CN110991539A - Spatial target high-frequency repetitive behavior identification method - Google Patents
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Abstract
The invention discloses a space target high-frequency repetitive behavior identification method, which comprises the following steps: acquiring motion data of a plurality of space targets; extracting an approximate repeated track of each target from all the motion data according to a preset extraction rule; respectively calculating a deviation value between any two approximate repeated tracks of each target, and calculating the association degree of each space target according to the deviation value; and determining the space target with high-frequency repeated behaviors according to the relevance. The method for identifying the high-frequency behavior of the space target based on the big data technology utilizes the big data distributed storage and processing technology, can efficiently identify the high-frequency repeated behavior of the target in mass space target data, realizes the automatic identification of the high-frequency behavior of the space target, presents the result in a list form, supports the manual intervention decision, and has obvious advantages under the condition of TB magnitude data and PB magnitude data.
Description
Technical Field
The invention relates to the field of radars, in particular to a method for identifying high-frequency repeated behaviors of a space target.
Background
The high frequency repetitive behavior of a spatial target is an important attribute of the target and is of high interest. At present, the identification of high-frequency behavior for spatial objects is mainly based on the experience of the observer and the comparison of historical data to draw conclusions.
The traditional method highly depends on manual participation, calls historical data for some sensitive targets to compare, gives confidence coefficient by means of expert experience, and is low in efficiency, poor in effect in a mass data environment and strong in restrictive property.
Disclosure of Invention
The invention aims to solve the technical problem of providing a space target high-frequency repetitive behavior identification method, a system, a device and a storage medium aiming at the defects of the prior art.
The technical scheme for solving the technical problems is as follows:
a spatial target high-frequency repetitive behavior identification method comprises the following steps:
acquiring motion data of a plurality of space targets;
extracting an approximate repeated track of each target from all the motion data according to a preset extraction rule;
respectively calculating a deviation value between any two approximate repeated tracks of each target, and calculating the association degree of each space target according to the deviation value;
and determining the space target with high-frequency repeated behaviors according to the relevance.
The invention has the beneficial effects that: the method for identifying the high-frequency behavior of the space target based on the big data technology utilizes the big data distributed storage and processing technology, can efficiently identify the high-frequency repeated behavior of the target in mass space target data, realizes the automatic identification of the high-frequency behavior of the space target, presents the result in a list form, supports the manual intervention decision, and has obvious advantages under the condition of TB magnitude data and PB magnitude data.
Another technical solution of the present invention for solving the above technical problems is as follows:
a spatial target high frequency repetitive behavior recognition system, comprising:
an acquisition unit configured to acquire motion data of a plurality of spatial objects;
the extracting unit is used for extracting the approximate repeated track of each target from all the motion data according to a preset extracting rule;
the calculation unit is used for calculating deviation values between any two approximate repeated tracks of each target respectively and calculating the association degree of each space target according to the deviation values;
and the processing unit is used for determining the space target with the high-frequency repeated behaviors according to the relevance.
Another technical solution of the present invention for solving the above technical problems is as follows:
a storage medium, wherein instructions are stored in the storage medium, and when a computer reads the instructions, the computer is caused to execute the method for identifying the spatial target high-frequency repetitive behaviors according to the above technical solution.
Another technical solution of the present invention for solving the above technical problems is as follows:
a spatial target high frequency repetitive behavior recognition apparatus, comprising:
a memory for storing a computer program;
and the processor is used for executing the computer program and realizing the spatial target high-frequency repetitive behavior identification method in the technical scheme.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic flow chart provided by an embodiment of the method for identifying the high-frequency repetitive behaviors of the spatial target according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
As shown in fig. 1, a method for identifying a spatial target high-frequency repetitive behavior includes:
s1, acquiring motion data of a plurality of space targets;
s2, extracting the approximate repeated track of each target from all the motion data according to a preset extraction rule;
for example, the data samples including the target category, the start and stop time, etc. may be first narrowed down according to the parameters input by the user, and then the software calculates the approximation degree and the confidence degree between the target track data based on the lcs (changecommon Sub-Sequence) algorithm and the cluster analysis method, and if the confidence degree exceeds the threshold, the track is the approximate repeated track.
S3, respectively calculating deviation values between any two approximate repeated tracks of each target, and calculating the association degree of each space target according to the deviation values;
the deviation value may be a weighted distance deviation average.
And S4, determining the space target with high-frequency repeated behaviors according to the relevance.
The spatial target high-frequency behavior identification method based on the big data technology utilizes the big data distributed storage and processing technology, can efficiently identify the target high-frequency repeated behavior in mass spatial target data, realizes automatic identification of the spatial target high-frequency behavior, presents the result in a list form, supports manual intervention decision making, and has obvious advantages under the condition of TB and PB magnitude data.
Optionally, in some embodiments, before extracting the approximate repeated trajectory of each target from all the motion data according to a preset extraction rule, the method further includes:
and detecting whether all the motion data meet preset requirements, and if not, processing the non-conforming motion data.
Optionally, in some embodiments, detecting whether all the motion data meet a preset requirement specifically includes:
detecting whether incomplete data exists in all the motion data;
detecting whether repeated data exist in all the motion data;
and detecting whether meaningless data exists in all the motion data.
Optionally, in some embodiments, the processing the non-compliant motion data specifically includes:
and removing the incomplete data.
Optionally, in some embodiments, the processing the non-compliant motion data specifically includes:
when the repeated data is continuous echo data of the same target in a continuous pulse repetition period, extracting the repeated data according to a preset time interval;
when the duplicate data are correlated and another data is available from one data, only the source data is selected.
Optionally, in some embodiments, the processing the non-compliant motion data specifically includes:
the meaningless data is removed.
Optionally, in some embodiments, the degree of association is calculated according to the following formula:
the association degree is (deviation value-threshold)/association coefficient;
wherein the threshold value and the correlation coefficient are preset values.
The following description is given as an example.
The method comprises the following steps:
data is prepared.
The motion data of a plurality of space targets can be echo data of the targets detected by radar, the data is stored or imported into an Hbase database through a data import tool of software, and some rough descriptions are made on the data, such as record number, attribute number and the like.
And setting parameters.
Selecting a category of objects, such as two-dimensional objects or three-dimensional objects; selecting a data start time and an end time; selecting an analysis model, such as cluster analysis, time series analysis and the like; and selecting unnecessary parameters such as a data source, a noise processing mode, a confidence threshold value and the like.
The data start time and end time are for setting a time period for detecting the repetitive tracks.
And (4) preprocessing data.
Checking the data quality, including whether the data is complete, whether the data has errors, whether missing values exist and other problems;
selecting proper data according to the data quality, wherein the proper data comprises table selection, record selection and attribute selection;
performing simple statistical analysis on the data, such as distribution of key attributes and the like;
data cleaning, such as noise removal, missing value filling, and merging several data sets together by means of table join; the task of data cleansing and screening is to filter out undesirable data to compress storage capacity. The unsatisfactory data is mainly three major categories of incomplete data, repetitive data and meaningless data.
1) Incomplete data
This type of data primarily refers to the presence of data that should have missing or obscured information. Such as low signal-to-noise ratio data, lack of identification data for the target class, etc., for which the situation may be directly discarded.
2) Repeated data
There are two main cases of repeated data: one is continuous echo data of the same target in continuous pulse repetition cycles, which are basically unchanged and can be extracted at certain intervals according to actual requirements; another is that there is a class of data that is interrelated and from which another data can be obtained, such data may choose to use only the source data.
3) Meaningless data
Meaningless data is also a relative concept, meaning data that is meaningless for some application tasks, and among the spatial target data, mainly noise data when there is neither target nor interference, and background environmental data, which can be directly discarded.
And (6) data processing.
The method comprises the steps of extracting approximate repeated tracks of a target in a selected time period, wherein before a client accesses data, a Zookeeper needs to be accessed first, position information of a-ROOT-table is obtained, then the-ROOT-table is accessed, information of a META-table is obtained, then the META-table is accessed, the required specific position of a Region is found, and the data can be read from a corresponding Region server, so that the indexing efficiency of mass data is greatly improved, then a deviation value between each track is calculated in a Spark component, the association degree is given, and sequencing is carried out according to the repetition frequency.
For example, for the targets o1, o2, o3, …, on, each target has a plurality of tracks t1, t2, t3, …, tn, the data distance between the tracks is calculated to form a distance matrix, a group of data is divided into different clusters, the association relationship of similar behaviors is given according to a threshold value defined by a user, and the association degree is calculated, wherein the association degree is (the weight value of the track distance and the threshold value)/the association coefficient.
And (5) manual proofreading.
And aiming at the sorted list, giving other supplementary data to the top-ranked target, and indexing other intelligence data of the target, such as AIS, text data, image data, historical identification record data and the like, by utilizing Rowkey in Hbase to help a user to manually check, mark or eliminate invalid results.
It is understood that some or all of the alternative embodiments described above may be included in some embodiments.
In other embodiments of the present invention, there is also provided a spatial target high-frequency repetitive behavior recognition system, including:
an acquisition unit configured to acquire motion data of a plurality of spatial objects;
the extracting unit is used for extracting the approximate repeated track of each target from all the motion data according to a preset extracting rule;
the calculation unit is used for calculating deviation values between any two approximate repeated tracks of each target respectively and calculating the association degree of each space target according to the deviation values;
and the processing unit is used for determining the space target with the high-frequency repeated behaviors according to the relevance.
Optionally, in some embodiments, before extracting the approximate repeated trajectory of each target from all the motion data according to a preset extraction rule, the method further includes:
and detecting whether all the motion data meet preset requirements, and if not, processing the non-conforming motion data.
Optionally, in some embodiments, detecting whether all the motion data meet a preset requirement specifically includes:
detecting whether incomplete data exists in all the motion data;
detecting whether repeated data exist in all the motion data;
and detecting whether meaningless data exists in all the motion data.
Optionally, in some embodiments, the processing the non-compliant motion data specifically includes:
and removing the incomplete data.
Optionally, in some embodiments, the processing the non-compliant motion data specifically includes:
when the repeated data is continuous echo data of the same target in a continuous pulse repetition period, extracting the repeated data according to a preset time interval;
when the duplicate data are correlated and another data is available from one data, only the source data is selected.
Optionally, in some embodiments, the processing the non-compliant motion data specifically includes:
the meaningless data is removed.
Optionally, in some embodiments, the degree of association is calculated according to the following formula:
the association degree is (deviation value-threshold)/association coefficient;
wherein the threshold value and the correlation coefficient are preset values.
It is understood that some or all of the alternative embodiments described above may be included in some embodiments.
In another embodiment of the present invention, there is also provided a storage medium having instructions stored therein, which when read by a computer, cause the computer to execute the spatial target high-frequency repetitive behavior identification method according to any of the above embodiments.
In another embodiment of the present invention, there is also provided a spatial target high-frequency repetitive behavior recognition apparatus, including:
a memory for storing a computer program;
a processor configured to execute the computer program to implement the method for identifying high-frequency repetitive behaviors of a spatial target according to any of the embodiments.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A spatial target high-frequency repetitive behavior identification method is characterized by comprising the following steps:
acquiring motion data of a plurality of space targets;
extracting an approximate repeated track of each target from all the motion data according to a preset extraction rule;
respectively calculating a deviation value between any two approximate repeated tracks of each target, and calculating the association degree of each space target according to the deviation value;
and determining the space target with high-frequency repeated behaviors according to the relevance.
2. The method for identifying spatial target high-frequency repetitive behaviors according to claim 1, wherein before extracting the approximate repetitive trajectory of each target from all the motion data according to a preset extraction rule, the method further comprises:
and detecting whether all the motion data meet preset requirements, and if not, processing the non-conforming motion data.
3. The method for identifying spatial target high-frequency repetitive behaviors according to claim 2, wherein detecting whether all the motion data meet preset requirements specifically comprises:
detecting whether incomplete data exists in all the motion data;
detecting whether repeated data exist in all the motion data;
and detecting whether meaningless data exists in all the motion data.
4. The method for spatial target high-frequency repetitive behavior recognition according to claim 3, wherein the processing of the non-compliant motion data specifically comprises:
and removing the incomplete data.
5. The method for spatial target high-frequency repetitive behavior recognition according to claim 3, wherein the processing of the non-compliant motion data specifically comprises:
when the repeated data is continuous echo data of the same target in a continuous pulse repetition period, extracting the repeated data according to a preset time interval;
when the duplicate data are correlated and another data is available from one data, only the source data is selected.
6. The method for spatial target high-frequency repetitive behavior recognition according to claim 3, wherein the processing of the non-compliant motion data specifically comprises:
the meaningless data is removed.
7. The spatial target high-frequency repetitive behavior recognition method according to any one of claims 1 to 6, wherein the degree of association is calculated according to the following formula:
the association degree is (deviation value-threshold)/association coefficient;
wherein the threshold value and the correlation coefficient are preset values.
8. A spatial target high frequency repetitive behavior recognition system, comprising:
an acquisition unit configured to acquire motion data of a plurality of spatial objects;
the extracting unit is used for extracting the approximate repeated track of each target from all the motion data according to a preset extracting rule;
the calculation unit is used for calculating deviation values between any two approximate repeated tracks of each target respectively and calculating the association degree of each space target according to the deviation values;
and the processing unit is used for determining the space target with the high-frequency repeated behaviors according to the relevance.
9. A storage medium having stored therein instructions that, when read by a computer, cause the computer to execute the spatial target high-frequency repetitive behavior recognition method according to any one of claims 1 to 7.
10. An apparatus for identifying a high frequency repetitive behavior of a spatial object, comprising:
a memory for storing a computer program;
a processor for executing the computer program for implementing the method for spatial target high-frequency repetitive behavior recognition according to any of claims 1 to 7.
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|---|---|---|---|---|
| CN113986926A (en) * | 2021-10-27 | 2022-01-28 | 北京无线电测量研究所 | Method and system for preloading multidimensional data of space target |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102253375A (en) * | 2011-04-02 | 2011-11-23 | 海华电子企业(中国)有限公司 | Radar multi-target data interconnection method |
| CN103439697A (en) * | 2013-08-23 | 2013-12-11 | 西安电子科技大学 | Target detection method based on dynamic programming |
| CN103593361A (en) * | 2012-08-14 | 2014-02-19 | 中国科学院沈阳自动化研究所 | Movement space-time trajectory analysis method in sense network environment |
| CN104077601A (en) * | 2014-07-08 | 2014-10-01 | 中国航空无线电电子研究所 | Method for carrying out synthetic target recognition through information of different types |
| CN104598566A (en) * | 2015-01-09 | 2015-05-06 | 四川九洲电器集团有限责任公司 | Method and system for association rule analysis of moving object |
| CN104715154A (en) * | 2015-03-23 | 2015-06-17 | 西安交通大学 | Nuclear K-mean value track correlation method based on KMDL criteria |
| CN104931960A (en) * | 2015-05-25 | 2015-09-23 | 中国电子科技集团公司第十研究所 | Trend message and radar target state information whole-track data correlation method |
| CN105303012A (en) * | 2014-06-17 | 2016-02-03 | 衣晓 | Asynchronous equal data rate track correlation algorithm based on interval grey number |
| CN107133269A (en) * | 2017-04-01 | 2017-09-05 | 中国人民解放军国防科学技术大学 | Frequent location track generation method and device based on mobile target |
| CN107169301A (en) * | 2017-06-08 | 2017-09-15 | 中国人民解放军海军航空工程学院 | One kind is divided and rule Data Association |
| CN107861123A (en) * | 2017-10-24 | 2018-03-30 | 武汉大学 | A kind of through-wall radar is under complex environment to the method for multiple mobile object real-time tracking |
| CN109444897A (en) * | 2018-09-13 | 2019-03-08 | 中国船舶重工集团公司第七〇五研究所 | A kind of more gusts of Data Associations based on multiple features |
| CN109814080A (en) * | 2018-12-11 | 2019-05-28 | 长沙莫之比智能科技有限公司 | A kind of millimetre-wave radar target following and certainty factor algebra and its device |
| CN110033017A (en) * | 2019-02-27 | 2019-07-19 | 中国人民解放军空军工程大学 | A kind of more radar track substep Interconnected Fuzzy clustering algorithms |
-
2019
- 2019-12-06 CN CN201911243507.9A patent/CN110991539B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102253375A (en) * | 2011-04-02 | 2011-11-23 | 海华电子企业(中国)有限公司 | Radar multi-target data interconnection method |
| CN103593361A (en) * | 2012-08-14 | 2014-02-19 | 中国科学院沈阳自动化研究所 | Movement space-time trajectory analysis method in sense network environment |
| CN103439697A (en) * | 2013-08-23 | 2013-12-11 | 西安电子科技大学 | Target detection method based on dynamic programming |
| CN105303012A (en) * | 2014-06-17 | 2016-02-03 | 衣晓 | Asynchronous equal data rate track correlation algorithm based on interval grey number |
| CN104077601A (en) * | 2014-07-08 | 2014-10-01 | 中国航空无线电电子研究所 | Method for carrying out synthetic target recognition through information of different types |
| CN104598566A (en) * | 2015-01-09 | 2015-05-06 | 四川九洲电器集团有限责任公司 | Method and system for association rule analysis of moving object |
| CN104715154A (en) * | 2015-03-23 | 2015-06-17 | 西安交通大学 | Nuclear K-mean value track correlation method based on KMDL criteria |
| CN104931960A (en) * | 2015-05-25 | 2015-09-23 | 中国电子科技集团公司第十研究所 | Trend message and radar target state information whole-track data correlation method |
| CN107133269A (en) * | 2017-04-01 | 2017-09-05 | 中国人民解放军国防科学技术大学 | Frequent location track generation method and device based on mobile target |
| CN107169301A (en) * | 2017-06-08 | 2017-09-15 | 中国人民解放军海军航空工程学院 | One kind is divided and rule Data Association |
| CN107861123A (en) * | 2017-10-24 | 2018-03-30 | 武汉大学 | A kind of through-wall radar is under complex environment to the method for multiple mobile object real-time tracking |
| CN109444897A (en) * | 2018-09-13 | 2019-03-08 | 中国船舶重工集团公司第七〇五研究所 | A kind of more gusts of Data Associations based on multiple features |
| CN109814080A (en) * | 2018-12-11 | 2019-05-28 | 长沙莫之比智能科技有限公司 | A kind of millimetre-wave radar target following and certainty factor algebra and its device |
| CN110033017A (en) * | 2019-02-27 | 2019-07-19 | 中国人民解放军空军工程大学 | A kind of more radar track substep Interconnected Fuzzy clustering algorithms |
Non-Patent Citations (1)
| Title |
|---|
| 谷晓琳等: "基于ｍ－ｂｅｓｔ数据关联和小轨迹关联多目标跟踪算", 《系统工程与电子技术》, pages 1640 - 1646 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113986926A (en) * | 2021-10-27 | 2022-01-28 | 北京无线电测量研究所 | Method and system for preloading multidimensional data of space target |
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