CN114609604B - Unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method - Google Patents
Unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method Download PDFInfo
- Publication number
- CN114609604B CN114609604B CN202210298426.4A CN202210298426A CN114609604B CN 114609604 B CN114609604 B CN 114609604B CN 202210298426 A CN202210298426 A CN 202210298426A CN 114609604 B CN114609604 B CN 114609604B
- Authority
- CN
- China
- Prior art keywords
- cluster
- target
- points
- unmanned aerial
- theta
- 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.)
- Active
Links
Images
Classifications
-
- 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
- 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
- G01S13/88—Radar or analogous systems specially adapted for specific applications
-
- 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
- G01S7/415—Identification of targets based on measurements of movement associated with the target
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Computer Networks & Wireless Communication (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Probability & Statistics with Applications (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a method for detecting a cluster target of an unmanned aerial vehicle, estimating a target contour and a cluster scale, and carrying out vector superposition on a single scatterer radar echo to obtain a cluster echo model; after pulse compression, introduce l 1 The norm and the nuclear norm are used as regularization constraint terms to limit deconvolution pathogenicity and fully utilize priori information to realize super-resolution of azimuth dimension; after CFAR detection, a group of neighborhood parameters (epsilon, minPts) are set on the basis of dense distribution of clusters, and spatial clustering based on density is carried out on CFAR results, so that cluster target points are detected; polar angle sequencing is carried out on the cluster target points, the cluster boundary points are obtained through traversal, the boundary points are sequentially connected to obtain the boundary information of the clusters, the cluster outline is estimated, statistics is carried out on the number of targets in the clusters under the outline, and the cluster scale is estimated. The method effectively solves the problems of accurate detection and information estimation of the dense unmanned aerial vehicle clusters by using the existing radar system, and has important significance for effectively counteracting the unmanned aerial vehicle clusters.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to an unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method based on radars.
Background
The cluster targets are target clusters formed by mixing a large number of individual targets with similar sizes and similar movement modes, and have the characteristics of small individuals, large whole scale and flexible spatial distribution, and can complete complex functions due to the intelligent clusters, so that the 'small' and 'large' can be realized. With the development of unmanned intelligent systems, unmanned aerial vehicle cluster combat will become a novel combat style. The unmanned aerial vehicle cluster is an unmanned aerial vehicle network which is formed by a plurality of small unmanned aerial vehicles and cooperatively executes unified tasks under the command or supervision of operators. The large-scale, low-cost and miniaturized unmanned aerial vehicle can realize the group capacity of far-reaching monomer capacity through information interaction, dynamic networking and collaborative combat, thereby forming a multifunctional integrated intelligent combat platform for investigation, interference, detection, hitting and the like. The detection of the unmanned aerial vehicle cluster and the acquisition of cluster information are necessary for judging the threat level of the unmanned aerial vehicle cluster and making corresponding countermeasures.
Currently, less detection research is performed on unmanned aerial vehicle clusters, and most of the detection research is performed on single unmanned aerial vehicles, such as radar, radio, photoelectric detection and the like. Radar-based single-frame unmanned aerial vehicle detection technology is also mature, so that detection of clustered targets, particularly unmanned aerial vehicle clustered targets, can be achieved through radar. However, the number of the cluster targets is large, the distribution is dense, the individual movement in the clusters has high autonomy, the cluster structure is changeable, and the situation that the individual clusters are increased or decreased is also involved. Therefore, the traditional detection means for single unmanned aerial vehicle is not suitable any more, and a method for detecting the clustered target radar needs to be found.
Document Fast and robust super-resolution DOA estimation for UAV swarms (Tianyuan Yang et al Signal Processing,2021, 11 th edition, pages 1-37) proposes a super-resolution DOA estimation algorithm for unmanned aerial vehicle clusters, and DOA estimation is performed on individual targets in the clusters by a grid-free sparse technique, so as to obtain an angle estimation value of each target. However, the method is based on a spectrum estimation theory, a large number of snapshots (namely long accumulation time) are needed, and the calculation complexity is high; the document (Wang Luosheng, et al, modern radar, 2021, 9 th stage, 6-13 pages) provides a self-adaptive single pulse group target resolution method, which utilizes an array radar single pulse angle measurement technology to resolve group targets, but the method needs known target number, can only aim at double-target and three-target scenes, and has larger limitation; CN111580093a discloses a radar system for detecting a cluster target of a drone, but a specific signal processing algorithm is not described.
The method only solves the problem of cluster target resolution in certain special scenes, has respective limitations, and does not consider acquisition of information such as the number of targets of the clusters, the cluster contour and the like.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides an unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method.
The technical scheme of the invention is as follows: a man-machine cluster target detection method comprises the following steps:
s1, carrying out vector superposition on radar echoes of a single unmanned aerial vehicle based on the relation between a single scatterer and a cluster to obtain a cluster target echo S (tau, t), wherein tau is a distance dimension fast time variable, t is an azimuth dimension slow time variable, and S (tau, t) is rewritten into S (R, theta) capable of more intuitively reflecting cluster information through the relation between the distance dimension delay and the distance and the relation between the azimuth dimension delay and the angle, wherein theta is an angle variable of the azimuth dimension, and R is a distance variable of the distance dimension;
s2, in order to distinguish the cluster targets on the distance dimension, a transmitting end transmits a Linear Frequency Modulation (LFM) signal with a large time-width bandwidth product, a matched filter is generated based on the transmitted linear frequency modulation signal, pulse compression processing is carried out on the distance dimension echo, and the resolution of the distance dimension is improved to
Wherein B is the bandwidth of the transmitted signal, c is the speed of light, and the distance dimension super-resolution result S' (R, theta) is obtained through pulse compression;
s3, in order to obtain more accurate resolution of the clustered targets, scattering coefficient distribution of the clustered targets in the azimuth dimension needs to be obtained, and the scattering coefficient distribution obtained by directly solving deconvolution has pathogenicity. Considering the airspace sparsity of unmanned aerial vehicle cluster targets and the low rank property of airspace background clutter, and taking l of azimuth dimension echo matrix 1 Norms and nuclear norms as regularitiesTransforming constraint terms, solving under the constraint
Wherein h (theta) is an antenna pattern modulation function with an independent variable of an angle theta, S (theta) is an azimuth dimension echo at each distance, inherent pathogenicity of deconvolution is limited by iteration solution of an optimal value, azimuth dimension scattering center distribution u (theta) of a cluster target is solved, and then a two-dimensional super-resolution result u (R, theta) of the cluster target is obtained;
s4, setting a proper false alarm rate and a reference window length, and performing Constant False Alarm Rate (CFAR) detection on the super-resolution result to initially obtain a detection result of the clustered target, wherein the detection result comprises false target points caused by noise and clutter.
S5, in order to better detect cluster target points and inhibit the influence of noise and clutter, a group of neighborhood parameters (epsilon, minPts) are set by utilizing the dense distribution characteristic of unmanned aerial vehicle clusters, wherein epsilon represents the minimum distance of the set targets in the clusters and is used for describing the cluster density; minPts represents the minimum number of targets within a cluster and is used to describe the cluster size. And using the group of neighborhood parameters to perform clustering processing based on space density on the CFAR detection result, eliminating false alarm points generated by noise or clutter in the CFAR process, and detecting cluster target points.
Based on the unmanned aerial vehicle cluster target detection method, the invention also provides an unmanned aerial vehicle cluster target contour and cluster scale estimation method, which further comprises the following steps:
s6, in order to obtain the estimation of the cluster scale and the contour, the cluster targets are better processed, polar angle ordering is carried out on the cluster target points detected in the step S5, all points are traversed, the boundary target points of the clusters are finally found through the stack inner elements of the boundary points, the points are sequentially connected to obtain the boundary information of the clusters, the contour estimation of the cluster targets is completed, then the number of the targets in the clusters is counted under the contour, and the contour information of the clusters is combined to complete the estimation of the cluster scale.
Further, the cluster target modeling process in step S1 is as follows:
where n is the number of targets in the cluster, u m Is the scattering coefficient of the mth target, h (t) is the antenna pattern modulation function with the independent variable of time t, rect (·) is the rectangular window function, τ m Is the distance dimension time delay of the mth target, f c Is the carrier frequency of the radar transmit signal, T is the time width of the radar transmit pulse, and k is the chirp rate of the radar transmit chirp signal.
Consider t= (θ - θ) m )/ω，τ＝2R/c，τ m ＝2R(θ m )/c，f c =c/λ, where θ m Is the angle of the mth target relative to the antenna normal direction, ω is the antenna scan speed, R (θ m ) Is the distance from the mth target to the radar, lambda is the wavelength of the radar emission signal, and after the carrier removal processing, the formula (1) becomes:
further, the step S2 results in pulse compression of the distance dimension as follows:
further, the step S3 processes the distance dimension pulse compression result in the azimuth dimension, and the azimuth dimension echo at each distance can be expressed as:
the azimuth dimension echo can be expressed as:
S(θ)＝u(θ)*h(θ)+n(θ) (5)
where u (θ) is the distribution of the target scattering coefficient in the azimuth dimension, h (θ) is the antenna pattern modulation function, and n (θ) is noise.
Because of the ill-condition of deconvolution, the azimuth dimension distribution u (θ) of the target cannot be obtained from the direct convolution inversion in equation (5). Considering the sparsity of the airspace of the target distribution and the low rank of the airspace background in the unmanned aerial vehicle cluster scene, and taking l of the azimuth dimension echo matrix 1 And taking the norms and the nuclear norms as constraint terms, and completing iterative solution to the target azimuth dimension distribution based on a regularization theory, so as to eliminate the influence caused by the pathogenicity of deconvolution. Using this approach, the super-resolution result of the azimuth dimension can be expressed as:
where s (θ) is the scattering coefficient distribution of the object of interest, b (θ) is the scattering coefficient distribution of the background environment, |·|| 2 Is l 2 The norm of the sample is calculated, I.I 1 Is l 1 The norm of the sample is calculated, I.I * The value of the core norm is the sum of all singular values of the matrix, lambda 1 And lambda (lambda) 2 Is a regularization parameter, and is constant.
And obtaining a super-resolution result u (R, theta) of the cluster target through super-resolution processing of the distance dimension and the azimuth dimension.
It should be noted that the false alarm rate in the step S4 is set to P fa ＝10 -6 The reference window length and the guard window length were set to 10 and 8, respectively, and detection was performed using cell average constant false alarm rate detection (CA-CFAR).
It should be noted that, in the step S5, the parameter (epsilon, minPts) set for the cluster target is epsilon used to limit the maximum distance between two scattering centers nearest to each other in the same cluster, and MinPts is used to limit the minimum number of scattering centers in each cluster. The method comprises the steps of setting MinPts to 10, setting epsilon to a smaller value by using dense distribution characteristics of a cluster target, carrying out density-based noise application spatial clustering on all CFAR detected points, and detecting the cluster target point.
In step S6, all the detected cluster target points P are to be detected i (R, θ), i=1, 2, …, N put into the same coordinate system, find the point with the smallest ordinate first, let this point be P 1 In P 1 As an origin, polar angle sequencing is carried out on all points, and the sequencing rule is as follows: the connecting line has small number with small included angle with the positive direction of the abscissa, if the included angle is the same, the distance P 1 The number of the near part is small; p after finishing the sequence 1 、P 2 、P N These three points must be points on the cluster envelope; from P 2 The points sequentially traverse all the points, and two points at the top of the stack are taken each time and connected into a straight line to form a current point P i Comparison, if P i Directly push on the left side of the straight line, if P i On the right side of the straight line, a point at the top of the stack is popped up, and the line formed by connecting two points in the stack and P are continuously taken i Compare until P i On the left side of the straight line, P is i After all points are traversed, the rest points in the stack are boundary points of the cluster, and the boundary information of the cluster is obtained by connecting the rest points in sequence, so that the estimation of the cluster outline can be obtained; and then counting the number of targets in the cluster under the contour, and completing the estimation of the cluster scale by combining the contour information of the cluster.
The invention has the beneficial effects that: according to the method, firstly, according to the relation between a single scatterer and a cluster, vector superposition is carried out on radar echoes of a single unmanned aerial vehicle to obtain a cluster echo; secondly, using priori information of cluster scene to make l of echo matrix 1 The norm and the nuclear norm are used as regularization constraint items to limit the pathogenicity of deconvolution and realize super-resolution of azimuth dimension; then, based on the dense distribution characteristic of the cluster target and the randomness of noise, performing spatial clustering on the CFAR result based on density by using a group of neighborhood parameters (epsilon, minPts), and detecting the cluster target point; finally, traversing the points after polar angle sequencing to obtain cluster boundary target points, and sequentially connecting the cluster boundary target points to obtain the boundary information of the clusters to obtain the estimation of the cluster contours; then at the wheelAnd counting the number of targets in the cluster under the profile, and completing the estimation of the cluster scale by combining the profile information of the cluster. The method can acquire important information of the clusters from radar echo signals by means of signal processing, has higher accuracy and good performance, effectively solves the problem of accurately detecting and estimating information of the dense unmanned aerial vehicle clusters by using the existing radar system, and has important significance for effectively countering the unmanned aerial vehicle clusters.
Drawings
Fig. 1 is a flowchart of a method for detecting a cluster target and estimating a target contour and a cluster scale of an unmanned aerial vehicle.
FIG. 2 is a diagram showing the effect of the present invention in practice.
FIG. 3 is a graph showing the comparison of the relative error of the cluster contour estimation by the method of the present invention and the conventional detection method
FIG. 4 is a graph showing the relative error of the cluster scale estimation by the method of the present invention and the conventional detection method.
Detailed Description
The invention is mainly verified by adopting a computer simulation method, and all steps and conclusions are verified to be correct on MATLAB-R2019 b. The invention is described in further detail below with reference to the accompanying drawings.
The specific flow of the method of the invention is shown in figure 1, and unlike the traditional single scatterer and rigid body expansion targets, the clustered targets are flexible time-varying targets composed of a plurality of scatterers. Firstly, initializing relevant parameters of a cluster target scene, which specifically comprises the following steps: the number of targets in the cluster is 100, the cluster expansion size is 300m multiplied by 300m, and the minimum radial distance of the cluster from the radar is 3200m. The system parameters of the real beam scanning radar are shown in table 1.
TABLE 1
| Parameters (parameters) | (symbol) | Numerical value |
| Carrier frequency | f c | 10GHz |
| Bandwidth of a communication device | B | 30MHz |
| Time width of transmitting signal | T | 2μs |
| Pulse sampling frequency | PRF | 2000Hz |
| Antenna scanning speed | ω | 120°/s |
| Antenna beam width | θ 3dB | 3° |
The method comprises the following specific steps:
step one: and generating radar original echoes of the cluster targets according to the simulation scene generated by the simulation parameters, wherein the signal-to-noise ratio is set to be 15dB. Pulse compression is carried out in the distance dimension, super-resolution processing is carried out in the azimuth dimension by using a regularization method based on low rank and sparse constraint, and a super-resolution processing result u (R, theta) is obtained.
Step two: for super-resolution processing result u(R, θ) detection using CA-CFAR with false alarm rate set to P fa ＝10 -6 And obtaining a detection result.
Step three: and (3) performing density-based spatial clustering on the CA-CFAR detection result, and removing false alarm points generated by noise or clutter from the cluster target points by utilizing the dense distribution characteristic of the cluster targets to obtain interested cluster target points, so as to obtain the detection of the cluster target points.
Step four: and (3) performing polar angle sequencing on the clustered interested cluster target points, traversing the clustered interested cluster target points, sequentially finding out boundary points of the clusters, connecting the boundary points, finding out a minimum polygon containing all the cluster target points and the boundary of the clusters, estimating cluster contours, counting the number of targets in the clusters under the contours, and completing the estimation of the cluster scale by combining the contour information of the clusters.
Finally, as shown in fig. 2, the detection results of the cluster target and the estimation results of the cluster information are as follows from left to right and from top to bottom: cluster scene graph, cluster original radar echo graph, two-dimensional super-resolution result graph, CFAR detection result graph, clustering result graph, and cluster scale and contour estimation result graph. Fig. 3 and fig. 4 show the error comparison of the method provided by the present invention with the conventional pulse pressure-CFAR detection method for cluster contour and cluster scale estimation, respectively. As can be seen from the figure, the method provided by the invention can detect the cluster target from the radar echo of which the target is indistinguishable, acquire the scale and contour information of the cluster, and has higher accuracy.
According to the embodiment of the invention, the method can detect the cluster target from the radar echo in a signal processing mode, estimate the scale and the outline of the cluster, has higher prediction accuracy under a certain signal-to-noise ratio and has good performance.
Claims (4)
1. The unmanned aerial vehicle cluster target detection method comprises the following steps:
s1, carrying out vector superposition on radar echoes of a single unmanned aerial vehicle based on the relation between a single scatterer and a cluster to obtain a cluster target echo S (tau, t), wherein tau is a distance dimension fast time variable, t is an azimuth dimension slow time variable, and the cluster target echo S (tau, t) is rewritten into S (R, theta) through the relation between the distance dimension delay and the distance and the relation between the azimuth dimension delay and the angle, wherein theta is an angle variable of the azimuth dimension, and R is a distance variable of the distance dimension;
the cluster target modeling process comprises the following steps:
where n is the number of targets in the cluster, u m Is the scattering coefficient of the mth target, h (t) is the antenna pattern modulation function with the independent variable of time t, rect (·) is the rectangular window function, τ m Is the distance dimension time delay of the mth target, f c Is the carrier frequency of the radar transmitting signal, T is the time width of the radar transmitting pulse, and k is the frequency modulation slope of the radar transmitting linear frequency modulation signal;
the S (R, theta) is specifically as follows:
wherein θ m Is the angle of the mth target relative to the antenna normal direction, R (θ m ) Is the distance from the mth target to the radar, and lambda is the wavelength of the radar emission signal;
s2, a transmitting end transmits a linear frequency modulation signal with a large time-width bandwidth product, a matched filter is generated based on the transmitted linear frequency modulation signal, pulse compression processing is carried out on a distance dimension echo, and the resolution of the distance dimension is improved to be
Wherein B is the bandwidth of the transmitted signal, c is the speed of light, and the distance dimension super-resolution result S' (R, theta) is obtained through pulse compression;
the S' (R, θ) is specifically:
s3, carrying out l of an azimuth dimension echo matrix 1 The norms and the nuclear norms act as regularization constraint terms under which to solve
Wherein u (theta) is the distribution of azimuth dimension scattering centers of the clustered targets, h (theta) is an antenna pattern modulation function with an independent variable of an angle theta, and S (theta) is an azimuth dimension echo at each distance; the optimal value is solved through iteration, inherent pathogenicity of deconvolution is limited, u (theta) is solved, and then a two-dimensional super-resolution result u (R, theta) of the cluster target is obtained;
s4, setting a false alarm rate and a reference window length, and performing Constant False Alarm Rate (CFAR) detection on a super-resolution result u (R, theta) to initially obtain a detection result of a cluster target;
s5, setting a group of neighborhood parameters (epsilon, minPts), wherein epsilon represents the minimum distance between targets in the set cluster, minPts represents the minimum target number in the cluster, performing clustering processing based on space density on CFAR detection results, eliminating false alarm points generated by noise or clutter in the CFAR process, and detecting cluster target points.
2. The method for radar-based unmanned aerial vehicle cluster target detection of claim 1, wherein,
the azimuth dimension echo S (θ) at each distance described in step S3 is expressed as:
the equation (4) is changed into a convolution form, and the azimuth dimension echo is expressed as:
S(θ)＝u(θ)*h(θ)+n(θ) (5)
wherein n (θ) is noise;
matrix of azimuth dimension echo 1 The norm and the nuclear norm are used as constraint terms, iteration solution of target azimuth dimension distribution is completed based on regularization theory, and a super-resolution result of the azimuth dimension is obtained:
where s (θ) is the scattering coefficient distribution of the object of interest, b (θ) is the scattering coefficient distribution of the background environment, |·|| 2 Is l 2 The norm of the sample is calculated, I.I 1 Is l 1 The norm of the sample is calculated, I.I * The value of the core norm is the sum of all singular values of the matrix, lambda 1 And lambda (lambda) 2 Is a regularization parameter;
and obtaining a super-resolution result u (R, theta) of the cluster target through super-resolution processing of the distance dimension and the azimuth dimension.
3. A method for estimating a target profile and a cluster size of a cluster of unmanned aerial vehicles based on the method for detecting a target of a cluster of unmanned aerial vehicles according to claim 1 or 2, further comprising the steps of:
s6, polar angle sequencing is carried out on the cluster target points detected in the step S5, all points are traversed, boundary target points of the clusters are found through continuously updating elements in stacks of the boundary points, the boundary target points are sequentially connected to obtain boundary information of the clusters, contour estimation of the cluster targets is completed, then the number of the targets in the clusters is counted under the contour, and the contour information of the clusters is combined to complete estimation of the cluster scale.
4. A method for estimating a cluster target profile and cluster size of an unmanned aerial vehicle according to claim 3, wherein in step S6, all detected cluster target points P are determined i (R, θ) is placed in the same coordinate system, wherein i=1, 2, …, N, N is the number of detected cluster target points, the point with the smallest ordinate is found first, and the point is set as P 1 In P 1 As an origin, polar angle sequencing is carried out on all points, and the sequencing rule is as follows: will P i And P 1 The connection, the point number that the contained angle of the connection line and the positive direction of the abscissa is small, if the contained angle is the same, the distance P 1 The number of the near part is small; p after finishing the sequence 1 、P 2 、P N These three points must be points on the cluster envelope; from P 2 The points sequentially traverse all the points, and two points at the top of the stack are taken each time and connected into a straight line to form a current point P i Comparison, if P i Directly push on the left side of the straight line, if P i On the right side of the straight line, a point at the top of the stack is popped up, and the line formed by connecting two points in the stack and P are continuously taken i Compare until P i On the left side of the straight line, P is i After all points are traversed, the rest points in the stack are boundary points of the cluster, and the boundary information of the cluster is obtained by connecting the rest points in sequence, so that the estimation of the cluster outline can be obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210298426.4A CN114609604B (en) | 2022-03-25 | 2022-03-25 | Unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210298426.4A CN114609604B (en) | 2022-03-25 | 2022-03-25 | Unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114609604A CN114609604A (en) | 2022-06-10 |
| CN114609604B true CN114609604B (en) | 2023-06-09 |
Family
ID=81866545
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210298426.4A Active CN114609604B (en) | 2022-03-25 | 2022-03-25 | Unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114609604B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109001700A (en) * | 2018-05-28 | 2018-12-14 | 电子科技大学 | A kind of radar foresight imaging method for realizing objective contour reconstruct |
| CN109959900A (en) * | 2017-12-21 | 2019-07-02 | 波音公司 | Light weight radar system |
| CN110632942A (en) * | 2019-09-29 | 2019-12-31 | 成都纳雷科技有限公司 | Tree contour detection method and device based on unmanned aerial vehicle obstacle avoidance radar |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8207887B2 (en) * | 2009-06-19 | 2012-06-26 | The United States Of America As Represented By The Secretary Of The Army | Computationally efficent radar processing method and sytem for SAR and GMTI on a slow moving platform |
| US11408978B2 (en) * | 2015-07-17 | 2022-08-09 | Origin Wireless, Inc. | Method, apparatus, and system for vital signs monitoring using high frequency wireless signals |
| US10222472B2 (en) * | 2016-09-30 | 2019-03-05 | Veoneer Us, Inc. | System and method for detecting heading and velocity of a target object |
| CN106896358A (en) * | 2017-04-27 | 2017-06-27 | 电子科技大学 | A kind of high-speed target phase-coherent accumulation detection method based on position rotation transformation |
| CN109344772B (en) * | 2018-09-30 | 2021-01-26 | 中国人民解放军战略支援部队信息工程大学 | Ultrashort wave specific signal reconnaissance method based on spectrogram and deep convolutional network |
| US11579287B2 (en) * | 2019-01-23 | 2023-02-14 | The Government Of The United States Of America, As Represented By The Secretary Of The Navy | Millimeter-wave radar for unmanned aerial vehicle swarming, tracking, and collision avoidance |
| EP3715901A1 (en) * | 2019-03-29 | 2020-09-30 | Robin Radar Facilities BV | Detection and classification of unmanned aerial vehicles |
| CN111123257B (en) * | 2019-12-30 | 2023-03-28 | 西安电子科技大学 | Radar moving target multi-frame joint detection method based on graph space-time network |
| CN111580093B (en) * | 2020-05-25 | 2022-11-04 | 南京理工大学 | Radar system for detecting unmanned aerial vehicle cluster target |
| CN111736131B (en) * | 2020-07-13 | 2023-09-01 | 深圳大学 | Method for eliminating false targets of one-bit signal harmonic waves and related components |
| CN112346030B (en) * | 2020-10-30 | 2023-06-30 | 西安电子科技大学 | Super-resolution direction-of-arrival estimation method for unmanned aerial vehicle group |
| CN113050059A (en) * | 2021-03-24 | 2021-06-29 | 西安电子科技大学 | Group target focusing super-resolution direction of arrival estimation method by using co-prime array radar |
| CN113806920B (en) * | 2021-08-25 | 2024-02-23 | 中山大学 | Unmanned aerial vehicle cluster electromagnetic scattering simulation method, device, equipment and medium |
| CN113807591B (en) * | 2021-09-22 | 2023-04-07 | 电子科技大学 | Cooperative optimization deployment method for communication distance-limited unmanned aerial vehicle cluster station |
| CN114114192B (en) * | 2021-12-02 | 2023-05-23 | 电子科技大学 | Cluster target detection method |
-
2022
- 2022-03-25 CN CN202210298426.4A patent/CN114609604B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109959900A (en) * | 2017-12-21 | 2019-07-02 | 波音公司 | Light weight radar system |
| CN109001700A (en) * | 2018-05-28 | 2018-12-14 | 电子科技大学 | A kind of radar foresight imaging method for realizing objective contour reconstruct |
| CN110632942A (en) * | 2019-09-29 | 2019-12-31 | 成都纳雷科技有限公司 | Tree contour detection method and device based on unmanned aerial vehicle obstacle avoidance radar |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114609604A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111352102A (en) | Multi-target number detection method and device based on frequency modulation continuous wave radar | |
| CN110412559A (en) | The non-coherent of distributed unmanned plane MIMO radar merges object detection method | |
| CN109324315B (en) | Space-time adaptive radar clutter suppression method based on double-layer block sparsity | |
| CN110297233B (en) | LFMCW array radar signal parallel pipelining processing method | |
| CN111521976B (en) | Space-time adaptive interference processing method, device and storage medium | |
| CN112731307B (en) | RATM-CFAR detector based on distance-angle joint estimation and detection method | |
| CN112612006B (en) | Deep learning-based non-uniform clutter suppression method for airborne radar | |
| CN112859014A (en) | Radar interference suppression method, device and medium based on radar signal sorting | |
| CN115061113B (en) | Target detection model training method and device for radar and storage medium | |
| CN112346030A (en) | Super-resolution direction-of-arrival estimation method for unmanned aerial vehicle cluster | |
| CN115291207A (en) | Multi-target detection method for small rotor unmanned aerial vehicle based on MIMO radar | |
| CN112147608A (en) | Rapid Gaussian gridding non-uniform FFT through-wall imaging radar BP method | |
| CN113376607B (en) | Airborne distributed radar small sample space-time self-adaptive processing method | |
| CN108196238B (en) | Clutter map detection method based on adaptive matched filtering under Gaussian background | |
| CN113466797A (en) | Bistatic SAR space-time clutter suppression method based on clutter ridge matching sparse recovery | |
| CN114609604B (en) | Unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method | |
| CN116184343A (en) | Three-dimensional space swarm target detection and information estimation method based on phased array radar | |
| CN115877380A (en) | SAR multi-moving-target imaging method and device and storage medium | |
| CN115546526A (en) | Three-dimensional point cloud clustering method and device and storage medium | |
| CN115575906A (en) | Fusion detection method for intelligent interference suppression under non-uniform background | |
| CN111965617B (en) | GPU-based time division MIMO radar signal processing method | |
| CN115015925A (en) | Airborne array radar super-resolution forward-looking imaging method and device based on improved matching pursuit | |
| CN114418068A (en) | Millimeter wave radar sparse point cloud reconstruction method based on multi-step neural network structure | |
| CN113093174A (en) | PHD filtering radar fluctuation weak multi-target-based track-before-detect method | |
| Yang et al. | A detection and information extraction approach for uav swarm |
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 |