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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CN109459752A (en
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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
    • 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
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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Abstract

The invention discloses a resource self-adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging, which relates to the technical field of phased array radar inverse synthetic aperture radar imaging, and solves the technical problem of how to reasonably allocate resources of a multi-target imaging task in a single radar from the aspects of target azimuth and distance direction so as to improve the overall performance of a system. The invention can realize the resource allocation of the single-step radar facing to multiple targets from the aspects of the azimuth direction and the distance direction of the targets, save radar resources and improve the overall performance of the system.

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. A resource self-adaptive scheduling method for inverse synthetic aperture radar two-dimensional sparse imaging is characterized by comprising the following steps of calculating the pulse resource demand of each target on the basis that the radar performs feature cognition on each target, further determining the sub-pulse transmitting position required to be distributed by each target according to the constraint condition selected by the radar, and finishing inverse synthetic aperture imaging tasks of a plurality of targets by alternately observing the targets and acquiring target echo signals, wherein the resource self-adaptive scheduling method comprises the following steps:
the radar transmits a small amount of pulses to each target, and target flight parameters are estimated;
(II) estimating the two-dimensional size of the target;
thirdly, determining the number of target transmission pulse trains of the radar and the number of sub-pulse transmissions under the pulse trains;
fourthly, estimating the azimuth sparsity and the distance sparsity of the target;
fifthly, determining the number of the radar to the target azimuth sparse transmission pulse trains and the number of the distance sparse transmission sub-pulses;
sixthly, calculating the threat degree of the target;
establishing a resource scheduling model and performing pulse resource allocation under the condition of meeting resource constraint;
(VIII) obtaining a target two-dimensional image by using a two-dimensional sparse imaging technology;
the specific process of the step (I) 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(ii) a 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 FDA0003372991110000011
Target speed
Figure FDA0003372991110000012
Target course
Figure FDA0003372991110000013
Target height
Figure FDA0003372991110000014
The specific process of the step (II) 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 FDA0003372991110000021
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 FDA0003372991110000022
And maximum distance to envelope position
Figure FDA0003372991110000023
According to the relation between the target distance and the envelope position, the distance dimension of the jth target can be obtained
Figure FDA0003372991110000024
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 FDA0003372991110000025
The azimuth dimension of the jth target can be obtained
Figure FDA0003372991110000026
Wherein
Figure FDA0003372991110000027
The azimuth resolution is;
the specific process of the step (III) is as follows:
setting a target distance to a reference dimension
Figure FDA0003372991110000028
Reference dimension of azimuth
Figure FDA0003372991110000029
And the distance-to-reference resolution ρ required for imagingrefrAzimuthal reference resolution ρrefaThen the distance resolution ρ required for imaging the jth targetjrAnd azimuthal resolution ρjaComprises the following steps:
Figure FDA00033729911100000210
wherein
Figure FDA00033729911100000211
Is the jth target range dimension,
Figure FDA00033729911100000212
Is the jth target azimuth dimension; setting the minimum value of the azimuth resolution as rhomina(ii) a 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 FDA00033729911100000213
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 FDA00033729911100000214
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 FDA0003372991110000031
wherein u is1A constant greater than 1 for adjusting the number of transmit bursts, λ being the signal wavelength, FPRFA sub-pulse repetition frequency;
the specific process of the step (IV) is as follows:
coarse resolution inverse synthetic aperture radar image S for jth targetrjThe 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 FDA0003372991110000032
Figure FDA0003372991110000033
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 FDA0003372991110000034
And distance to sparsity
Figure FDA0003372991110000035
Comprises the following steps:
Figure FDA0003372991110000036
the concrete process of the step (V) 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 FDA0003372991110000037
wherein c is1The value is a constant related to the recovery precision and is between 0.5 and 2;
the specific process of the step (six) is as follows:
calculation of the threat degree mainly considers the distance of the target
Figure FDA0003372991110000038
Speed of rotation
Figure FDA0003372991110000039
Course of course
Figure FDA00033729911100000310
Height
Figure FDA00033729911100000311
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:
Figure FDA0003372991110000047
in the formula, aijA 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 the factors according to equation (11)Converted into YijThe weight of each factor is calculated as beta by the equation (12)i：
Figure FDA0003372991110000041
Figure FDA0003372991110000042
Wherein each factor occurs with a probability
Figure FDA0003372991110000043
If p isijWhen 0, then
Figure FDA0003372991110000044
Calculating the threat degree of the jth target according to the following formula:
Figure FDA0003372991110000045
wherein beta is1、β2、β3、β4Obtaining 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 respectively;
the specific process of the step (VII) 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 FDA0003372991110000046
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 FDA0003372991110000051
wherein N istotallThe total number of targets for applying for executing the imaging tasks in the scheduling interval;
(3) the pulse resource utilization ratio is the ratio of the number of sub-pulses occupied by completing the imaging task to the total number of sub-pulses in the scheduling interval within the scheduling interval T:
Figure FDA0003372991110000052
(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 FDA0003372991110000053
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 trainjAn observation sub-pulse;
step 5: if j < NtotallIf j is j +1, returning 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: calculating the sum of target threat degrees, scheduling success rate, pulse resource utilization rate and performance index of the radar completing imaging tasks in the priority ranking mode;
adding the above-mentioned NtotallEach combination in the priority sorting mode is substituted into the model for resource allocation according to the principle that the target priority is high and high, through the allocation process from Step1 to Step7, the SPI of each combination is calculated, and a pulse resource allocation sequence with the largest Sum of Performance Indexes (SPI) and meeting the conditions of 3 constraints in the model is selected as a final resource scheduling sequence result;
the specific process of the step (eight) 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 a two-dimensional sparse imaging technology.
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