CN114609604A - Unmanned aerial vehicle cluster target detection and target contour and cluster scale estimation method - Google Patents
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Abstract
The invention discloses a method for detecting a cluster target of an unmanned aerial vehicle and estimating a target profile and a cluster scale, which carries out vector superposition on radar echoes of a single scatterer to obtain a cluster echo model; after pulse compression, introduce l1The norm and the nuclear norm are used as regularization constraint terms to limit deconvolution ill-conditioning and fully utilize prior information to realize super-resolution on the azimuth dimension; after CFAR detection, setting a group of neighborhood parameters (epsilon, MinPts) based on the dense distribution of the clusters to perform density-based spatial clustering on CFAR results, and detecting cluster target points; polar angle sorting and traversing the cluster target points to obtain the boundary points of the cluster, connecting the boundary points in sequence to obtain the boundary information of the cluster,and estimating a cluster contour, counting the number of targets in the cluster under the contour, and estimating the cluster scale. The method effectively solves the problems of accurate detection and information estimation of the dense unmanned aerial vehicle cluster by using the conventional radar system, and has important significance for effectively countering the unmanned aerial vehicle cluster.
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 target is a target cluster formed by mixing a large number of individual targets with similar sizes and similar motion modes, has the characteristics of small individual, large integral scale and flexible spatial distribution time variation, can complete complex functions due to the group intelligence, and realizes the purpose of ' small ' beating ' large. Along with the development of unmanned intelligent system, unmanned aerial vehicle cluster combat will become a novel combat style. The unmanned aerial vehicle cluster is an unmanned aerial vehicle network which is composed of a plurality of small unmanned aerial vehicles and cooperatively executes unified tasks under the command or supervision of operators. The mass, low-cost and miniaturized unmanned aerial vehicle can realize the group capability far exceeding the single capability through information interaction, dynamic networking and cooperative operation, and further form an intelligent operation platform with multifunctional integration of investigation, interference, detection, attack 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 counter measures.
At present, the research on the detection of the unmanned aerial vehicle cluster is less, and most of the research is directed to the detection of a single unmanned aerial vehicle, such as radar, radio, photoelectric detection and the like. The radar-based single unmanned aerial vehicle detection technology is mature, so that the cluster target, particularly the unmanned aerial vehicle cluster target, can be detected through the radar. However, the number of the cluster targets is large, the distribution is dense, the movement of the individual in the cluster has high autonomy, the cluster structure is variable, and the increase and decrease of the cluster individuals are involved. Therefore, the traditional detection means for a single unmanned aerial vehicle is no longer applicable, and a radar detection method for dealing with the cluster target needs to be found.
Document Fast and robust super-resolution DOA estimation for UAV swarms (Tianyuan Yang et al, Signal Processing, No. 11 of 2021, pages 1-37) proposes a super-resolution DOA estimation algorithm for unmanned aerial vehicle clusters, and performs DOA estimation on a single target in a cluster by a mesh-free sparse technology, so as to obtain an angle estimation value of each target. However, the method is based on the spectrum estimation theory, needs a large amount of fast beats (namely, long accumulation time) and has higher calculation complexity; the document "array radar monopulse group target resolution method" (royal bin and bin, and the like, modern radar, 9 th in 2021, pages 6-13) proposes an adaptive monopulse group target resolution method, which uses array radar monopulse angle measurement technology to resolve group targets, but the method needs known number of targets, and can only be used for double-target and triple-target scenes, and has large limitation; CN111580093A discloses a radar system for detecting drone swarm targets, but does not describe a specific signal processing algorithm.
The method only solves the problem of cluster target resolution in some special scenes, has respective limitations, and does not consider the acquisition of information such as the number of the cluster targets, the cluster outline and the like.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for detecting the cluster target of an unmanned aerial vehicle and estimating the target contour and the cluster scale.
The technical scheme of the invention is as follows: a man-machine cluster target detection method comprises the following steps:
s1, performing vector superposition on radar echoes of a single unmanned aerial vehicle based on the relation between a single scatterer and a cluster to obtain cluster target echoes S (tau, t), wherein tau is a distance dimension fast time variable, t is an orientation dimension slow time variable, and S (tau, t) is rewritten into S (R, theta) capable of reflecting cluster information more intuitively through the relations between distance dimension time delay and distance and between orientation dimension time delay and angle, theta is an orientation dimension angle variable, and R is a distance dimension distance variable;
s2, in order to distinguish the cluster target in the distance dimension, a transmitting terminal transmits a Linear Frequency Modulation (LFM) signal with a large time-width bandwidth product, a matched filter is generated based on the transmitted LFM signal, distance dimension echo waves are subjected to pulse compression processing, and the resolution of the distance dimension is improved to the degree that the distance dimension is not limited
B is the bandwidth of a transmitting signal, c is the speed of light, and a distance dimension super-resolution result S' (R, theta) is obtained through pulse compression;
s3, in order to obtain more accurate resolution of the cluster target, scattering coefficient distribution of the cluster target on the azimuth dimension needs to be obtained, and the scattering coefficient distribution obtained by directly solving the deconvolution is ill-conditioned. Considering the airspace sparsity of the unmanned aerial vehicle cluster target and the low rank of the airspace background clutter, and converting the l of the azimuth dimension echo matrix1Norm and nuclear norm as regularized constraint terms under which solution is performed
H (theta) is an antenna directional diagram modulation function with an independent variable of an angle theta, S (theta) is an azimuth dimension echo at each distance, an optimal value is solved through iteration, inherent ill-conditioned performance of deconvolution is limited, azimuth dimension scattering center distribution u (theta) of the cluster target is solved, and then a two-dimensional super-resolution result u (R, theta) of the cluster target is obtained;
and S4, setting a proper false alarm rate and a proper reference window length, and performing Constant False Alarm Rate (CFAR) detection on the super-resolution result to preliminarily obtain a detection result of the cluster target, wherein the detection result comprises a false target point brought by noise and clutter.
S5, in order to better detect a cluster target point and simultaneously inhibit the influence of noise and clutter, a group of neighborhood parameters (epsilon, MinPts) are set by utilizing the dense distribution characteristic of the unmanned aerial vehicle cluster, wherein epsilon represents the set minimum target distance in the cluster 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 a cluster target point.
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 estimation of cluster scale and contour and better correspond to a cluster target, polar angle sorting 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 by continuously updating stack elements of the boundary points, the points are sequentially connected to obtain boundary information of the clusters, contour estimation of the cluster target is completed, then statistics is carried out on the number of targets in the clusters under the contour, and estimation of the cluster scale is completed by combining the contour information of the clusters.
Further, the modeling process of the cluster target in step S1 is:
where n is the number of targets in the cluster, umIs the scattering coefficient of the mth target, h (t) is the antenna pattern modulation function with time t as the independent variable, rect () is a rectangular window function, taumIs the distance dimension time delay of the mth target, fcIs the carrier frequency of the radar transmitted signal, T is the time width of the radar transmitted pulse, and k is the chirp rate of the radar transmitted chirp signal.
Consider t ═ theta-thetam)/ω，τ＝2R/c，τm＝2R(θm)/c，fcC/λ, wherein θmIs the angle of the mth target with respect to the normal direction of the antenna, ω is the antenna scanning speed, R (θ)m) Is the distance from the mth target to the radar, λ is the wavelength of the radar transmission signal, and after the frequency hopping processing, equation (1) becomes:
further, the result of the step S2 on the distance dimension pulse compression is:
further, the step S3 is to process the distance-dimensional pulse compression result in the azimuth dimension, and the azimuth-dimensional echo at each distance can be represented as:
changing equation (4) to a convolution form and considering the noise effect, the azimuth-dimension echo can be expressed as:
S(θ)＝u(θ)*h(θ)+n(θ) (5)
wherein u (theta) is the distribution of the scattering coefficient of the target in the azimuth dimension, h (theta) is the modulation function of the antenna directional diagram, and n (theta) is noise.
Due to the ill-conditioned nature of deconvolution, the azimuthal distribution u (θ) of the target cannot be obtained by direct convolution inversion from equation (5). Considering the space domain sparsity of target distribution and the low rank of space domain background in the cluster scene of the unmanned aerial vehicle, and converting the l of the azimuth dimension echo matrix1And the norm and the nuclear norm are used as constraint terms, and iterative solution of target orientation dimensional distribution is completed based on a regularization theory so as to eliminate the influence caused by the ill-conditioned property of deconvolution. Using this method, the super-resolution result in the azimuth dimension can be expressed as:
wherein s (θ) is the scattering coefficient distribution of the object of interest, b (θ) is the scattering coefficient distribution of the background environment, | | · | | survival2Is 12Norm, | \ | circumflecting1Is 11Norm, | · | luminance*Is a matrix kernel norm, the value of the kernel norm is the sum of all singular values of the matrix,λ1and λ2Is a regularization parameter, is a constant.
And performing super-resolution processing on the distance dimension and the orientation dimension to obtain a super-resolution result u (R, theta) of the cluster target.
It should be noted that the false alarm rate in step S4 is set to Pfa＝10-6The reference window length and the protection window length are set to 10 and 8, respectively, and are detected using a cell average constant false alarm rate detection (CA-CFAR).
It should be noted that, in step S5, the parameter (e, MinPts) is set for the cluster target, where e is used to limit the maximum distance between the two nearest neighboring scattering centers in the same cluster, and MinPts is used to limit the minimum number of scattering centers in each cluster. The method utilizes the prior knowledge that the positions of false alarm points generated by noise or clutter are random and irrelevant to a cluster target, sets MinPts to be 10 in order to distinguish the cluster target points from the false alarm points generated by the noise or the clutter, simultaneously utilizes the dense distribution characteristic of the cluster target to set epsilon to be a smaller value, performs noise application space clustering based on density on all CFAR detection points, and detects cluster target points.
It should be noted that, in the step S6, all the detected cluster target points P are required to be detectedi(R, θ), i is 1,2, …, N is put into the same coordinate system, and the point with the smallest ordinate is found first, and this point is defined as P1With P1And (3) as an original point, carrying out polar angle sequencing on all points, wherein the sequencing rule is as follows: the number of the connecting line with small included angle with the positive direction of the abscissa is small, and if the included angles are the same, the distance P is1The number of the last is small; after finishing the sequence P1、P2、PNThese three points must be points on the cluster envelope; from P2The 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 be connected with the current point PiBy comparison, if PiStack directly on the left of the line if PiAt the right side of the straight line, a point at the top of the stack is popped out, and a line formed by connecting two points in the stack and the P are continuously takeniComparison until PiOn the left side of the line, PiStacking, after all the points are traversed, the remaining points in the stack are the boundary points of the cluster, and the cluster boundary points are used for stackingThe boundary information of the clusters is obtained by sequential connection, 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 finishing the estimation of the cluster scale by combining the contour information of the cluster.
The invention has the beneficial effects that: firstly, according to the relation between a single scatterer and a cluster, carrying out vector superposition on radar echoes of a single unmanned aerial vehicle to obtain cluster echoes; secondly, the prior information of the cluster scene is utilized to convert the l of the echo matrix1The norm and the nuclear norm are used as regularization constraint terms to limit the ill-conditioned character of deconvolution, and the super-resolution of the orientation dimension is realized; secondly, based on the dense distribution characteristics of the cluster target and the randomness of noise, carrying out density-based spatial clustering on the CFAR result by using a group of neighborhood parameters (epsilon, MinPts) to detect a cluster target point; finally, the points are subjected to polar angle sorting and then traversed to obtain cluster boundary target points, the cluster boundary target points are sequentially connected to obtain cluster boundary information, and estimation of a cluster contour is obtained; and then counting the number of targets in the cluster under the contour, and finishing the estimation of the cluster scale by combining the contour information of the cluster. The method can acquire important cluster information from the radar echo signal by means of signal processing, has high accuracy and good performance, effectively solves the problems of accurate detection and information estimation of the dense unmanned aerial vehicle cluster by using the conventional radar system, and has important significance for effectively countering the unmanned aerial vehicle cluster.
Drawings
Fig. 1 is a flowchart of a method for detecting an unmanned aerial vehicle cluster target and estimating a target contour and a cluster scale.
FIG. 2 is a diagram illustrating the effect of the present invention.
FIG. 3 is a schematic diagram 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 diagram showing the comparison of the relative error of the cluster size estimation by the method of the present invention and the conventional detection method.
Detailed Description
The invention mainly adopts a computer simulation method for verification, and all steps and conclusions are verified to be correct on MATLAB-R2019 b. The present invention is described in further detail below with reference to the attached drawing figures.
The specific flow of the method of the invention is shown in fig. 1, and unlike the traditional single scatterer and rigid body extension target, the clustering target is a flexible time-varying target composed of a plurality of scatterers. Firstly, initializing relevant parameters of a cluster target scene, specifically: the number of targets in the cluster is 100, the cluster expansion size is 300m multiplied by 300m, and the cluster distance is 3200m from the radar minimum radial distance. The system parameters of the real beam scanning radar are shown in table 1.
TABLE 1
| Parameter(s) | (symbol) | Numerical value |
| Carrier frequency | fc | 10GHz |
| Bandwidth of | B | 30MHz |
| Transmission signal time width | T | 2μs |
| Pulse sampling frequency | PRF | 2000Hz |
| Scanning speed of antenna | ω | 120°/s |
| Antenna beam width | θ3dB | 3° |
The method comprises the following specific steps:
the method comprises the following steps: and generating original radar echoes of the cluster targets for the simulation scenes generated according to the simulation parameters, wherein the signal-to-noise ratio is set to be 15 dB. And performing pulse compression in a distance dimension, and performing super-resolution processing in an orientation dimension by using a low-rank and sparse constraint-based regularization method to obtain a super-resolution processing result u (R, theta).
Step two: detecting the super-resolution processing result u (R, theta) by using CA-CFAR, and setting the false alarm rate as Pfa＝10-6And obtaining a detection result.
Step three: and carrying out 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 using the dense distribution characteristic of the cluster target to obtain interested cluster target points, namely detecting the cluster target points.
Step four: and sequencing the interested cluster target points obtained after clustering and traversing the cluster target points, sequentially finding the boundary points of the clusters, connecting the boundary points, finding a minimum polygon containing all the cluster target points and the boundary of the clusters, estimating the cluster contour, counting the number of targets in the clusters under the contour, and finishing the estimation of the cluster scale by combining the contour information of the clusters.
The final results of detecting the cluster target and estimating the cluster information are shown in fig. 2, and sequentially from left to right and from top to bottom: the method comprises the steps of clustering scene graphs, clustering original radar echo graphs, two-dimensional super-resolution result graphs, CFAR detection result graphs, clustering result graphs and result graphs for cluster scale and contour estimation. Fig. 3 and 4 respectively show the comparison of the method provided by the present invention and the conventional pulse pressure-CFAR detection method on the estimation error of the cluster contour and the cluster size. As can be seen from the figure, the method provided by the invention can detect the cluster target from the radar echo with indistinguishable targets, and can acquire the scale and contour information of the cluster, thereby having higher accuracy.
The embodiment of the invention shows that 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, and has higher prediction accuracy and good performance under a certain signal-to-noise ratio.
Claims (7)
1. An unmanned aerial vehicle cluster target detection method comprises the following steps:
s1, performing vector superposition on radar echoes of a single unmanned aerial vehicle based on the relation between a single scatterer and a cluster to obtain cluster target echoes S (tau, t), wherein tau is a distance dimension fast time variable, t is an orientation dimension slow time variable, and rewriting the cluster target echoes S (tau, t) into S (R, theta) through the relation between a distance dimension time delay and a distance and between the orientation dimension time delay and an angle, theta is an orientation dimension angle variable, and R is a distance dimension distance variable;
s2, the 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 distance dimension echo waves, and the resolution of the distance dimension is improved to the degree that the distance dimension is not limited
B is the bandwidth of a transmitting signal, c is the speed of light, and a distance dimension super-resolution result S' (R, theta) is obtained through pulse compression;
s3, converting the l of the azimuth dimension echo matrix1Norm and nuclear norm as regularized constraint terms under which solution is performed
Where u (θ) is the cluster targetH (theta) is an antenna directional diagram modulation function with an independent variable of an angle theta, and S (theta) is an azimuth dimension echo at each distance; solving an optimal value through iteration, limiting inherent ill-posed characteristics of deconvolution, solving u (theta), and further obtaining a two-dimensional super-resolution result u (R, theta) of a cluster target;
s4, setting a false alarm rate and a reference window length, and carrying out Constant False Alarm Rate (CFAR) detection on the super-resolution result u (R, theta) to preliminarily obtain a detection result of the cluster target;
s5, a group of neighborhood parameters (epsilon, MinPts) is set, wherein epsilon represents the set minimum distance of targets in the cluster, MinPts represents the minimum number of targets in the cluster, clustering processing based on space density is carried out on CFAR detection results, false alarm points generated by noise or clutter in the CFAR process are removed, and cluster target points are detected.
2. The unmanned aerial vehicle cluster target detection method of claim 1,
the cluster target modeling process described in step S1 is:
where n is the number of targets in the cluster, umIs the scattering coefficient of the mth target, h (t) is the antenna pattern modulation function with the independent variable as time t, rect (-) is a rectangular window function, taumIs the distance dimension time delay of the mth target, fcIs the carrier frequency of the radar transmitted signal, T is the time width of the radar transmitted pulse, and k is the chirp rate of the radar transmitted chirp signal.
3. The method for detecting the unmanned aerial vehicle cluster target according to claim 2, wherein S (R, θ) in step S1 is specifically:
wherein, thetamIs the angle of the mth target with respect to the normal direction of the antenna, R (theta)m) Is the distance from the mth target to the radar, and λ is the radar transmitted signal wavelength.
4. The unmanned aerial vehicle cluster target detection method of claim 3,
s' (R, θ) in step S2 is specifically:
5. a method for radar-based drone cluster object detection according to claim 4,
the azimuth-dimensional echo S (θ) at each distance described in step S3 is represented as:
changing equation (4) to a convolution form and considering the noise effect, the azimuth-dimension echo is expressed as:
S(θ)＝u(θ)*h(θ)+n(θ) (5)
where n (θ) is noise.
L of azimuth dimension echo matrix1The norm and the nuclear norm are used as constraint terms, iterative solution of target azimuth dimension distribution is completed on the basis of a regularization theory, and an azimuth dimension super-resolution result is obtained:
wherein s (θ) is the scattering coefficient distribution of the target of interest, b (θ) is the scattering coefficient distribution of the background environment, | · calvert | | caldol2Is 12Norm, | · | luminance1Is 11Norm of，||·||*Is the matrix kernel norm whose value is the sum of all singular values of the matrix, lambda1And λ2Is a regularization parameter;
and (4) carrying out super-resolution processing on the distance dimension and the azimuth dimension to obtain a super-resolution result u (R, theta) of the cluster target.
6. An unmanned aerial vehicle cluster target contour and cluster scale estimation method based on the unmanned aerial vehicle cluster target detection method of claims 1-5, characterized by further comprising the steps of:
s6, polar angle sorting is carried out on the cluster target points detected in the step S5, all the points are traversed, the boundary target points of the clusters are found by continuously updating the elements in the stacks of the boundary points, the boundary target points are sequentially connected to obtain the boundary information of the clusters, the contour estimation of the cluster target is completed, then the number of the targets in the clusters is counted under the contour, and the estimation of the cluster scale is completed by combining the contour information of the clusters.
7. The method for estimating unmanned aerial vehicle cluster target profile and cluster size of claim 6, wherein all the detected cluster target points P in step S6i(R, θ) is put into the same coordinate system, where i is 1,2, …, N is the number of detected cluster target points, the point with the smallest ordinate is found first, and the point is assumed to be P1With P1And (3) as an original point, carrying out polar angle sequencing on all points, wherein the sequencing rule is as follows: will PiAnd P1The connection, the number of the points with small included angle between the connecting line and the positive direction of the abscissa is small, and if the included angles are the same, the distance P is1The number of the last is small; after finishing the sequence P1、P2、PNThese three points must be points on the cluster envelope; from P2The points sequentially traverse all the points, two points at the top of the stack are taken each time and are connected into a straight line with the current point PiBy comparison, if PiStack directly on the left of the line if PiAt the right side of the straight line, a point at the top of the stack is popped out, and a line formed by connecting two points in the stack and the P are continuously takeniComparison until PiTo the left of a straight lineSide by side, PiAnd (4) stacking, namely after all the points are traversed, connecting the remaining points in the stack in sequence to obtain the boundary information of the cluster, so as to obtain the estimation of the cluster outline.
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Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110006944A1 (en) * | 2009-06-19 | 2011-01-13 | U.S. Government As Represented By The Secretary Of The Army | Computationally efficent radar processing method and sytem for sar and gmti on a slow moving platform |
| 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 |
| WO2018063612A1 (en) * | 2016-09-30 | 2018-04-05 | Autoliv Asp, Inc. | System and method for detecting heading and velocity of a target object |
| 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 |
| CN109344772A (en) * | 2018-09-30 | 2019-02-15 | 中国人民解放军战略支援部队信息工程大学 | Ultrashort wave signal specific reconnaissance method based on spectrogram and depth convolutional network |
| 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 |
| CN111123257A (en) * | 2019-12-30 | 2020-05-08 | 西安电子科技大学 | Radar moving target multi-frame joint detection method based on graph space-time network |
| CN111580093A (en) * | 2020-05-25 | 2020-08-25 | 南京理工大学 | Radar system for detecting unmanned aerial vehicle cluster target |
| US20200284901A1 (en) * | 2019-01-23 | 2020-09-10 | 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 |
| US20200300972A1 (en) * | 2015-07-17 | 2020-09-24 | Origin Wireless, Inc. | Method, apparatus, and system for vital signs monitoring using high frequency wireless signals |
| EP3715901A1 (en) * | 2019-03-29 | 2020-09-30 | Robin Radar Facilities BV | Detection and classification of unmanned aerial vehicles |
| CN112346030A (en) * | 2020-10-30 | 2021-02-09 | 西安电子科技大学 | Super-resolution direction-of-arrival estimation method for unmanned aerial vehicle cluster |
| 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 |
| CN113806920A (en) * | 2021-08-25 | 2021-12-17 | 中山大学 | Unmanned aerial vehicle cluster electromagnetic scattering simulation method, device, equipment and medium |
| CN113807591A (en) * | 2021-09-22 | 2021-12-17 | 电子科技大学 | Cooperative optimization deployment method for communication distance-limited unmanned aerial vehicle cluster station |
| WO2022012213A1 (en) * | 2020-07-13 | 2022-01-20 | 深圳大学 | Method for eliminating one-bit signal harmonic false target, and related component |
| CN114114192A (en) * | 2021-12-02 | 2022-03-01 | 电子科技大学 | Cluster target detection method |
-
2022
- 2022-03-25 CN CN202210298426.4A patent/CN114609604B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110006944A1 (en) * | 2009-06-19 | 2011-01-13 | U.S. Government As Represented By The Secretary Of The Army | Computationally efficent radar processing method and sytem for sar and gmti on a slow moving platform |
| US20200300972A1 (en) * | 2015-07-17 | 2020-09-24 | Origin Wireless, Inc. | Method, apparatus, and system for vital signs monitoring using high frequency wireless signals |
| WO2018063612A1 (en) * | 2016-09-30 | 2018-04-05 | Autoliv Asp, 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 |
| 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 |
| CN109344772A (en) * | 2018-09-30 | 2019-02-15 | 中国人民解放军战略支援部队信息工程大学 | Ultrashort wave signal specific reconnaissance method based on spectrogram and depth convolutional network |
| US20200284901A1 (en) * | 2019-01-23 | 2020-09-10 | 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 |
| 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 |
| CN111123257A (en) * | 2019-12-30 | 2020-05-08 | 西安电子科技大学 | Radar moving target multi-frame joint detection method based on graph space-time network |
| CN111580093A (en) * | 2020-05-25 | 2020-08-25 | 南京理工大学 | Radar system for detecting unmanned aerial vehicle cluster target |
| WO2022012213A1 (en) * | 2020-07-13 | 2022-01-20 | 深圳大学 | Method for eliminating one-bit signal harmonic false target, and related component |
| CN112346030A (en) * | 2020-10-30 | 2021-02-09 | 西安电子科技大学 | Super-resolution direction-of-arrival estimation method for unmanned aerial vehicle cluster |
| 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 |
| CN113806920A (en) * | 2021-08-25 | 2021-12-17 | 中山大学 | Unmanned aerial vehicle cluster electromagnetic scattering simulation method, device, equipment and medium |
| CN113807591A (en) * | 2021-09-22 | 2021-12-17 | 电子科技大学 | Cooperative optimization deployment method for communication distance-limited unmanned aerial vehicle cluster station |
| CN114114192A (en) * | 2021-12-02 | 2022-03-01 | 电子科技大学 | Cluster target detection method |
Non-Patent Citations (5)
| Title |
|---|
| P. WELLIG, P. SPEIRS, C. SCHUEPBACH, R. OECHSLIN, M. RENKER, U.: "Radar systems and challenges for C-UAV", 《2018 19TH INTERNATIONAL RADAR SYMPOSIUM》, pages 1 - 8 * |
| W. WANG, Z. TANG, J. XIANG, Y. CHEN, Y. ZHANG, H. XIAO: "A sorties recognition algorithm of formation targets for wide-band radar", 《2019 INTERNATIONAL CON-FERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (ICCAIS)》, pages 1 - 5 * |
| 井田;王涛;王维平;李小波;周鑫;: "一种持续侦察无人机集群规模自适应调控方法", 计算机研究与发展, no. 06, pages 140 - 148 * |
| 孙立宏: "高速运动雷达弱小目标检测方法研究", 《系统工程与电子技术》, vol. 30, no. 2, pages 257 - 260 * |
| 时晨光;丁琳涛;周建江;: "基于Stackelberg博弈的有人机/无人机混合集群辐射功率控制算法", 《无人系统技术》, no. 04, pages 33 - 45 * |
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