CN111208484A - Main lobe dense false target removing method based on angle information - Google Patents

Main lobe dense false target removing method based on angle information Download PDF

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CN111208484A
CN111208484A CN202010042763.8A CN202010042763A CN111208484A CN 111208484 A CN111208484 A CN 111208484A CN 202010042763 A CN202010042763 A CN 202010042763A CN 111208484 A CN111208484 A CN 111208484A
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point
trace
traces
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angle
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CN111208484B (en
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赵永波
李嘉隆
刘宏伟
苏涛
苏洪涛
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Xidian University
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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
    • 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
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • 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
    • G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
    • 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
    • G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures

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

Abstract

The invention belongs to the technical field of radar signal processing, and particularly relates to a method for removing a main lobe dense false target based on angle information, which comprises the following steps: acquiring a trace point set comprising azimuth angle information and pitch angle information; calculating the spatial angle difference between each point trace and other point traces in the point trace set to obtain a plurality of spatial angle differences; obtaining the field of each trace in the trace point set according to the plurality of spatial angle differences; clustering all the points according to the DBSCAN clustering algorithm and the field of each point in the point set to obtain a plurality of point clusters; and removing the plurality of point trace clusters serving as main lobe dense decoys from the point trace set to obtain a removed point trace set. The method has the advantages of reducing false alarm probability, improving target detection performance and clustering accuracy, and improving performance when the method is applied to radar signal processing.

Description

Main lobe dense false target removing method based on angle information
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a method for removing a main lobe dense false target based on angle information.
Background
In recent years, with the increase of interference types and the progress of interference technologies, radar anti-interference technologies have become hot spots of research in the industry. The radar anti-jamming technology aims to weaken various interference signals influencing the normal work of the radar to an allowable degree and guarantee the normal work of the radar, so the radar anti-jamming technology plays a vital role in modern war.
The radar finds the existence of a target and measures parameter information of the target through detection of an echo signal, and the purpose of interference is to destroy or prevent the radar from finding the target and measuring the parameter of the target. The radar jammer can analyze each parameter of a radar transmitting signal, generates an interference signal matched with an original transmitting signal, and enters a radar receiver, the interference signal and a target echo signal in the radar receiver are difficult to distinguish, so that a large number of false point traces (main lobe dense false targets) can be generated in the radar signal processing process, and a real target point trace is contained in the false point traces, so that the false alarm probability is high, the target detection performance is difficult to improve, and the difficulty is brought to the tracking and the identification of a radar target.
The DBSCAN is a clustering algorithm based on density information, is applied to point trace clustering, generally only uses distance information, and does not use two-dimensional angle information. The robustness and accuracy of clustering false target points with dense main lobes caused by interference resistance are poor.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for removing the dense false target of the main lobe based on angle information. The technical problem to be solved by the invention is realized by the following technical scheme:
a main lobe dense false target removing method based on angle information comprises the following steps:
acquiring a trace point set comprising azimuth angle information and pitch angle information;
calculating the spatial angle difference between each point trace and other point traces in the point trace set to obtain a plurality of spatial angle differences;
obtaining the field of each trace in the trace point set according to the plurality of spatial angle differences;
clustering all the points according to the DBSCAN clustering algorithm and the field of each point in the point set to obtain a plurality of point clusters;
and removing the plurality of point trace clusters serving as main lobe dense decoys from the point trace set to obtain a removed point trace set.
In one embodiment of the present invention, the spatial angle difference expression is:
Figure BDA0002368329900000021
wherein, yiFor the ith trace, yjRepresents the jth trace point, i is 1, 2., N, j is 1, 2., N is the total number of trace points included in the trace point set, s (y is y)i,yj) Is the spatial angular difference between the ith and jth traces, θiIs the azimuth angle of the ith point trace, thetajFor the azimuth angle of the j-th trace,
Figure BDA0002368329900000022
the pitch angle of the ith trace is,
Figure BDA0002368329900000023
pitch angle for the j-th trace, w1Is an azimuth space weighting coefficient, w2Is a pitch angle spatial weighting factor.
In an embodiment of the present invention, a domain expression of each trace in the trace point set is:
Mi＝{yj∈Y|s(yi,yj)≤Eps,i≠j}，
and Eps is a domain space angle difference threshold value, and Y is a point trace set.
In an embodiment of the present invention, clustering all the dots according to the DBSCAN clustering algorithm and the field of each dot in the dot set to obtain a plurality of dot clusters, including:
(4a) inputting N traces, and setting the minimum contained point number MinPts and the minimum point number MinCus in the cluster;
(4b) marking the N traces as not-retrieved and sorting the N traces according to sequence numbers;
(4c) and selecting trace points from the N trace points according to the sequence and judging whether the trace points are searched: if the trace is searched, judging the next trace in sequence; otherwise, executing the step (4 d);
(4d) marking the trace point as searched, judging the trace point, and returning to the step (4c) if the trace point is an isolated point; if the point trace is the core point, marking all point traces in the core point field as searched, and executing the step (4 e);
(4e) taking the core point and all the point traces in the field as a new point trace cluster, and marking a new cluster label;
(4f) judging whether all the point traces in the core point field are core points or not; if the result is the core point, marking all the point traces in the core point field as retrieved, annotating the cluster labels same as those in the step (4e) and supplementing the cluster labels into the point trace cluster, repeating the step (4f) to continue expanding the core point and the point trace cluster until no core point exists, and executing the step (4 g);
(4g) returning to the step (4c), and executing the step (4h) until the N point traces are all retrieved;
(4h) judging whether the number of the points contained in each obtained point cluster is less than the minimum number MinCus in the cluster or not; if so, deleting the point trace cluster; otherwise, the point trace cluster is reserved.
In an embodiment of the present invention, the core point determining method in the step (4d) is:
for trace point yiIf m is satisfiediIf the trace is more than or equal to MinPts, the trace point is a core point; otherwise, the trace of points is an isolated point, wherein:
mias a point trace yiField M ofiThe number of all traces in the trace.
The invention has the beneficial effects that:
firstly, the point trace is spatially divided and point trace cluster extraction is carried out based on angle information, so that the influence of false main lobe concentration targets caused by interference can be avoided, the number of false main lobe concentration target points caused by interference after target detection can be reduced, the false alarm probability is reduced, and the target detection performance is improved;
secondly, the two-dimensional space weight coefficient and the minimum number of points in the cluster are added into the DBSCAN clustering algorithm, so that the clustered point trace cluster better conforms to the characteristic of the false target with dense main lobes caused by interference, the clustering accuracy is improved, and the performance of the clustered point trace cluster when being applied to radar signal processing is improved.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a schematic step diagram of a main lobe dense false target elimination method based on angle information according to an embodiment of the present invention;
fig. 2 is a comparison graph of a pulse compression result of radar echo data and a CFAR detection threshold in a main lobe dense false target elimination method based on angle information according to an embodiment of the present invention;
fig. 3 is an azimuth dimension beam scanning directional diagram of a method for removing a dense false target of a main lobe based on angle information according to an embodiment of the present invention;
fig. 4 is a pitch-dimension beam scanning directional diagram of a main lobe dense false target elimination method based on angle information according to an embodiment of the present invention;
FIG. 5 is a two-dimensional polar coordinate display interface diagram of a radar of a method for removing dense false targets of main lobes based on angle information according to an embodiment of the present invention;
fig. 6 is a two-dimensional polar coordinate display interface diagram of a radar processed by a main lobe dense false target elimination method based on angle information according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Referring to fig. 1, fig. 1 is a schematic diagram of a method for rejecting dense false targets of main lobes based on angle information according to an embodiment of the present invention, including:
acquiring a trace point set comprising azimuth angle information and pitch angle information;
calculating the spatial angle difference between each point trace and other point traces in the point trace set to obtain a plurality of spatial angle differences;
obtaining the field of each trace in the trace point set according to the plurality of spatial angle differences;
clustering all the points according to the DBSCAN clustering algorithm and the field of each point in the point set to obtain a plurality of point clusters;
and removing the plurality of point trace clusters serving as main lobe dense decoys from the point trace set to obtain a removed point trace set.
In one embodiment of the present invention, the spatial angle difference expression is:
Figure BDA0002368329900000051
wherein, yiFor the ith trace, yjRepresents the jth trace point, i is 1, 2., N, j is 1, 2., N is the total number of trace points included in the trace point set, s (y is y)i,yj) Is the spatial angular difference between the ith and jth traces, θiIs the azimuth angle of the ith point trace, thetajFor the azimuth angle of the j-th trace,
Figure BDA0002368329900000052
the pitch angle of the ith trace is,
Figure BDA0002368329900000053
pitch angle for the j-th trace, w1Is an azimuth space weighting coefficient, w2Is a pitch angle spatial weighting factor.
In particular, w1And w2The selection of the radar is combined with the angle measurement precision of the actual radar in the azimuth dimension and the pitch dimension, and the weighting coefficient of the dimension with higher angle measurement precision is required to be high. In the embodiment of the invention, the number of N is 7The angle measurement precision of the position dimension and the pitch dimension is consistent, so the azimuth angle space weighting coefficient w11, pitch angle spatial weighting coefficient w2＝1。
In an embodiment of the present invention, a domain expression of each trace in the trace point set is:
Mi＝{yj∈Y|s(yi,yj)≤Eps,i≠j}，
and Eps is a domain space angle difference threshold, Eps is 0.7, and Y is a trace point set.
In an embodiment of the present invention, clustering all the dots according to the DBSCAN clustering algorithm and the field of each dot in the dot set to obtain a plurality of dot clusters, including:
(4a) inputting N traces, and setting the minimum contained point number MinPts and the minimum point number MinCus in the cluster;
(4b) marking the N traces as not-retrieved and sorting the N traces according to sequence numbers;
(4c) and selecting trace points from the N trace points according to the sequence and judging whether the trace points are searched: if the trace is searched, judging the next trace in sequence; otherwise, executing the step (4 d);
(4d) marking the trace point as searched, judging the trace point, and returning to the step (4c) if the trace point is an isolated point; if the point trace is the core point, marking all point traces in the core point field as searched, and executing the step (4 e);
(4e) taking the core point and all the point traces in the field as a new point trace cluster, and marking a new cluster label;
(4f) judging whether all the point traces in the core point field are core points or not; if the result is the core point, marking all the point traces in the core point field as retrieved, annotating the cluster labels same as those in the step (4e) and supplementing the cluster labels into the point trace cluster, repeating the step (4f) to continue expanding the core point and the point trace cluster until no core point exists, and executing the step (4 g);
(4g) returning to the step (4c), and executing the step (4h) until the N point traces are all retrieved;
(4h) judging whether the number of the points contained in each obtained point cluster is less than the minimum number MinCus in the cluster or not; if so, deleting the point trace cluster; otherwise, the point trace cluster is reserved.
Specifically, MinPts is 2 and MinCus is 4.
In an embodiment of the present invention, the core point determining method in the step (4d) is:
for trace point yiIf m is satisfiediIf the trace is more than or equal to MinPts, the trace point is a core point; otherwise, the trace of points is an isolated point, wherein:
mias a point trace yiField M ofiThe number of all traces in the trace.
The technical effects of the invention are further explained by combining simulation experiments as follows:
1. simulation conditions are as follows:
the software conditions of the simulation of the invention are as follows: MATLAB R2017a Windows 10 (Pro).
The hardware conditions of the simulation of the invention are as follows: intel (R) core (TM) i7-6700K CPU 4.00 GHz.
2. Simulation content and simulation result analysis:
the simulation experiment of the invention sets 2 real target points, the radar jammer generates interference signals with the same wave form as the radar emission signals, the interference generates 5 dense false target points of the main lobe, the azimuth angle of the interference is 0 degree, the pitch angle is 15 degrees, and the interference is generated once at intervals; the azimuth angle of the real target 1 is 5 degrees, the pitch angle is 13 degrees, and the distance unit is 1000 degrees; the true target 2 has an azimuth angle of 10 °, a pitch angle of 20 °, and a range bin of 1500.
Referring to fig. 2, fig. 2 is a pulse compression result and CFAR detection threshold comparison diagram of radar echo data according to a main-lobe dense false target elimination method based on angle information, where the pulse compression result and the CFAR detection threshold comparison diagram are obtained after pulse compression and constant false alarm detection are performed on the echo data.
As can be seen from fig. 2, after CFAR detection, a total of 7 targets can be detected and output as trace points, and from the result of pulse compression, the amplitudes of real targets and false targets with dense main lobes caused by interference are similar, and there is no feature at an obvious distance and cannot be distinguished, so that there are false alarm points in the 7 trace points output at this time, which brings trouble to subsequent data processing.
Referring to fig. 3 and 4, fig. 3 is an azimuth dimensional beam scanning direction diagram of a main lobe dense false target removing method based on angle information according to an embodiment of the present invention, and fig. 4 is a pitch dimensional beam scanning direction diagram of a main lobe dense false target removing method based on angle information according to an embodiment of the present invention, and beam scanning angle measurement is performed on the 7 tracks to obtain a beam scanning angle measurement direction diagram of the 7 tracks. Fig. 3 shows a beam scanning pattern in the azimuth dimension, and fig. 4 shows a beam scanning pattern in the elevation dimension.
As can be seen from fig. 3 and 4, the interference points have an obvious "aggregation" characteristic in terms of angle, the information of 7 traces is sorted, and the trace number, the azimuth angle, the pitch angle and the distance unit are in one-to-one correspondence to be sorted into a trace information table, as shown in the following table.
Trace point information table
Dot trace numbering Azimuth angle/° Pitch angle/° Distance unit
1 -0.1 14.8 100
2 -0.2 15 500
3 -0.1 15.1 900
4 0.2 15.2 1300
5 0 15 1700
6 5 12.8 1000
7 9.9 19.9 1500
Referring to fig. 5, fig. 5 is a two-dimensional polar coordinate display interface diagram of a radar of a method for rejecting a dense main lobe decoy based on angle information according to an embodiment of the present invention, where 7 traces are drawn on the two-dimensional polar coordinate display interface diagram of the radar. Wherein, the polar diameter represents the distance unit, the polar angle represents the angle, the radar is located at the polar center, and the position of the "+" symbol represents the trace point position.
As can be seen from fig. 5, 7 point traces after target detection appear simultaneously on the two-dimensional coordinate graph, in which real targets cannot be distinguished.
After the field of each trace is obtained, clustering all the traces by adopting an improved DBSCAN clustering algorithm to obtain a trace cluster which comprises 5 traces, wherein the trace numbers are 1,2, 3, 4 and 5. All the traces in the trace point cluster are taken as main lobe dense false targets and are removed from the trace point set, only two trace points are left in the final output trace point set, namely the trace points with trace point numbers of 6 and 7, and the simulation condition is combined, so that the real trace point is extracted, and the main lobe dense false target points are all removed.
Referring to fig. 6, fig. 6 is a two-dimensional polar coordinate display interface diagram of a radar processed by a main lobe dense false target elimination method based on angle information according to an embodiment of the present invention, and 2 final output traces are drawn in the two-dimensional polar coordinate display interface diagram of the radar.
As can be seen from FIG. 6, the real target is still remained on the display interface, and the false target point with dense main lobe generated by the interference is completely removed, which shows that the method has high accuracy and good adaptability.
In summary, the method for rejecting the main-lobe dense false targets based on the angle information can effectively reduce the number of main-lobe dense false target points caused by interference after target detection, reduce false alarm probability and improve target detection performance.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (5)

1. A main lobe dense false target eliminating method based on angle information is characterized by comprising the following steps:
acquiring a trace point set comprising azimuth angle information and pitch angle information;
calculating the spatial angle difference between each point trace and other point traces in the point trace set to obtain a plurality of spatial angle differences;
obtaining the field of each trace in the trace point set according to the plurality of spatial angle differences;
clustering all the points according to the DBSCAN clustering algorithm and the field of each point in the point set to obtain a plurality of point clusters;
and removing the plurality of point trace clusters serving as main lobe dense decoys from the point trace set to obtain a removed point trace set.
2. The method for eliminating angle-information-based main-lobe dense false target as claimed in claim 1, wherein the spatial angle difference expression is:
Figure FDA0002368329890000011
wherein, yiFor the ith trace, yjRepresents the jth trace point, i is 1, 2., N, j is 1, 2., N is the total number of trace points included in the trace point set, s (y is y)i,yj) Is the spatial angular difference between the ith and jth traces, θiIs the azimuth angle of the ith point trace, thetajFor the azimuth angle of the j-th trace,
Figure FDA0002368329890000012
the pitch angle of the ith trace is,
Figure FDA0002368329890000013
pitch angle for the j-th trace, w1Is an azimuth space weighting coefficient, w2Is a pitch angle spatial weighting factor.
3. The method for eliminating the main lobe dense false target based on the angle information as claimed in claim 2, wherein the domain expression of each point in the point trace set is as follows:
Mi＝{yj∈Y|s(yi,yj)≤Eps,i≠j}，
and Eps is a domain space angle difference threshold value, and Y is a point trace set.
4. The method for eliminating angle information-based main-lobe dense false targets according to claim 1, wherein clustering all the point traces according to a DBSCAN clustering algorithm and a field of each point trace in the point trace set to obtain a plurality of point trace clusters comprises:
(4a) inputting N traces, and setting the minimum contained point number MinPts and the minimum point number MinCus in the cluster;
(4b) marking the N traces as not-retrieved and sorting the N traces according to sequence numbers;
(4c) and selecting trace points from the N trace points according to the sequence and judging whether the trace points are searched: if the trace is searched, judging the next trace in sequence; otherwise, executing the step (4 d);
(4d) marking the trace point as searched, judging the trace point, and returning to the step (4c) if the trace point is an isolated point; if the point trace is the core point, marking all point traces in the core point field as searched, and executing the step (4 e);
(4e) taking the core point and all the point traces in the field as a new point trace cluster, and marking a new cluster label;
(4f) judging whether all the point traces in the core point field are core points or not; if the result is the core point, marking all the point traces in the core point field as retrieved, annotating the cluster labels same as those in the step (4e) and supplementing the cluster labels into the point trace cluster, repeating the step (4f) to continue expanding the core point and the point trace cluster until no core point exists, and executing the step (4 g);
(4g) returning to the step (4c), and executing the step (4h) until the N point traces are all retrieved;
(4h) judging whether the number of the points contained in each obtained point cluster is less than the minimum number MinCus in the cluster or not; if so, deleting the point trace cluster; otherwise, the point trace cluster is reserved.
5. The method for removing main lobe dense false targets based on angle information as claimed in claim 4, wherein the core point determining method in step (4d) is:
for trace point yiIf m is satisfiediIf the trace is more than or equal to MinPts, the trace point is a core point; otherwise, the trace of points is an isolated point, wherein:
mias a point trace yiField M ofiThe number of all traces in the trace.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113390406A (en) * 2021-06-16 2021-09-14 电子科技大学 Multi-target data association and positioning method based on passive multi-sensor system
CN113608178A (en) * 2021-07-30 2021-11-05 上海无线电设备研究所 Anti-drag deception jamming method based on dual-band information fusion
WO2023082347A1 (en) * 2021-11-12 2023-05-19 苏州苏试试验集团股份有限公司 Signal processing method and apparatus, storage medium and electronic terminal

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140028489A1 (en) * 2012-03-02 2014-01-30 Hidetoshi Furukawa Target tracking apparatus, storage medium stored a target tracking program, target tracking system, and target tracking method
CN109633598A (en) * 2019-01-25 2019-04-16 西安电子科技大学 Array radar object detection method based on signature analysis

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140028489A1 (en) * 2012-03-02 2014-01-30 Hidetoshi Furukawa Target tracking apparatus, storage medium stored a target tracking program, target tracking system, and target tracking method
CN109633598A (en) * 2019-01-25 2019-04-16 西安电子科技大学 Array radar object detection method based on signature analysis

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王国宏等: "基于信号-数据联合处理的压制-距离欺骗复合干扰抑制算法", 《电子与信息学报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113390406A (en) * 2021-06-16 2021-09-14 电子科技大学 Multi-target data association and positioning method based on passive multi-sensor system
CN113390406B (en) * 2021-06-16 2022-05-24 电子科技大学 Multi-target data association and positioning method based on passive multi-sensor system
CN113608178A (en) * 2021-07-30 2021-11-05 上海无线电设备研究所 Anti-drag deception jamming method based on dual-band information fusion
CN113608178B (en) * 2021-07-30 2024-01-02 上海无线电设备研究所 Anti-drag deception jamming method based on dual-band information fusion
WO2023082347A1 (en) * 2021-11-12 2023-05-19 苏州苏试试验集团股份有限公司 Signal processing method and apparatus, storage medium and electronic terminal

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