It should be noted that the second benchmark change amount is the same as the second benchmark change amount involved in the radar historical data processing, and the processing method will not be explained again; calculate the difference between the first benchmark change amount and the second benchmark change amount, and determine whether the difference is within a preset range. If so, calculate the average between the two as the benchmark change amount of the tracking target with respect to the target point track data under the environmental parameter data to achieve tracking of the tracking target. If not, continue to use the first benchmark change amount of the tracking target with respect to the target point track data to achieve tracking of the tracking target.

[0036] Among them, this embodiment can not only improve the efficiency and level of radar data application by efficiently analyzing and processing massive amounts of radar real-time data and historical data, but also more efficiently capture targets and more accurately measure targets, ultimately improving the accuracy and stability of target tracking.

[0037] In some embodiments, the method further comprises: Establish a database to store all data sets; Based on the database, target feature data of the same target is obtained and recorded as third target feature data, and target point trace data corresponding to all third target feature data are simultaneously obtained and recorded as third target point trace data, and corresponding environmental parameter data are recorded as first environmental parameter data; Based on all the third target point trace data and all the first environmental parameter data, a first preset method is used to process and obtain a second baseline variation of the third target point trace data of the same target under different environments; Based on the second benchmark change, tracking of the same target is achieved.

[0038] In some embodiments, the first preset method includes: Obtain multiple sample targets of the same target; Based on the database, obtaining the third target point trace data and the first environmental parameter data of each sample target in different time periods; Processing the first environmental parameter data corresponding to each sample target in different time periods to obtain the baseline environmental parameters; Under the same reference environmental parameters, calculate the average of the first reference variation of the third target trace data corresponding to all sample targets to obtain the second reference variation of the third target trace data of the same target in the same environment; All reference environmental parameters are combined to calculate the second reference variation of the third target point trace data under different environments for the same target.

[0039] In some embodiments, processing the first environmental parameter data corresponding to each sample target in different time periods to obtain the baseline environmental parameters includes: Obtain any time period as a first time period, and obtain the first environmental parameter data within the first time period; The first environmental parameter data includes a plurality of environmental parameter sub-data, and an influence coefficient is preset for each environmental parameter sub-data; sequentially calculating the mean of each environmental parameter sub-data corresponding to the first environmental parameter data within the first time period to obtain a third calculation result; Based on the third calculation result, the sum of the mean value of each environmental parameter sub-data in the first time period multiplied by the corresponding influence coefficient is calculated as the reference environmental parameter in the first time period.

[0040] In some embodiments, based on the first target characteristic data, locking the tracked target and matching the second target characteristic data corresponding to the tracked target in the real-time data of the remaining radars includes: Acquire the first target feature data, and perform normalization processing on the data under a preset data protocol to obtain a corresponding feature value recorded as a first feature value; Acquire target feature data of different targets from the real-time data of the remaining radars, and perform normalization processing under the preset data protocol to obtain feature values ​​of the target feature data corresponding to the different targets; Calculating the similarity between the first characteristic value and characteristic values ​​of target characteristic data corresponding to different targets to obtain a first calculation result; Based on the first target characteristic data, a tracking target is locked, and based on the first calculation result, second target characteristic data corresponding to the tracking target is matched in the real-time data of the remaining radars.

[0041] In some embodiments, processing to obtain a first baseline variation of the target point trace data corresponding to the tracking target includes: Storing the first variation and all second variations in a preset set; Calculating 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 with the smallest difference from the mean change is selected from the preset set, and recorded as a first initial benchmark change; All remaining variations in the preset set except the first initial reference variation are sequentially convolved with the first initial reference variation, and the first initial reference variation is corrected multiple times to obtain a first reference variation.

[0042] In some embodiments, based on the BIT information data, cleaning the remaining real-time data in the data set includes: Obtaining BIT information data from any radar real-time data as first BIT information data; Determine whether the first BIT information data is within a first preset range. If so, do nothing. If not, clean the corresponding target point data, target feature data, and environmental parameter data in the data set.

[0043] In some embodiments, the method further comprises: Obtain any tracking target as a first tracking target, and obtain a first reference change of the first tracking target with respect to target point trace data; The database is traversed to determine whether there is a target identical to the first tracking target; if not, the first tracking target is tracked based on a first benchmark change of the first tracking target with respect to the target point trace data; if so, the first tracking target is tracked using a second preset method.

[0044] In some embodiments, the second preset method further includes: Acquire first environmental parameter data corresponding to the first tracking target, and simultaneously acquire, under the first environmental parameter data, a second baseline variation of target point trace data corresponding to the same target as the first tracking target in the database; Calculating a difference between a first baseline variation of the first tracking target with respect to the target point trace data and a second baseline variation of the target point trace data corresponding to the same target, determining whether the difference is within a second preset range, and if so, calculating an average of the first baseline variation of the first tracking target with respect to the target point trace data and the second baseline variation of the target point trace data corresponding to the same target as the baseline variation of the target point trace data of the first tracking target under the first environmental parameter data, thereby tracking the first tracking target; If not, tracking the first tracking target is achieved based on a first reference variation of the first tracking target with respect to the target point trace data.

[0045] Example 2 Based on the method for processing large amounts of radar data provided in the first embodiment of the present application, the second embodiment of the present application also provides a system for processing large amounts of radar data, such as Figure 2 As shown, including: A data set unit is used to obtain real-time data of N target tracking radars and establish a data set for each radar for storing real-time data, wherein the real-time data includes target feature data, target point data, BIT information data and environmental parameter data, where N>1; a data cleaning unit, configured to clean the remaining real-time data in the data set based on the BIT information data; A first target point trace unit is configured to obtain target feature data in any radar real-time data based on the data set after data cleaning and record the data as first target feature data, and record target point trace data corresponding to the first target feature data as first target point trace data; A second target point tracking unit is configured to lock and track a target based on the first target feature data, match the second target feature data corresponding to the tracked target in the real-time data of the remaining radars, and synchronously obtain the corresponding second target point tracking data; The tracking unit is used to calculate the first change of the first target point trace data within a preset time, and the second change of all second target point trace data within the preset time; based on the first change and all second change, the first baseline change of the target point trace data corresponding to the tracking target is obtained by processing, and based on the first baseline change, the tracking of the tracking target is achieved.

[0046] The specific principles and execution processes of each unit in the system for processing large amounts of radar data disclosed in the above-mentioned embodiment 2 of the present application are the same as the method for processing large amounts of radar data disclosed in the above-mentioned embodiment 1 of the present application. Please refer to the corresponding parts of the method for processing large amounts of radar data disclosed in the above-mentioned embodiment 1 of the present application, and no further details will be given here.

[0047] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computing software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional 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.

[0049] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for processing large amounts of radar data, characterized in that: include: Acquire real-time data from N target tracking radars and establish a data set for each radar for storing real-time data, wherein the real-time data includes target feature data, target point data, BIT information data, and environmental parameter data, where N>1; Based on the BIT information data, cleaning the remaining real-time data in the data set; Based on the data set after data cleaning, target feature data in any radar real-time data 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 characteristic data, the tracking target is locked, and the second target characteristic data corresponding to the tracking target is matched in the real-time data of the remaining radars, and the corresponding second target point track data is synchronously obtained; Calculating a first change in the first target point trace data within a preset time, and a second change in all second target point trace data within the preset time; Based on the first variation and all the second variations, a first reference variation of the target point trace data corresponding to the tracking target is obtained through processing, and based on the first reference variation, the tracking target is tracked.

2. The method for processing large amounts of radar data according to claim 1, characterized in that: The method further comprises: Establish a database to store all data sets; Based on the database, target feature data of the same target is obtained and recorded as third target feature data, and target point trace data corresponding to all third target feature data are simultaneously obtained and recorded as third target point trace data, and corresponding environmental parameter data are recorded as first environmental parameter data; Based on all the third target point trace data and all the first environmental parameter data, a first preset method is used to process and obtain a second baseline variation of the third target point trace data of the same target under different environments; Based on the second benchmark change, tracking of the same target is achieved.

3. The method for processing large amounts of radar data according to claim 2, characterized in that: The first preset method includes: Obtain multiple sample targets of the same target; Based on the database, obtaining the third target point trace data and the first environmental parameter data of each sample target in different time periods; Processing the first environmental parameter data corresponding to each sample target in different time periods to obtain the baseline environmental parameters; Under the same reference environmental parameters, calculate the average of the first reference variation of the third target trace data corresponding to all sample targets to obtain the second reference variation of the third target trace data of the same target in the same environment; All reference environmental parameters are combined to calculate the second reference variation of the third target point trace data under different environments for the same target.

4. The method for processing large amounts of radar data according to claim 2, characterized in that: The first environmental parameter data corresponding to each sample target in different time periods is processed to obtain the following baseline environmental parameters: Obtain any time period as a first time period, and obtain the first environmental parameter data within the first time period; The first environmental parameter data includes a plurality of environmental parameter sub-data, and an influence coefficient is preset for each environmental parameter sub-data; sequentially calculating the mean of each environmental parameter sub-data corresponding to the first environmental parameter data within the first time period to obtain a third calculation result; Based on the third calculation result, the sum of the mean value of each environmental parameter sub-data in the first time period multiplied by the corresponding influence coefficient is calculated as the baseline environmental parameter in the first time period.

5. The method for processing large amounts of radar data according to claim 1, characterized in that: Locking the tracked target based on the first target characteristic data and matching the second target characteristic data corresponding to the tracked target in the real-time data of the remaining radars includes: Acquire the first target feature data, and perform normalization processing on the data under a preset data protocol to obtain a corresponding feature value recorded as a first feature value; Acquire target feature data of different targets from the real-time data of the remaining radars, and perform normalization processing under the preset data protocol to obtain feature values ​​of the target feature data corresponding to the different targets; Calculating the similarity between the first characteristic value and characteristic values ​​of target characteristic data corresponding to different targets to obtain a first calculation result; Based on the first target characteristic data, a tracking target is locked, and based on the first calculation result, second target characteristic data corresponding to the tracking target is matched in the real-time data of the remaining radars.

6. The method for processing large amounts of radar data according to claim 1, characterized in that: Processing to obtain a first reference variation of the target point trace data corresponding to the tracking target includes: Storing the first variation and all second variations in a preset set; Calculating 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 with the smallest difference from the mean change is selected from the preset set, and recorded as a first initial benchmark change; All remaining variations in the preset set except the first initial reference variation are sequentially convolved with the first initial reference variation, and the first initial reference variation is corrected multiple times to obtain a first reference variation.

7. The method for processing large amounts of radar data according to claim 1, characterized in that: Based on the BIT information data, cleaning the remaining real-time data in the data set includes: Obtaining BIT information data from any radar real-time data as first BIT information data; Determine whether the first BIT information data is within a first preset range. If so, do nothing. If not, clean the corresponding target point data, target feature data, and environmental parameter data in the data set.

8. The method for processing large amounts of radar data according to claim 2, characterized in that: The method further comprises: Obtain any tracking target as a first tracking target, and obtain a first reference change of the first tracking target with respect to target point trace data; The database is traversed to determine whether there is a target identical to the first tracking target; if not, the first tracking target is tracked based on a first benchmark change of the first tracking target with respect to the target point trace data; if so, the first tracking target is tracked using a second preset method.

9. The method for processing large amounts of radar data according to claim 8, characterized in that: The second preset method further includes: Acquire first environmental parameter data corresponding to the first tracking target, and simultaneously acquire, under the first environmental parameter data, a second baseline variation of target point trace data corresponding to the same target as the first tracking target in the database; Calculating a difference between a first baseline variation of the first tracking target with respect to the target point trace data and a second baseline variation of the target point trace data corresponding to the same target, determining whether the difference is within a second preset range, and if so, calculating an average of the first baseline variation of the first tracking target with respect to the target point trace data and the second baseline variation of the target point trace data corresponding to the same target as the baseline variation of the target point trace data of the first tracking target under the first environmental parameter data, thereby tracking the first tracking target; If not, tracking the first tracking target is achieved based on a first reference variation of the first tracking target with respect to the target point trace data.

10. A system for processing large amounts of radar data, characterized in that: include: A data set unit is used to obtain real-time data of N target tracking radars and establish a data set for each radar for storing real-time data, wherein the real-time data includes target feature data, target point data, BIT information data and environmental parameter data, where N>1; a data cleaning unit, configured to clean the remaining real-time data in the data set based on the BIT information data; A first target point trace unit is configured to obtain target feature data in any radar real-time data based on the data set after data cleaning and record the data as first target feature data, and record target point trace data corresponding to the first target feature data as first target point trace data; A second target point tracking unit is configured to lock and track a target based on the first target feature data, match the second target feature data corresponding to the tracked target in the real-time data of the remaining radars, and synchronously obtain the corresponding second target point tracking data; a tracking unit, configured to calculate a first change in the first target trace data within a preset time, and a second change in all second target trace data within the preset time; Based on the first variation and all the second variations, a first reference variation of the target point trace data corresponding to the tracking target is obtained through processing, and based on the first reference variation, the tracking target is tracked.

Images