CN109459752B - Resource self-adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging - Google Patents

Resource self-adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging Download PDF

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CN109459752B
CN109459752B CN201811376967.4A CN201811376967A CN109459752B CN 109459752 B CN109459752 B CN 109459752B CN 201811376967 A CN201811376967 A CN 201811376967A CN 109459752 B CN109459752 B CN 109459752B
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廖可非
杜毅
欧阳缮
白钊铭
李长树
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Guilin University of Electronic Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/904—SAR modes
    • G01S13/9064—Inverse SAR [ISAR]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

本发明公开了一种逆合成孔径雷达二维稀疏成像的资源自适应调度方法，涉及相控阵雷达逆合成孔径雷达成像技术领域，解决的技术问题是如何从目标方位向和距离向角度考虑进行多目标成像任务在单部雷达中合理资源分配从而提升系统整体性能，该方法是在雷达对每个目标进行特征认知的基础上，计算每个目标的脉冲资源需求量，进而根据雷达选取的约束条件确定每个目标所需要分配的子脉冲发射位置，通过对目标的交替观测并获取目标回波信号，从而完成对多个目标的逆合成孔径成像任务。本发明可从目标方位向和距离向角度考虑实现单步雷达面对多目标时的资源分配，节省雷达资源，提升系统整体性能。

Figure 201811376967

The invention discloses a resource adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging, and relates to the technical field of phased array radar inverse synthetic aperture radar imaging. The multi-target imaging task allocates resources reasonably in a single radar to improve the overall performance of the system. This method calculates the pulse resource demand of each target on the basis of the radar's feature recognition of each target, and then selects the target according to the radar. Constraints determine the sub-pulse emission positions that each target needs to allocate, and through alternate observation of targets and acquisition of target echo signals, the task of inverse synthetic aperture imaging for multiple targets is completed. The invention can realize resource allocation when single-step radar faces multiple targets from the perspective of target azimuth and distance, save radar resources and improve the overall performance of the system.

Figure 201811376967

Description

Resource self-adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging
Technical Field
The invention relates to the technical field of phased array radar inverse synthetic aperture radar imaging, in particular to a resource self-adaptive scheduling method for two-dimensional sparse imaging of a synthetic aperture radar.
Background
Due to the beam agility and the beam self-adaption capability of the array antenna of the multifunctional phased array radar, the multifunctional phased array radar can execute a task of alternately observing a plurality of targets, and how to distribute the task under the resource constraint has a decisive influence on the radar performance. In the prior art, most researches on a phased array radar resource optimization scheduling strategy only consider how to realize optimization of multi-target searching and tracking performance under the condition of limited time, frequency spectrum, space, power and other resources, and the influence of imaging requirements on the scheduling strategy is rarely considered. Therefore, in the phased array radar resource optimization scheduling, if observation imaging of a plurality of targets can be realized in limited resources, the radar resources can be saved, and the overall performance of a radar system is greatly improved.
Under the random sparse observation imaging mode, the traditional inverse synthetic aperture radar imaging method cannot be used. In recent years, the Compressive Sensing (CS) theory, proposed by d.donoho, e.cand, and chef.tao et al, provides a new technical route for random sparse observation inverse synthetic aperture radar imaging. Researchers at home and abroad carry out more researches on the radar imaging technology based on the CS, a series of sparse aperture inverse synthetic aperture radar imaging methods based on the CS are provided, and effective technical support is provided for bringing imaging task requirements into a resource optimization scheduling model.
For example, chenyijun et al introduces the cognitive imaging idea into radar resource adaptive scheduling, proposes a radar resource adaptive scheduling algorithm based on sparse aperture cognitive inverse synthetic aperture radar imaging, and provides specific performance evaluation indexes, and the algorithm is mainly a method for allocating time resources of one-dimensional sparse inverse synthetic aperture radar imaging based on chirp signals. Mengdi et al propose an imaging radar resource scheduling algorithm based on pulse interleaving and a resource scheduling algorithm for digital array radar search, tracking and imaging tasks, and an optimized scheduling algorithm for Digital Array Radar (DAR) tasks, for the scheduling problem of multi-functional phased array radar imaging tasks.
The prior arts do not have the problem of reasonable resource allocation in a single radar by considering multi-target imaging tasks from the aspects of target azimuth and distance direction, so that the radar resource utilization is insufficient, and the overall performance of the system is deficient.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problem solved by the invention is how to reasonably allocate resources of a multi-target imaging task in a single radar from the perspective of the target direction and the distance direction so as to improve the overall performance of the system.
In order to solve the technical problems, the technical scheme adopted by the invention is a resource self-adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging, on the basis that radar performs feature cognition on each target, the pulse resource demand of each target is calculated, the sub-pulse transmitting position required to be allocated to each target is further determined according to the constraint condition selected by the radar, and echoes are obtained through alternate observation of the targets, so that the inverse synthetic aperture imaging task of a plurality of targets is completed, and the method comprises the following steps:
the radar transmits a small amount of pulses to each target and estimates the flight parameters of the target, and the specific process is as follows:
the waveform parameters for estimating the target parameters of a small number of pulses transmitted by each target are set as follows: carrier frequency frcPulse width T of sub-pulserpFrequency step length Δ frSub-pulse repetition frequency FPRFBandwidth Br. After the radar echo signal is obtained, the distance of the jth target from the radar can be measured by a radar conventional algorithm
Figure GDA0003372991120000024
Target speed
Figure GDA0003372991120000021
Target course
Figure GDA0003372991120000022
Target height
Figure GDA0003372991120000023
(II) estimating the two-dimensional size of the target, wherein the specific process is as follows:
for radar echo signals, an inverse synthetic aperture radar imaging method in the prior art is adopted to obtain a j-th target coarse resolution inverse synthetic aperture radar image Srj(k, l) normalizing the inverse synthetic aperture radar image using the following equation:
Figure GDA0003372991120000031
wherein k and l respectively represent the position of the target distance direction and the position direction envelope, and a proper threshold T is setsCalculating distance to envelope position minimum
Figure GDA0003372991120000032
And maximum distance to envelope position
Figure GDA0003372991120000033
According to the relation between the target distance and the envelope position, the distance dimension of the jth target can be obtained
Figure GDA00033729911200000314
Where ρ isrr＝c/(2Br) Is the distance resolution; the minimum and maximum values of the position envelope position in the azimuth direction are calculated by the same method respectively
Figure GDA0003372991120000034
The azimuth dimension of the jth target can be obtained
Figure GDA0003372991120000035
Wherein
Figure GDA0003372991120000036
Is the azimuth resolution.
(III) determining the number of target transmission pulse trains and the number of sub-pulse transmissions under the pulse trains by the radar, and the specific process is as follows:
setting a target distance to a reference dimension
Figure GDA0003372991120000037
Reference dimension of azimuth
Figure GDA0003372991120000038
And the distance-to-reference resolution ρ required for imagingrefrAzimuthal reference resolution ρrefaThen imaging of jth targetDirection-distance resolution ρjrAnd azimuthal resolution ρjaComprises the following steps:
Figure GDA0003372991120000039
wherein
Figure GDA00033729911200000310
Is the jth target range dimension,
Figure GDA00033729911200000311
Is the jth target azimuth dimension. Setting the minimum value of the azimuth resolution as rhomina. The maximum frequency step length Δ f required to allow the radar to show the size profile of all targets without ambiguity at alljComprises the following steps:
Figure GDA00033729911200000312
where c is the speed of light, the number of transmitted minimum sub-pulses N in the pulse train of the jth targetjComprises the following steps:
Figure GDA00033729911200000313
in order to meet the requirement of the required azimuth resolution for imaging the jth target, the number M of the required transmission pulse trainsjComprises the following steps:
Figure GDA0003372991120000041
wherein u is1A constant greater than 1 for adjusting the number of transmit bursts, λ being the signal wavelength, FPRFThe sub-pulse repetition frequency.
And (IV) estimating the target azimuth sparsity and the distance sparsity, wherein the specific process is as follows:
coarse resolution inverse synthetic aperture for jth targetRadial radar image SrjThe row with the largest element sum value in (k, l) was normalized to S 'as follows'rj(l) The element sum maximum column is normalized as S ″, as followsrj(k)：
Figure GDA0003372991120000042
Figure GDA0003372991120000043
Setting a suitable threshold value to TMIs prepared from S'rj(l) Discretized representation is vector S'rjWill be S ″)rj(k) The discretization is represented as vector S ″rjThen the azimuth sparsity of the jth target
Figure GDA0003372991120000044
And distance to sparsity
Figure GDA0003372991120000045
Comprises the following steps:
Figure GDA0003372991120000046
(V) determining the number L of target azimuth sparse transmission pulse trains by the radarjNumber Z of sub-pulses emitted to sparse by sum distancejThe specific process is as follows:
according to the compressed sensing theory, the number of pulse trains and the number of sub-pulses which are sparsely transmitted in the azimuth direction and the distance direction required by the jth target are respectively as follows:
Figure GDA0003372991120000047
wherein c is1Is a constant related to the recovery accuracy, and usually takes a value between 0.5 and 2.
And (VI) calculating the threat degree of the target by the following specific process:
calculation of the threat degree mainly considers the distance of the target
Figure GDA0003372991120000051
Speed of rotation
Figure GDA0003372991120000052
Course of course
Figure GDA0003372991120000053
Height
Figure GDA0003372991120000054
Four parameters, assuming that the radar needs to be on NtotallImaging an object, NtotallThese four factors affecting the threat for each target are written in the form of a matrix:
A＝(aij)4×Ntotall (10)
in the formula, aij(i＝1，2…，4；j＝1，2…，Ntotall) A threat coefficient representing an ith factor of a jth target, i ═ 1,2 …,4 represent the distance of the target from the radar, the speed of the target, the heading of the target, and the altitude of the target, respectively, and j ═ 1,2 …, Ntotall；
Normalizing each factor to Y according to formula (11)ijThe weight of each factor is calculated as beta by the equation (12)i：
Figure GDA0003372991120000055
Figure GDA0003372991120000056
Wherein each factor occurs with a probability
Figure GDA0003372991120000057
If p isijWhen 0, then
Figure GDA0003372991120000058
Calculating the threat degree of the jth target according to the following formula:
Figure GDA0003372991120000059
wherein beta is1、β2、β3、β4And respectively obtaining weight coefficients for the distance from the target to the radar, the speed of the target, the course of the target and the height of the target.
(VII) under the condition of meeting the resource constraint, establishing a resource scheduling model and carrying out resource allocation, wherein the concrete process is as follows:
suppose the scheduling interval is T and the sub-pulse repetition frequency is FPRFThen the total number of sub-pulses within the scheduling interval: ptotall＝T×FPRF；
Based on the basis, the following performance indexes are defined for the radar imaging resource scheduling algorithm:
(1) the sum of the target threat degrees of the radar completing the imaging task is as follows:
Figure GDA0003372991120000061
wherein N issRepresenting a target sequence number sequence for completing the imaging task in a scheduling interval T, wherein Nu is the number of targets for completing the imaging task in the scheduling interval;
(2) the scheduling success rate is as follows: the ratio of the number of targets actually and successfully executing the imaging tasks to the number of targets applying for executing the imaging tasks in the scheduling interval T is as follows:
Figure GDA0003372991120000062
wherein N istotallThe total number of targets for applying for executing the imaging tasks in the scheduling interval;
(3) the utilization rate of pulse resources is as follows: in the scheduling interval T, the ratio of the number of the sub-pulses occupied by the imaging task to the total number of the sub-pulses in the scheduling interval is completed:
Figure GDA0003372991120000063
(4) sum of performance indexes:
SPI＝w1.STC+w2.SSR+w3.PRU (17)
wherein w1、w2、w3A weight coefficient.
Based on the performance indexes, a resource scheduling model is established as follows:
Figure GDA0003372991120000064
any pulse train of I'jiThe sum of the number of the sub-pulses in the pulse train is less than or equal to Nj
The resource scheduling model according to equation (18) can be described as:
step 1: setting the initial time j as 1, using the most front sub-pulse in the current rest idle pulse as the start position start for observing the j th target emission sub-pulsej；
Step 2: according to the number M of the sub-pulses required during the full-aperture observationj×NjDetermining the position of a terminator pulse of the task;
step 3: from the starting position to the end position by NjFor spacing, uniformly dividing MjA pulse train;
step 4: randomly picking out L from the currentjEach pulse train being randomly inserted Z within each selected pulse trainjAnd observing the sub-pulses.
Step 5: if j < NtotallAnd j equals j +1 and returns to the first step.
Step 6: obtaining a target sequence number sequence N for completing the imaging task within the scheduling interval T under the priority ordering modesThe number Nu of targets for completing the imaging task;
step 7: and calculating the sum of target threat degrees, scheduling success rate, pulse resource utilization rate and performance indexes of the radar for completing the imaging task in the priority sorting mode.
Adding the above-mentioned NtotallAnd (3) according to the principle that each combination in the priority sorting mode is substituted into the model to perform resource allocation according to the target priority, calculating the SPI of each combination through the allocation processes from Step1 to Step7, and selecting a pulse resource allocation sequence with the maximum Sum of Performance Indexes (SPI) and meeting the conditions of 3 constraints in the model as a final resource scheduling sequence result.
(VIII) obtaining a target two-dimensional image by using the existing two-dimensional sparse imaging technology, wherein the specific process is as follows:
and reasonably distributing radar pulse resources according to the resource scheduling model, alternately observing targets, and after receiving target echo signals, realizing inverse synthetic aperture radar two-dimensional imaging on each target by using the conventional two-dimensional sparse imaging technology.
By adopting the technical scheme of the invention, the resource allocation of the single-step radar facing to multiple targets can be realized from the aspects of the target azimuth direction and the distance direction, the radar resources are saved, and the overall performance of a radar system is improved.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a diagram of the corresponding SPI in each priority ranking mode;
FIG. 3 is a diagram illustrating a radar resource scheduling result according to the present invention.
Detailed Description
The following description will be made with reference to the accompanying drawings and examples, but the present invention is not limited thereto.
Example (b):
let T be 1s for scheduling interval and F for sub-pulse repetition frequencyPRF10kHz, the total number of sub-pulses within the scheduling interval: ptotall＝T×F PRF10000, N in the scheduling interval TtotallThe individual target applies for an imaging task.
Fig. 1 shows a resource adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging, which is implemented by calculating the pulse resource demand of each target on the basis of feature cognition of each target by a radar, determining the sub-pulse transmitting position required to be allocated to each target according to the constraint condition selected by the radar, and acquiring echoes through alternate observation of the targets, thereby completing the inverse synthetic aperture imaging task of multiple targets, and comprises the following steps:
the radar transmits a small amount of pulses to each target and estimates the flight parameters of the target, and the specific process is as follows:
the waveform parameters for estimating the target parameters of a small number of pulses transmitted by each target are set as follows: carrier frequency frcSub-pulse width T of 10GHzrp0.3 mus, frequency step length Δ fr3M, bandwidth Br300 MHz. Randomly selecting 300 sub-pulses to transmit to each target, and measuring the distance between the jth target and the radar by using a radar conventional algorithm after radar echo signals are obtained
Figure GDA0003372991120000081
Target speed
Figure GDA0003372991120000082
Target course
Figure GDA0003372991120000083
Target height
Figure GDA0003372991120000084
The respective target parameters are shown in table 1.
TABLE 1 target parameter tables
Figure GDA0003372991120000091
(II) estimating the two-dimensional size of the target, wherein the specific process is as follows:
reconstruction F for filling zero in missing part of received echo dataPRFThe data of each observation point is used as the echo signal of the target coarse resolution inverse synthetic aperture radar image, and the echo signal of each target is processedThe wave signals are processed by inverse synthetic aperture radar imaging to obtain a rough image of each target, which is recorded as Srj(k, l). And normalizing the image to obtain a normalized image, which is recorded as S'rj(k，l)：
Figure GDA0003372991120000092
Where k and l represent the target distance and azimuth envelope positions, respectively, according to the formula ρrr＝c/(2Br) Calculating the distance resolution according to
Figure GDA0003372991120000093
And calculating the azimuth resolution. Selecting a threshold value Ts0.38, the calculated distance is minimal towards the envelope position
Figure GDA0003372991120000094
And maximum distance to envelope position
Figure GDA0003372991120000099
According to the relation between the target distance and the envelope position, the distance dimension of the jth target can be obtained
Figure GDA0003372991120000095
Where ρ isrr＝c/(2Br) Is the distance resolution; the minimum and maximum values of the position envelope position in the azimuth direction are calculated by the same method respectively
Figure GDA0003372991120000096
The azimuth dimension of the jth target can be obtained
Figure GDA0003372991120000097
Wherein
Figure GDA0003372991120000098
For azimuthal resolution, the two-dimensional recognition results for each target were calculated as shown in table 2 below.
TABLE 2 target feature recognition results
Figure GDA0003372991120000101
(III) determining the number of target transmission pulse trains and the number of sub-pulse transmissions under the pulse trains by the radar, and the specific process is as follows:
setting a target distance to a reference dimension
Figure GDA0003372991120000102
Reference dimension of azimuth
Figure GDA0003372991120000103
And the distance-to-reference resolution ρ required for imagingrefrAzimuthal reference resolution ρrefaThe distance direction rho of the j th target required for imagingjrAnd azimuthal resolution ρjaComprises the following steps:
Figure GDA0003372991120000104
wherein
Figure GDA0003372991120000105
Is the jth target range dimension,
Figure GDA0003372991120000106
Is the jth target azimuth dimension. Setting the minimum value of the azimuth resolution as rhomina. The target distance is set to the reference dimension
Figure GDA0003372991120000107
An azimuth reference dimension of
Figure GDA0003372991120000108
Distance direction reference resolution is rhorefr0.5m and an azimuth reference resolution ρrefa0.5m, the maximum frequency step length Δ f required to allow the radar to show the size profile of all targets completely unambiguousjComprises the following steps:
Figure GDA0003372991120000111
where c is the speed of light, the number of transmitted minimum sub-pulses N in the pulse train of the jth targetjComprises the following steps:
Figure GDA0003372991120000112
in order to meet the requirement of the required azimuth resolution for imaging the jth target, the number M of the required transmission pulse trainsjComprises the following steps:
Figure GDA0003372991120000113
wherein u is1In order to adjust a constant greater than 1 for the number of the transmitted bursts, the value is 1.1, λ is the signal wavelength, and the results of calculating the number of the bursts to be transmitted for each target and the number of the sub-pulses transmitted under the bursts are shown in table 2 above.
And (IV) estimating the target azimuth sparsity and the distance sparsity, wherein the specific process is as follows:
coarse resolution inverse synthetic aperture radar image S for jth targetrjThe line normalization process for which the element sum value in (k, l) is the maximum is S'rj(l) The column normalization process with the largest sum of elements is S ″rj(k)：
Figure GDA0003372991120000114
Figure GDA0003372991120000115
Setting the threshold value to TM0.3, mixing S'rj(l) Discretized representation is vector S'rjThen the azimuth sparsity of the jth target
Figure GDA0003372991120000116
Is vector S'rjIs greater than TMThe number of elements (c). S ″)rj(k) The discretization is represented as vector S ″rjThen the distance of the jth target is sparse
Figure GDA0003372991120000117
Is vector S ″)rjIs greater than TMThe number of elements (c):
Figure GDA0003372991120000121
the results of estimation of the azimuth sparsity and the range sparsity of each target are shown in table 2 above.
And (V) determining the number of the target azimuth sparse transmission pulse trains and the number of the distance sparse transmission sub-pulses, wherein the specific process is as follows:
according to the compressed sensing theory, the number L of the pulse trains which are sparsely transmitted in the azimuth direction and the distance direction and are required by the jth targetjNumber of and subpulses ZjRespectively as follows:
Figure GDA0003372991120000122
wherein c is1The value of the constant is 0.51, and the estimation results of the number of the pulse trains emitted sparsely in each target direction and the number of the sub-pulses emitted sparsely in each target direction are shown in the table 2.
And (VI) calculating the threat degree of the target by the following specific process:
calculation of the threat degree mainly considers the distance of the target
Figure GDA0003372991120000123
Speed of rotation
Figure GDA0003372991120000124
Course of course
Figure GDA0003372991120000125
Height
Figure GDA0003372991120000126
Four parameters, assuming that the radar needs to be on NtotallImaging an object, NtotallThese four factors affecting the threat for each target are written in the form of a matrix:
A＝(aij)4×Ntotall (10)
in the formula, aij(i＝1，2…，4；j＝1，2…，Ntotall) A threat coefficient representing an ith factor of a jth target, i ═ 1,2 …,4 represent the distance of the target from the radar, the speed of the target, the heading of the target, and the altitude of the target, respectively, and j ═ 1,2 …, Ntotall；
Normalizing each factor to Y according to formula (11)ijThe weight of each factor is calculated as beta by the equation (12)i：
Figure GDA0003372991120000127
Figure GDA0003372991120000131
Wherein each factor occurs with a probability
Figure GDA0003372991120000132
If p isijWhen 0, then
Figure GDA0003372991120000133
Calculating the threat degree of the jth target according to the following formula:
Figure GDA0003372991120000134
wherein beta is1、β2、β3、β4Weight coefficients are obtained for the distance from the target to the radar, the speed of the target, the heading of the target and the height of the target, respectively, and estimation results of the threat degrees of the targets are shown in table 2 above.
And (seventhly), under the condition of meeting the resource constraint, carrying out the specific process of resource allocation according to the resource scheduling model as follows:
setting 4 different priorities from high to low is: 0.4, 0.3, 0.2, 0.1, N for which there is a task application in the scheduling intervaltotallThe free ranking of 4 targets with 4 different priorities results in 24 prioritization as shown in table 3 below.
TABLE 3 target priority ranking list
Figure GDA0003372991120000135
Figure GDA0003372991120000141
Calculating the SPI of each combination in the 24 priority sorting modes according to the principle that each combination is firstly substituted into the following model for resource allocation according to the target priority:
Figure GDA0003372991120000142
Figure GDA0003372991120000143
pulse train I'jiThe sum of the number of the sub-pulses in the pulse train is less than or equal to Nj
The impulse resource allocation procedure according to equation (18) can be described as:
step 1: setting the initial time j as 1, using the most front sub-pulse in the current rest idle pulse as the start position start for observing the j th target emission sub-pulsej；
Step 2: according to the number M of the sub-pulses required during the full-aperture observationj×NjDetermining the position of a terminator pulse of the task;
step 3: from the starting position to the end position by NjFor spacing, uniformly dividing MjA pulse train;
step 4: randomly picking out L from the currentjEach pulse train being randomly inserted Z within each selected pulse trainjAnd observing the sub-pulses.
Step 5: if j < NtotallAnd j equals j +1 and returns to the first step.
Step 6: obtaining a target sequence number sequence N for completing the imaging task within the scheduling interval T under the priority ordering modesThe number Nu of targets for completing the imaging task;
step 7: the sum of the target threat degrees, the scheduling success rate, the pulse resource utilization rate and the performance index of the radar completing the imaging task under the priority ranking mode is calculated as follows:
(1) the sum of the target threat degrees of the radar completing the imaging task is as follows:
Figure GDA0003372991120000151
wherein N issRepresenting a target sequence number sequence for completing the imaging task in a scheduling interval T, wherein Nu is the number of targets for completing the imaging task in the scheduling interval;
(2) the scheduling success rate is as follows: the ratio of the number of targets actually and successfully executing the imaging tasks to the number of targets applying for executing the imaging tasks in the scheduling interval T is as follows:
Figure GDA0003372991120000152
wherein N istotallAnd applying the total number of targets for executing the imaging tasks in the scheduling interval.
(3) The utilization rate of pulse resources is as follows: in the scheduling interval T, the ratio of the number of the sub-pulses occupied by the imaging task to the total number of the sub-pulses in the scheduling interval is completed:
Figure GDA0003372991120000153
(4) sum of performance indexes:
SPI＝w1·STC+w2·SSR+w3·PRU (17)
wherein the weight coefficient is w1＝0.3、w2＝0.5、w3＝0.2；
The ith pulse train transmitted by the radar to the jth target is I'ji。
And (3) according to the principle that each combination in the 24 priority ordering modes is substituted into the model to perform resource allocation according to the target priority, calculating the SPI of each combination through the allocation processes from Step1 to Step7, and selecting a pulse resource allocation sequence with the largest sum of the performance indexes and the SPI meeting the conditions of 3 constraints in the model as a final resource scheduling sequence result as shown in fig. 3 as shown in fig. 2.
(eighth) finally, obtaining a target two-dimensional image by using the existing two-dimensional sparse imaging technology, wherein the specific process is as follows:
and reasonably distributing radar pulse resources according to the resource scheduling model, alternately observing targets, and after receiving target echo signals, realizing inverse synthetic aperture radar two-dimensional imaging on each target by using the conventional two-dimensional sparse imaging technology.
By adopting the technical scheme of the invention, the resource allocation of the single-step radar facing to multiple targets can be realized from the aspects of the target azimuth direction and the distance direction, the radar resources are saved, and the overall performance of a radar system is improved.
The embodiments of the present invention have been described in detail with reference to the drawings and examples, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention.

Claims (1)

1.一种逆合成孔径雷达二维稀疏成像的资源自适应调度方法，在雷达对每个目标进行特征认知的基础上，计算每个目标的脉冲资源需求量，进而根据雷达选取的约束条件确定每个目标所需要分配的子脉冲发射位置，通过对目标的交替观测并获取目标回波信号，从而完成对多个目标的逆合成孔径成像任务，其特征在于，包括如下步骤：1. A resource adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging. Based on the radar's feature recognition of each target, the pulse resource demand of each target is calculated, and then the constraints selected by the radar are selected. Determine the sub-pulse emission positions that need to be allocated for each target, and complete the inverse synthetic aperture imaging task of multiple targets by alternately observing the targets and acquiring target echo signals, which is characterized in that the following steps are included: (一)雷达对每个目标发射少量脉冲，估算目标飞行参数；(1) The radar transmits a small number of pulses to each target to estimate the flight parameters of the target; (二)估算目标二维尺寸；(2) Estimate the two-dimensional size of the target; (三)确定雷达对目标发射脉冲串个数及脉冲串下发射子脉冲个数；(3) Determining the number of pulse trains transmitted by the radar to the target and the number of sub-pulses emitted under the pulse train; (四)估计目标方位向稀疏度、距离向稀疏度；(4) Estimate the azimuth sparsity and range sparsity of the target; (五)确定雷达对目标方位向稀疏发射脉冲串个数和距离向稀疏发射子脉冲个数；(5) Determining the number of sparsely transmitted pulse trains in the azimuth direction and the number of sub-pulses sparsely transmitted in the distance direction by the radar to the target; (六)计算目标的威胁度；(6) Calculate the threat degree of the target; (七)在满足资源约束的条件下，建立资源调度模型并进行脉冲资源分配；(7) Under the condition that the resource constraints are met, establish a resource scheduling model and perform pulse resource allocation; (八)用二维稀疏成像技术获得目标二维像；(8) Obtaining a two-dimensional image of the target by using two-dimensional sparse imaging technology; 步骤(一)具体过程如下：Step (1) The specific process is as follows: 设定对各目标发射少量脉冲进行目标参数估算所用的波形参数为：载频frc、子脉冲脉宽Trp、频率步进长度Δfr、子脉冲重复频率FPRF、带宽Br；获取雷达回波信号后，可通过雷达常规算法测定出第j个目标距离雷达的距离
Figure FDA0003372991110000011
目标速度
Figure FDA0003372991110000012
目标航向
Figure FDA0003372991110000013
目标高度
Figure FDA0003372991110000014
Set the waveform parameters used to estimate the target parameters by transmitting a small number of pulses from each target as follows: carrier frequency f rc , sub-pulse pulse width T rp , frequency step length Δf r , sub-pulse repetition frequency F PRF , bandwidth B r ; obtain radar After the echo signal, the distance of the jth target from the radar can be determined by the conventional radar algorithm
Figure FDA0003372991110000011
target speed
Figure FDA0003372991110000012
target heading
Figure FDA0003372991110000013
target height
Figure FDA0003372991110000014
 步骤(二)具体过程如下：The specific process of step (2) is as follows: 对雷达回波信号，采用现有技术的逆合成孔径雷达成像方法，得到第j个目标的粗分辨逆合成孔径雷达像Srj(k,l)，对该逆合成孔径雷达像采用以下公式进行归一化处理：For the radar echo signal, the inverse synthetic aperture radar imaging method of the prior art is used to obtain the coarse-resolution inverse synthetic aperture radar image S rj (k,l) of the j-th target, and the inverse synthetic aperture radar image is calculated by the following formula: Normalized processing:
Figure FDA0003372991110000021
Figure FDA0003372991110000021
 其中k和l分别表示目标距离向和方位向包络位置，设置一个合适的门限Ts，计算距离向包络位置最小
Figure FDA0003372991110000022
及距离向包络位置最大值
Figure FDA0003372991110000023
根据目标距离与包络位置关系，可得第j个目标的距离向尺寸
Figure FDA0003372991110000024
其中ρrr＝c/(2Br)为距离向分辨率；同理计算方位向包络位置最小最大值分别为
Figure FDA0003372991110000025
可得第j个目标的方位向尺寸
Figure FDA0003372991110000026
其中
Figure FDA0003372991110000027
为方位向分辨率；where k and l represent the target range and azimuth envelope positions respectively, set an appropriate threshold T s to calculate the minimum range envelope position
Figure FDA0003372991110000022
and the maximum value of the distance envelope position
Figure FDA0003372991110000023
According to the relationship between the target distance and the envelope position, the distance dimension of the jth target can be obtained
Figure FDA0003372991110000024
where ρ rr =c/(2B r ) is the range resolution; similarly, the minimum and maximum azimuth envelope positions are calculated as
Figure FDA0003372991110000025
The azimuth size of the jth target can be obtained
Figure FDA0003372991110000026
in
Figure FDA0003372991110000027
is the azimuth resolution; 步骤(三)具体过程如下：The specific process of step (3) is as follows: 设定目标距离向基准尺寸
Figure FDA0003372991110000028
方位向基准尺寸
Figure FDA0003372991110000029
及成像所需的距离向基准分辨率ρrefr、方位向基准分辨率ρrefa，则第j个目标成像所需方距离向分辨率ρjr与方位向分辨率ρja为：Set the target distance to the base size
Figure FDA0003372991110000028
Azimuth reference size
Figure FDA0003372991110000029
and the range reference resolution ρ refr and the azimuth reference resolution ρ refa required for imaging, then the range resolution ρ jr and azimuth resolution ρ ja required for imaging the j-th target are:
Figure FDA00033729911100000210
Figure FDA00033729911100000210
 其中
Figure FDA00033729911100000211
为第j个目标距离向尺寸、
Figure FDA00033729911100000212
为第j个目标方位向尺寸；设定方位向分辨率的最小值为ρmina；要使雷达完全无模糊的显示出所有目标的尺寸轮廓，则所需要的最大频率步进长度Δfj为：in
Figure FDA00033729911100000211
is the distance dimension of the jth target,
Figure FDA00033729911100000212
is the azimuth size of the jth target; the minimum value of the azimuth resolution is set to be ρ mina ; to make the radar display the size contours of all targets completely without ambiguity, the required maximum frequency step length Δf j is:
Figure FDA00033729911100000213
Figure FDA00033729911100000213
 其中c为光速，第j个目标的脉冲串下发射最少子脉冲个数Nj为：where c is the speed of light, and the minimum number of sub-pulses N j emitted under the pulse train of the j-th target is:
Figure FDA00033729911100000214
Figure FDA00033729911100000214
 为达到第j个目标成像所需方位向分辨率的要求，则所需发射脉冲串个数Mj为：In order to achieve the azimuth resolution required for the imaging of the jth target, the required number of transmitted pulse trains M j is:
Figure FDA0003372991110000031
Figure FDA0003372991110000031
 其中u1为调节发射脉冲串个数的一个大于1的常数，λ为信号波长，FPRF子脉冲重复频率；where u 1 is a constant greater than 1 for adjusting the number of transmitted pulse trains, λ is the signal wavelength, and F PRF sub-pulse repetition frequency; 步骤(四)具体过程如下：The specific process of step (4) is as follows: 对第j个目标的粗分辨逆合成孔径雷达像Srj(k,l)中元素和值最大的行作如下归一化处理为S′rj(l)，元素和值最大列作如下归一化处理为S″rj(k)：The row with the largest element sum value in the coarse-resolution inverse synthetic aperture radar image S rj (k,l) of the j-th target is normalized as S′ rj (l) as follows, and the column with the largest element sum value is normalized as follows Converted to S″ rj (k):
Figure FDA0003372991110000032
Figure FDA0003372991110000032
Figure FDA0003372991110000033
Figure FDA0003372991110000033
 设置合适的阈值为TM，将S′rj(l)离散化表示为向量S′rj，将S″rj(k)离散化表示为向量S″rj，则第j个目标的方位向稀疏度
Figure FDA0003372991110000034
和距离向稀疏度
Figure FDA0003372991110000035
为：Set the appropriate threshold as T M , denote S′ rj (l) as a vector S′ rj , and denote S″ rj (k) as a vector S″ rj , then the azimuth sparsity of the j-th target
Figure FDA0003372991110000034
and distance sparsity
Figure FDA0003372991110000035
for:
Figure FDA0003372991110000036
Figure FDA0003372991110000036
 步骤(五)具体过程如下：The specific process of step (5) is as follows: 根据压缩感知理论，第j个目标所需的方位向和距离向稀疏发射的脉冲串个数与子脉冲个数分别为：According to the compressed sensing theory, the number of pulse trains and sub-pulses required for the jth target to be sparsely transmitted in azimuth and range directions are:
Figure FDA0003372991110000037
Figure FDA0003372991110000037
 其中c1为是一个与恢复精度有关的常数，取值为0.5到2之间；Among them, c 1 is a constant related to the recovery accuracy, and its value is between 0.5 and 2; 步骤(六)具体过程如下：The specific process of step (6) is as follows: 威胁度的计算主要考虑目标的距离
Figure FDA0003372991110000038
速度
Figure FDA0003372991110000039
航向
Figure FDA00033729911100000310
高度
Figure FDA00033729911100000311
四个参数，假设雷达需要对Ntotall个目标成像，把Ntotall个目标的这四种影响威胁度的因素写成矩阵的形式为A：The calculation of the threat degree mainly considers the distance of the target
Figure FDA0003372991110000038
speed
Figure FDA0003372991110000039
course
Figure FDA00033729911100000310
high
Figure FDA00033729911100000311
Four parameters, assuming that the radar needs to image N totall targets, the four factors that affect the threat degree of N totall targets are written as a matrix in the form of A:
Figure FDA0003372991110000047
Figure FDA0003372991110000047
 式中，aij表示第j个目标的第i个因素的威胁系数，i＝1,2…,4分别表示目标距离雷达的距离、目标的速度、目标的航向和目标的高度，并且j＝1,2…,Ntotall；In the formula, a ij represents the threat coefficient of the i-th factor of the j-th target, i=1, 2..., 4 represent the distance of the target from the radar, the speed of the target, the heading of the target and the height of the target, and j= 1,2…, Ntotal ; 根据式(11)将各因素归一化后为Yij，通过式(12)计算出各因素的权重为βi：According to formula (11), each factor is normalized to Y ij , and the weight of each factor is calculated by formula (12) to be β i :
Figure FDA0003372991110000041
Figure FDA0003372991110000041
Figure FDA0003372991110000042
Figure FDA0003372991110000042
 其中每种因素出现得概率
Figure FDA0003372991110000043
如果pij＝0，则定义
Figure FDA0003372991110000044
The probability of occurrence of each factor
Figure FDA0003372991110000043
If p ij = 0, then define
Figure FDA0003372991110000044
 根据下式计算出第j个目标的威胁度：Calculate the threat level of the jth target according to the following formula:
Figure FDA0003372991110000045
Figure FDA0003372991110000045
 其中β1、β2、β3、β4分别为目标距离雷达的距离、目标的速度、目标的航向和目标的高度得权值系数；Among them, β 1 , β 2 , β 3 , and β 4 are the weighted coefficients of the distance between the target and the radar, the speed of the target, the course of the target and the height of the target, respectively; 步骤(七)具体过程如下：The specific process of step (7) is as follows: 假设调度间隔为T，子脉冲重复频率为FPRF，则调度间隔内子脉冲的总个数：Ptotall＝T×FPRF；Assuming that the scheduling interval is T and the sub-pulse repetition frequency is F PRF , the total number of sub-pulses in the scheduling interval: P totall =T×F PRF ; 基于上述基础，对雷达成像资源调度算法定义了如下性能指标：Based on the above foundation, the following performance indicators are defined for the radar imaging resource scheduling algorithm: (1)雷达完成成像任务的目标威胁度之和为：(1) The sum of the target threat degree for the radar to complete the imaging task is:
Figure FDA0003372991110000046
Figure FDA0003372991110000046
 其中Ns表示在调度间隔T内完成成像任务的目标序号序列，Nu为调度间隔内完成成像任务的目标个数；where N s represents the target sequence number sequence for completing the imaging task within the scheduling interval T, and Nu is the number of targets for completing the imaging task within the scheduling interval; (2)调度成功率：指调度间隔T内，实际成功执行成像任务的目标数目与申请执行成像任务的目标数目之比：(2) Scheduling success rate: refers to the ratio of the number of targets actually successfully executing imaging tasks to the number of targets applying for executing imaging tasks within the scheduling interval T:
Figure FDA0003372991110000051
Figure FDA0003372991110000051
 其中Ntotall为调度间隔内申请执行成像任务的目标总个数；Among them, N totall is the total number of targets applying to perform imaging tasks within the scheduling interval; (3)脉冲资源利用率:在调度间隔T内，完成成像任务所占用的子脉冲个数与调度间隔内总的子脉冲个数之比：(3) Pulse resource utilization rate: in the scheduling interval T, the ratio of the number of sub-pulses occupied to complete the imaging task to the total number of sub-pulses in the scheduling interval:
Figure FDA0003372991110000052
Figure FDA0003372991110000052
 (4)性能指标之和：(4) The sum of performance indicators: SPI＝w1·STC+w2·SSR+w3·PRU (17)SPI=w1·STC+w2·SSR+w3·PRU (17) 其中w1、w2、w3权重系数；Wherein w 1 , w 2 , w 3 weight coefficients; 基于以上性能指标，建立资源调度模型如下：Based on the above performance indicators, the resource scheduling model is established as follows:
Figure FDA0003372991110000053
Figure FDA0003372991110000053
 任意脉冲串I′ji,满足脉冲串内子脉冲个数之和≤Nj Arbitrary pulse train I′ ji , satisfying the sum of the number of sub-pulses in the pulse train≤N j 根据式(18)资源调度模型可描写为：According to formula (18), the resource scheduling model can be described as: Step1：初始时刻j＝1，将当前剩余空闲脉冲中最靠前的子脉冲作为观测第j个目标发射子脉冲的起始位置startj；Step1: initial time j=1, take the most advanced sub-pulse in the current remaining idle pulse as the starting position start j of observing the j-th target emission sub-pulse; Step2：根据全孔径观测时需要的子脉冲个数Mj×Nj，确定任务的终止子脉冲位置；Step2: According to the number of sub-pulses M j ×N j required for full-aperture observation, determine the position of the termination sub-pulse of the task; Step3：从起始位置到终止位置按Nj为间隔，均匀的划分出Mj个脉冲串；Step3: From the start position to the end position, according to the interval of N j , evenly divide M j pulse trains; Step4：现从中随机挑选出Lj个脉冲串，在挑选出的每一个脉冲串内随机的插入Zj个观测子脉冲；Step4: Now randomly select L j pulse trains, and randomly insert Z j observation sub-pulses into each selected pulse train; Step5：如果j＜Ntotall,则j＝j+1并返回第一步；Step5: If j<N totall , then j=j+1 and return to the first step; Step6：得到该种优先级排序方式下调度间隔T内完成成像任务的目标序号序列Ns，完成成像任务的目标个数Nu；Step 6: Obtain the sequence N s of target sequence numbers N s for completing imaging tasks within the scheduling interval T under this priority sorting method, and the number Nu of targets for completing imaging tasks; Step7：计算该种优先级排序方式下雷达完成成像任务的目标威胁度之和、调度成功率、脉冲资源利用率、性能指标之和；Step7: Calculate the sum of the target threat degree, the scheduling success rate, the pulse resource utilization rate, and the performance index of the radar completing the imaging task under this priority sorting method; 将上述Ntotall种优先级排序方式中的每一种组合按照目标优先级高优的先代入上述模型中进行资源分配的原则，经过上述分配过程Step1至Step7，计算出每一种组合下的SPI，选取性能指标之和SPI最大且满足模型中3个约束的条件的脉冲资源分配序列，作为最终的资源调度序列结果；Substitute each combination of the above N totall priority sorting methods into the above model for resource allocation according to the principle of high target priority. After the above allocation process Step1 to Step7, calculate the SPI under each combination. , select the pulse resource allocation sequence with the largest sum of performance indicators and the three constraints in the model as the final resource scheduling sequence result; 步骤(八)具体过程如下：The specific process of step (8) is as follows: 根据资源调度模型合理分配雷达脉冲资源并对目标进行交替观测，接收目标回波信号后，用二维稀疏成像技术，对各目标实现逆合成孔径雷达二维成像。According to the resource scheduling model, the radar pulse resources are allocated reasonably and the targets are observed alternately. After receiving the target echo signal, the two-dimensional sparse imaging technology is used to realize the two-dimensional inverse synthetic aperture radar imaging of each target.
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