CN112632463A - Target data association method and device based on multiple attributes - Google Patents
Target data association method and device based on multiple attributes Download PDFInfo
- Publication number
- CN112632463A CN112632463A CN202011527005.1A CN202011527005A CN112632463A CN 112632463 A CN112632463 A CN 112632463A CN 202011527005 A CN202011527005 A CN 202011527005A CN 112632463 A CN112632463 A CN 112632463A
- Authority
- CN
- China
- Prior art keywords
- target
- attribute
- data
- association
- target data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 15
- 239000011159 matrix material Substances 0.000 claims abstract description 23
- 238000005259 measurement Methods 0.000 claims abstract description 19
- 238000001514 detection method Methods 0.000 claims abstract description 11
- 238000013507 mapping Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000013506 data mapping Methods 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 230000000875 corresponding effect Effects 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 239000000969 carrier Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application belongs to the technical field of data processing, and particularly relates to a target data association method and device based on multiple attributes. The method comprises the steps of S1, acquiring at least 2 sensor detection data of different working principles to obtain multi-attribute target measurement data; step S2, mapping the measured data into a fuzzy set on a multi-attribute discourse domain; s3, establishing a membership degree relation between every two multi-attribute targets through a preset membership degree function; step S4, establishing a target data association matrix according to the membership degree relation; and step S5, resolving the optimal target matching association scheme and determining the target association relation. The method and the device solve the problem that the measured target data of the multiple sensors cannot correspond to each other, achieve correct association relation of the multiple attribute target data, are suitable for engineering practice, and are effective and feasible.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a target data association method and device based on multiple attributes.
Background
The target data generally comprises multiple attributes, the target data association relation of different detection devices is established, the premise and the basis of information fusion and target tracking are provided, and the accuracy and the rationality of subsequent information processing are directly influenced. Under the air complex situation environment, the airborne detection equipment has errors in multi-attribute measurement of target data, and the target data measured by the special-shaped sensor is unrelated, so that the detection efficiency of 1+1 > 2 cannot be exerted. In order to fully exert the measurement advantages of multiple sensors and solve the problem of corresponding ambiguity between target data measured by the multiple sensors, an association relation needs to be established for the target data with the same attribute detected by the special-shaped sensor, so that the detected target attribute data is more complete.
The non-relevance of the target data measured by the special-shaped sensor is mainly shown in that the targets measured by the special-shaped sensor cannot be effectively correlated, for example, two carriers for formation need to detect two fixed targets, the first carrier detects two targets AB, the second carrier detects two targets CD, and it cannot be determined whether the target a detected by the first carrier corresponds to the target C or the target D detected by the second carrier according to the detection data of distance and direction, and the existing correlation relationship is only the correlation through coordinates, so that the method has great limitation.
Disclosure of Invention
The invention aims to solve the problems, solve the problem that the multi-sensor measurement target data cannot be corresponded, and realize the correct association relation of the multi-attribute target data.
A first aspect of the present application provides a target data association method based on multiple attributes, which mainly includes:
s1, acquiring at least 2 sensor detection data of different working principles to obtain multi-attribute target measurement data;
step S2, mapping the measured data into a fuzzy set on a multi-attribute discourse domain;
s3, establishing a membership degree relation between every two multi-attribute targets through a preset membership degree function;
step S4, establishing a target data association matrix according to the membership degree relation;
and step S5, resolving the optimal target matching association scheme and determining the target association relation.
Preferably, in step S1, the sensors of different operating principles are provided on different drones.
Preferably, in step S3, the membership function includes:
where h denotes a characteristic attribute of the target data, αhCov (h) is the mean of the single feature attribute, and cov (h) is the variance of the single feature attribute.
Preferably, in step S5, the optimal target matching association scheme is solved by the hungarian algorithm.
A second aspect of the present application provides a target data association apparatus based on multiple attributes, including:
the target measurement data acquisition module is used for acquiring at least 2 sensor detections with different working principles to obtain multi-attribute target measurement data;
the data mapping module is used for mapping the measured data into a fuzzy set on the multi-attribute discourse domain;
the membership calculation module is used for establishing a membership relation between every two multi-attribute targets through a preset membership function;
the incidence matrix building module is used for building a target data incidence matrix according to the membership degree relation;
and the target incidence relation determining module is used for resolving the optimal target matching incidence scheme and determining the target incidence relation.
Preferably, in the target measurement data acquisition module, sensors of different working principles are arranged on different unmanned aerial vehicles.
Preferably, in the membership calculation module, the membership function includes:
where h denotes a characteristic attribute of the target data, αhCov (h) is the mean of the single feature attribute, and cov (h) is the variance of the single feature attribute.
Preferably, in the target association relation determining module, an optimal target matching association scheme is solved through a Hungarian algorithm.
The method and the device solve the problem that the measured target data of the multiple sensors cannot correspond to each other, achieve correct association relation of the multiple attribute target data, are suitable for engineering practice, and are effective and feasible.
Drawings
FIG. 1 is a flowchart of a multi-attribute-based target data association method according to the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all embodiments of the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application, and should not be construed as limiting the present application. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application. Embodiments of the present application will be described in detail below with reference to the drawings.
The general idea of the invention is as follows: a sensor (such as a radar and a light radar) for observing a target carries out target measurement according to a sensor coordinate system, measurement data have certain uncertainty and ambiguity, and the measurement data are mapped into an ambiguity set on a domain of discourse. Carrying out fuzzy membership degree analysis on the fuzzy set data, establishing a fuzzy incidence matrix, and solving the optimal combination distribution of the incidence matrix, namely, the target incidence result
In order to achieve the above object, a first aspect of the present invention provides a multi-attribute-based target data association and optimization algorithm, as shown in fig. 1, which mainly includes the following steps:
s1, acquiring at least 2 sensor detection data of different working principles to obtain multi-attribute target measurement data;
step S2, mapping the measured data into a fuzzy set on a multi-attribute discourse domain;
s3, establishing a membership degree relation between every two multi-attribute targets through a preset membership degree function;
step S4, establishing a target data association matrix according to the membership degree relation;
and step S5, resolving the optimal target matching association scheme and determining the target association relation.
The embodiments of the present invention are as follows:
firstly, detecting by at least 2 sensors with different working principles to obtain multi-attribute target measurement data;
the F1 machine and the F2 machine form a formation flying machine to detect a T1 target and a T2 target respectively.
An F1 machine detects parameters such as the distance d11, the direction r11, the speed v11 and the distance change rate a11 of a T1 target relative to a local machine, an F1 machine detects parameters such as the distance d12, the direction r12, the speed v12 and the distance change rate a12 of a T2 target relative to a local machine, an F2 machine detects parameters such as the distance d21, the direction r21, the speed v21 and the frequency spectrum p21 of a T1 target relative to a local machine, and an F2 machine detects parameters such as the distance d22, the direction r22, the speed v22 and the frequency spectrum p22 of a T2 target relative to a local machine.
For example:
F1T1＝{80.7，5°2′，305，1.2}；
F1T2＝{62.5，11°9′，270，1.8}；
F2T1＝{80.5，5°4′，310，0.7}；
F2T2＝{62.2，12°1′，275，1.2}；
wherein, F1T1 indicates that the F1 machine detects the T1 target, the same is as follows.
And step two, mapping the target data with the same attribute in the step one into a fuzzy set on a domain of discourse, wherein the fuzzy set comprises a distance d, a direction r and a speed v.
And step three, establishing a membership relation between every two targets for the fuzzy set in the step two through a preset membership function, and obtaining the following table.
TABLE 1 target membership matrix
| Degree of membership | Distance between two adjacent plates | Azimuth angle | Speed of rotation |
| F1T1×F2T1 | 0.9441 | 0.9978 | 0.5816 |
| F1T1×F2T2 | 0.9736 | 0.9698 | 0.5998 |
| F1T2×F2T1 | 0.9121 | 0.9961 | 0.5452 |
| F1T2×F2T2 | 0.9497 | 0.9590 | 0.6363 |
Wherein the membership function is:
in the formula, h represents the characteristic attributes of the target data, namely, the distance d, the direction r, the speed v, alphahCov (h) is the mean of the single feature attribute, and cov (h) is the variance of the single feature attribute.
Step four, establishing a target incidence matrix by utilizing the membership relation in the step three, and obtaining a table below;
TABLE 2 target Association matrix
| Association relation | F2T1 | F2T2 |
| F1T1 | 0.8726 | 0.8353 |
| F1T2 | 0.8371 | 0.8802 |
And step five, resolving an optimal target matching association scheme, namely a final target association mapping relation, by using the target association matrix in the step four.
In some optional embodiments, the optimal target matching association scheme is solved through a hungarian algorithm, the hungarian algorithm meets the condition that the minimum elements of each row (column) of the objective function matrix are respectively subtracted from the elements of each row (column) to obtain a reduction matrix, and then the optimal solution obtained by taking the reduction matrix as the objective function matrix is the same as the optimal solution of the original objective function matrix. By using the optimal solution property of the distribution problem, the original objective function matrix can be transformed into a new matrix containing a plurality of 0 elements, and the optimal solution is kept unchanged, as shown in table 3.
TABLE 3 target matching scheme
| Association relation | F2T1 | F2T2 |
| F1T1 | 0 | 1 |
| F1T2 | 1 | 0 |
In the hungarian algorithm, 1 except for 0 indicates that the related ranks have an association relationship, so that it can be known that a target T1 detected by the F1 plane is a target T2 detected by the F2 plane, and similarly, a target T2 detected by the F1 plane is a target T1 detected by the F2 plane, so that the target relationships detected by the two unmanned planes are associated.
The multi-attribute target data are analyzed, the problem that the multi-sensor measurement target data cannot be corresponded can be solved through a fuzzy membership function and an optimal solution distribution algorithm, and correct correlation of the multi-attribute target data is achieved.
A second aspect of the present application provides a target data association apparatus based on multiple attributes corresponding to the foregoing method, including:
the target measurement data acquisition module is used for acquiring at least 2 sensor detections with different working principles to obtain multi-attribute target measurement data;
the data mapping module is used for mapping the measured data into a fuzzy set on the multi-attribute discourse domain;
the membership calculation module is used for establishing a membership relation between every two multi-attribute targets through a preset membership function;
the incidence matrix building module is used for building a target data incidence matrix according to the membership degree relation;
and the target incidence relation determining module is used for resolving the optimal target matching incidence scheme and determining the target incidence relation.
In some optional embodiments, in the target metrology data acquisition module, sensors of different working principles are disposed on different drones.
In some optional embodiments, in the membership calculation module, the membership function includes:
where h denotes a characteristic attribute of the target data, αhCov (h) is the mean of the single feature attribute, and cov (h) is the variance of the single feature attribute.
In some optional embodiments, in the target association relation determining module, an optimal target matching association scheme is solved through a hungarian algorithm.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A target data association method based on multiple attributes is characterized by comprising the following steps:
s1, acquiring at least 2 sensor detection data of different working principles to obtain multi-attribute target measurement data;
step S2, mapping the measured data into a fuzzy set on a multi-attribute discourse domain;
s3, establishing a membership degree relation between every two multi-attribute targets through a preset membership degree function;
step S4, establishing a target data association matrix according to the membership degree relation;
and step S5, resolving the optimal target matching association scheme and determining the target association relation.
2. The multi-attribute based target data association method of claim 1, wherein in step S1, the sensors of different working principles are disposed on different drones.
3. The multi-attribute based target data association method of claim 1, wherein in step S3, the membership function comprises:
where h denotes a characteristic attribute of the target data, αhCov (h) is the mean of the single feature attribute, and cov (h) is the variance of the single feature attribute.
4. The multi-attribute based target data association method as claimed in claim 1, wherein in step S5, the optimal target matching association scheme is solved by Hungarian algorithm.
5. A multi-attribute based target data association apparatus, comprising:
the target measurement data acquisition module is used for acquiring at least 2 sensor detections with different working principles to obtain multi-attribute target measurement data;
the data mapping module is used for mapping the measured data into a fuzzy set on the multi-attribute discourse domain;
the membership calculation module is used for establishing a membership relation between every two multi-attribute targets through a preset membership function;
the incidence matrix building module is used for building a target data incidence matrix according to the membership degree relation;
and the target incidence relation determining module is used for resolving the optimal target matching incidence scheme and determining the target incidence relation.
6. The multi-attribute based target data association apparatus of claim 5, wherein in the target metrology data acquisition module, sensors of different working principles are disposed on different drones.
7. The multi-attribute based target data correlation apparatus of claim 5, wherein in the membership calculation module, the membership function comprises:
where h denotes a characteristic attribute of the target data, αhCov (h) is the mean of the single feature attribute, and cov (h) is the variance of the single feature attribute.
8. The multi-attribute-based target data association device as claimed in claim 5, wherein in the target association relation determination module, an optimal target matching association scheme is solved through Hungarian algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011527005.1A CN112632463B (en) | 2020-12-22 | 2020-12-22 | Multi-attribute-based target data association method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011527005.1A CN112632463B (en) | 2020-12-22 | 2020-12-22 | Multi-attribute-based target data association method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112632463A true CN112632463A (en) | 2021-04-09 |
| CN112632463B CN112632463B (en) | 2024-06-11 |
Family
ID=75321557
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011527005.1A Active CN112632463B (en) | 2020-12-22 | 2020-12-22 | Multi-attribute-based target data association method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112632463B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040078125A1 (en) * | 2002-09-30 | 2004-04-22 | U.S.A. As Represented By The Administrator Of The National Aeronautics And Space Administration | Autonomous health monitoring system |
| US7692573B1 (en) * | 2008-07-01 | 2010-04-06 | The United States Of America As Represented By The Secretary Of The Navy | System and method for classification of multiple source sensor measurements, reports, or target tracks and association with uniquely identified candidate targets |
| 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 |
| CN105548983A (en) * | 2015-12-02 | 2016-05-04 | 中国人民解放军军械工程学院 | Radar networking identification target method |
| WO2017185688A1 (en) * | 2016-04-26 | 2017-11-02 | 深圳大学 | Method and apparatus for tracking on-line target |
| CN109190647A (en) * | 2018-06-26 | 2019-01-11 | 中国电子科技集团公司第二十九研究所 | A kind of active passive data fusion method |
| CN110888126A (en) * | 2019-12-06 | 2020-03-17 | 西北工业大学 | Unmanned ship information perception system data comprehensive processing method based on multi-source sensor |
-
2020
- 2020-12-22 CN CN202011527005.1A patent/CN112632463B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040078125A1 (en) * | 2002-09-30 | 2004-04-22 | U.S.A. As Represented By The Administrator Of The National Aeronautics And Space Administration | Autonomous health monitoring system |
| US7692573B1 (en) * | 2008-07-01 | 2010-04-06 | The United States Of America As Represented By The Secretary Of The Navy | System and method for classification of multiple source sensor measurements, reports, or target tracks and association with uniquely identified candidate targets |
| 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 |
| CN105548983A (en) * | 2015-12-02 | 2016-05-04 | 中国人民解放军军械工程学院 | Radar networking identification target method |
| WO2017185688A1 (en) * | 2016-04-26 | 2017-11-02 | 深圳大学 | Method and apparatus for tracking on-line target |
| CN109190647A (en) * | 2018-06-26 | 2019-01-11 | 中国电子科技集团公司第二十九研究所 | A kind of active passive data fusion method |
| CN110888126A (en) * | 2019-12-06 | 2020-03-17 | 西北工业大学 | Unmanned ship information perception system data comprehensive processing method based on multi-source sensor |
Non-Patent Citations (8)
| Title |
|---|
| AZIZ, A.M.: "Fuzzy track-to track association and track fusion approach in distributed multi-sensor multi-target multiple-attributive environment", 《SIGNAL PROCESSING》 * |
| 倪忠德;马宝华;: "海上编队多辐射源目标数据关联模型及仿真分析", 探测与控制学报, no. 04, pages 24 - 26 * |
| 关欣;孙祥威;曹昕莹;: "基于模糊聚类的多传感器特征关联算法研究", 中国电子科学研究院学报, no. 04, pages 363 - 367 * |
| 尉强;刘忠;: "一种粗-精结合的航天侦察航迹关联算法", 雷达科学与技术, no. 01, pages 29 - 34 * |
| 程进军, 肖明清, 谢希权: "基于雷达和红外传感器的目标检测方法", 传感器技术, no. 08, pages 54 - 56 * |
| 肖文凯;肖圣龙;林华;石章松;: "基于匈牙利求解的航迹关联算法研究", 舰船电子工程, no. 10, pages 132 - 134 * |
| 董红波;刘进忙;张春梅;杨少春;: "机载异类传感器模糊航迹关联算法的研究", 传感器与微系统, no. 07, pages 8 - 13 * |
| 马敏;张承志;薛萍冰;: "一种无源雷达的数据关联技术", 雷达科学与技术, no. 01, pages 49 - 53 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112632463B (en) | 2024-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111983600B (en) | Target detection method, device and equipment | |
| US4806936A (en) | Method of determining the position of multiple targets using bearing-only sensors | |
| CN108614980A (en) | A kind of the dynamic object positioning system and method for combining RFID and laser intelligence | |
| CN106772218B (en) | Localization method is classified based on mobile RFID reader warehouse package plan-position | |
| CN105866636B (en) | Transformer substation positioning method based on time difference positioning | |
| CN111046877A (en) | Millimeter wave image suspicious article detection method and system | |
| CN110738275A (en) | UT-PHD-based multi-sensor sequential fusion tracking method | |
| CN111208484B (en) | Main lobe dense false target removing method based on angle information | |
| CN112632463B (en) | Multi-attribute-based target data association method and device | |
| Alexiev et al. | A Hough transform track initiation algorithm for multiple passive sensors | |
| CN106371087B (en) | A kind of space lattice multichannel measurement information method for registering based on extremum search | |
| CN110196409B (en) | Anti-difference step track correlation method based on relative distance of regional set | |
| CN109459723B (en) | Pure orientation passive positioning method based on meta-heuristic algorithm | |
| CN113390406B (en) | Multi-target data association and positioning method based on passive multi-sensor system | |
| CN115144851A (en) | Multi-station positioning tracking method based on pitch angle | |
| US6466158B2 (en) | Identifying closely clustered moving targets | |
| JP3298397B2 (en) | Multi-target tracking device | |
| Bulychev et al. | Optimization of the cluster-variant method of constructing a multi-position direction finding system for conditions of a priori uncertainty | |
| CN107945240B (en) | Target imaging method | |
| US11994580B2 (en) | Electronic device and method for positioning, imaging, detecting, posture discrimination, and shape discrimination | |
| Dai et al. | Phantom excludability algorithm based on speciality of sensor and target | |
| CN113468242B (en) | Multi-node track quick association method and system based on multi-dimensional order matching | |
| CN108279409A (en) | A kind of single moving observer angle measurement location information passive location method | |
| CN113484855B (en) | Single-station positioning method of external radiation source | |
| Guo et al. | OTHR Multipath Tracking with Correlated Virtual Ionospheric Heights |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |