CN114935736A - Moving platform distributed coherent radar grating lobe suppression method and device and computer equipment - Google Patents

Moving platform distributed coherent radar grating lobe suppression method and device and computer equipment Download PDF

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CN114935736A
CN114935736A CN202210874431.5A CN202210874431A CN114935736A CN 114935736 A CN114935736 A CN 114935736A CN 202210874431 A CN202210874431 A CN 202210874431A CN 114935736 A CN114935736 A CN 114935736A
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CN114935736B (en
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王宏强
王元昊
杨琪
曾旸
易俊
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National University of Defense Technology
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Abstract

The application relates to a method and a device for suppressing grating lobes of a distributed coherent radar of a moving platform and computer equipment. Designing a motion mode for nodes of the moving platform distributed coherent radar, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent; constructing an initial accumulation beam forming model according to the node distance and designing a model to optimize the accumulation time, constructing and solving an accumulation beam forming optimization model according to the model optimized accumulation time and the beam weight vector to obtain a candidate beam weight matrix; obtaining a node distance measurement value and a relative angle between a node and a reference node, taking a candidate beam weight vector corresponding to a node distance closest to the node distance measurement value as a middle beam weight vector, and processing the middle beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector, so as to obtain an optimized accumulated beam for forming grating lobe suppression. The invention can improve the non-fuzzy measurement effect and ensure the real-time performance.

Description

Moving platform distributed coherent radar grating lobe suppression method and device and computer equipment
Technical Field
The application relates to the technical field of wireless communication, in particular to a method and a device for restraining grating lobes of a distributed coherent radar of a moving platform and computer equipment.
Background
The distributed radar has multiple working modes, has outstanding advantages when the distributed radar performs fusion work at a signal level, can obtain the same performance as a phased array antenna with the same aperture theoretically, and can be called as a distributed coherent radar at the moment. Because different node radars are in different working platforms, and considering that different platforms have independent carriers, the antenna spacing of the distributed coherent radar often far exceeds half wavelength, and the direct detection without processing can bring serious grating lobe problems.
The existing grating lobe suppression means can be summarized into a directional diagram comprehensive problem which is essentially a mathematical optimization problem and can be roughly divided into two types, one type is a convex optimization method and an improvement method thereof, wherein an iterative Fourier transform algorithm improves the calculation efficiency of the convex optimization problem, the number of array elements cannot be accurately controlled by a thin-cloth linear array with a pencil beam directional diagram, and the method for searching the optimal position through scaling iteration of the analog position of the array elements has fewer addable constraint conditions; the other is a global optimization approach, which tends to require a large number of array elements. Although these methods work well in certain situations where the main objective is to reduce the number of arrays, they are not suitable for distributed coherent radars, because for distributed coherent radars all adjacent nodes are spaced more than half a wavelength apart and it is difficult to achieve a large number of nodes under the prior art conditions, and the number of nodes reported in the literature is at most 2.
Therefore, researchers propose to perform grating lobe suppression by using the limited number of node motion compensation nodes, and analyze the minimum value of the node distance change required for suppressing the grating lobe while ensuring the width of the main lobe under an ideal condition, but the method does not consider the problem that the nodes have motion errors under an actual condition, and simultaneously adopts a uniform weighting mode to directly accumulate and form the grating lobe which can be suppressed, but has higher side lobes.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus and a computer device for grating lobe suppression of a distributed coherent radar with a moving platform, so as to ensure low level side lobes during grating lobe suppression.
A dynamic platform distributed coherent radar grating lobe suppression method comprises the following steps:
designing a motion mode for nodes of the moving platform distributed coherent radar, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent;
constructing an initial accumulation beam forming model according to the node distance; the node distance is the distance between a node and a preset reference node; the node distance is obtained by calculating the adjacent node distance;
designing a model optimization accumulation moment according to the initial accumulation beam forming model, constructing and solving an accumulation beam forming optimization model according to the model optimization accumulation moment and the beam weight vector, and obtaining a candidate beam weight matrix; the candidate beam weight matrix comprises candidate beam weight vectors corresponding to a plurality of model optimization accumulation moments;
acquiring a node distance measurement value and a relative angle between a node and the reference node, taking a candidate beam weight vector corresponding to a node distance closest to the node distance measurement value as a middle beam weight vector, and processing the middle beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector;
and obtaining the optimized accumulated beam according to the optimized beam weight vector and performing grating lobe suppression.
In one embodiment, optimizing the number of accumulation instants according to the initial accumulation beamforming model design model includes:
obtaining a combined constraint model of accumulation time number and interval increment and an independent constraint model of the interval increment according to the initial accumulation beam forming model;
solving the combined constraint model and the independent constraint model to obtain the optimal accumulation time number and the optimal interval increment;
and optimizing the number of accumulated time according to the optimal number of accumulated time and the optimal spacing increment design model.
In one embodiment, the constructing the initial cumulative beam forming model according to the node distance includes:
and constructing an array manifold vector according to the node distance:
Figure 859981DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 903024DEST_PATH_IMAGE002
to represent
Figure 977159DEST_PATH_IMAGE003
The array manifold vector of the time of day,
Figure 738442DEST_PATH_IMAGE004
the antenna directional gain of the node is represented,
Figure 454594DEST_PATH_IMAGE005
which represents the direction of the incoming wave,
Figure 617722DEST_PATH_IMAGE006
expressed in terms of wavelength
Figure 3704DEST_PATH_IMAGE007
The distance between adjacent nodes at a time is,
Figure 642495DEST_PATH_IMAGE008
representing the number of nodes of the distributed coherent radar.
And constructing an initial accumulation beam forming model according to the array manifold vector:
Figure 772125DEST_PATH_IMAGE009
Figure 412929DEST_PATH_IMAGE010
Figure 969812DEST_PATH_IMAGE011
Figure 564742DEST_PATH_IMAGE012
wherein the content of the first and second substances,
Figure 232483DEST_PATH_IMAGE013
which represents the initial cumulative beam-forming,
Figure 229258DEST_PATH_IMAGE014
indicating the distance between adjacent nodes at the initial time,
Figure 957043DEST_PATH_IMAGE015
indicating the increment of the pitch between adjacent time instants,
Figure 304848DEST_PATH_IMAGE016
the initial array factor is represented as a function of,
Figure 510701DEST_PATH_IMAGE017
representing a dynamic factor.
In one embodiment, obtaining a joint constraint model of accumulation time number and spacing increment according to the initial accumulation beamforming model includes:
obtaining a combined constraint model of accumulation time number and interval increment according to the dynamic factor and the initial array factor of the initial accumulation beam forming model:
Figure 394606DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 293291DEST_PATH_IMAGE019
the position of the first side lobe of the dynamic factor,
Figure 3758DEST_PATH_IMAGE020
the position of the first grating lobe of the initial array factor.
In one embodiment, obtaining the independent constraint model of the distance increment according to the initial cumulative beamforming model includes:
and constructing an independent constraint model of the spacing increment according to the dynamic factors of the initial cumulative beam forming model and the antenna directional gain:
Figure 137937DEST_PATH_IMAGE021
Figure 515828DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure 444470DEST_PATH_IMAGE023
the main lobe width representing the antenna directional gain.
In one embodiment, constructing a cumulative beamforming optimization model according to the model-optimized cumulative number of time instants and the beam weight vector includes:
and constructing an accumulation beam forming optimization model according to the model optimization accumulation time number and the beam weight vector, wherein the accumulation beam forming optimization model comprises the following steps:
Figure 642233DEST_PATH_IMAGE024
Figure 314523DEST_PATH_IMAGE025
Figure 15763DEST_PATH_IMAGE026
Figure 613841DEST_PATH_IMAGE027
Figure 298900DEST_PATH_IMAGE028
Figure 774881DEST_PATH_IMAGE029
Figure 861786DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 397809DEST_PATH_IMAGE032
indicating the ideal single-pass beam-forming,
Figure 304585DEST_PATH_IMAGE033
to represent
Figure 318678DEST_PATH_IMAGE034
The unit matrix of (a) is,
Figure 260089DEST_PATH_IMAGE035
a region of the main lobe is represented,
Figure 842380DEST_PATH_IMAGE036
a side lobe region is shown and,
Figure 862551DEST_PATH_IMAGE037
a set of discrete angles of interest is represented,
Figure 555700DEST_PATH_IMAGE038
the representation model optimizes the number of integration instants,
Figure 476252DEST_PATH_IMAGE039
is shown in
Figure 495023DEST_PATH_IMAGE040
At the first moment
Figure 501025DEST_PATH_IMAGE041
The beam weight of each node.
In one embodiment, optimizing the number of integration moments according to the optimal number of integration moments and the optimal pitch increment design model includes:
designing a model to optimize the pitch increment according to the optimal pitch increment:
Figure 997866DEST_PATH_IMAGE042
wherein, the first and the second end of the pipe are connected with each other,
Figure 648290DEST_PATH_IMAGE043
the model-optimized coefficients are represented by,
Figure 962597DEST_PATH_IMAGE044
，
Figure 596840DEST_PATH_IMAGE045
the model is represented as an optimized pitch increment,
Figure 631793DEST_PATH_IMAGE046
representing an optimal pitch increment;
obtaining the model optimized accumulation time number according to the model optimized interval increment and the optimal accumulation time number:
Figure 759892DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 120466DEST_PATH_IMAGE048
the number of the best accumulated time instants is indicated,
Figure 101061DEST_PATH_IMAGE050
the representation model optimizes the cumulative number of moments.
In one embodiment, the processing the intermediate beam weight vector according to the relative angle and the inter-node distance measurement value to obtain an optimized beam weight vector includes:
processing the intermediate beam weight according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector as follows:
Figure 939704DEST_PATH_IMAGE051
wherein the content of the first and second substances,
Figure 564720DEST_PATH_IMAGE052
represent
Figure 220829DEST_PATH_IMAGE053
The optimized beam weight vector for a time instant,
Figure 564086DEST_PATH_IMAGE054
to represent
Figure 206420DEST_PATH_IMAGE055
The intermediate beam weight vector for a time instant,
Figure 810577DEST_PATH_IMAGE056
is shown in
Figure 512953DEST_PATH_IMAGE057
At the first moment
Figure 704026DEST_PATH_IMAGE058
The relative angle of the individual nodes to the reference node,
Figure 884471DEST_PATH_IMAGE059
is shown in
Figure 484080DEST_PATH_IMAGE060
At the first moment
Figure 481992DEST_PATH_IMAGE061
The node distance measurement of each node from the reference node.
A moving platform distributed coherent radar grating lobe suppression apparatus, the apparatus comprising:
the motion mode design module is used for designing a motion mode for the nodes of the moving platform distributed coherent radar, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent;
the initial model building module is used for building an initial accumulation beam forming model according to the node distance; the node distance is the distance between a node and a preset reference node; the node distance is obtained by calculating the adjacent node distance;
the optimization model solving module is used for optimizing the accumulation time number according to the initial accumulation beam forming model design model, and constructing and solving an accumulation beam forming optimization model according to the model optimized accumulation time number and the beam weight vector to obtain a candidate beam weight matrix; the candidate beam weight matrix comprises candidate beam weight vectors corresponding to a plurality of model optimization accumulation moments;
the weight vector optimization module is used for acquiring a node distance measurement value and a relative angle between a node and the reference node, taking a candidate beam weight vector corresponding to a node distance closest to the node distance measurement value as a middle beam weight vector, and processing the middle beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector;
and the grating lobe suppression module is used for obtaining the optimized accumulated beam according to the optimized beam weight vector and performing grating lobe suppression.
A computer device comprising a memory storing a computer program and a processor implementing the following steps when the computer program is executed:
designing a motion mode for nodes of the distributed coherent radar of the moving platform, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent;
constructing an initial accumulation beam forming model according to the node distance; the node distance is the distance between a node and a preset reference node; the node distance is obtained by calculating the adjacent node distance;
designing a model optimization accumulation time number according to the initial accumulation beam forming model, constructing and solving an accumulation beam forming optimization model according to the model optimization accumulation time number and a beam weight vector, and obtaining a candidate beam weight matrix; the candidate beam weight matrix comprises candidate beam weight vectors corresponding to a plurality of model optimization accumulation moments;
acquiring a node distance measurement value and a relative angle between a node and the reference node, taking a candidate beam weight vector corresponding to a node distance closest to the node distance measurement value as a middle beam weight vector, and processing the middle beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector;
and obtaining the optimized accumulated beam according to the optimized beam weight vector and performing grating lobe suppression.
According to the method, the device and the computer equipment for restraining the grating lobes of the distributed coherent radar of the moving platform, firstly, a motion mode is designed for the nodes of the distributed coherent radar of the moving platform, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent; then, an initial accumulation beam forming model is constructed according to the node distance, wherein the node distance is the distance between a node and a preset reference node; then, designing a model according to the initial cumulative beam forming model to optimize the cumulative time number, and constructing and solving a cumulative beam forming optimization model according to the model optimized cumulative time number and the beam weight vector to obtain a candidate beam weight matrix, wherein the candidate beam weight matrix comprises a plurality of candidate beam weight vectors corresponding to the model optimized cumulative time; acquiring a node spacing measurement value and a relative angle between a node and a reference node, taking a candidate beam weight vector corresponding to a node spacing closest to the node spacing measurement value as a middle beam weight vector, processing the middle beam weight vector according to the relative angle and the node spacing measurement value to obtain an optimized beam weight vector, and obtaining an optimized accumulated beam according to the optimized beam weight vector to perform grating lobe suppression. Due to the design of the first-step motion mode, the node distance can be directly obtained through the number difference between the node and a preset reference node and the distance between adjacent nodes; it can be known that the variation value of the adjacent node spacing is reduced along with the increase of the accumulation time within a certain period of time, considering that the actual position of each node is unknown, the model is designed according to the initial accumulation beam forming model to optimize the accumulation time, so that the accumulation beam forming optimization model is solved by using the smaller variation value of the adjacent node spacing, namely the smaller space sampling spacing, a plurality of candidate beam weight vectors under the model optimization accumulation time can be correspondingly obtained, then the node position is measured in real time in the actual movement process of the node, a proper intermediate beam weight vector is selected from the candidate beam weight vectors according to the nearest neighbor criterion, the intermediate beam weight vector is subjected to motion compensation according to the relative angle and the node spacing measurement value to obtain the final optimized beam weight vector, and thus the dynamic weighting replaces the fixed weighting and finally achieves the grating lobe suppression, the method has the advantages that the purpose of reducing side lobes is achieved, in addition, the calculation amount of a mode for acquiring the weight vector of the middle wave beam is small, and comprehensively, the method can improve the non-fuzzy measurement effect of the distributed coherent radar of the moving platform and ensure the real-time performance of measurement.
Drawings
FIG. 1 is a schematic flow chart of a dynamic platform distributed coherent radar grating lobe suppression method in one embodiment;
FIG. 2 is a diagram of nearest neighbor criteria in one embodiment;
FIG. 3 is a diagram illustrating an actual geometry of an actual distributed coherent radar in one embodiment;
FIG. 4 is a graph comparing actual motion and ideal motion of a node 2 provided in one embodiment;
FIG. 5 is a diagram illustrating simulation results of cumulative beam forming of distributed coherent radar of the mobile platform according to an embodiment of the present invention, (2)
Figure 534261DEST_PATH_IMAGE062
）；
FIG. 6 is a block diagramIn the embodiment, a simulation result diagram of cumulative beam forming of the distributed coherent radar of the moving platform is shown (
Figure 518398DEST_PATH_IMAGE063
，
Figure 362726DEST_PATH_IMAGE064
）；
FIG. 7 is a block diagram of a distributed coherent radar grating lobe suppression device of a moving platform according to an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
In one embodiment, as shown in fig. 1, a method for suppressing grating lobes of a distributed coherent radar with a moving platform is provided, which includes the following steps:
102, designing a motion mode for nodes of the moving platform distributed coherent radar, so that all the nodes are located on a straight line under an ideal condition and the distances between adjacent nodes at the same time are kept consistent.
Assuming that the number of nodes of the dynamic platform distributed coherent radar is 3, the designed motion mode is shown in table 1:
TABLE 1 node motion patterns
Figure 141326DEST_PATH_IMAGE065
It can be seen that, in the motion mode shown in table 1, the difference values of the initial positions of the adjacent nodes are 5, the difference values of the speeds of the adjacent nodes are 10, the motion model is a constant speed, and it can be considered that the nodes only do uniform linear motion in the X direction in the X-Y two-dimensional coordinate system, and then the distances between the adjacent nodes at the same time are kept consistent:
Figure 946471DEST_PATH_IMAGE066
wherein
Figure 593353DEST_PATH_IMAGE067
Expressed in terms of wavelength
Figure 167554DEST_PATH_IMAGE068
The adjacent node spacing at a time.
Taking the node 1 as a reference node:
Figure 740224DEST_PATH_IMAGE069
it can be known that, as long as the difference values of the initial positions of the adjacent nodes are consistent, the difference values of the speeds of the adjacent nodes are consistent, and all the nodes make uniform linear motion in the same direction, all the nodes are located on a straight line under an ideal condition and the distance between the adjacent nodes at the same time is kept consistent:
Figure 767086DEST_PATH_IMAGE070
therefore, the movement pattern of the node is not limited to the data shown in table 1, and can be designed according to the actual situation under the condition of meeting the requirement.
And 104, constructing an initial accumulation beam forming model according to the node distance.
The node distance is a distance between a node and a preset reference node, and due to the design of the motion mode in step 102, the node distance can be directly calculated by the difference between the serial numbers of the node and the preset reference node and the distance between adjacent nodes at corresponding moments.
The contradiction between the prior art and the grating lobe suppression of the distributed coherent radar is that the number of nodes is very limited while the distributed coherent radar does not have the distance between adjacent nodes which is smaller than or close to half wavelength, so that a beam without the grating lobe is difficult to form by single emission.
And 106, designing a model according to the initial cumulative beam forming model to optimize the cumulative time number, and constructing and solving a cumulative beam forming optimization model according to the model optimized cumulative time number and the beam weight vector to obtain a candidate beam weight matrix.
The candidate beam weight matrix comprises a plurality of candidate beam weight vectors corresponding to the model optimization accumulation time instant.
As shown in step 102, the distances between adjacent nodes at the same time are consistent, but as time goes by, the distances between adjacent nodes also change:
Figure 93025DEST_PATH_IMAGE071
Figure 646366DEST_PATH_IMAGE072
in the actual measurement process, it is not practical to select too short sampling interval time, but a smaller sampling interval time can be set when the model is optimized, and the change value of the distance between the adjacent nodes is smaller
Figure 766769DEST_PATH_IMAGE073
. Because the variation value of the adjacent node distance is reduced along with the increase of the accumulation time within a certain period of time, a smaller one is selected
Figure 546506DEST_PATH_IMAGE074
Namely, a larger model is designed to optimize the accumulated time number. Because the sampling interval selected in the actual measurement process is larger than the sampling interval selected when the model is optimized and the corresponding accumulation time is smaller than the model optimized accumulation time, the candidate beam weight matrix can be known to cover the beam weight vector at each time in the actual measurement process.
And step 108, acquiring the node distance measurement value and the relative angle between the node and the reference node, taking the candidate beam weight vector corresponding to the node distance closest to the node distance measurement value as an intermediate beam weight vector, and processing the intermediate beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector.
In an actual situation, the movement of the node has a control error, that is, the distances between adjacent nodes in the actual situation may not be consistent, and then after the distance between nodes is calculated according to the difference between the numbers of the node and the preset reference node and the distance between adjacent nodes at the corresponding moment, the distance error caused by the movement of the node needs to be considered; in practical cases, all nodes may not always be located on a straight line, i.e. ideally, the relative angle is 0, and the true relative angle between the node and the reference node, i.e. the relative angle error, needs to be considered.
According to the content of step 106, since the measurement values of the node distance and the relative angle cannot be obtained in advance, the invention proposes to use smaller values first
Figure 535191DEST_PATH_IMAGE075
And (5) refining, constructing and solving an accumulated beam forming optimization model to obtain a candidate beam weight matrix.
As shown in fig. 2, a diagram of the nearest neighbor criterion is provided. In FIG. 2
Figure 818405DEST_PATH_IMAGE076
Indicates that node n is at
Figure 499922DEST_PATH_IMAGE077
The sample points at the time and reference node's distance measurement, and closest to the distance measurement of node n, are the sample points in the 1 st box in figure 2, and, similarly,
Figure 501376DEST_PATH_IMAGE078
indicates that node n is at
Figure 903538DEST_PATH_IMAGE079
Time of dayAnd selecting a candidate beam weight vector corresponding to the sampling point closest to the distance measurement value as an intermediate beam weight vector through a nearest neighbor criterion, namely dynamically weighting the node according to a measurement result and the nearest neighbor criterion, wherein the calculation amount of the obtained intermediate beam weight vector is smaller, and the requirement of real-time property can be better met. In addition, the sampling point and the measured value corresponding to the intermediate beam weight vector can be known to have deviation, and in order to further improve the grating lobe suppression effect, the optimized beam weight vector is obtained after the intermediate beam weight vector is further compensated according to the relative angle and the measured value of the node distance.
And step 110, obtaining the optimized accumulated beam forming according to the optimized beam weight vector and performing grating lobe suppression.
In the method for suppressing the grating lobe of the moving platform distributed coherent radar, firstly, a motion mode is designed for the nodes of the moving platform distributed coherent radar, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent; then, an initial accumulation beam forming model is constructed according to the node distance, wherein the node distance is the distance between a node and a preset reference node; then, designing a model according to the initial cumulative beam forming model to optimize the cumulative time number, and constructing and solving a cumulative beam forming optimization model according to the model optimized cumulative time number and the beam weight vector to obtain a candidate beam weight matrix, wherein the candidate beam weight matrix comprises a plurality of candidate beam weight vectors corresponding to the model optimized cumulative time; acquiring a node spacing measurement value and a relative angle between a node and a reference node, taking a candidate beam weight vector corresponding to a node spacing closest to the node spacing measurement value as a middle beam weight vector, processing the middle beam weight vector according to the relative angle and the node spacing measurement value to obtain an optimized beam weight vector, and obtaining an optimized accumulated beam according to the optimized beam weight vector to perform grating lobe suppression. Due to the design of the first-step motion mode, the node distance can be directly obtained through the number difference between the node and a preset reference node and the distance between adjacent nodes; it can be known that the variation value of the adjacent node spacing is reduced along with the increase of the accumulation time within a certain period of time, considering that the actual position of each node is unknown, the model is designed according to the initial accumulation beam forming model to optimize the accumulation time, so that the accumulation beam forming optimization model is solved by using the smaller variation value of the adjacent node spacing, namely the smaller space sampling spacing, a plurality of candidate beam weight vectors under the model optimization accumulation time can be correspondingly obtained, then the node position is measured in real time in the actual movement process of the node, a proper intermediate beam weight vector is selected from the candidate beam weight vectors according to the nearest neighbor criterion, the intermediate beam weight vector is subjected to motion compensation according to the relative angle and the node spacing measurement value to obtain the final optimized beam weight vector, and thus the dynamic weighting replaces the fixed weighting and finally achieves the grating lobe suppression, the method has the advantages that the purpose of reducing side lobes is achieved, in addition, the calculation amount of a mode for acquiring the weight vector of the middle wave beam is small, and comprehensively, the method can improve the non-fuzzy measurement effect of the distributed coherent radar of the moving platform and ensure the real-time performance of measurement.
In one embodiment, constructing an initial cumulative beamforming model from node spacings comprises:
consider a two-dimensional scene containing
Figure 932936DEST_PATH_IMAGE080
The dynamic platform distributed coherent radar of the uniformly distributed nodes is characterized in that because each node radar is in motion, a time-varying form of an array manifold vector is given firstly, namely the array manifold vector is constructed according to the node distance:
Figure 660721DEST_PATH_IMAGE081
wherein, the first and the second end of the pipe are connected with each other,
Figure 883892DEST_PATH_IMAGE082
to represent
Figure 479958DEST_PATH_IMAGE083
The array manifold vector of the time of day,
Figure 472185DEST_PATH_IMAGE084
the antenna directional gain of the node is represented,
Figure 495505DEST_PATH_IMAGE085
which is representative of the direction of the incoming wave,
Figure 205972DEST_PATH_IMAGE086
expressed in terms of wavelength
Figure 481095DEST_PATH_IMAGE087
The distance between adjacent nodes at a time is,
Figure 452462DEST_PATH_IMAGE088
representing the number of nodes of the distributed coherent radar.
Constructing an initial cumulative beam forming model according to the array manifold vector:
Figure 787629DEST_PATH_IMAGE089
Figure 342982DEST_PATH_IMAGE090
Figure 156217DEST_PATH_IMAGE091
Figure 247670DEST_PATH_IMAGE092
wherein the content of the first and second substances,
Figure 488158DEST_PATH_IMAGE093
which represents the initial cumulative beam-forming,
Figure 173217DEST_PATH_IMAGE094
indicating the distance between adjacent nodes at the initial time,
Figure 914777DEST_PATH_IMAGE095
indicating pitch increments of adjacent time instants, i.e.
Figure 736103DEST_PATH_IMAGE096
In the range of variation of (a) or (b),
Figure 147493DEST_PATH_IMAGE097
the initial array factor is represented by a matrix index,
Figure 178902DEST_PATH_IMAGE098
representing a dynamic factor.
To reduce the computational complexity, the design node motions are all linear motions with uniform velocity, as described in step 102, and thus
Figure 599519DEST_PATH_IMAGE099
And time of day
Figure 901450DEST_PATH_IMAGE100
Regardless, as can be seen from the above equation, the single node antenna directional gain is removed
Figure 483741DEST_PATH_IMAGE101
Besides, the air conditioner is provided with a fan,
Figure 143393DEST_PATH_IMAGE102
the module value of (a) is mainly influenced by two terms, one term is an initial matrix factor, and the other term is an accumulative influence caused by motion, namely a dynamic factor.
In one embodiment, optimizing the number of accumulation instants according to an initial accumulation beamforming model design model comprises:
a. and solving the joint constraint model and the independent constraint model according to the initial accumulation beam forming model to obtain the optimal accumulation time number and the optimal spacing increment.
In order to suppress the grating lobes,
Figure 226755DEST_PATH_IMAGE103
the first side lobe should be located less than
Figure 22673DEST_PATH_IMAGE104
The position of the first grating lobe, wherein
Figure 41444DEST_PATH_IMAGE105
Approximately at the first side-lobe of
Figure 47447DEST_PATH_IMAGE106
The joint constraint model of the cumulative time number and the spacing increment is:
Figure 544287DEST_PATH_IMAGE107
it is considered that in the case of a small angle,
Figure 460290DEST_PATH_IMAGE108
the range of node pitch variation is obtained, i.e. the pitch increment should
Figure 509018DEST_PATH_IMAGE109
。
Figure 143261DEST_PATH_IMAGE110
Taking the maximum value as
Figure 801383DEST_PATH_IMAGE111
To avoid
Figure 571892DEST_PATH_IMAGE112
To be received
Figure 666887DEST_PATH_IMAGE113
A plurality of peak points appear in the main lobe of the single-node directional diagram,
Figure 647482DEST_PATH_IMAGE114
there are also constraints, assumptions
Figure 486125DEST_PATH_IMAGE115
Has a main lobe width of
Figure 235775DEST_PATH_IMAGE116
Then, then
Figure 767250DEST_PATH_IMAGE117
Should satisfy
Figure 110507DEST_PATH_IMAGE118
I.e., the independent constraint model of the pitch increment is:
Figure 611896DEST_PATH_IMAGE119
Figure 356998DEST_PATH_IMAGE120
and solving the joint constraint model and the independent constraint model to obtain the optimal accumulation time number and the optimal interval increment.
b. And optimizing the accumulation time number according to the optimal accumulation time number and the optimal spacing increment design model.
Designing a model to optimize the pitch increment according to the optimal pitch increment:
Figure 793795DEST_PATH_IMAGE121
wherein the content of the first and second substances,
Figure 250447DEST_PATH_IMAGE122
the model-optimized coefficients are represented by,
Figure 430892DEST_PATH_IMAGE123
，
Figure 296080DEST_PATH_IMAGE124
the model is represented as an optimized pitch increment,
Figure 28413DEST_PATH_IMAGE125
the optimum pitch increment is indicated.
Obtaining the model optimized accumulation time number according to the model optimized interval increment and the optimal accumulation time number:
Figure 80682DEST_PATH_IMAGE126
wherein the content of the first and second substances,
Figure 189453DEST_PATH_IMAGE127
the number of the best accumulated time instants is indicated,
Figure 909147DEST_PATH_IMAGE128
the representation model optimizes the number of integration instants.
In one embodiment, constructing a cumulative beamforming optimization model based on the model-optimized cumulative number of time instants and the beam weight vector comprises:
Figure 687747DEST_PATH_IMAGE129
the beam weight vector for a time instant may be expressed as:
Figure 351947DEST_PATH_IMAGE130
。
the beam weight vector is designed to concentrate the received energy in the desired direction, assuming that the distributed coherent radar transmits narrowband signals and the array operates in far field conditions, the single transmit beam pattern can be expressed as:
Figure 874195DEST_PATH_IMAGE131
。
in which power constraints, requirements are taken into account
Figure 448396DEST_PATH_IMAGE132
。
Because the node spacing is much larger than
Figure 44504DEST_PATH_IMAGE133
At this time, the transmitting beam pattern has serious grating lobes, and the distance between two adjacent grating lobes is recorded as
Figure 336945DEST_PATH_IMAGE134
It can be expressed as follows:
Figure 521938DEST_PATH_IMAGE135
according to the formula, the compound has the advantages of,
Figure 216225DEST_PATH_IMAGE136
can influence the grating lobe position, which further decorrelates the grating lobe position spatially, and the cumulative transmit beam pattern can be written as
Figure 336628DEST_PATH_IMAGE137
In the formula
Figure 975419DEST_PATH_IMAGE138
Representing the number of accumulations. At this time, discrete angle ranges of interest are considered
Figure 105049DEST_PATH_IMAGE139
，
Figure 388263DEST_PATH_IMAGE140
The selection of (c) can be converted into the following optimization problem:
Figure 804201DEST_PATH_IMAGE141
in the formula
Figure 805655DEST_PATH_IMAGE142
Representing an ideal transmit beam pattern, having the following expression
Figure 99495DEST_PATH_IMAGE143
In the formula
Figure 502795DEST_PATH_IMAGE144
And
Figure 965000DEST_PATH_IMAGE145
respectively represent a main lobe region and a side lobe region, and
Figure 578384DEST_PATH_IMAGE146
. The optimization problem can be further simplified as:
Figure 49817DEST_PATH_IMAGE147
Figure 307623DEST_PATH_IMAGE148
Figure 65363DEST_PATH_IMAGE149
wherein the content of the first and second substances,
Figure 41410DEST_PATH_IMAGE150
indicating the ideal single-pass beam-forming,
Figure 175588DEST_PATH_IMAGE151
a region of the main lobe is represented,
Figure 287900DEST_PATH_IMAGE152
the side lobe region is represented as a region of side lobes,
Figure 357487DEST_PATH_IMAGE153
a set of discrete angles of interest is represented,
Figure 178419DEST_PATH_IMAGE154
the representation model optimizes the number of accumulated time instants,
Figure 991655DEST_PATH_IMAGE155
is shown in
Figure 958474DEST_PATH_IMAGE156
At the first moment
Figure 323596DEST_PATH_IMAGE157
The beam weight of each node.
In one embodiment, it can be known that solving the joint constraint model and the independent constraint model described above can result in the optimal cumulative time number and the optimal spacing increment, but in practical cases, the motion of the node has control errors, and the actual array manifold can be expressed as
Figure 8655DEST_PATH_IMAGE158
Wherein
Figure 484636DEST_PATH_IMAGE159
Which represents the true pitch of the nodes and,
Figure 571541DEST_PATH_IMAGE160
indicating the distance error due to node motion,
Figure 982930DEST_PATH_IMAGE161
finger nodenAnd the relative angle error between the node 1 (reference node) is 0 in an ideal state, and a schematic diagram of the actual geometric configuration of the actual distributed coherent radar is shown in fig. 3.
Note the book
Figure 279919DEST_PATH_IMAGE162
And
Figure 434957DEST_PATH_IMAGE163
the measurement results are respectively
Figure 376368DEST_PATH_IMAGE164
And
Figure 850337DEST_PATH_IMAGE165
the measured variances are respectively
Figure 244410DEST_PATH_IMAGE166
And
Figure 62193DEST_PATH_IMAGE167
here, assuming that the measurement accuracy is sufficiently high, the actual cumulative beam can be written as:
Figure 123690DEST_PATH_IMAGE168
due to failure to obtain in advance
Figure 611303DEST_PATH_IMAGE169
And
Figure 882884DEST_PATH_IMAGE170
and therefore cannot directly build the cumulative beamforming optimization model described above. Therefore, the optimization problem is established by fine refinement at small intervals, then the coefficient selection is carried out according to the nearest neighbor criterion after the measurement result of each moment is obtained, and the acquisition of the dynamic weighting coefficient is realized, namely, the order is made
Figure 379725DEST_PATH_IMAGE171
Taking a smaller value, it is generally preferable
Figure 154783DEST_PATH_IMAGE172
An optimization problem is established as follows, and an optimization result is obtained by a sequence quadratic programming method:
Figure 78876DEST_PATH_IMAGE173
then obtaining the measurement result
Figure 447541DEST_PATH_IMAGE174
And
Figure 105662DEST_PATH_IMAGE175
then selects the nearest in real time
Figure 876172DEST_PATH_IMAGE176
Is/are as follows
Figure 361380DEST_PATH_IMAGE177
At the same time to
Figure 951761DEST_PATH_IMAGE178
Compensating the phase error to obtain an optimized beam weight vector of each node at each moment:
Figure 55983DEST_PATH_IMAGE179
it should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The present invention has been verified by simulation. The design simulation parameters are shown in table 2:
TABLE 2 simulation parameters
Figure 71213DEST_PATH_IMAGE180
As shown in fig. 4, a comparison graph of the actual movement and the ideal movement of the node 2 is provided, and it can be seen that the actual movement of the node has an error in the distance and the angle from the ideal movement.
As shown in fig. 5, a simulation result diagram of the cumulative beam forming of the distributed coherent radar of the mobile platform is provided (
Figure 337109DEST_PATH_IMAGE181
）。
As shown in fig. 6, a simulation result diagram of the cumulative beam forming of the distributed coherent radar of the mobile platform is provided (
Figure 539420DEST_PATH_IMAGE182
，
Figure 181754DEST_PATH_IMAGE183
）。
Under the condition of no node motion error, the highest sidelobe level is-13.79 dB, the highest sidelobe level of the uniformly weighted cumulative beam is about-6.99 dB, and the highest sidelobe level can be suppressed to-10.21 dB and-12.23 dB through the method and the device respectively corresponding to the graph 6
Figure 661277DEST_PATH_IMAGE184
，
Figure 989752DEST_PATH_IMAGE185
And FIG. 5
Figure 554726DEST_PATH_IMAGE186
. Simulation results show the effectiveness of the invention in suppressing side lobes, and the method of the invention is universal and can be easily expanded when the number of nodes is increased.
In one embodiment, as shown in fig. 7, there is provided a moving platform distributed coherent radar grating lobe suppression device, including: the device comprises a motion mode design module, an initial model construction module, an optimization model solving module, a weight vector optimization module and a grating lobe suppression module, wherein:
the motion mode design module is used for designing a motion mode for the nodes of the distributed coherent radar of the moving platform, so that all the nodes are positioned on a straight line under an ideal condition and the distances between the adjacent nodes at the same moment are kept consistent;
the initial model building module is used for building an initial accumulation beam forming model according to the node distance; the node distance is the distance between a node and a preset reference node; the node distance is obtained by calculating the distance between adjacent nodes;
the optimization model solving module is used for optimizing the accumulation time number according to the initial accumulation beam forming model design model, building and solving an accumulation beam forming optimization model according to the model optimization accumulation time number and the beam weight vector to obtain a candidate beam weight matrix; the candidate beam weight matrix comprises candidate beam weight vectors corresponding to a plurality of model optimization accumulation moments;
the weight vector optimization module is used for acquiring a node distance measurement value and a relative angle between a node and a reference node, taking a candidate beam weight vector corresponding to a node distance closest to the node distance measurement value as a middle beam weight vector, and processing the middle beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector;
and the grating lobe suppression module is used for obtaining the optimized accumulated beam according to the optimized beam weight vector and performing grating lobe suppression.
In one embodiment, the initial model building module is further configured to build the array manifold vector according to the pitch of the nodes:
Figure 859805DEST_PATH_IMAGE187
wherein the content of the first and second substances,
Figure 459414DEST_PATH_IMAGE188
to represent
Figure 67113DEST_PATH_IMAGE189
The array manifold vector of the time of day,
Figure 509595DEST_PATH_IMAGE190
the antenna directional gain of the node is represented,
Figure 493732DEST_PATH_IMAGE191
which represents the direction of the incoming wave,
Figure 947847DEST_PATH_IMAGE192
expressed in terms of wavelength
Figure 851081DEST_PATH_IMAGE055
The distance between adjacent nodes at a time is,
Figure 656226DEST_PATH_IMAGE193
representing the number of nodes of the distributed coherent radar.
And constructing an initial accumulation beam forming model according to the array manifold vector:
Figure 801643DEST_PATH_IMAGE194
Figure 110265DEST_PATH_IMAGE195
Figure 325345DEST_PATH_IMAGE196
Figure 742420DEST_PATH_IMAGE197
wherein the content of the first and second substances,
Figure 802780DEST_PATH_IMAGE198
which represents the initial cumulative beam-forming,
Figure 231487DEST_PATH_IMAGE199
indicating the distance between adjacent nodes at the initial time,
Figure 742103DEST_PATH_IMAGE200
indicating the increment of the pitch between adjacent time instants,
Figure 256261DEST_PATH_IMAGE201
the initial array factor is represented by a matrix index,
Figure 244946DEST_PATH_IMAGE202
representing a dynamic factor.
In one embodiment, the optimization model solving module is further configured to obtain a joint constraint model of the accumulation time number and the spacing increment and an independent constraint model of the spacing increment according to the initial accumulation beamforming model;
solving the joint constraint model and the independent constraint model to obtain the optimal accumulation time number and the optimal interval increment;
and optimizing the accumulation time number according to the optimal accumulation time number and the optimal spacing increment design model.
In one embodiment, the optimization model solving module is further configured to design the model optimized pitch increment according to the optimal pitch increment:
Figure 793739DEST_PATH_IMAGE203
wherein the content of the first and second substances,
Figure 85043DEST_PATH_IMAGE204
the model-optimized coefficients are represented by,
Figure 712596DEST_PATH_IMAGE205
，
Figure 380337DEST_PATH_IMAGE206
the model is represented as an optimized pitch increment,
Figure 783637DEST_PATH_IMAGE207
representing an optimal pitch increment;
obtaining the model optimized accumulation time number according to the model optimized interval increment and the optimal accumulation time number:
Figure 370476DEST_PATH_IMAGE208
wherein the content of the first and second substances,
Figure 859226DEST_PATH_IMAGE209
the number of the best accumulated time instants is indicated,
Figure 189713DEST_PATH_IMAGE210
the representation model optimizes the number of integration instants.
In one embodiment, the optimization model solving module is further configured to obtain a joint constraint model of the accumulation time number and the spacing increment according to the dynamic factor and the initial array factor of the initial accumulation beamforming model:
Figure 447519DEST_PATH_IMAGE211
wherein the content of the first and second substances,
Figure 346205DEST_PATH_IMAGE212
the position of the first side lobe of the dynamic factor,
Figure 446885DEST_PATH_IMAGE213
the position of the first grating lobe being the initial array factor;
constructing an independent constraint model of the spacing increment according to the dynamic factors and the antenna directional gain of the initial accumulation beam forming model:
Figure 190850DEST_PATH_IMAGE214
Figure 568742DEST_PATH_IMAGE215
wherein the content of the first and second substances,
Figure 995919DEST_PATH_IMAGE216
the main lobe width representing the antenna directional gain.
In one embodiment, the optimization model solving module is further configured to construct an accumulated beamforming optimization model according to the model optimized accumulated time number and the beam weight vector as follows:
Figure 193682DEST_PATH_IMAGE217
Figure 131551DEST_PATH_IMAGE218
Figure 98370DEST_PATH_IMAGE219
Figure 604438DEST_PATH_IMAGE220
Figure 148552DEST_PATH_IMAGE221
Figure 765478DEST_PATH_IMAGE222
Figure 711437DEST_PATH_IMAGE223
wherein the content of the first and second substances,
Figure 388406DEST_PATH_IMAGE224
representing an ideal single-pass beam-forming,
Figure 295182DEST_PATH_IMAGE225
to represent
Figure 76319DEST_PATH_IMAGE226
The unit matrix of (a) is,
Figure 17730DEST_PATH_IMAGE227
a region of the main lobe is represented,
Figure 865600DEST_PATH_IMAGE228
the side lobe region is represented as a region of side lobes,
Figure 384306DEST_PATH_IMAGE229
a set of discrete angles of interest is represented,
Figure 343035DEST_PATH_IMAGE230
the representation model optimizes the number of accumulated time instants,
Figure 998007DEST_PATH_IMAGE231
is shown in
Figure 16779DEST_PATH_IMAGE232
At the first moment
Figure 163726DEST_PATH_IMAGE233
The beam weight of each node.
In one embodiment, the weight vector optimization module is further configured to process the intermediate beam weights according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector:
Figure 785200DEST_PATH_IMAGE234
wherein the content of the first and second substances,
Figure 701204DEST_PATH_IMAGE235
to represent
Figure 359718DEST_PATH_IMAGE236
The optimized beam weight vector for a time instant,
Figure 617131DEST_PATH_IMAGE237
to represent
Figure 652083DEST_PATH_IMAGE238
The intermediate beam weight vector for a time instant,
Figure 547227DEST_PATH_IMAGE239
is shown in
Figure 642222DEST_PATH_IMAGE240
At the first moment
Figure 763761DEST_PATH_IMAGE241
The relative angle of the individual nodes to the reference node,
Figure 867984DEST_PATH_IMAGE242
is shown in
Figure 352055DEST_PATH_IMAGE243
At the first moment
Figure 211426DEST_PATH_IMAGE244
The node distance measurement of each node from the reference node.
For specific limitations of the moving platform distributed coherent radar grating lobe suppression device, reference may be made to the above limitations of the moving platform distributed coherent radar grating lobe suppression method, and details are not described here. All modules in the dynamic platform distributed coherent radar grating lobe suppression device can be wholly or partially realized through software, hardware and a combination of the software and the hardware. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The database of the computer device is used for storing radar node position and angle data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a moving platform distributed coherent radar grating lobe suppression method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for suppressing grating lobes of a distributed coherent radar of a moving platform is characterized by comprising the following steps:
designing a motion mode for nodes of the moving platform distributed coherent radar, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent;
constructing an initial accumulation beam forming model according to the node distance; the node distance is the distance between a node and a preset reference node; the node distance is obtained by calculating the adjacent node distance;
designing a model optimization accumulation moment according to the initial accumulation beam forming model, constructing and solving an accumulation beam forming optimization model according to the model optimization accumulation moment and the beam weight vector, and obtaining a candidate beam weight matrix; the candidate beam weight matrix comprises candidate beam weight vectors corresponding to a plurality of model optimization accumulation moments;
acquiring a node distance measurement value and a relative angle between a node and the reference node, taking a candidate beam weight vector corresponding to a node distance closest to the node distance measurement value as a middle beam weight vector, and processing the middle beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector;
and obtaining optimized accumulated beam forming according to the optimized beam weight vector and performing grating lobe suppression.
2. The method of claim 1, wherein optimizing the number of accumulation instants according to the initial cumulative beamforming model design model comprises:
obtaining a combined constraint model of accumulation time number and interval increment and an independent constraint model of the interval increment according to the initial accumulation beam forming model;
solving the combined constraint model and the independent constraint model to obtain the optimal accumulation time number and the optimal interval increment;
and optimizing the number of accumulated time according to the optimal number of accumulated time and the optimal spacing increment design model.
3. The method of claim 1, wherein the constructing an initial cumulative beam forming model according to node spacing comprises:
and constructing an array manifold vector according to the node spacing:
Figure 325421DEST_PATH_IMAGE001
wherein, the first and the second end of the pipe are connected with each other,
Figure 101616DEST_PATH_IMAGE002
to represent
Figure 350194DEST_PATH_IMAGE003
The array manifold vector of the time of day,
Figure 479824DEST_PATH_IMAGE004
the antenna directional gain of the node is represented,
Figure 153251DEST_PATH_IMAGE005
which represents the direction of the incoming wave,
Figure 444555DEST_PATH_IMAGE006
expressed in terms of wavelength
Figure 446009DEST_PATH_IMAGE007
The distance between adjacent nodes at a time is,
Figure 238385DEST_PATH_IMAGE008
representing the number of nodes of the distributed coherent radar;
and constructing an initial accumulation beam forming model according to the array manifold vector:
Figure 641684DEST_PATH_IMAGE009
Figure 729988DEST_PATH_IMAGE010
Figure 218738DEST_PATH_IMAGE011
Figure 690171DEST_PATH_IMAGE012
wherein, the first and the second end of the pipe are connected with each other,
Figure 807032DEST_PATH_IMAGE013
which represents the initial cumulative beam-forming,
Figure 705717DEST_PATH_IMAGE014
indicating the distance between adjacent nodes at the initial time,
Figure 681764DEST_PATH_IMAGE015
indicating the increment of the pitch between adjacent time instants,
Figure 815942DEST_PATH_IMAGE016
the initial array factor is represented by a matrix index,
Figure 928254DEST_PATH_IMAGE017
representing a dynamic factor.
4. The method of claim 3, wherein obtaining a joint constraint model of accumulated time instants and spacing increments from the initial accumulated beamforming model comprises:
obtaining a combined constraint model of accumulation time number and interval increment according to the dynamic factor and the initial array factor of the initial accumulation beam forming model:
Figure 997841DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 585818DEST_PATH_IMAGE019
the position of the first side lobe of the dynamic factor,
Figure 133474DEST_PATH_IMAGE020
the position of the first grating lobe of the initial array factor.
5. The method of claim 3, wherein deriving the independently constrained model of the spacing increment from the initial cumulative beamforming model comprises:
and constructing an independent constraint model of the spacing increment according to the dynamic factors of the initial cumulative beam forming model and the antenna directional gain:
Figure 365872DEST_PATH_IMAGE021
Figure 229529DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure 914589DEST_PATH_IMAGE023
the main lobe width representing the antenna directional gain.
6. The method of claim 3, wherein constructing a cumulative beamforming optimization model based on the model-optimized cumulative number of time instants and beam weight vectors comprises:
and constructing an accumulation beam forming optimization model according to the model optimization accumulation time number and the beam weight vector, wherein the accumulation beam forming optimization model comprises the following steps:
Figure 531515DEST_PATH_IMAGE024
Figure 477474DEST_PATH_IMAGE025
Figure 154443DEST_PATH_IMAGE026
Figure 185853DEST_PATH_IMAGE027
Figure 340891DEST_PATH_IMAGE028
Figure 282302DEST_PATH_IMAGE029
Figure 989227DEST_PATH_IMAGE030
wherein, the first and the second end of the pipe are connected with each other,
Figure 648878DEST_PATH_IMAGE031
indicating the ideal single-pass beam-forming,
Figure 968126DEST_PATH_IMAGE032
to represent
Figure 764044DEST_PATH_IMAGE033
The unit matrix of (a) is,
Figure 782816DEST_PATH_IMAGE034
a region of the main lobe is represented,
Figure 788818DEST_PATH_IMAGE035
the side lobe region is represented as a region of side lobes,
Figure 551237DEST_PATH_IMAGE036
a set of discrete angles of interest is represented,
Figure 201662DEST_PATH_IMAGE037
the representation model optimizes the number of accumulated time instants,
Figure 250389DEST_PATH_IMAGE038
is shown in
Figure 619053DEST_PATH_IMAGE039
At the first moment
Figure 919585DEST_PATH_IMAGE040
The beam weight of each node.
7. The method of claim 2, wherein optimizing the number of integration moments based on the optimal number of integration moments and the optimal pitch delta design model comprises:
designing a model to optimize the pitch increment according to the optimal pitch increment:
Figure 814728DEST_PATH_IMAGE041
wherein the content of the first and second substances,
Figure 909723DEST_PATH_IMAGE042
the model-optimized coefficients are represented by,
Figure 31263DEST_PATH_IMAGE043
，
Figure 493075DEST_PATH_IMAGE044
the model is represented as an optimized pitch increment,
Figure 852512DEST_PATH_IMAGE045
representing an optimal pitch increment;
obtaining the model optimized accumulation time number according to the model optimized interval increment and the optimal accumulation time number:
Figure 508622DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 851878DEST_PATH_IMAGE047
the number of the best accumulated time instants is indicated,
Figure 494212DEST_PATH_IMAGE048
the representation model optimizes the cumulative number of moments.
8. The method of claim 1, wherein processing the intermediate beam weight vector according to the relative angle and the inter-node distance measurement value to obtain an optimized beam weight vector comprises:
processing the intermediate beam weight according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector as follows:
Figure 363948DEST_PATH_IMAGE049
wherein the content of the first and second substances,
Figure 800746DEST_PATH_IMAGE050
to represent
Figure 631298DEST_PATH_IMAGE051
The optimized beam weight vector for a time instant,
Figure 936378DEST_PATH_IMAGE052
represent
Figure 535986DEST_PATH_IMAGE053
The intermediate beam weight vector for a time instant,
Figure 409264DEST_PATH_IMAGE054
is shown in
Figure 87633DEST_PATH_IMAGE055
At the first moment
Figure 71769DEST_PATH_IMAGE056
The relative angle of the individual nodes to the reference node,
Figure 650518DEST_PATH_IMAGE057
is shown in
Figure 694697DEST_PATH_IMAGE058
At the first moment
Figure 234263DEST_PATH_IMAGE059
The node distance measurement of each node from the reference node.
9. A moving platform distributed coherent radar grating lobe suppression device, the device comprising:
the motion mode design module is used for designing a motion mode for the nodes of the moving platform distributed coherent radar, so that all the nodes are positioned on a straight line under an ideal condition and the distances between adjacent nodes at the same moment are kept consistent;
the initial model building module is used for building an initial accumulation beam forming model according to the node distance; the node distance is the distance between a node and a preset reference node; the node distance is obtained by calculating the adjacent node distance;
the optimization model solving module is used for optimizing the accumulation time number according to the initial accumulation beam forming model design model, and constructing and solving an accumulation beam forming optimization model according to the model optimized accumulation time number and the beam weight vector to obtain a candidate beam weight matrix; the candidate beam weight matrix comprises candidate beam weight vectors corresponding to a plurality of model optimization accumulation moments;
the weight vector optimization module is used for acquiring a node distance measurement value and a relative angle between a node and the reference node, taking a candidate beam weight vector corresponding to a node distance closest to the node distance measurement value as a middle beam weight vector, and processing the middle beam weight vector according to the relative angle and the node distance measurement value to obtain an optimized beam weight vector;
and the grating lobe suppression module is used for obtaining the optimized accumulated beam according to the optimized beam weight vector and performing grating lobe suppression.
10. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the method of any one of claims 1 to 8 when executing the computer program.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103728597A (en) * 2014-01-16 2014-04-16 西安电子科技大学 Auxiliary array based networked radar suppression type main lobe jamming restraining method
CN103728595A (en) * 2014-01-16 2014-04-16 西安电子科技大学 Subspace-projection-based networked radar inhibition pressing type main lobe interference method
US20170102458A1 (en) * 2014-06-09 2017-04-13 Src, Inc. Multiplatform GMTI Radar With Adaptive Clutter Suppression
US20180059237A1 (en) * 2016-08-24 2018-03-01 Mitsubishi Electric Research Laboratories, Inc. System and Method for Radar Imaging Using Distributed Moving Platforms Under Unknown Position Perturbations
CN112130139A (en) * 2020-08-21 2020-12-25 西安空间无线电技术研究所 Distributed full-coherent sparse linear array radar system optimization array arrangement method
CN112332894A (en) * 2020-10-28 2021-02-05 香港中文大学（深圳） Method for forming robust beam with punishment cone constraint and mobile terminal
CN114371461A (en) * 2022-03-21 2022-04-19 中国人民解放军国防科技大学 Distributed coherent radar coherent parameter acquisition method based on coherent parameter separation
CN114384529A (en) * 2020-10-22 2022-04-22 中国科学院声学研究所 Multi-base multi-target positioning method and system based on mobile platform

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103728597A (en) * 2014-01-16 2014-04-16 西安电子科技大学 Auxiliary array based networked radar suppression type main lobe jamming restraining method
CN103728595A (en) * 2014-01-16 2014-04-16 西安电子科技大学 Subspace-projection-based networked radar inhibition pressing type main lobe interference method
US20170102458A1 (en) * 2014-06-09 2017-04-13 Src, Inc. Multiplatform GMTI Radar With Adaptive Clutter Suppression
US20180059237A1 (en) * 2016-08-24 2018-03-01 Mitsubishi Electric Research Laboratories, Inc. System and Method for Radar Imaging Using Distributed Moving Platforms Under Unknown Position Perturbations
CN112130139A (en) * 2020-08-21 2020-12-25 西安空间无线电技术研究所 Distributed full-coherent sparse linear array radar system optimization array arrangement method
CN114384529A (en) * 2020-10-22 2022-04-22 中国科学院声学研究所 Multi-base multi-target positioning method and system based on mobile platform
CN112332894A (en) * 2020-10-28 2021-02-05 香港中文大学（深圳） Method for forming robust beam with punishment cone constraint and mobile terminal
CN114371461A (en) * 2022-03-21 2022-04-19 中国人民解放军国防科技大学 Distributed coherent radar coherent parameter acquisition method based on coherent parameter separation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
YUANHAO WANG等: ""Generalised Signal Processing Flow for Distributed Radars on Moving Platform Coherently Combing"", 《2021 INTERNATIONAL CONFERENCE ON MICROWAVE AND MILLIMETER WAVE TECHNOLOGY (ICMMT)》 *
卢佳欣等: ""动平台分布式相参雷达系统分析"", 《信号处理》 *
宋靖: ""分布式全相参雷达相参性能分析"", 《电子与信息学报》 *

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