CN116520307A - Radar cooperative detection system and method based on target tracking - Google Patents

Radar cooperative detection system and method based on target tracking Download PDF

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CN116520307A
CN116520307A CN202310133634.3A CN202310133634A CN116520307A CN 116520307 A CN116520307 A CN 116520307A CN 202310133634 A CN202310133634 A CN 202310133634A CN 116520307 A CN116520307 A CN 116520307A
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target
radar
association
characteristic
judgment
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CN116520307B (en
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李继锋
陈铭
李晃
朱文明
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Yangzhou Yuan Electronic Technology Co Ltd
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Yangzhou Yuan Electronic Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/40—Means for monitoring or calibrating
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention relates to the technical field of target detection of war radar, in particular to a radar cooperative detection system and method based on target tracking, comprising the following steps: extracting characteristic attribute items from each node radar; the method comprises the steps of respectively capturing target characteristic parameter sets transmitted by all node radars in networking radars to a fusion center, capturing and extracting target judgment association structures with target judgment association relations by combining characteristic attribute items corresponding to all the node radars in each history target tracking detection record; calculating a correlation index for the target decision correlation structure; screening out a characteristic target judgment association structure; and feeding the characteristic target judgment association structure back to a fusion center of the networking radar, and prompting the fusion center to select and reject the judgment parameters before information fusion of the target characteristic parameter set transmitted from the related node radar when a judgment scene meeting the occurrence of the characteristic target judgment association structure appears.

Description

Radar cooperative detection system and method based on target tracking
Technical Field
The invention relates to the technical field of target detection of war radars, in particular to a radar collaborative detection system and method based on target tracking.
Background
With the development of science, technology and weaponry, the operational environment facing modern radars is more and more complex; it is difficult to continuously detect and track modern flying objects by means of a single type of single-part radar; the advantage of radar formation obtained by networking after tactical configuration aiming at the characteristics of each radar is incomparable with that of a single radar in terms of some performances;
the networking radar system is an important means widely applied to the current electronic countermeasure, is not a simple radar combination, but an organic whole formed by networking technology by single-station radars which are different in frequency bands, different in systems and different in polarization modes and can work independently; the information of each radar in the network is collected and transmitted in a network mode, and comprehensive processing, control and management are carried out through a central station, so that detection, positioning and tracking tasks in the whole coverage range are completed.
Disclosure of Invention
The invention aims to provide a radar collaborative detection system and a radar collaborative detection method based on target tracking, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a radar collaborative detection method based on target tracking includes:
step S100: extracting characteristic attribute items from each node radar based on index value deviation conditions of each node radar and the whole networking radar among various performance indexes in the networking radar;
step S200: information collection is carried out on each historical target tracking detection record of the networking radar; in each historical target tracking detection record, respectively capturing a target characteristic parameter set transmitted by each node radar in the networking radar to a fusion center, and capturing and extracting a target judgment association structure with a target judgment association relationship from each historical target tracking detection record by combining a characteristic attribute item corresponding to each node radar in each historical target tracking detection record;
step S300: calculating a correlation index of a target judgment correlation structure obtained by capturing and extracting from each historical target tracking detection record; screening out a characteristic target judgment association structure based on the association index;
step S400: and feeding back all the screened characteristic target judgment association structures to a fusion center of the networking radar, and prompting the fusion center to select and reject the judgment parameters before information fusion of the target characteristic parameter set transmitted from the related node radar when a judgment scene meeting the occurrence of the characteristic target judgment association structures appears.
Further, step S100 includes:
step S101: collecting information presented by the whole networking radar on each performance index, and collecting to obtain a performance index set { [ a ] of the whole networking radar 1 ,Ya 1 ],[a 2 ,Ya 2 ],…,[a n ,Ya n ]-a }; the method comprises the steps of respectively collecting information presented by each node radar in the networking radar on each performance index, and respectively collecting to obtain a performance index set { [ a ] of each node radar 1 ,X i a 1 ],[a 2 ,X i a 2 ],…,[a n ,X i a n ]-a }; wherein a is 1 、a 2 、…、a n Respectively representing the 1 st, 2 nd, … th and n th performance indexes; wherein Ya is 1 、Ya 2 、…、Ya n Respectively showing the whole of the networking radar in a 1 、a 2 、…、a n The index value presented above; wherein X is i a 1 、X i a 2 、…、X i a n Respectively representing the ith node radar in the networking radar as a 1 、a 2 、…、a n The index value presented above;
step S102: calculating the performance index deviation value presented on the same performance index with the whole networking radar for each node radar in the networking radar respectively, D k [a j ]＝Ya j -X k a j The method comprises the steps of carrying out a first treatment on the surface of the Wherein D is k [a j ]Representing the integral j performance index a of the kth node radar and the networking radar in the networking radar j Index deviation values present thereon; ya j Representing the j-th performance index a of the whole networking radar j The index value presented above; x is X k a j Representing the performance index a of the kth node radar in the networking radar at the jth item j The index value presented above;
step S103: sequentially screening all target performance index items with performance index deviation values larger than 0 and larger than a deviation threshold value from each node radar in the networking radar; taking the target performance index item as a characteristic attribute item of each node radar; respectively capturing corresponding characteristic attribute items for each node radar in the networking radar;
because a plurality of radars can be improved to a certain extent on each performance index after tactical configuration and networking, the index value displayed on a certain performance index by radar formation is generally influenced by the specific index value distribution of all the node radars in the formation on the performance index, and the upper limit which can be reached by the networking radars on the performance index is determined to a certain extent by the node radars with the worst index value on the performance index.
Further, step S200 includes:
step S201: in each historical target tracking detection record, a target characteristic parameter set transmitted by each node radar in the networking radar to the fusion center is set as a first target characteristic parameter set, and an actual target characteristic parameter set corresponding to each historical target tracking detection record is set as a second target characteristic parameter set; respectively comparing the deviation between each first target characteristic parameter set and the corresponding second target characteristic parameter set in each historical target tracking detection record, and screening out characteristic parameter items with the deviation value larger than a deviation threshold value between the first target characteristic parameter set and the second target characteristic parameter set;
step S202: if the first target characteristic parameter set captured in a certain historical target tracking detection record comprises { A } 1 ,A 2 ,…,A g -a }; wherein A is 1 ,A 2 ,…,A g Respectively represent the first of the networking radars1. 2, …, g target characteristic parameter sets transmitted to a fusion center by node radars; a second target characteristic parameter set in a certain historical target tracking detection record is B; let { A } 1 ,A 2 ,…,A g Any first set of target feature parameters A within } r The characteristic parameter items obtained by screening after deviation comparison with B comprise { F 1 ,F 2 ,…,F z -a }; wherein F is 1 ,F 2 ,…,F z Respectively represent A r 1 st, 2 nd, … th and z th characteristic parameter items with the deviation value larger than the deviation threshold value;
step S203: capturing and arbitrary first target feature parameter set A r The corresponding node radar extracts the characteristic attribute items corresponding to the node radar and comprises { U } 1 ,U 2 ,…,U p -a }; wherein U is 1 ,U 2 ,…,U p Respectively represent and arbitrary first target characteristic parameter set A r 1 st, 2 nd, … th and p th characteristic attributes of the corresponding node radars; sequentially { U } 1 ,U 2 ,…,U p Characteristic attributes within { F }, respectively 1 ,F 2 ,…,F z Establishing a target judgment association relation among all characteristic parameter items in the sequence, and constructing a plurality of target judgment association structures: u (U) e →F h The method comprises the steps of carrying out a first treatment on the surface of the Wherein F is h ∈{F 1 ,F 2 ,…,F z }；
Step S204: step S202-step S203 are looped to capture and aggregate the target decision association structure present in each historical target tracking detection record.
Further, step S300 includes:
step S301: setting a target judgment association structure with the characteristic attribute items being identical with the characteristic parameter items as a target judgment association structure; classifying and sorting the target judgment association structures captured and extracted from all the historical target tracking and detecting records;
step S302: calculating a first association index for each target decision association structure:wherein w1 is as followsShowing the total number of occurrences of various target decision association structures; m1 represents the total number of target decision association structures captured and extracted from all historical target tracking detection records;
step S303: calculating a second association index for each target decision association structure:g represents the total number of the node radars involved in the construction of various target judgment association structures; m2 represents the total number of node radars existing in the networking radars;
step S304: calculating comprehensive association indexes mu=ρ for various target decision association structures 1 ＊ρ 2 The method comprises the steps of carrying out a first treatment on the surface of the And judging the target judgment association structure with the comprehensive association index larger than the threshold value of the comprehensive association index as a characteristic target judgment association structure.
Further, step S400 includes:
step S401: feeding back all the screened characteristic target judgment association structures to a fusion center of the networking radar; in each target detection task, the operation fusion center respectively performs parameter value comparison on each characteristic parameter item in a target characteristic parameter set transmitted from all node radars; calculating the cumulative similarity of the parameter values of the node radars and other node radars on each characteristic parameter item: ω=θ 1 ＊θ 2 ＊…＊θ d The method comprises the steps of carrying out a first treatment on the surface of the Wherein θ 1 、θ 2 、…、θ d The similarity of the parameter values of each node radar and other 1 st, 2 nd, … nd and d node radars on a certain characteristic parameter item is respectively expressed;
step S402: when the accumulated similarity of the parameter value of a certain node radar on a certain characteristic parameter item is smaller than an accumulated similarity threshold value, and the characteristic attribute item belonging to a characteristic target judgment association structure exists in all characteristic attribute items forming the characteristic target judgment association structure with the certain characteristic parameter item, judging that a judgment scene of the characteristic target judgment association structure is met at the moment, prompting a fusion center to remove the parameter value of the corresponding certain characteristic parameter item from a target characteristic parameter set transmitted by the certain node radar, and then fusing information between the target characteristic parameter set transmitted by the certain node radar and a target characteristic parameter set transmitted by other node radars.
In the above steps, the process of judging whether the judging scene of the feature target judging association structure is met is to judge whether the situation that the detection of a certain node radar on a certain feature parameter item has larger deviation due to the performance of the certain node radar and the distribution feature of the target occurs at present, and the data situation is early-warned or eliminated before data fusion is carried out, so that the judging accuracy of the radar information fusion center can be effectively improved.
The radar collaborative detection system comprises a characteristic attribute item extraction management module, a target judgment association relation establishment management module, a target judgment association structure construction module, a characteristic target judgment association structure screening management module and a fusion center management module;
the characteristic attribute item extraction management module is used for extracting characteristic attribute items from each node radar according to the index value deviation condition of each node radar and the whole networking radar in the networking radar among each performance index;
the target judgment association relation establishment management module is used for collecting information of each historical target tracking detection record of the networking radar; in each historical target tracking detection record, respectively capturing a target characteristic parameter set transmitted by each node radar in the networking radar to a fusion center, and capturing a characteristic attribute item and a characteristic parameter item with a target judgment association relation from each historical target tracking detection record by combining a characteristic attribute item corresponding to each node radar in each historical target tracking detection record;
the target judgment association structure construction module is used for receiving the data in the target judgment association relation construction management module and constructing a target judgment association structure between the characteristic attribute items and the characteristic parameter items with the target judgment association relation;
the characteristic target judgment association structure screening management module is used for receiving the data in the target judgment association structure construction module, calculating association indexes for each target judgment association structure and screening out characteristic target judgment association structures based on the association indexes;
the fusion center management module is used for receiving the data in the characteristic target judgment association structure screening management module, feeding back all the screened characteristic target judgment association structures to the fusion center of the networking radar, and prompting the fusion center to select and reject the judgment parameters before information fusion of the target characteristic parameter set transmitted from the related node radar when a judgment scene meeting the occurrence of the characteristic target judgment association structure appears.
Further, the characteristic attribute item extraction management module comprises a performance index deviation value calculation unit and a characteristic attribute item capturing and extracting unit;
the performance index deviation value calculation unit is used for calculating the performance index deviation value which is presented on the same performance index as the whole networking radar for each node radar in the networking radar respectively;
the characteristic attribute item capturing and extracting unit is used for receiving the data in the performance index deviation value calculating unit and extracting characteristic attribute items for the radar of each node.
Further, the characteristic target judgment association structure screening management module comprises an association index calculation unit and a characteristic target judgment association structure screening unit;
the association index calculation unit is used for receiving the data in the target judgment association structure construction module and calculating association indexes for each target judgment association structure;
and the characteristic target judgment association structure screening unit is used for receiving the data in the association index calculation unit and screening out the characteristic target judgment association structure based on the association index.
Compared with the prior art, the invention has the following beneficial effects: the method is suitable for carrying out performance attribute analysis on the node radars of the layout construction networking radar by adopting the information fusion and networking radars, namely, capturing the characteristic attribute of each node radar based on the deviation detection relation presented between the self performance of each node radar and the distribution characteristic of the target, constructing a judgment association structure between the characteristic attribute and the characteristic parameter item of the detection target, providing help for the fusion center before carrying out information fusion, and assisting the fusion center in finally judging that the obtained target detection result is as accurate as possible.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a radar cooperative detection system based on target tracking according to the present invention;
fig. 2 is a schematic flow chart of a radar collaborative detection method based on target tracking according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: a radar collaborative detection method based on target tracking includes:
step S100: extracting characteristic attribute items from each node radar based on index value deviation conditions of each node radar and the whole networking radar among various performance indexes in the networking radar;
wherein, step S100 includes:
step S101: collecting information presented by the whole networking radar on each performance index, and collecting to obtain a performance index set { [ a ] of the whole networking radar 1 ,Ya 1 ],[a 2 ,Ya 2 ],…,[a n ,Ya n ]-a }; the information presented by each node radar in the networking radar on each performance index is respectively collected, and each section is respectively collectedPerformance index set of Point Radar { [ a ] 1 ,X i a 1 ],[a 2 ,X i a 2 ],…,[a n ,X i a n ]-a }; wherein a is 1 、a 2 、…、a n Respectively representing the 1 st, 2 nd, … th and n th performance indexes; wherein Ya is 1 、Ya 2 、…、Ya n Respectively showing the whole of the networking radar in a 1 、a 2 、…、a n The index value presented above; wherein X is i a 1 、X i a 2 、…、X i a n Respectively representing the ith node radar in the networking radar as a 1 、a 2 、…、a n The index value presented above;
step S102: calculating the performance index deviation value presented on the same performance index with the whole networking radar for each node radar in the networking radar respectively, D k [a j ]＝Ya j -X k a j The method comprises the steps of carrying out a first treatment on the surface of the Wherein D is k [a j ]Representing the integral j performance index a of the kth node radar and the networking radar in the networking radar j Index deviation values present thereon; ya j Representing the j-th performance index a of the whole networking radar j The index value presented above; x is X k a j Representing the performance index a of the kth node radar in the networking radar at the jth item j The index value presented above;
step S103: sequentially screening all target performance index items with performance index deviation values larger than 0 and larger than a deviation threshold value from each node radar in the networking radar; taking the target performance index item as a characteristic attribute item of each node radar; respectively capturing corresponding characteristic attribute items for each node radar in the networking radar;
step S200: information collection is carried out on each historical target tracking detection record of the networking radar; in each historical target tracking detection record, respectively capturing a target characteristic parameter set transmitted by each node radar in the networking radar to a fusion center, and capturing and extracting a target judgment association structure with a target judgment association relationship from each historical target tracking detection record by combining a characteristic attribute item corresponding to each node radar in each historical target tracking detection record;
wherein, step S200 includes:
step S201: in each historical target tracking detection record, a target characteristic parameter set transmitted by each node radar in the networking radar to the fusion center is set as a first target characteristic parameter set, and an actual target characteristic parameter set corresponding to each historical target tracking detection record is set as a second target characteristic parameter set; respectively comparing the deviation between each first target characteristic parameter set and the corresponding second target characteristic parameter set in each historical target tracking detection record, and screening out characteristic parameter items with the deviation value larger than a deviation threshold value between the first target characteristic parameter set and the second target characteristic parameter set;
step S202: if the first target characteristic parameter set captured in a certain historical target tracking detection record comprises { A } 1 ,A 2 ,…,A g -a }; wherein A is 1 ,A 2 ,…,A g Respectively representing target characteristic parameter sets transmitted to a fusion center by 1 st, 2 nd, … th and g th node radars in the networking radars; a second target characteristic parameter set in a certain historical target tracking detection record is B; let { A } 1 ,A 2 ,…,A g Any first set of target feature parameters A within } r The characteristic parameter items obtained by screening after deviation comparison with B comprise { F 1 ,F 2 ,…,F z -a }; wherein F is 1 ,F 2 ,…,F z Respectively represent A r 1 st, 2 nd, … th and z th characteristic parameter items with the deviation value larger than the deviation threshold value;
step S203: capturing and arbitrary first target feature parameter set A r The corresponding node radar extracts the characteristic attribute items corresponding to the node radar and comprises { U } 1 ,U 2 ,…,U p -a }; wherein U is 1 ,U 2 ,…,U p Respectively represent and arbitrary first target characteristic parameter set A r 1 st, 2 nd, … th and p th characteristic attributes of the corresponding node radars; sequentially { U } 1 ,U 2 ,…,U p Characteristic attributes within { F }, respectively 1 ,F 2 ,…,F z Establishing a target judgment association relation among all characteristic parameter items in the sequence, and constructing a plurality of target judgment association structures: u (U) e →F h The method comprises the steps of carrying out a first treatment on the surface of the Wherein F is h ∈{F 1 ,F 2 ,…,F z }；
Step S204: step S202-step S203 are circulated, and target judgment association structures existing in each historical target tracking detection record are captured and collected;
step S300: calculating a correlation index of a target judgment correlation structure obtained by capturing and extracting from each historical target tracking detection record; screening out a characteristic target judgment association structure based on the association index;
wherein, step S300 includes:
step S301: setting a target judgment association structure with the characteristic attribute items being identical with the characteristic parameter items as a target judgment association structure; classifying and sorting the target judgment association structures captured and extracted from all the historical target tracking and detecting records;
step S302: calculating a first association index for each target decision association structure:wherein w1 represents the total number of occurrences of various target decision association structures; m1 represents the total number of target decision association structures captured and extracted from all historical target tracking detection records;
step S303: calculating a second association index for each target decision association structure:g represents the total number of the node radars involved in the construction of various target judgment association structures; m2 represents the total number of node radars existing in the networking radars;
for example, the networking radars include a node radar 1, a node radar 2, a node radar 3, and a node radar 4;
wherein:
the node radar 1 has a 1 st characteristic attribute item, a 2 nd characteristic attribute item and a 3 rd characteristic attribute item;
the node radar 2 has a 1 st characteristic attribute item and a 2 nd characteristic attribute item;
the node radar 3 gathers the 1 st characteristic attribute item and the 2 nd characteristic attribute item;
the node radar 4 gathers the 3 rd characteristic attribute items and the 4 th characteristic attribute items;
in the 1 st historical target tracking detection record, capturing a target judgment association relation between a 1 st characteristic attribute item and a characteristic parameter item E of the node radar 1, and constructing a 1 st characteristic attribute item-characteristic parameter item E of a target judgment association structure; the 1 st characteristic attribute item and the characteristic parameter item E of the node radar 2 are captured to meet the target judgment association relation, and the 1 st characteristic attribute item-characteristic parameter item E of the target judgment association structure is constructed;
meanwhile, in the 2 nd historical target tracking detection record, capturing a target judgment association relation between a 1 st characteristic attribute item and a characteristic parameter item E of the node radar 3, and constructing a 1 st characteristic attribute item-characteristic parameter item E of a target judgment association structure;
in summary, regarding the 1 st characteristic attribute item- & gt characteristic parameter item E, the total number of extracted node radars is 3;
step S304: calculating comprehensive association indexes mu=ρ for various target decision association structures 1 ＊ρ 2 The method comprises the steps of carrying out a first treatment on the surface of the Judging the target judgment association structure with the comprehensive association index larger than the threshold value of the comprehensive association index as a characteristic target judgment association structure;
step S400: feeding back all the screened characteristic target judgment association structures to a fusion center of the networking radar, and prompting the fusion center to perform judgment parameter choice before information fusion on a target characteristic parameter set transmitted from the related node radar when a judgment scene meeting the occurrence of the characteristic target judgment association structures appears;
wherein, step S400 includes:
step S401: feeding back all the screened characteristic target judgment association structures to a fusion center of the networking radar; in each target detection task, the operation is fusedThe center performs parameter value comparison on each characteristic parameter item in a target characteristic parameter set transmitted from all node radars; calculating the cumulative similarity of the parameter values of the node radars and other node radars on each characteristic parameter item: ω=θ 1 ＊θ 2 ＊…＊θ d The method comprises the steps of carrying out a first treatment on the surface of the Wherein θ 1 、θ 2 、…、θ d The similarity of the parameter values of each node radar and other 1 st, 2 nd, … nd and d node radars on a certain characteristic parameter item is respectively expressed;
step S402: when the accumulated similarity of the parameter value of a certain node radar on a certain characteristic parameter item is smaller than an accumulated similarity threshold value, and the characteristic attribute item belonging to a characteristic target judgment association structure exists in all characteristic attribute items forming the characteristic target judgment association structure with the certain characteristic parameter item, judging that a judgment scene of the characteristic target judgment association structure is met at the moment, prompting a fusion center to remove the parameter value of the corresponding certain characteristic parameter item from a target characteristic parameter set transmitted by the certain node radar, and then fusing information between the target characteristic parameter set transmitted by the certain node radar and a target characteristic parameter set transmitted by other node radars.
The radar collaborative detection system comprises a characteristic attribute item extraction management module, a target judgment association relation establishment management module, a target judgment association structure construction module, a characteristic target judgment association structure screening management module and a fusion center management module;
the characteristic attribute item extraction management module is used for extracting characteristic attribute items from each node radar according to the index value deviation condition of each node radar and the whole networking radar in the networking radar among each performance index;
the characteristic attribute item extraction management module comprises a performance index deviation value calculation unit and a characteristic attribute item capturing and extracting unit;
the performance index deviation value calculation unit is used for calculating the performance index deviation value which is presented on the same performance index as the whole networking radar for each node radar in the networking radar respectively;
the characteristic attribute item capturing and extracting unit is used for receiving the data in the performance index deviation value calculating unit and extracting characteristic attribute items for the radars of all nodes;
the target judgment association relation establishment management module is used for collecting information of each historical target tracking detection record of the networking radar; in each historical target tracking detection record, respectively capturing a target characteristic parameter set transmitted by each node radar in the networking radar to a fusion center, and capturing a characteristic attribute item and a characteristic parameter item with a target judgment association relation from each historical target tracking detection record by combining a characteristic attribute item corresponding to each node radar in each historical target tracking detection record;
the target judgment association structure construction module is used for receiving the data in the target judgment association relation construction management module and constructing a target judgment association structure between the characteristic attribute items and the characteristic parameter items with the target judgment association relation;
the characteristic target judgment association structure screening management module is used for receiving the data in the target judgment association structure construction module, calculating association indexes for each target judgment association structure and screening out characteristic target judgment association structures based on the association indexes;
the characteristic target judgment association structure screening management module comprises an association index calculation unit and a characteristic target judgment association structure screening unit;
the association index calculation unit is used for receiving the data in the target judgment association structure construction module and calculating association indexes for each target judgment association structure;
the characteristic target judgment association structure screening unit is used for receiving the data in the association index calculation unit and screening out a characteristic target judgment association structure based on the association index;
the fusion center management module is used for receiving the data in the characteristic target judgment association structure screening management module, feeding back all the screened characteristic target judgment association structures to the fusion center of the networking radar, and prompting the fusion center to select and reject the judgment parameters before information fusion of the target characteristic parameter set transmitted from the related node radar when a judgment scene meeting the occurrence of the characteristic target judgment association structure appears.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A radar collaborative detection method based on target tracking, the method comprising:
step S100: extracting characteristic attribute items from each node radar based on index value deviation conditions of each node radar and the whole networking radar in the networking radar among various performance indexes;
step S200: information collection is carried out on each historical target tracking detection record of the networking radar; in each historical target tracking detection record, respectively capturing a target characteristic parameter set transmitted by each node radar in the networking radar to a fusion center, and capturing and extracting a target judgment association structure with a target judgment association relationship from each historical target tracking detection record by combining a characteristic attribute item corresponding to each node radar in each historical target tracking detection record;
step S300: calculating a correlation index of a target judgment correlation structure obtained by capturing and extracting from each historical target tracking detection record; screening out a characteristic target judgment association structure based on the association index;
step S400: and feeding back all the screened characteristic target judgment association structures to a fusion center of the networking radar, and prompting the fusion center to select and reject the judgment parameters before information fusion of the target characteristic parameter set transmitted from the related node radar when a judgment scene meeting the occurrence of the characteristic target judgment association structures appears.
2. The radar collaborative detection method according to claim 1, wherein the step S100 includes:
step S101: collecting information presented by the whole networking radar on each performance index, and collecting to obtain a whole performance index set of the networking radarRespectively collecting information presented by each node radar in the networking radar on each performance index, and respectively collecting to obtain a performance index set { [ a ] of each node radar 1 ,X i a 1 ],[a 2 ,X i a 2 ],…,[a n ,X i a n ]-a }; wherein a is 1 、a 2 、…、a n Respectively representing the 1 st, 2 nd, … th and n th performance indexes; wherein,,respectively showing the whole of the networking radar in a 1 、a 2 、…、a n The index value presented above; wherein X is i a 1 、X i a 2 、…、X i a n Respectively representing the ith node radar in the networking radar as a 1 、a 2 、…、a n The index value presented above;
step S102: calculating the performance index deviation value presented on the same performance index with the whole networking radar for each node radar in the networking radar respectively, D k [a j ]＝Ya j -X k a j The method comprises the steps of carrying out a first treatment on the surface of the Wherein D is k [a j ]Representing the integral j performance index a of the kth node radar and the networking radar in the networking radar j Index deviation values present thereon; ya j Representing the j-th performance index a of the whole networking radar j The index value presented above; x is X k a j Representing the performance index a of the kth node radar in the networking radar at the jth item j The index value presented above;
step S103: sequentially screening all target performance index items with performance index deviation values larger than 0 and larger than a deviation threshold value from each node radar in the networking radar; taking the target performance index item as a characteristic attribute item of each node radar; and respectively capturing corresponding characteristic attribute items for each node radar in the networking radar.
With the development of science, technology and weaponry, the operational environment facing modern radars is more and more complex; it is difficult to continuously detect and track modern flying objects by means of a single type of single-part radar; the advantage of radar formation obtained by networking after tactical configuration aiming at the characteristics of each radar is incomparable with that of a single radar in terms of some performances;
because a plurality of radars can be improved to a certain extent on each performance index after tactical configuration and networking, the index value displayed on a certain performance index by radar formation is generally influenced by the specific index value distribution of all the node radars in the formation on the performance index, and the upper limit which can be reached by the networking radars on the performance index is determined to a certain extent by the node radars with the worst index value on the performance index.
3. The method of claim 1, wherein the step S200 includes:
step S201: in each historical target tracking detection record, a target characteristic parameter set transmitted by each node radar in the networking radar to a fusion center is set as a first target characteristic parameter set, and an actual target characteristic parameter set corresponding to each historical target tracking detection record is set as a second target characteristic parameter set; respectively comparing the deviation between each first target characteristic parameter set and a corresponding second target characteristic parameter set in each historical target tracking detection record, and screening out characteristic parameter items with the deviation value larger than a deviation threshold value between the first target characteristic parameter set and the second target characteristic parameter set;
step S202: if the first target characteristic parameter set captured in a certain historical target tracking detection record comprises { A } 1 ,A 2 ,…,A g -a }; wherein A is 1 ,A 2 ,…,A g Respectively representing target characteristic parameter sets transmitted to a fusion center by 1 st, 2 nd, … th and g th node radars in the networking radars; the second target characteristic parameter set in the certain historical target tracking detection record is B; let { A } 1 ,A 2 ,…,A g Any first set of target feature parameters A within } r The characteristic parameter items obtained by screening after deviation comparison with B comprise { F 1 ,F 2 ,…,F z -a }; wherein F is 1 ,F 2 ,…,F z Respectively represent A r 1 st, 2 nd, … th and z th characteristic parameter items with the deviation value larger than the deviation threshold value;
step S203: capturing the first target characteristic parameter set A r The corresponding node radar extracts the characteristic attribute items corresponding to the node radar and comprises { U } 1 ,U 2 ,…,U p -a }; wherein U is 1 ,U 2 ,…,U p Respectively represent the first target characteristic parameter set A r 1 st, 2 nd, … th and p th characteristic attributes of the corresponding node radars; sequentially { U } 1 ,U 2 ,…,U p Each characteristic attribute in the { F } and the { F }, respectively 1 ,F 2 ,…,F z Establishing a target judgment association relation among all characteristic parameter items in the sequence, and constructing a plurality of target judgment association structures:U e →F h the method comprises the steps of carrying out a first treatment on the surface of the Wherein F is h ∈{F 1 ,F 2 ,…,F z }；
Step S204: step S202-step S203 are looped to capture and aggregate the target decision association structure present in each historical target tracking detection record.
4. The method of claim 1, wherein the step S300 includes:
step S301: setting a target judgment association structure with the characteristic attribute items being identical with the characteristic parameter items as a target judgment association structure; classifying and sorting the target judgment association structures captured and extracted from all the historical target tracking and detecting records;
step S302: calculating a first association index for each target decision association structure:wherein w1 represents the total number of occurrences of various target decision association structures; m1 represents the total number of target decision association structures captured and extracted from all historical target tracking detection records;
step S303: calculating a second association index for each target decision association structure:wherein G represents the total number of node radars involved in constructing the various target decision association structures; m2 represents the total number of node radars existing in the networking radars;
step S304: calculating comprehensive association indexes mu=ρ for various target decision association structures 1 *ρ 2 The method comprises the steps of carrying out a first treatment on the surface of the And judging the target judgment association structure with the comprehensive association index larger than the threshold value of the comprehensive association index as a characteristic target judgment association structure.
5. The radar collaborative detection method according to claim 1, wherein the step S400 includes:
step S401: feeding back all the screened characteristic target judgment association structures to a fusion center of the networking radar; in each target detection task, the operation fusion center respectively performs parameter value comparison on each characteristic parameter item in a target characteristic parameter set transmitted from all node radars; calculating the cumulative similarity of the parameter values of the node radars and other node radars on each characteristic parameter item: ω=θ 1 *θ 2 *…*θ d The method comprises the steps of carrying out a first treatment on the surface of the Wherein θ 1 、θ 2 、…、θ d The similarity of the parameter values of the node radars and other 1 st, 2 nd, … nd and d node radars on a certain characteristic parameter item is respectively represented;
step S402: when the accumulated similarity of the parameter value of a certain node radar on a certain characteristic parameter item is smaller than an accumulated similarity threshold value, and the characteristic attribute item belonging to a characteristic target judgment association structure exists in all characteristic attribute items forming the characteristic target judgment association structure with the certain characteristic parameter item, judging that a judgment scene of the characteristic target judgment association structure is met at the moment, prompting a fusion center to remove the parameter value corresponding to the certain characteristic parameter item from a target characteristic parameter set transmitted by the certain node radar, and then fusing information between a target characteristic parameter set transmitted by the certain node radar and a target characteristic parameter set transmitted by other node radars.
In the above steps, the process of judging whether the judging scene of the feature target judging association structure is met is to judge whether the situation that the detection of a certain node radar on a certain feature parameter item has larger deviation due to the performance of the certain node radar and the distribution feature of the target occurs at present, and the data situation is early-warned or eliminated before data fusion is carried out, so that the judging accuracy of the radar information fusion center can be effectively improved.
6. A radar collaborative detection system applying the radar collaborative detection method based on target tracking according to any one of claims 1-5, characterized in that the system comprises a characteristic attribute item extraction management module, a target decision association relationship establishment management module, a target decision association structure construction module, a characteristic target decision association structure screening management module and a fusion center management module;
the characteristic attribute item extraction management module is used for extracting characteristic attribute items from each node radar according to the index value deviation condition of each node radar and the whole networking radar in the networking radar among various performance indexes;
the target judgment association relation establishment management module is used for collecting information of each historical target tracking detection record of the networking radar; in each historical target tracking detection record, respectively capturing a target characteristic parameter set transmitted by each node radar in the networking radar to a fusion center, and capturing a characteristic attribute item and a characteristic parameter item with a target judgment association relation from each historical target tracking detection record by combining a characteristic attribute item corresponding to each node radar in each historical target tracking detection record;
the target judgment association structure construction module is used for receiving the data in the target judgment association relation establishment management module and constructing a target judgment association structure between the characteristic attribute items and the characteristic parameter items with the target judgment association relation;
the characteristic target judgment association structure screening management module is used for receiving the data in the target judgment association structure construction module, calculating association indexes for each target judgment association structure, and screening out characteristic target judgment association structures based on the association indexes;
the fusion center management module is used for receiving the data in the characteristic target judgment association structure screening management module, feeding back all the screened characteristic target judgment association structures to the fusion center of the networking radar, and prompting the fusion center to select and reject the judgment parameters before information fusion of the target characteristic parameter set transmitted from the related node radar when a judgment scene meeting the occurrence of the characteristic target judgment association structure appears.
7. The radar collaborative detection system according to claim 6, wherein the feature attribute item extraction management module includes a performance index bias value calculation unit, a feature attribute item capture extraction unit;
the performance index deviation value calculation unit is used for calculating the performance index deviation value which is presented on the same performance index as the whole networking radar for each node radar in the networking radar;
the characteristic attribute item capturing and extracting unit is used for receiving the data in the performance index deviation value calculating unit and extracting characteristic attribute items for the node radars.
8. The radar collaborative detection system according to claim 6, wherein the feature target decision association structure screening management module includes an association index calculation unit, a feature target decision association structure screening unit;
the association index calculation unit is used for receiving the data in the target judgment association structure construction module and calculating association indexes for each target judgment association structure;
the characteristic target judgment association structure screening unit is used for receiving the data in the association index calculation unit and screening out the characteristic target judgment association structure based on the association index.
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