CN115618166A - Time resource scheduling method and device based on multi-task radar - Google Patents

Time resource scheduling method and device based on multi-task radar Download PDF

Info

Publication number
CN115618166A
CN115618166A CN202211276849.2A CN202211276849A CN115618166A CN 115618166 A CN115618166 A CN 115618166A CN 202211276849 A CN202211276849 A CN 202211276849A CN 115618166 A CN115618166 A CN 115618166A
Authority
CN
China
Prior art keywords
target
radar
time
time resource
task
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202211276849.2A
Other languages
Chinese (zh)
Other versions
CN115618166B (en
Inventor
马林
严峻坤
关永胜
孙旭敏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CETC Information Science Research Institute
Original Assignee
CETC Information Science Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CETC Information Science Research Institute filed Critical CETC Information Science Research Institute
Priority to CN202211276849.2A priority Critical patent/CN115618166B/en
Publication of CN115618166A publication Critical patent/CN115618166A/en
Application granted granted Critical
Publication of CN115618166B publication Critical patent/CN115618166B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F41—WEAPONS
    • F41H—ARMOUR; ARMOURED TURRETS; ARMOURED OR ARMED VEHICLES; MEANS OF ATTACK OR DEFENCE, e.g. CAMOUFLAGE, IN GENERAL
    • F41H11/00—Defence installations; Defence devices
    • F41H11/02—Anti-aircraft or anti-guided missile or anti-torpedo defence installations or systems
    • 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
    • G01S13/66—Radar-tracking systems; Analogous systems
    • 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
    • G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Mathematics (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Algebra (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Operations Research (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The embodiment of the invention provides a time resource scheduling method and device based on a multitask radar, and is applied to the technical field of radars. The method comprises the following steps: determining the total time resource of the radar for completing a search task in a low-frequency-band single-beam target search mode; multiplying the time of the identification task allocated to the target farthest from the radar by the total number of the targets identified in the time resource allocation period to obtain the total time resource of the radar for completing the identification task; determining the total time resource of the radar for completing the tracking task according to the total time resource of the radar for completing the searching task, the total time resource for completing the identification task and the total time resource corresponding to the time resource allocation period; according to the total time resource of the radar for completing the tracking task, and based on a preset resource scheduling model, solving by adopting an optimization algorithm to obtain the revisit time of the radar; and controlling the radar to transmit radar signals to different wave positions according to the revisiting time.

Description

Time resource scheduling method and device based on multi-task radar
Technical Field
The invention relates to the technical field of radars, in particular to a time resource scheduling method and device based on a multitask radar.
Background
Ballistic missiles have the advantages of long range, high power, strong maneuverability and high precision, which are incomparable with other weapons. Ballistic missiles and ballistic missile defense are the current focus of research.
The ballistic missile defense system mainly relates to key technologies such as detection, tracking, identification and interception, and can be generally divided into three stages of early warning, tracking and interception. As a core detector of a ballistic missile defense system, the radar plays an irreplaceable important role in the aspects of detection, tracking and guidance of ballistic missile defense. Therefore, the method is combined with a ballistic target motion model and a nonlinear filtering algorithm to research the tracking of the ballistic target and the resource allocation technology of the radar, and has important significance on a ballistic missile defense system.
For radar, tasks of searching, identifying and tracking a target need to be completed within limited time resources, but a performance evaluation method of each task is different. In the case of multiple tasks that the radar needs to complete the search, identification and tracking, if only the completion of the tracking task and the scheduling of resources are considered, the performance of the search task and the identification task may be affected.
Therefore, for the multi-task radar, a more reasonable and fine resource scheduling scheme is needed, an effective resource allocation model is established and solved, and the method is very critical for improving the performance of the radar.
Disclosure of Invention
The invention provides a time resource scheduling method and device based on a multi-task radar. The technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a time resource scheduling method based on a multitask radar, including:
determining the total time resource of the radar for completing a search task in a low-frequency-band single-beam target search mode;
multiplying the time of the identification task allocated to the target farthest from the radar by the total number of the targets identified in the time resource allocation period to obtain the total time resource of the radar for completing the identification task;
determining the total time resource of the radar for completing the tracking task according to the total time resource of the radar for completing the searching task, the total time resource for completing the identification task and the total time resource corresponding to the time resource allocation period;
according to the total time resource of the radar for completing the tracking task, and based on a preset resource scheduling model, solving by adopting an optimization algorithm to obtain the revisit time of the radar;
and controlling the radar to transmit radar signals to different wave positions according to the revisiting time.
Optionally, the determining, in the target search mode of the low-frequency-band single beam, total time resources of the radar for completing a search task includes:
using formulas
Figure BDA0003895368990000021
Obtaining the wave position number N covering the whole search area S, wherein θk For searching the field angle, theta, of the sector area at the current time s Is the beam angle, and θ s ＜＜θ k ；
Using a formula
Figure BDA0003895368990000022
Obtaining the total time resource of the radar for completing the search task under the target search mode of low-frequency band single wave beam
Figure BDA0003895368990000023
wherein ts ＝max[t 1 ，t 2 ，…t n ]t n Is the search time of a single wave position, n is the current wave position, and n is a positive integer.
Optionally, the multiplying the time of the identification task allocated to the target farthest from the radar by the total number of targets identified in the time resource allocation period to obtain the total time resource of the radar for completing the identification task includes:
determining the time resource of the identification task allocated to the target farthest from the radar in the current scene
Figure BDA0003895368990000024
Using formulas
Figure BDA0003895368990000025
Obtaining total time resources of the radar for completing the identification task
Figure BDA0003895368990000026
wherein Nr And allocating the total number of the targets identified in the period for the time resource.
Optionally, in determining the current scenario, the time resource of the recognition task assigned by the target farthest from the radar
Figure BDA0003895368990000027
Previously, the method further comprises:
for each target, determining whether the target is a key target or not according to the narrow-band characteristics of the target;
if the target is not the key target, determining that the target is a general target;
if the target is the key target, further moving the broadband one-dimensional image of the target to determine whether the target is a suspected target;
if the target is not a suspected target, determining that the target is a key target;
if the target is a suspected target, further determining whether the target is a threat target through micro-motion detection;
if the target is not a threat target, determining that the target is a suspected target;
if the target is the threat target, determining the target as the threat target;
wherein the threat target is the target furthest from the radar.
Optionally, the method further comprises:
after the target type of each target is determined, determining the threat degree score of each target according to different target types; wherein the target types include the general target, the emphasized target, the suspected target, and the threat target.
Optionally, the method further comprises:
and judging whether interference exists in each target or not.
Optionally, determining the total time resource of the radar for completing the tracking task according to the total time resource of the radar for completing the search task, the total time resource of the radar for completing the identification task, and the total time resource corresponding to the time resource allocation period includes:
using formulas
Figure BDA0003895368990000031
Obtaining total time resources of the radar completing the tracking task
Figure BDA0003895368990000032
wherein
Figure BDA0003895368990000033
Allocating total time resources corresponding to the period for the kth time resource, wherein k is a positive number,
Figure BDA0003895368990000034
the total time resources to complete a search task for the radar,
Figure BDA0003895368990000035
a total time resource to complete an identification task for the radar.
Optionally, the preset resource scheduling model is:
Figure BDA0003895368990000036
Figure BDA0003895368990000037
Figure BDA0003895368990000038
wherein the radar operates in X-band and S-band, w q，k Is the threat degree weight, T, of the target q at the kth tracking k A period is allocated for the time resource at the k-th trace,
Figure BDA0003895368990000039
the dwell time of the irradiation of the S-band wave position i once at the kth tracking,
Figure BDA00038953689900000310
the dwell time of one irradiation of the X wave band wave position i in the kth tracking,
Figure BDA00038953689900000311
the revisit time of the wave position i of the S wave band at the kth tracking,
Figure BDA00038953689900000312
the revisit time of the wave position i of the X wave band at the k tracking time,
Figure BDA0003895368990000041
is the minimum value of the revisit time of the S band,
Figure BDA0003895368990000042
is the minimum value of the revisit time of the X band,
Figure BDA0003895368990000043
is the maximum value of the revisit time of the S band,
Figure BDA0003895368990000044
is the maximum value of the revisit time of the X band,
Figure BDA0003895368990000045
the time resource for target tracking identification of the radar at the k tracking time,
Figure BDA0003895368990000046
η k is the radar tracking time proportion at the k tracking time,
Figure BDA0003895368990000047
N L number of targets in wave position L, B L Is the L-th wave position and is,
Figure BDA0003895368990000048
Λ is a matrix of utilizations, F q And (t) is a value obtained by extracting the sum of diagonal elements of the Cramer-Rao boundary matrix and is used for reflecting the error magnitude of target tracking, and B (t) is an error lower bound factor.
Optionally, B (t) includes an error lower bound factor in the absence of interference and an error lower bound factor in the presence of interference; wherein,
the lower bound factor B (t) of the error under the condition of no interference is as follows:
Figure BDA0003895368990000049
the lower bound factor of error B (t) in the presence of interference is:
Figure BDA00038953689900000410
wherein ,
Figure BDA00038953689900000411
represents the attenuation factor, Q, of the information matrix of the target in the case of the m-th measured value received by band i tracking at the current time q，k Is and tracks time interval T q，k Correlated process noise, J P (x q，k )＝[Q q，k +F q，k J -1 (x q，k-1 )(F q，k ) T ] -1 ，J -1 (x q，k-1 ) Indicates the Cramer-Row bound, Y, of the previous moment q，k Is the residual matrix, P, of the target covariance matrix q，k Mean power, τ, assigned to target q at the time of target assignment kth q，k For the residence time allocated by target q at the time of the kth allocation,
Figure BDA00038953689900000412
and a Jacobian matrix representing the mth target state data to be fused at the kth tracking moment, wherein m, i and k are positive numbers.
In a second aspect, an embodiment of the present invention provides a time resource scheduling apparatus based on a multitasking radar, including: a memory, a processor and a computer program stored on the memory and running on the processor, the processor implementing the method for time resource scheduling based on multitask radar as provided in the first aspect when executing the computer program.
The technical scheme of the invention has the beneficial effects that:
the time resource scheduling method and device based on the multitask radar, provided by the embodiment of the invention, complete the time scheduling of radar multitask in a multi-target scene and the optimal allocation of time resources in a tracking task. Meanwhile, the time resource of the radar can be optimally distributed by optimizing revisit time under the background of multi-task time scheduling, so that the target tracking precision is improved, the threat score of the target is reduced, and a foundation is laid for further follow-up research.
Drawings
FIG. 1 is a diagram of a radar resource allocation provided by an embodiment of the present invention;
fig. 2 is a flowchart of a method for scheduling time resources based on a multitasking radar according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for solving an optimization problem according to an embodiment of the present invention;
FIG. 4 is a flowchart of radar single-task periodic scheduling provided by an embodiment of the present invention;
FIG. 5 is a diagram of target scenarios and task assignments provided by an embodiment of the present invention;
FIG. 6 is a graph of the average assigned and optimized assigned average target scores provided by an embodiment of the present invention;
FIG. 7 is a graph of the average target score boost ratio provided by an embodiment of the present invention;
FIG. 8 is a diagram illustrating a variation of a target wave position according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a time resource scheduling apparatus based on a multitask radar according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided only to help the full understanding of the embodiments of the present invention. Thus, it will be apparent to those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In various embodiments of the present invention, it should be understood that the sequence numbers of the following processes do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic of the process, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The embodiment of the invention develops research aiming at radar resource scheduling, designs an integral time resource allocation scheme aiming at a multi-task radar, completes the search, identification and tracking tasks of the radar, and provides a corresponding model modeling and solution scheme for a key technology of a multi-band radar beam-level integrated scheduling optimization algorithm. The specific content comprises the steps of scheduling and modeling from single task to multi-task, establishing a relevant tracking resource allocation model by taking revisit time as an optimization variable, and providing a performance evaluation method under the conditions of non-sequential tracking and interference.
Specifically, embodiments of the present invention relate to improvements in three general aspects:
1. performance evaluation
For optimization of the revisit time, an evaluation of the state estimate after a period of time is made. In consideration of simplifying the model and improving the evaluation efficiency, a performance evaluation method of non-sequential tracking is used in the performance evaluation, and meanwhile, the interference existing in the current target environment is considered, and the performance evaluation method of the interference is also adopted in the tracking so that the tracking evaluation is more accurate.
2. Resource on-demand scheduling
Radar resource allocation diagram as shown in fig. 1, resource scheduling and allocation in a plurality of task cycles is a dynamic process, and resource can be influenced by search time and identification time for optimizable tracking time. In a particular resource scheduling assignment model,
Figure BDA0003895368990000061
represents the total time resources that can be allocated in the kth time resource allocation period,
Figure BDA0003895368990000062
represents a time resource for a target search,
Figure BDA0003895368990000063
represents a time resource for object recognition,
Figure BDA0003895368990000064
representing the time resources for target tracking.
3. Intelligent optimization solution
For the non-convex and nonlinear constraint optimization problem given by the embodiment of the present invention, a suitable optimization algorithm needs to be used to solve the optimization problem.
Specifically, referring to fig. 2, an embodiment of the present invention provides a time resource scheduling method based on a multi-task radar, and specifically provides a resource scheduling allocation model of a radar under searching, identifying, and tracking multi-tasks. The method comprises the following steps:
step 201, determining the total time resource of the radar for completing the search task in the low-frequency-band single-beam target search mode.
Firstly, for the time resource allocation of the search task, the embodiment of the invention completes the search task by using the low frequency band. Suppose that the sector area searched at the current time is S k Angle of opening theta k Angle of beam theta s And θ s ＜＜θ k The entire search area requires a search of multiple wave bits to cover the entire spatial domain.
Thus, formulas can be specifically utilized
Figure BDA0003895368990000071
Obtaining the wave position number N covering the whole search area S 。
In the same wave position, the searching time mainly depends on the time of receiving the echo of the farthest target, and the searching time t in one wave position can be obtained n Comprises the following steps:
Figure BDA0003895368990000072
wherein Rn Is the distance of the farthest target in the wave position where the current beam is located, and n is the current wave position. Since the target is located in each wave position differently, due to the fact thatSimplifying the consideration of the search time model, taking the search time t of a single wave position s ＝max[t 1 ，t 2 ，…t n ]t n Is the search time of a single wave position, n is the current wave position, and n is a positive integer. The total time resource of the radar completing the search task at the current moment in the low-frequency band single-beam target search mode
Figure BDA0003895368990000073
Step 202, multiplying the time of the identification task allocated to the target farthest from the radar by the total number of the targets identified in the time resource allocation period to obtain the total time resource of the radar for completing the identification task.
Specifically, the embodiment of the invention determines the time resource of the identification task allocated to the target farthest from the radar in the current scene
Figure BDA0003895368990000074
Further using the formula
Figure BDA0003895368990000075
Obtaining total time resource of the radar for completing the identification task
Figure BDA0003895368990000076
wherein Nr And allocating the total number of the targets identified in the period for the time resource.
In the practical application process of the embodiment of the invention, the current scene can include a plurality of targets with different target types, and the target types can include a general target, a key target, a suspected target and a threat target. The embodiment of the present invention may sequentially classify all the targets in the searched scene, specifically for example:
for each target, determining whether the target is a key target or not according to the narrow-band characteristics of the target;
if the target is not the key target, determining that the target is a general target;
if the target is the key target, further moving the broadband one-dimensional image of the target to determine whether the target is a suspected target;
if the target is not a suspected target, determining that the target is a key target;
if the target is a suspected target, further determining whether the target is a threat target through micro-motion detection;
if the target is not a threat target, determining that the target is a suspected target;
if the target is the threat target, determining the target as the threat target;
wherein the threat object in an embodiment of the invention is the object furthest from the radar.
After a target is tracked for T seconds, whether the target is a key target is determined through narrow-band characteristics of the target, then the key target can transfer a wide-band one-dimensional image of the target to determine whether the target is a suspected target, and finally whether the suspected target is a threat target is determined through micro-motion detection. Furthermore, it is further preferred that after determining the object type of each object, the threat level score of each object may also be determined according to different object types. For example, the threat level score corresponding to a general target is 0.5, the threat level score corresponding to a key target is 5, the threat level score corresponding to a suspected target is 10, and the threat level score corresponding to a threat target is 25. And in the whole identification process, whether each target has interference or not can be judged simultaneously.
Assuming that the recognition time resources required for the above different types of targets are T N 、T B 、T F. wherein TN Time consumption for mobilizing narrowband identification, T B Time resource consumption, T, to mobilize a broadband one-dimensional image F Time resource consumption for mobilizing the micro-motion detection. The total time resource needed for identification is recorded as T R Then general target and key target T R ＝T N Suspected target T R ＝T N +T B Threat target T R ＝T N +T B +T F (T F ＞T B ). It can be seen that, when a target is identified, the maximum time resource and the minimum time resource consumed by the identification are respectively T R and TN . When identifying multiple targetsThe time resource consumed by the threat target furthest from the radar is the largest, and is noted as
Figure BDA0003895368990000081
The time resources consumed by the general target closest to the radar are minimal, noted as
Figure BDA0003895368990000082
Setting the target farthest from the radar in the scene as a threat target corresponding to the threat target in consideration of simplifying the model and simultaneously ensuring that each target can be accurately identified in the multi-target scene
Figure BDA0003895368990000083
As a time resource allocated to each target in the recognition task.
Thus, the total consumption of time resources for identification in the current time resource allocation period
Figure BDA0003895368990000091
N r Is a target number recognizable by the current time resource allocation period.
Step 203, determining the total time resource of the radar for completing the tracking task according to the total time resource of the radar for completing the search task, the total time resource for completing the identification task and the total time resource corresponding to the time resource allocation period.
In the embodiment of the invention, after target searching and identification, the tracking time resource can be used for domination
Figure BDA0003895368990000092
wherein
Figure BDA0003895368990000093
Allocating total time resources corresponding to the period for the kth time resource, wherein k is a positive number,
Figure BDA0003895368990000094
completion of search tasks for the radarThe time resource is used for the time resource,
Figure BDA0003895368990000095
a total time resource to complete an identification task for the radar.
And 204, solving by adopting an optimization algorithm to obtain the revisit time of the radar according to the total time resource of the radar for completing the tracking task and based on a preset resource scheduling model.
The preset resource scheduling model in the embodiment of the invention specifically comprises the following steps:
Figure BDA0003895368990000096
Figure BDA0003895368990000097
Figure BDA0003895368990000098
wherein the radar operates in X-band and S-band, w q，k Is the threat degree weight, T, of the target q at the kth tracking k A period is allocated for the time resource at the k-th trace,
Figure BDA0003895368990000099
the dwell time of irradiation of the S-band wave position i once at the kth tracking,
Figure BDA00038953689900000910
the dwell time of one irradiation of the X-band wave position i at the kth tracking,
Figure BDA00038953689900000911
the revisit time of the wave position i of the S wave band at the kth tracking,
Figure BDA00038953689900000912
the revisit time of the wave position i of the X wave band at the k tracking time,
Figure BDA00038953689900000913
is the minimum value of the revisit time of the S band,
Figure BDA00038953689900000914
is the minimum value of the revisit time of the X wave band,
Figure BDA00038953689900000915
is the maximum value of the revisit time of the S band,
Figure BDA00038953689900000916
is the maximum value of the revisit time of the X band,
Figure BDA00038953689900000917
the time resource for target tracking identification of the radar at the k tracking time,
Figure BDA00038953689900000918
η k is the radar tracking time proportion at the k tracking time,
Figure BDA00038953689900000919
N L to the number of targets in the wave position L, B L For the L-th wave position, the wave position is,
Figure BDA0003895368990000101
Λ is a matrix of utilizations, F q And (t) is a value obtained by extracting the sum of diagonal elements of the Cramer-Rao boundary matrix and is used for reflecting the error magnitude of target tracking, and B (t) is an error lower bound factor.
And further, B (t) in the embodiment of the present invention further includes: an error lower bound factor in the absence of interference and an error lower bound factor in the presence of interference. Wherein:
the lower bound factor of error B (t) without interference is:
Figure BDA0003895368990000102
the lower bound factor of error B (t) in the presence of interference is:
Figure BDA0003895368990000103
wherein ,
Figure BDA0003895368990000104
represents the attenuation factor, Q, of the information matrix of the target in the case of the m-th measured value received by band i tracking at the current time q，k Is and tracks time interval T q，k Correlated process noise, J P (x q，k )＝[Q q，k +F q，k J -1 (x q，k-1 )(F q，k ) T ] -1 ，，J -1 (x q，k-1 ) Indicating the Cramer-Row bound, Y, of the previous moment q,k The residual matrix, P, of the target covariance matrix q,k Mean power, τ, assigned to target q at k-th assignment of target q,k The assigned dwell time for target q at the kth assignment,
Figure BDA0003895368990000105
and a Jacobian matrix representing the mth target state data to be fused at the kth tracking moment, wherein m, i and k are positive numbers.
The applicant of the present invention will now describe in detail a resource scheduling model, a lower bound error factor B (t) in the case of no interference, a lower bound error factor B (t) in the case of interference, and an intelligent optimization solution according to embodiments of the present invention.
First, the applicant introduces a lower bound factor for error B (t) in the absence of interference and a lower bound factor for error B (t) in the presence of interference.
Knowing that Q targets exist in a scene and are in a two-dimensional motion state, the motion state accords with a discretized first-order correlation time Singer model, and a discretized discrete state equation of the Singer model is as follows: x is the number of q，k+1 ＝F q x q，k +w q，k wherein
Figure BDA0003895368990000111
The position, velocity, and acceleration of the target q at the time k are associated with the information.
State transition matrix
Figure BDA0003895368990000112
Noise w q，k With the following covariance matrix:
Figure BDA0003895368990000113
Figure BDA0003895368990000114
q 12 ＝q 21 ＝(e -2αT +1-2e -αT +2αTe -αT -2αT+α 2 T 2 )/(2α 4 )
q 13 ＝q 31 ＝(1-e -2αT -2αTe -αT )/(2α 3 )
q 22 ＝(4e -αT -3-e -2αT +2αT)/(2α 3 )
q 23 ＝q 32 ＝(e -2αT +1-2e -αT )/(2α 2 )
q 33 ＝(1-e -2αT )/(2α)
where T is the tracking time interval, α is the inverse of the maneuver time constant, and the variance of the acceleration is
Figure BDA0003895368990000115
The process is a target motion state of a target in a one-dimensional state, and is expanded to two-dimensional state
Figure BDA0003895368990000116
wherein ,I2 Is an identity matrix. The covariance of the corresponding noise is:
Figure BDA0003895368990000117
the target state vector is expanded to
Figure BDA0003895368990000118
The corresponding measurement model is:
z q，k ＝h(x q，k )+v q，k
Figure BDA0003895368990000121
where (x, y) is the position of the radar in the coordinate system, (x) q，k ，y q，k ) Is the location information of the object. v. of q，k Is a mean of 0 and a covariance of ∑ q，k White gaussian noise of (1):
Figure BDA0003895368990000122
Figure BDA0003895368990000123
covariance matrix Σ q，k Can be simplified as follows:
Figure BDA0003895368990000124
Figure BDA0003895368990000125
Y q，k the residual matrix, P, of the target covariance matrix q，k Mean power, τ, assigned to target q at k-th assignment of target q，k Residence time, R, assigned to target q at the kth assignment q，k Represents the radial distance of the target q to the radar at the k-th tracking,
Figure BDA0003895368990000126
c is the speed of light and c is the speed of light,
Figure BDA0003895368990000127
Figure BDA0003895368990000128
denotes the reflectivity, β, of the target q at the k-th allocation q，k For the k-th allocation of the effective bandwidth, beta, of the signal q，NN Is the receive beamwidth.
In order to simplify the model, non-sequential tracking is used, assuming that the motion states of a plurality of targets and the measurement model are unchanged, Z q，k Represents a vector consisting of M measurement information:
Figure BDA0003895368990000131
processing a fusion of multiple radar data with a maximum likelihood function (ML) estimation:
Figure BDA0003895368990000132
and (3) iteratively solving state estimation by using a least square method:
Figure BDA0003895368990000133
h q，k () Representing a non-linear measurement function vector, Σ q，k Is Z q，k Corresponding noise covariance matrix, H q，k，j And the Jacobian matrix represents the information of how many states of transition are fused, and the expressions of the Jacobian matrix are as follows:
Figure BDA0003895368990000134
Figure BDA0003895368990000135
Figure BDA0003895368990000136
Figure BDA0003895368990000137
a Jacobian matrix representing the mth target state data to be fused at the kth tracking time.
The clarmerola bound that has fused a plurality of state data is:
Figure BDA0003895368990000138
through calculation and simplification and for the consideration of real-time iteration, it is possible to obtain:
Figure BDA0003895368990000139
simultaneous prior information matrix:
J P (x q，k )＝[Q q，k +F q，k J -1 (x q，k-1 )(F q，k ) T ] -1
wherein ,J-1 (x q，k-1 ) Indicates the Cramer-Role bound, Q, of the last moment in time q，k Is from the tracking time interval T q，k Correlated process noise.
The final fusion error lower bound factor is:
Figure BDA0003895368990000141
from the analysis and derivation, it can be seen that the hybrid measurement can reduce the amount of computation, improve the timeliness of state estimation, and has important significance for optimization of revisit time.
For a multiband radar, under the condition of no interference, the measurement information of one target can come from different wavebands, in the resource allocation period of the whole target, the radar receives the measurement information of M targets in total, the data information matrix of the target corresponding to each measurement information cannot be attenuated, and the target can be normally tracked. In the presence of interference in a certain band, the data information matrix of the target will be lost.
Here, assuming that the radar operates in two bands, 1 band and 2 band, respectively, considering the influence of the interference power on the attenuation of the information matrix of the target, the following attenuation factor of the information matrix of the target data is defined:
IRF1＝1/(r 1 +1)
IRF2＝1/(r 2 +1)
wherein r1 ，r 2 Expressing the interference ratio, the expression is as follows:
r 1 ＝P/P 1
r 2 ＝P/P 2
p is the interference power, P 1 and P2 The reference interference power reflects the magnitude of the degree of influence on the target in the state of interference in a certain waveband, and the smaller the value of the reference interference power, the greater the influence on the target in the waveband, that is, the greater the interference ratio, the greater the information attenuation.
Under the condition that the target is interfered by the waveband, the lower error bound under the specific fusion state estimation can be obtained by introducing an interference attenuation factor:
Figure BDA0003895368990000151
in the above formula, the first and second carbon atoms are,
Figure BDA0003895368990000152
which represents the information matrix attenuation factor of the target in case the mth measurement value is received with band i tracking at the current time. It can be seen that the stronger the interference, the smaller the attenuation factor and the higher the lower bound of the errorIs large.
Next, the applicant further introduces a resource scheduling model in the embodiment of the present invention.
On the distribution of the tracking time resources, after target searching and identification, the tracking time resources which can be used for domination
Figure BDA0003895368990000153
At this time, a variable eta is introduced k ，
Figure BDA0003895368990000154
Note the book
Figure BDA0003895368990000155
Figure BDA0003895368990000156
By the formula F q (t) as a function of a criterion for measuring the accuracy of the target, where Λ is the matrix used, F q And (t) is a value obtained by squaring the sum of diagonal elements of the Cramer-Rao bound matrix, and is used for reflecting the error magnitude of target tracking.
In the embodiment of the invention, for a scene with a plurality of targets, the radar needs to artificially divide the scene into a plurality of wave positions, and each wave position is scanned by a single wave beam, namely, one wave beam simultaneously tracks a plurality of targets, and the targets in the same wave position use the same time resource. In the embodiment of the invention, revisit time is taken as an optimization variable, a radar works in an X wave band and an S wave band, and an obtained resource scheduling model is as follows:
Figure BDA0003895368990000157
Figure BDA0003895368990000158
Figure BDA0003895368990000159
it should be noted that the resource allocation model reflects a relationship between optimization of revisit time and improvement of target tracking effect under an interference-free condition, and if there is interference in a target, performance evaluation under an interference condition is used in tracking evaluation of the target. The resource allocation model used in the embodiment of the present invention is the same as that used in the absence of interference, except that the target is interfered, i.e. the above description
Figure BDA0003895368990000161
The way of calculation of (c) is different.
Finally, the intelligent optimization solution in the embodiment of the invention is concerned.
In the embodiment of the present invention, the resource allocation problem generally needs to find the optimal task performance under the resource or logic constraint, which is generally expressed as a constraint optimization problem, and some methods that can be used to solve the optimization problem are shown in fig. 3.
According to the integral scheme design, intelligent algorithms such as a genetic algorithm, a simulated annealing algorithm and the like can be used, and after nonlinear constraint of the constraint problem is changed into linear constraint, optimization algorithms such as a gradient projection method, a near-end ADMM algorithm and the like can be adopted.
Regarding the resource allocation model provided in the embodiment of the present invention, the objective function and the constraint are non-linear and non-convex, if some intelligent solving algorithms are used, the time consumption is huge, and for the specificity of the constraint function, the problem is simplified by converting the variables into linear constraints through variable substitution, and the method for solving the problem is more diverse, wherein a method with higher solving efficiency is selected, and the real-time scheduling performance of the corresponding time resource is improved.
And step 205, controlling the radar to transmit radar signals to different wave positions according to the revisiting time.
The time resource scheduling method based on the multi-task radar completes the multi-task time scheduling of the radar in a multi-target scene and the optimal allocation of time resources in a tracking task. Meanwhile, the time resource of the radar can be optimally distributed by optimizing revisit time under the background of multi-task time scheduling, so that the target tracking precision is improved, the threat score of the target is reduced, and a foundation is laid for further follow-up research.
The applicant further verifies and explains the implementation effect of the invention through simulation experiments.
(I) simulation conditions
The simulation running system of the embodiment of the invention is, for example, an Inter (R) Core (TM) i5-4590 CPU @3.30GHZ and 64-bit Windows 10 operating system, and MATLAB (R2016 b) is adopted as simulation software.
(II) simulation content and result analysis
In a simulation scene, after a ballistic target enters an alert airspace of a radar, the ballistic target is subjected to monomer splitting, and a total of 100 targets such as warheads, bait bullets, false targets and rocket wreckages are gradually formed, wherein the warheads, the false targets and the like continue to move, and continuously press a ballistic target defense system of one party, specifically: at s 1 there is one target (i.e. a ballistic target) and at s 5 the target is split for the first time, when there are two targets in the surveillance airspace of the radar. The missile is split again at the 13 th s and the 25 th s, and the number of targets is gradually changed from 2 to 100 preset targets. Where table 1 is the initial state information for the top 10 targets. The radar warning time of our party is 100S, the single fusion period is 4S, 25 task allocation periods are provided in total, and an S-band and X-band dual-band working mode is adopted. Table 2 shows radar band information. The interference setting during the warning process is as follows: when the target is interfered by one waveband, the interference ratio of the two wavebands is set to be 3 and 0.6, and when the target is interfered by the two wavebands, the interference ratio is 3. Table 3 sets up for specific interference information.
TABLE 1
Target Location (Km) Speed (m/s)
1 (1200,1600) (-3000,3000)
2 (1190,1590) (-2850,3100)
3 (1210,1610) (-3150,2900)
4 (1170,1590) (-2900,3150)
5 (1160,1600) (-2850,2900)
6 (1180,1590) (-2700,3000)
7 (1170,1610) (-3100,3100)
8 (1160,1590) (-2880,2900)
9 (1180,1610) (-2950,3150)
10 (1170,1590) (-2900,2900)
TABLE 2
Wave band Bandwidth/MHz Beam width/degree
X wave band 10 1
S wave band 5 1
TABLE 3
Figure BDA0003895368990000171
Figure BDA0003895368990000181
As further shown in fig. 4, fig. 4 is a flowchart of radar single task cycle scheduling provided by an embodiment of the present invention. In the above-mentioned scenario of 100 targets, the false alarm time needs to be divided into a plurality of task cycles, each task cycle needs to complete four tasks of searching, identifying, tracking, and threat level evaluation, the four tasks are performed sequentially, and considering that the previous task cycles do not need to allocate time to identification, a determination of whether to schedule identification is preferably added to the process.
Referring to fig. 5, fig. 5 is a diagram of a target scenario and task allocation provided by an embodiment of the invention. In the simulation, the search time distribution of the targets is determined according to the existing condition of the targets in the determined fusion period, the wave positions are divided aiming at the targets, and the detection time of the farthest target is selected as the scheme of the detection time of each target. The identification of the target needs at least two fusion periods (8 s) of information accumulation, the broadband one-dimensional image needs 4 echo times, and the micro-motion detection time consumption is fixed 0.06s. In a fusion task period, 10 targets can be identified simultaneously, the first 10 targets are identified at the 21 st s, then the remaining targets are identified at the 33 st s, and 10 targets are randomly selected in each fusion period until all targets are identified. After identification, the identification target needs to be given a score of threat level, the threat target score is set to 25, the suspected target is 10, the emphasized target is 5, and the general target is 0.5, and once the target is identified, the threat level score of the target is determined. Table 4 below is the threat attribute set for each target. The target tracking time allocation is an optimization problem, the allocatable tracking time resource is dynamically changed, the minimum revisiting time requirement of the dual-waveband is larger than or equal to the residence time resource and is set to be 0.01s, and the maximum revising time is set to be 0.8s, so that more than two times of measurement data are obtained in the tracking process.
TABLE 4
Figure BDA0003895368990000182
Figure BDA0003895368990000191
Referring to fig. 6, fig. 6 is a graph of target scores for the average allocation and the optimized allocation provided by the embodiment of the present invention. It can be seen from the figure that optimizing revisit time may well reduce the threat score of the target for the current fusion cycle.
Referring to fig. 7, fig. 7 is a graph of the average target score boost ratio provided by the embodiment of the invention. At the initial time, the optimization result and the improvement ratio are not obvious, because the number of targets at the initial time is small, the targets are concentrated, the wave position division is concentrated, the targets with high threat degree and the targets with low threat degree use the same revisit time resource, the targets with low threat degree can obtain a large amount of resources, the resource waste is caused, and the optimization result is not obvious; at most times, the optimal allocation may reduce the target threat score by approximately 10% -15%.
Referring to fig. 8, fig. 8 is a diagram of a target wave position variation according to an embodiment of the present invention. According to the motion state and the process of the target, the target is gradually dispersed, and the wave position division of the target is gradually increased.
In the embodiment of the invention, at the 16 th fusion time, the region with high target threat degree is mainly concentrated with 4,5,6 wave bits of the wave bit division region, the optimization scheme divides more resources into 4,5,6 wave bits compared with the average allocation scheme, the wave bits at two sides of the region are divided into fewer resources, and the following conclusion can be obtained: compared with the average allocation scheme, the optimal allocation reduces the resources of wave bits with small threats, and allocates the wave bits with higher threat degrees to some wave bits with the mechanism to reduce the target threat score.
In the implementation example of the invention, at the 16 th fusion time, the threat score proportion distribution of the wave position at the time is the same as the change trend of the total threat degree: the threat score with large threat is high, the wave position threat score with high threat score is reduced in proportion through optimized distribution, the rate with low threat degree is increased, and the total threat score is reduced.
In summary, the above simulation experiments verify the correctness, validity and reliability of the embodiments of the present invention.
Based on the foregoing text, an embodiment of the present invention provides a method for scheduling time resources based on a multitasking radar, and an embodiment of the present invention further provides a device for scheduling time resources based on a multitasking radar, as shown in fig. 9, including:
a first determining module 100, configured to determine, in a target search mode of a low-frequency-band single beam, a total time resource for a radar to complete a search task;
a second determining module 200, configured to multiply the time of the identification task allocated to the target farthest from the radar by the total number of targets identified in the time resource allocation period, to obtain a total time resource for the radar to complete the identification task;
a third determining module 300, configured to determine, according to the total time resource for the radar to complete the search task, the total time resource for the radar to complete the identification task, and the total time resource corresponding to the time resource allocation period, the total time resource for the radar to complete the tracking task;
the solving module 400 is used for solving the revisit time of the radar by adopting an optimization algorithm based on a preset resource scheduling model according to the total time resource of the radar for completing the tracking task;
and the control module 500 is configured to control the radar to transmit radar signals to different wave positions according to the revisit time.
The first determining module 100 may include:
a first determination submodule for utilizing a formula
Figure BDA0003895368990000201
Obtaining the wave position number N covering the whole search area S, wherein θk Searching the opening angle of the sector area at the current moment,θ s is the beam angle, and θ s ＜＜θ k ；
A second determination submodule for utilizing the formula
Figure BDA0003895368990000202
Obtaining the total time resource of the radar for completing the search task under the target search mode of the low-frequency single beam
Figure BDA0003895368990000211
wherein ts ＝max[t 1 ，t 2 ，…t n ]，t n The search time of a single wave bit, n is the current wave bit, and n is a positive integer.
Wherein, the second determining module 200 may include:
a third determining submodule for determining the time resource of the recognition task assigned to the target farthest from the radar in the current scene
Figure BDA0003895368990000212
A fourth determination submodule for utilizing a formula
Figure BDA0003895368990000213
Obtaining total time resources of the radar for completing the identification task
Figure BDA0003895368990000214
wherein Nr And allocating the total number of the targets identified in the period for the time resource.
And, optionally, the second determining module 200 may be further configured to:
determining a time resource of an identification task assigned to a target farthest from the radar in a current scenario
Figure BDA0003895368990000215
Before, for each target, determining whether the target is a key target or not through the narrow-band characteristics of the target;
if the target is not the key target, determining that the target is a general target;
if the target is the key target, further moving the broadband one-dimensional image of the target to determine whether the target is a suspected target;
if the target is not a suspected target, determining that the target is a key target;
if the target is a suspected target, further determining whether the target is a threat target through micro-motion detection;
if the target is not a threat target, determining that the target is a suspected target;
if the target is the threat target, determining the target as the threat target;
wherein the threat target is a target furthest from the radar.
And, further optionally, the second determining module 200 may be further configured to: after the target type of each target is determined, determining the threat degree score of each target according to different target types; wherein the target types include the general target, the important target, the suspected target, and the threat target.
Optionally, the time resource scheduling apparatus based on a multitask radar provided in the embodiment of the present invention may further include: and the judging module is used for judging whether each target has interference.
The third determining module 300 may be specifically configured to:
using formulas
Figure BDA0003895368990000216
Obtaining total time resources of the radar completing the tracking task
Figure BDA0003895368990000217
wherein
Figure BDA0003895368990000221
Allocating total time resources corresponding to the period for the kth time resource, wherein k is a positive number,
Figure BDA0003895368990000222
the total time resources to complete the search task for the radar,
Figure BDA0003895368990000223
total time resources to complete an identification task for the radar.
In the embodiment of the present invention, the preset resource scheduling model may be:
Figure BDA0003895368990000224
Figure BDA0003895368990000225
Figure BDA0003895368990000226
wherein the radar operates in an X-band and an S-band,
Figure BDA0003895368990000227
is the threat degree weight, T, of the target q at the kth tracking k A period is allocated for the time resource at the k-th trace,
Figure BDA0003895368990000228
the dwell time of irradiation of the S-band wave position i once at the kth tracking,
Figure BDA0003895368990000229
the dwell time of one irradiation of the X-band wave position i at the kth tracking,
Figure BDA00038953689900002210
the revisit time of the wave position i of the S wave band at the k tracking time,
Figure BDA00038953689900002211
the revisit time of the wave position i of the X wave band at the kth tracking,
Figure BDA00038953689900002212
is the minimum value of the revisit time of the S band,
Figure BDA00038953689900002213
is the minimum value of the revisit time of the X wave band,
Figure BDA00038953689900002214
is the maximum value of the revisit time of the S band,
Figure BDA00038953689900002215
is the maximum value of the revisit time of the X band,
Figure BDA00038953689900002216
the time resource used by the radar for target tracking identification at the kth tracking time,
Figure BDA00038953689900002217
η k is the radar tracking time proportion at the k tracking time,
Figure BDA00038953689900002218
N L number of targets in wave position L, B L For the L-th wave position, the wave position is,
Figure BDA00038953689900002219
Λ is a matrix of utilizations, F q And (t) is a value obtained by extracting the sum of diagonal elements of the Cramer-Rao boundary matrix and is used for reflecting the error magnitude of target tracking, and B (t) is an error lower bound factor.
Further B (t) may include a lower bound factor for error in the absence of interference and a lower bound factor for error in the presence of interference; wherein,
the lower bound factor of error B (t) without interference is:
Figure BDA0003895368990000231
the lower bound factor of error B (t) in the presence of interference is:
Figure BDA0003895368990000232
wherein ,
Figure BDA0003895368990000233
represents the attenuation factor, Q, of the information matrix of the target in the case of receiving the mth measurement value by means of band i tracking at the current time q，k Is a time interval T with the tracking q，k Correlated process noise, J P (x q，k )＝[Q q，k +F q，k J -1 (x q，k-1 )(F q，k ) T ] -1 ，J -1 (x q，k-1 ) Indicates the Cramer-Row bound, Y, of the previous moment q，k The residual matrix, P, of the target covariance matrix q，k Mean power, τ, assigned to target q at k-th assignment of target q，k For the residence time allocated by target q at the time of the kth allocation,
Figure BDA0003895368990000234
and a Jacobian matrix representing the mth target state data to be fused at the kth tracking time, wherein m, i and k are positive numbers.
It should be noted that the time resource scheduling control device based on the multitask radar is a device corresponding to the time resource scheduling method based on the multitask radar in the foregoing embodiment, and all implementation means in the above method embodiment are applicable to the embodiment of the time resource scheduling device based on the multitask radar, and the same technical effect can be achieved.
In addition, an embodiment of the present invention further provides a time resource scheduling apparatus based on a multitask radar, including: a memory, a processor and a computer program stored on the memory and running on the processor, the processor when executing the computer program implementing the method for time resource scheduling based on multitask radar as described in the previous embodiments.
While the foregoing is directed to the preferred embodiment of the present invention, it will be appreciated by those skilled in the art that various changes and modifications may be made therein without departing from the principles of the invention as set forth in the appended claims.

Claims (10)

1. A time resource scheduling method based on a multitask radar is characterized by comprising the following steps:
determining the total time resource of the radar for completing a search task in a low-frequency-band single-beam target search mode;
multiplying the time of the identification task allocated to the target farthest from the radar by the total number of the targets identified in the time resource allocation period to obtain the total time resource of the radar for completing the identification task;
determining the total time resource of the radar for completing the tracking task according to the total time resource of the radar for completing the searching task, the total time resource for completing the identification task and the total time resource corresponding to the time resource allocation period;
according to the total time resource of the radar for completing the tracking task, and based on a preset resource scheduling model, solving by adopting an optimization algorithm to obtain the revisit time of the radar;
and controlling the radar to transmit radar signals to different wave positions according to the revisiting time.
2. The method of claim 1, wherein determining the total time resource of the radar for completing the search task in the low-frequency-band single-beam target search mode comprises:
using formulas
Figure FDA0003895368980000011
Obtaining the wave position number N covering the whole search area S, wherein θk For searching the opening angle theta of the sector area at the current time s Is the beam angle, and θ s ＜＜θ k ；
Using a formula
Figure FDA0003895368980000012
Obtaining the total time resource of the radar for completing the search task under the target search mode of low-frequency band single wave beam
Figure FDA0003895368980000013
wherein ts ＝max[t 1 ，t 2 ，...t n ]，t n Is the search time of a single wave position, n is the current wave position, and n is a positive integer.
3. The method of claim 1, wherein multiplying the time of the identification task assigned to the object farthest from the radar by the total number of objects identified in the time resource assignment period to obtain the total time resource of the radar for completing the identification task comprises:
determining the time resource of the identification task allocated to the target farthest from the radar in the current scene
Figure FDA0003895368980000014
Using formulas
Figure FDA0003895368980000015
Obtaining total time resources of the radar for completing the identification task
Figure FDA0003895368980000016
wherein Nr And allocating the total number of the targets identified in the period for the time resource.
4. Method according to claim 3, characterized in that in determining the current scenario, the time resources of the recognition tasks assigned to the targets furthest away from the radar are determined
Figure FDA0003895368980000021
The method described aboveFurther comprising:
for each target, determining whether the target is a key target or not according to the narrow-band characteristics of the target;
if the target is not the key target, determining that the target is a general target;
if the target is the key target, further moving the broadband one-dimensional image of the target to determine whether the target is a suspected target;
if the target is not a suspected target, determining that the target is a key target;
if the target is a suspected target, further determining whether the target is a threat target through micro-motion detection;
if the target is not a threat target, determining that the target is a suspected target;
if the target is the threat target, determining the target as the threat target;
wherein the threat target is a target furthest from the radar.
5. The method of claim 4, further comprising:
after the target type of each target is determined, determining the threat degree score of each target according to different target types; wherein the target types include the general target, the emphasized target, the suspected target, and the threat target.
6. The method according to any one of claims 3-5, further comprising:
and judging whether interference exists in each target or not.
7. The method of claim 1, wherein determining the total time resource of the radar for completing the tracking task according to the total time resource of the radar for completing the search task, the total time resource of the radar for completing the identification task, and the total time resource corresponding to the time resource allocation period comprises:
using formulas
Figure FDA0003895368980000022
Obtaining total time resources of the radar completing the tracking task
Figure FDA0003895368980000023
wherein
Figure FDA0003895368980000024
Allocating total time resources corresponding to the period for the kth time resource, wherein k is a positive number,
Figure FDA0003895368980000025
the total time resources to complete the search task for the radar,
Figure FDA0003895368980000026
a total time resource to complete an identification task for the radar.
8. The method of claim 1, wherein the predetermined resource scheduling model is:
Figure FDA0003895368980000031
Figure FDA0003895368980000032
Figure FDA0003895368980000033
wherein the radar operates in X-band and S-band, w q，k Is the threat degree weight, T, of the target q at the kth tracking k A period is allocated for the time resource at the kth tracking,
Figure FDA0003895368980000034
the dwell time of the irradiation of the S-band wave position i once at the kth tracking,
Figure FDA0003895368980000035
the dwell time of one irradiation of the X wave band wave position i in the kth tracking,
Figure FDA0003895368980000036
the revisit time of the wave position i of the S wave band at the k tracking time,
Figure FDA0003895368980000037
the revisit time of the wave position i of the X wave band at the kth tracking,
Figure FDA0003895368980000038
is the minimum value of the revisit time of the S band,
Figure FDA0003895368980000039
is the minimum value of the revisit time of the X band,
Figure FDA00038953689800000310
is the maximum value of the revisit time of the S band,
Figure FDA00038953689800000311
is the maximum value of the revisit time of the X band,
Figure FDA00038953689800000312
the time resource for target tracking identification of the radar at the k tracking time,
Figure FDA00038953689800000313
η k is the ratio of the radar tracking time at the kth tracking,
Figure FDA00038953689800000314
N L number of targets in wave position L, B L For the L-th wave position, the wave position is,
Figure FDA00038953689800000315
Λ is a matrix of utilizations, F q And (t) is a value obtained by extracting the sum of diagonal elements of the Cramer-Rao boundary matrix and is used for reflecting the error magnitude of target tracking, and B (t) is an error lower bound factor.
9. The method of claim 8, wherein B (t) comprises a lower bound error factor in the absence of interference and a lower bound error factor in the presence of interference; wherein,
the lower bound factor of error B (t) without interference is:
Figure FDA00038953689800000316
the lower bound factor of error B (t) in the presence of interference is:
Figure FDA0003895368980000041
wherein ,
Figure FDA0003895368980000042
represents the attenuation factor, Q, of the information matrix of the target in the case of the m-th measured value received by band i tracking at the current time q，k Is and tracks time interval T q，k Correlated process noise, J p (x q，k )＝[Q q，k +F q，k J -1 (x q，k-1 )(F q，k ) T ] -1 ，J -1 (x q，k-1 ) Indicates the Cramer-Row bound, Y, of the previous moment q，k The residual matrix, P, of the target covariance matrix q，k Mean power, τ, assigned to target q at k-th assignment of target q，k For the residence time allocated by target q at the time of the kth allocation,
Figure FDA0003895368980000043
and a Jacobian matrix representing the mth target state data to be fused at the kth tracking time, wherein m, i and k are positive numbers.
10. A time resource scheduling device based on a multitask radar is characterized by comprising: memory, processor and computer program stored on the memory and run on the processor, the processor implementing the method of multitask radar based time resource scheduling according to any of claims 1-9 when executing the computer program.
CN202211276849.2A 2022-10-18 2022-10-18 Time resource scheduling method and device based on multi-task radar Active CN115618166B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211276849.2A CN115618166B (en) 2022-10-18 2022-10-18 Time resource scheduling method and device based on multi-task radar

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211276849.2A CN115618166B (en) 2022-10-18 2022-10-18 Time resource scheduling method and device based on multi-task radar

Publications (2)

Publication Number Publication Date
CN115618166A true CN115618166A (en) 2023-01-17
CN115618166B CN115618166B (en) 2023-09-12

Family

ID=84862308

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211276849.2A Active CN115618166B (en) 2022-10-18 2022-10-18 Time resource scheduling method and device based on multi-task radar

Country Status (1)

Country Link
CN (1) CN115618166B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115993596A (en) * 2023-03-27 2023-04-21 中国人民解放军63921部队 Characteristic parameter measurement radar resource allocation method and device and computer storage medium
CN116506947A (en) * 2023-04-20 2023-07-28 中国人民解放军93209部队 Active-passive-cooperation-based space-time frequency resource intelligent scheduling method and system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107728139A (en) * 2017-09-12 2018-02-23 电子科技大学 A kind of phased-array radar group network system method for managing resource based on multiple target tracking
CN108376105A (en) * 2018-03-26 2018-08-07 电子科技大学 A kind of radar fence method for allocating tasks constraining combination Double Auction based on timeliness
CN109581342A (en) * 2018-12-07 2019-04-05 中国船舶重工集团公司第七二四研究所 A kind of rotating phased array radar complete period time resource distribution method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107728139A (en) * 2017-09-12 2018-02-23 电子科技大学 A kind of phased-array radar group network system method for managing resource based on multiple target tracking
CN108376105A (en) * 2018-03-26 2018-08-07 电子科技大学 A kind of radar fence method for allocating tasks constraining combination Double Auction based on timeliness
CN109581342A (en) * 2018-12-07 2019-04-05 中国船舶重工集团公司第七二四研究所 A kind of rotating phased array radar complete period time resource distribution method

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115993596A (en) * 2023-03-27 2023-04-21 中国人民解放军63921部队 Characteristic parameter measurement radar resource allocation method and device and computer storage medium
CN116506947A (en) * 2023-04-20 2023-07-28 中国人民解放军93209部队 Active-passive-cooperation-based space-time frequency resource intelligent scheduling method and system
CN116506947B (en) * 2023-04-20 2023-11-10 中国人民解放军93209部队 Active-passive-cooperation-based space-time frequency resource intelligent scheduling method and system

Also Published As

Publication number Publication date
CN115618166B (en) 2023-09-12

Similar Documents

Publication Publication Date Title
CN115618166A (en) Time resource scheduling method and device based on multi-task radar
Pegoraro et al. Multiperson continuous tracking and identification from mm-wave micro-Doppler signatures
Yi et al. Particle filtering based track-before-detect method for passive array sonar systems
CN109581353B (en) Multi-target tracking method and system based on automobile radar
CN107329136A (en) MIMO radar multiple target adaptive tracking method based on the variable analysis moment
KR101929512B1 (en) Apparatus for predicting target’s angle of attack using angle information combination technique of millimeter wave air-to-ground radar and method thereof
CN108398678A (en) A kind of quick self-adapted method for tracking target of centralization MIMO radar
CN105223557A (en) Based on the airborne early warn ing radar clutter suppression method of accessory channel
CN103728615B (en) Phased array secondary radar multi-target detection method and system
Ehrman et al. Using target RCS when tracking multiple Rayleigh targets
Xia et al. Extended object tracking with automotive radar using learned structural measurement model
Kim et al. Gaussian mixture probability hypothesis density filter against measurement origin uncertainty
Suvorova et al. Multi step ahead beam and waveform scheduling for tracking of manoeuvering targets in clutter
CN111198366B (en) Method for quickly selecting finite array elements under distributed MIMO radar multitasking
KR20220120321A (en) Apparatus and method for processing signal of active phased array radar
Ding et al. Collaborative route optimization and resource management strategy for multi-target tracking in airborne radar system
CN110046326B (en) Time-frequency DOA estimation method
KR102005099B1 (en) Apparatus identifying target using dual polarized channel signal reflecting target indentification rate of single channel signal in w-band millimeter wave air-to-ground radar and method thereof
Gao et al. Intelligent multiframe detection aided by Doppler information and a deep neural network
CN115561748A (en) Networked radar target search tracking resource allocation method based on radio frequency stealth
CN108107415B (en) Centralized MIMO radar multi-beam power distribution method based on opportunity constraint
CN113406580B (en) MIMO radar low-interception performance evaluation method, device and storage medium
CN112083423B (en) Multi-base sound source high-precision positioning method
CN108333583B (en) Resource allocation method based on dual-target optimization of phased array radar search and tracking
Cabrera et al. Radar Resource Management for Multiple Hypothesis Tracking

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant