CN111415073B - Multi-sensor collaborative detection task planning method, system and medium under multi-constraint - Google Patents
Multi-sensor collaborative detection task planning method, system and medium under multi-constraint Download PDFInfo
- Publication number
- CN111415073B CN111415073B CN202010167175.7A CN202010167175A CN111415073B CN 111415073 B CN111415073 B CN 111415073B CN 202010167175 A CN202010167175 A CN 202010167175A CN 111415073 B CN111415073 B CN 111415073B
- Authority
- CN
- China
- Prior art keywords
- target
- sensor
- detection
- detection time
- period
- 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.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 302
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000005457 optimization Methods 0.000 claims description 78
- 239000013598 vector Substances 0.000 claims description 39
- 238000013178 mathematical model Methods 0.000 claims description 37
- 239000011159 matrix material Substances 0.000 claims description 17
- 230000007704 transition Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 5
- 239000000523 sample Substances 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000007123 defense Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- 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
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a multi-sensor collaborative detection task planning method, a multi-sensor collaborative detection task planning system and a multi-sensor collaborative detection task planning medium. The multi-sensor collaborative detection method solves the problem of collaborative detection of multiple targets by the multi-sensor considering various constraints such as sensor performance, detection task requirements and the like, can reduce the workload of problem solving, is beneficial to improving the efficiency and speed of task planning, has better guarantee on the continuity of target detection, and can adapt to multi-sensor collaborative detection tasks with specific requirements on target detection time periods.
Description
Technical Field
The invention relates to the technical field of collaborative detection, in particular to a multi-sensor collaborative detection task planning method, a multi-sensor collaborative detection task planning system and a multi-sensor collaborative detection task planning medium under multiple constraints.
Background
In the battle process, the command information system needs to efficiently manage and schedule a plurality of sensors, and continuous and reliable target information is provided for the command control system through the collaborative detection of a plurality of sensors, so that information guarantee is provided for battle decision, weapon guidance, battle effect evaluation and the like. The multi-sensor collaborative detection task planning is to design the detection task of each sensor to the target according to the estimation to the target motion and a certain criterion, so as to form the detection time sequence of the multi-sensor to the multi-target.
Currently, the existing methods mostly allocate sensor resources in a fixed time period or a defined time period, in a time sequence and with a certain optimization principle (such as Cui Boxin, etc., a dynamic multi-sensor management scheme based on task control, 12 months in 2012 of system engineering and electronic technology, li Zhihui, etc., a resource optimization problem of multi-target sensor collaborative detection, 11 months in 2015 of firepower and command control, a multi-sensor multi-target optimization allocation algorithm based on time-varying measurement variance, 4 months in 2015 of fire and command control, dong Chen, etc., a multi-sensor collaborative detection task plan based on missile defense, 12 months in 2018 of modern defense technology), and the traditional methods have the problems of finer time period or time period division, large calculation amount of task plan, low planning efficiency, etc., for the multi-sensor collaborative detection task plan under complex scenes with more sensor numbers and target numbers.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a multi-sensor collaborative detection task planning method, a multi-sensor collaborative detection task planning system and a multi-sensor collaborative detection task planning medium under multiple constraints.
The invention provides a multi-sensor collaborative detection task planning method under multi-constraint, which comprises the following steps:
the information acquisition step: acquiring the number of targets supporting task planning, the total number of target channels of each sensor, the starting detection time and the ending detection time of each sensor target channel to each target, defining the starting detection time to the ending detection time of each sensor target channel to each target as a detectable period, and defining the starting detection time to the ending detection time to each target as a desired detection period;
and (3) establishing a model: establishing a multi-constraint optimization mathematical model of task planning, wherein the multi-constraint optimization mathematical model comprises an optimization variable, constraint conditions and an optimization target;
task generation: solving a multi-constraint optimization mathematical model to obtain a pairing relation between a target and a target channel of a sensor, inquiring a detectable period of the allocated sensor target channel to a corresponding target, and taking an intersection of the detectable period of the target and a desired detection period of the target as a detection period which is optimally allocated to the target at one time to form a collaborative detection task scheme represented by 'the target-sensor target channel-detection period' pairing;
and a coverage judging step: judging whether the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets, if so, completing task planning, otherwise, updating a multi-constraint optimization mathematical model, subtracting the transition time period of the two sensor target channel handover targets from the ending detection time of the targets in the collaborative detection task scheme for the targets with the detection time periods which do not completely cover the expected detection time periods, updating the expected starting detection time of the targets, reconstructing the multi-constraint optimization mathematical model, and solving until the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets.
Preferably, in the step of acquiring information, the target number is set to m; the total number of target channels of each sensor is set to n; the initial detection time and the final detection time of the target channel number of each sensor to each target are respectively expressed as an n multiplied by m dimensional matrix T 0 And T is f ，
Wherein t is ij0 Representing the initial detection time, t, of the ith sensor target channel to the jth target ijf Representing the termination detection moment of the ith sensor target channel to the jth target, if the ith sensor target channel cannot detect the jth target, t ij0 ＝t ijf ＝-1；
The expected initial detection time and the expected final detection time of each target are respectively expressed as m-dimensional row vectors T e0 And T is ef ，
T e0 ＝[t e10 …t em0 ]，T ef ＝[t e1f …t emf ]
t ej0 Representing the expected initial detection time, t, for the jth target ejf Representing the expected termination detection time for the jth target.
Preferably, in the modeling step, the optimization variable is a "target-sensor target channel" pairing;
constraints include two types of sensor performance constraints and detection task constraints, wherein:
sensor performance constraints include:
no more than one target is allocated to one target channel of each sensor;
the target allocated to one target channel of each sensor cannot be the target which cannot be detected by the sensor;
the probing task constraints include:
the sensor target channels are distributed to each target for detection;
the initial detection time of each target is smaller than or equal to the expected initial detection time of the target;
the detection of each target should be continuous;
the optimization objective is to design the pairing of the objective and the sensor objective channel so that the detectable time period of the allocated sensor objective channel to the objective covers the expected detection time period of the corresponding objective to the greatest extent.
Preferably, the optimization variable "target-sensor target channel" pairing is expressed as an n x m dimensional matrix D,
its element d ij =0 or 1, d ij =1 represents that the ith sensor target channel detects the jth target, d ij =0 represents that the ith sensor target channel does not detect the jth target;
the constraint that no more than one target is allocated to one target channel of each sensor is expressed as D.I m1 -I n1 ≤0 n1
I m1 For m-dimensional unit column vectors, I n1 Is n-dimensional unit column vector, 0 n1 Is an n-dimensional zero column vector;
the constraint is one purpose for each sensorThe target that the target channel assigns cannot be the target that the sensor cannot detect is denoted as-D x T 0 ≤0 nm
Operator represents multiplication of elements of the same row and column of two same-dimensional matrices or vectors, 0 nm Is an n x m dimensional zero matrix;
the constraint of "assigning sensor target channels to targets" is expressed as
I 1n ·D-I 1m ＝0 1m
I 1n For n-dimensional unit row vectors, I 1m For m-dimensional unit row vector, 0 1m Is an m-dimensional zero line vector;
the constraint that the initial detection time of each target is less than or equal to the expected initial detection time of the target is expressed as I 1n ·(D*T 0 )-T e0 ≤0 1m
The constraint "detection of objects should be continuous" is denoted as T e0 -I 1n ·(D*T f )＜0 1m ，T sf *(D·I m1 )-(D*T 0 )·I m1 ＜0 n1
T sf For an n-dimensional column vector, if the ith sensor target channel has been assigned a probe task, then T sf Is the ith element t of (2) sif The end detection moment representing the detection period of the ith sensor target channel, t if no detection task is allocated sif ＝0；
The optimization objective is expressed as
J＝{[I 1n ·(D*T f )-T e0 ]//[T ef -T e0 ]}·I m1
Operator// represents the division of elements of the same row and column of two co-dimensional matrices or vectors;
the multi-constraint optimization mathematical model is expressed as
Preferably, in the cooperative detection step, the allocation is performedThe detection period of the object, for the jth object, the detection period assigned to it is denoted as [ t ] TjSi0 t TjSif ]，
[t TjSi0 t TjSif ]＝[t cj0 t cjf ]∩[t ej0 t ejf ]
Wherein the method comprises the steps of
[t c10 …t cj0 …t cm0 ]＝I 1n ·(D*T 0 )，[t c1f …t cjf …t cmf ]＝I 1n ·(D*T f )
The "target-sensor target channel-detection period" pairing is denoted (T j ,S i ,[t TjSi0 t TjSif ])。
Preferably, in the coverage determination step, it is determined whether or not the detection period allocated to each target completely covers the desired detection period of the corresponding target, and for the jth target, the percentage of coverage of the detection period allocated to the target to the desired detection period is
Preferably, in the coverage determination step, the transition period duration of the two sensor target channel handover targets is subtracted from the end detection time of the target given by the collaborative detection task scheme, the expected start detection time of the target is updated, and the j-th target is provided with
t ej0 ＝t TjSif -t TjΔ ，
t TjΔ When representing that two sensor target channels detect the same target in relay, allowing the two sensor target channels to detect the transition period duration of the target simultaneously in the process of delivering the target;
the addition of the new collaborative detection task solution to the previously formed collaborative detection task solution may be expressed as (T j ,S 1 ,[t TjS10 t TjS1f ],S 2 ,[t TjS20 t TjS2f ],…)。
According to the present invention there is provided a computer readable storage medium storing a computer program which when executed by a processor performs the steps of the method described above.
According to the multi-sensor collaborative detection mission planning system under multi-constraint provided by the invention, the system comprises:
and an information acquisition module: acquiring the number of targets supporting task planning, the total number of target channels of each sensor, the starting detection time and the ending detection time of each sensor target channel to each target, defining the starting detection time to the ending detection time of each sensor target channel to each target as a detectable period, and defining the starting detection time to the ending detection time to each target as a desired detection period;
and (3) establishing a model module: establishing a multi-constraint optimization mathematical model of task planning, wherein the multi-constraint optimization mathematical model comprises an optimization variable, constraint conditions and an optimization target;
the task generation module: solving a multi-constraint optimization mathematical model to obtain a pairing relation between a target and a target channel of a sensor, inquiring a detectable period of the allocated sensor target channel to a corresponding target, and taking an intersection of the detectable period of the target and a desired detection period of the target as a detection period which is optimally allocated to the target at one time to form a collaborative detection task scheme represented by 'the target-sensor target channel-detection period' pairing;
and a coverage judging module: judging whether the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets, if so, completing task planning, otherwise, updating a multi-constraint optimization mathematical model, subtracting the transition time period of the two sensor target channel handover targets from the ending detection time of the targets in the collaborative detection task scheme for the targets with the detection time periods which do not completely cover the expected detection time periods, updating the expected starting detection time of the targets, reconstructing the multi-constraint optimization mathematical model, and solving until the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets.
Compared with the prior art, the invention has the following beneficial effects:
1. compared with the traditional method of dividing a plurality of time periods and distributing sensor resources on each time period, the method selects the part which covers the expected detection period of the corresponding target as much as possible from the detectable period of the sensor to the target, and splices the part to form a collaborative detection task scheme which is represented by pairing of the target and the target channel of the sensor and the detection period, so that the number of optimization rounds of sensor resource distribution can be reduced, the workload of problem solving is reduced, and the efficiency and the speed of task planning are improved.
2. In the multi-round optimization process of task planning, the dimensionality of the optimization problem can be adjusted according to the meeting condition of the target expected detection period, the workload of problem solving is reduced, and the efficiency and the speed of task planning are improved.
3. When two sensor target channels are considered to relay and detect one target in task planning, the transition period of the two sensor target channels for connecting the target is considered, so that the sensor target channels for subsequent detection can intercept and stably track the target, and the continuity of target detection is ensured.
4. The expected detection time period requirements for each target are introduced into the task planning, so that the planned collaborative detection task scheme meets the expected detection time period requirements for each target, and the multi-sensor collaborative detection task with requirements for the target detection time period can be adapted, such as providing target indication and guiding information, identifying specific Shi Min targets and the like.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of the steps of the method of the present invention;
fig. 2 is a schematic diagram of a collaborative detection task scheme generated by an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
According to the multi-sensor collaborative detection task planning, under the condition that multiple constraint conditions are met, the targets are selected and spliced in a detectable time period, collaborative detection time sequences of the multiple targets are formed, the workload of solving task planning problems can be reduced, and the efficiency and the speed of task planning are improved; when two sensors relay to detect one target, a transition period for the two sensors to cross the target can be set, so that continuous detection of the target is ensured; the method is suitable for multi-sensor collaborative detection tasks with specific requirements on the target detection period, such as providing target indication and guiding information, identifying specific Shi Min targets and the like.
In order to solve the problem of collaborative detection of multiple targets by multiple sensors taking into account various constraints such as sensor performance, detection task requirements and the like, the method comprises the following steps in the implementation:
step 1: acquiring relevant information supporting task planning, wherein the relevant information comprises the number of targets, the total number of target channels of each sensor, the initial detection time and the final detection time of each sensor target channel to each target, the expected initial detection time and the expected final detection time of each target, defining the period from the initial detection time to the final detection time of each sensor target channel to each target as a detectable period, and defining the period from the expected initial detection time to the expected final detection time of each target as a expected detection period;
step 2: establishing a multi-constraint optimization mathematical model of task planning, wherein the model comprises optimization variables, constraint conditions and an optimization target, and the method comprises the following steps of:
2.1 optimization variables are "target-sensor target channel" pairing;
2.2 constraints include two categories, sensor performance constraints and detection task constraints, wherein:
sensor performance constraints include:
no more than one target is allocated to one target channel of each sensor;
the target allocated to one target channel of each sensor cannot be the target which cannot be detected by the sensor;
the probing task constraints include:
the sensor target channels are distributed to each target for detection;
the initial detection time of each target is smaller than or equal to the expected initial detection time of the target;
the detection of each target should be continuous;
2.3 optimizing the targets by designing a 'target-sensor target channel' pairing so that the detectable time period of the allocated sensor target channel to the targets maximally covers the expected detection time period of the corresponding targets;
step 3: solving the multi-constraint optimization mathematical model to obtain an optimized 'target-sensor target channel' pairing, and inquiring the detectable time period of the allocated sensor target channel to the corresponding target based on the pairing so as to use the intersection of the detectable time period of the target and the expected detection time period of the target as the detection time period allocated to the target in the optimization, thereby forming a collaborative detection task scheme represented by the 'target-sensor target channel-detection time period' pairing;
step 4: evaluating the collaborative detection task scheme, judging whether the detection time period allocated to each target completely covers the expected detection time period of the corresponding target, if so, completing task planning, and if not, executing the following steps;
step 5: updating the multi-constraint optimization mathematical model, subtracting the transition time period duration of the two sensor target channel handover targets from the ending detection time of the target given by the collaborative detection task scheme for the target when the detection time does not completely cover the target of the expected detection time period, returning to the step 2 to reconstruct the multi-constraint optimization mathematical model, solving in the step 3 to obtain a new collaborative detection task scheme, supplementing the new collaborative detection task scheme to the collaborative detection task scheme formed before, and executing the step 4.
In the step 1, the target number is set as m; the total number of target channels of each sensor is set to n; the initial detection time and the final detection time of each sensor target channel number to each target can be expressed as an n multiplied by m dimensional matrix T 0 And T is f ，
t ij0 Representing the initial detection time, t, of the ith sensor target channel to the jth target ijf Representing the termination detection moment of the ith sensor target channel to the jth target, if the ith sensor target channel cannot detect the jth target, t ij0 ＝t ijf -1; the desired start detection time and the desired end detection time for each target can be expressed as m-dimensional row vectors T e0 And T is ef ，
T e0 ＝[t e10 …t em0 ]，T ef ＝[t e1f …t emf ]t ej0 Representing the expected initial detection time, t, for the jth target ejf Representing the expected termination detection time for the jth target.
In step 2, the optimization variable "target-sensor target channel" pairing is expressed as an n x m dimensional matrix D,
its element d ij =0 or 1, d ij =1 represents that the ith sensor target channel detects the jth target, d ij =0 represents that the ith sensor target channel does not detect the jth target; the constraint that no more than one target is allocated to one target channel of each sensor is expressed as D.I m1 -I n1 ≤0 n1
I m1 For m-dimensional unit column vectors, I n1 Is n-dimensional unit column vector, 0 n1 Is an n-dimensional zero column vector; the constraint that the target allocated to one target channel of each sensor cannot be detected by the sensor is expressed as-D x T 0 ≤0 nm
Operator represents multiplication of elements of the same row and column of two same-dimensional matrices or vectors, 0 nm Is an n x m dimensional zero matrix; the constraint of "assigning sensor target channels to targets" is expressed as
I 1n ·D-I 1m ＝0 1m
I 1n For n-dimensional unit row vectors, I 1m For m-dimensional unit row vector, 0 1m Is an m-dimensional zero line vector; the constraint that the initial detection time of each target is less than or equal to the expected initial detection time of the target is expressed as I 1n ·(D*T 0 )-T e0 ≤0 1m
The constraint "detection of objects should be continuous" is denoted as T e0 -I 1n ·(D*T f )＜0 1m ，T sf *(D·I m1 )-(D*T 0 )·I m1 ＜0 n1
T sf For an n-dimensional column vector, if the ith sensor target channel has been assigned a probe task, then T sf Is the ith element t of (2) sif The end detection moment representing the detection period of the ith sensor target channel, t if no detection task is allocated sif =0; the optimization objective is expressed as
J＝{[I 1n ·(D*T f )-T e0 ]//[T ef -T e0 ]}·I m1
Operator// represents the division of elements of the same row and column of two co-dimensional matrices or vectors; the multi-constraint optimization mathematical model is expressed as
In step 3, the detection period allocated to the target is represented as [ t ] for the j-th target TjSi0 t TjSif ]，
[t TjSi0 t TjSif ]＝[t cj0 t cjf ]∩[t ej0 t ejf ]
Wherein the method comprises the steps of
[t c10 …t cj0 …t cm0 ]＝I 1n ·(D*T 0 )，[t c1f …t cjf …t cmf ]＝I 1n ·(D*T f )
The "target-sensor target channel-detection period" pairing is denoted (T j ,S i ,[t TjSi0 t TjSif ])。
In step 4, the detection period allocated to each target is determined to completely cover the expected detection period of the corresponding target, and for the jth target, the coverage percentage of the detection period allocated to the target to the expected detection period is
In step 5, the ending detection time of the target given by the collaborative detection task scheme minus the transition time period of the two sensor target channel handover targets updates the expected starting detection time of the target, and there is a j-th target
t ej0 ＝t TjSif -t TjΔ ，
t TjΔ When representing that two sensor target channels detect the same target in relay, allowing the two sensor target channels to detect the transition period duration of the target simultaneously in the process of delivering the target; the addition of the new collaborative detection task solution to the previously formed collaborative detection task solution may be expressed as (T j ,S 1 ,[t TjS10 t TjS1f ],S 2 ,[t TjS20 t TjS2f ],…)。
The method comprises the steps of determining targets to be detected, available sensor target channels, detectable time periods of each sensor target channel for each target and expected detection time periods of each target through step 1.
And 2, establishing a multi-constraint optimization mathematical model of task planning and solving the multi-constraint optimization mathematical model by the step 3, wherein the essence is that under the condition that all the constraints are met, the part which covers the expected detection period of the corresponding target as much as possible is selected from the detectable period of the sensor on the target, so as to form the pairing of 'target-sensor target channel-detection period' after one optimization.
Step 4, whether the detection time period of each target completely covers the expected detection time period of each target after one-time optimization is evaluated, and if so, task planning is completed; if not, go to step 5.
And 5, updating a multi-constraint optimization mathematical model, carrying out multi-round optimization until the detection time period of each target completely covers the expected detection time period of each target, splicing the pairs of the target-sensor target channel-detection time period formed by each round of optimization, and finally forming a collaborative detection task scheme.
The embodiment is suitable for processing application scenes of 5 targets cooperatively detected by 6 target channels of a plurality of sensors, and the protection scope of the patent is not limited by the specific implementation of the embodiment.
A plurality of sensors are arranged to have 6 sensor target channels which are respectively S 1 、S 2 、S 3 、S 4 、S 5 、S 6 5 targets are T 1 、T 2 、T 3 、T 4 、T 5 With reference to fig. 1, the following is a specific description of an embodiment of the present invention:
step 1: acquiring related information supporting task planning, wherein the target number m=5, the total number n=6 of sensor target channels, the initial detection time and the final detection time of each sensor target channel to each target,
a desired start detection time and a desired end detection time for each target,
T e0 ＝[250 300 350 275 375]，T ef ＝[1800 900 600 400 1600]。
step 2: establishing a multi-constraint optimization mathematical model of task planning, wherein the optimization variable is a 6 multiplied by 5 dimensional matrix
Establishing a multi-constraint optimization mathematical model containing the optimization variables, constraint conditions and optimization targets
Step 3: solving the multi-constraint optimization mathematical model by utilizing a branch and bound algorithm to obtain a matrix D
According to D, T 0 、T f 、T e0 、T ef Calculating the detection time interval allocated to the target, and further obtaining a cooperative detection task scheme, namely 'target-sensor target channel-detection time interval' pairing
Step 4: evaluating the cooperative detection task scheme, and calculating the coverage percentage of the detection time period allocated to each target to the expected detection time period of each target to be T 1 :24.5％，T 2 :100％，T 3 :100％，T 4 :100％，T 5 :85.6％，
For T 1 And T 5 The allocated probing period does not fully cover its desired probing period.
Step 5 and subsequent steps: updating the multi-constraint optimization mathematical model aiming at target T 1 And T 5 The target number is updated to be m=2, and the transition period duration t is set TjΔ =10s, let T 1 And T 5 Respectively updated to the expected initial detection time of (a)
t e10 ＝629-10＝619s，t e50 ＝1424-10＝1414s，
The optimization variable D is updated as a 6 x 2 dimensional matrix,
solving the updated multi-constraint optimization mathematical model to obtain a matrix D
According to D, T 0 、T f 、T e0 、T ef Calculating the detection time interval allocated to the target, and further obtaining a new collaborative detection task scheme
Calculating the coverage percentage of the detection period allocated to each target to the expected detection period of each target to be T 1 :59.4％，T 5 :100％，
For T 1 Does not fully cover its desired detection period, for a target T 1 Updating the target number to m=1, and setting T 1 The expected initial detection time is updated to
t e10 ＝1320-10＝1310s，
The optimization variable D is updated as a 6 x 1 dimensional matrix,
solving the updated multi-constraint optimization mathematical model to obtain a matrix D
According to D, T 0 、T f 、T e0 、T ef Calculating the detection time interval allocated to the target, and further obtaining a new collaborative detection task scheme
(T 1 ,S 6 ,[1310 1800])，
Calculating the coverage percentage of the detection period allocated to each target for the expected detection period to be T 1 :100％，
The new collaborative detection task scheme is used for completing task planning by fully covering the expected detection time periods of the corresponding targets for the detection time periods distributed by the targets, and supplementing the new collaborative detection task scheme into the collaborative detection task scheme formed before to obtain a final collaborative detection task scheme:
a schematic diagram of the collaborative detection task scheme is shown in FIG. 2, which shows that the scheme is applied to T 1 From S 1 At 250S-629S, S 5 At 619S-1320S, S 6 Relay detection is carried out for 1310s to 1800s, and T is detected 2 From S 3 Detecting at 300-900 s, and detecting T 3 From S 4 Detecting at 350-600 s, and detecting T 4 From S 5 Detecting at 275 s-400 s, and detecting T 5 From S 2 At 375S-1424S, S 4 And performing relay detection at 1414 s-1600 s.
Those skilled in the art will appreciate that the systems, apparatus, and their respective modules provided herein may be implemented entirely by logic programming of method steps such that the systems, apparatus, and their respective modules are implemented as logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc., in addition to the systems, apparatus, and their respective modules being implemented as pure computer readable program code. Therefore, the system, the apparatus, and the respective modules thereof provided by the present invention may be regarded as one hardware component, and the modules included therein for implementing various programs may also be regarded as structures within the hardware component; modules for implementing various functions may also be regarded as being either software programs for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (7)
1. The multi-sensor collaborative detection task planning method under the multi-constraint is characterized by comprising the following steps of:
the information acquisition step: acquiring the number of targets supporting task planning, the total number of target channels of each sensor, the starting detection time and the ending detection time of each sensor target channel to each target, defining the starting detection time to the ending detection time of each sensor target channel to each target as a detectable period, and defining the starting detection time to the ending detection time to each target as a desired detection period;
and (3) establishing a model: establishing a multi-constraint optimization mathematical model of task planning, wherein the multi-constraint optimization mathematical model comprises an optimization variable, constraint conditions and an optimization target;
task generation: solving a multi-constraint optimization mathematical model to obtain a pairing relation between a target and a target channel of a sensor, inquiring a detectable period of the allocated sensor target channel to a corresponding target, and taking an intersection of the detectable period of the target and a desired detection period of the target as a detection period which is optimally allocated to the target at one time to form a collaborative detection task scheme represented by 'the target-sensor target channel-detection period' pairing;
and a coverage judging step: judging whether the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets, if so, indicating that task planning is completed, otherwise, updating a multi-constraint optimization mathematical model, subtracting the transition time period of the two sensor target channel handover targets from the ending detection time of the targets in the collaborative detection task scheme for the targets of which the detection time periods do not completely cover the expected detection time periods, updating the expected starting detection time of the targets, reconstructing the multi-constraint optimization mathematical model, and solving until the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets;
in the modeling step, the optimization variable is 'target-sensor target channel' pairing;
constraints include two types of sensor performance constraints and detection task constraints, wherein:
sensor performance constraints include:
no more than one target is allocated to one target channel of each sensor;
the target allocated to one target channel of each sensor cannot be the target which cannot be detected by the sensor;
the probing task constraints include:
the sensor target channels are distributed to each target for detection;
the initial detection time of each target is smaller than or equal to the expected initial detection time of the target;
the detection of each target should be continuous;
the optimization target is that the detectable time period of the allocated sensor target channel to the target is covered with the expected detection time period of the corresponding target to the greatest extent by designing the pairing of the target and the sensor target channel;
the optimization variable "target-sensor target channel" pairing is expressed as an n x m dimensional matrix D,
its element d ij =0 or 1, d ij =1 represents that the ith sensor target channel detects the jth target, d ij =0 represents that the ith sensor target channel does not detect the jth target;
the constraint that no more than one target is allocated to one target channel of each sensor is expressed as
D·I m1 -I n1 ≤0 n1
I m1 For m-dimensional unit column vectors, I n1 Is n-dimensional unit column vector, 0 n1 Is an n-dimensional zero column vector;
the constraint that the target allocated to one target channel of each sensor cannot be the target that the sensor cannot detect is expressed as
-D*T 0 ≤0 nm
Operator represents multiplication of elements of the same row and column of two same-dimensional matrices or vectors, 0 nm Is an n x m dimensional zero matrix;
the constraint of "assigning sensor target channels to targets" is expressed as
I 1n ·D-I 1m ＝0 1m
I 1n For n-dimensional unit row vectors, I 1m For m-dimensional unit row vector, 0 1m Is an m-dimensional zero line vector;
the constraint that the initial detection time of each target is less than or equal to the expected initial detection time of the target is expressed as
I 1n ·(D*T 0 )-T e0 ≤0 1m
The constraint "detection of objects should be continuous" is expressed as
T e0 -I 1n ·(D*T f )＜0 1m ，T sf *(D·I m1 )-(D*T 0 )·I m1 ＜0 n1
T sf For an n-dimensional column vector, if the ith sensor target channel has been assigned a probe task, then T sf Is the ith element t of (2) sif The end detection moment representing the detection period of the ith sensor target channel, t if no detection task is allocated sif ＝0；
The optimization objective is expressed as
J＝{[I 1n ·(D*T f )-T e0 ]//[T ef -T e0 ]}·I m1
Operator// represents the division of elements of the same row and column of two co-dimensional matrices or vectors;
the multi-constraint optimization mathematical model is expressed as
2. The multi-sensor collaborative detection mission planning method under the multi-constraint of claim 1, wherein in the obtaining information step, the target number is set to be m; the total number of target channels of each sensor is set to n; the initial detection time and the final detection time of the target channel number of each sensor to each target are respectively expressed as an n multiplied by m dimensional matrix T 0 And T is f ，
Wherein t is ij0 Representing the initial detection time, t, of the ith sensor target channel to the jth target ijf Representing the termination detection moment of the ith sensor target channel to the jth target, if the ith sensor target channel cannot detect the jth target, t ij0 ＝t ijf ＝-1；
The expected initial detection time and the expected final detection time of each target are respectively expressed as m-dimensional row vectors T e0 And T is ef ，
I e0 ＝[t e10 …t em0 ]，T ef ＝[t e1f …t emf ]
t ej0 Representing the expected initial detection time, t, for the jth target ejf Representing the expected termination detection time for the jth target.
3. The multi-sensor collaborative exploration mission planning method under multiple constraints of claim 1, wherein the co-ordinatesIn the same detection step, the detection period allocated to the target is represented as [ t ] for the jth target TjSi0 t TjSif ]，
[t TjSi0 t TjSif ]＝[t cj0 t cjf ]∩[t ej0 t ejf ]
Wherein the method comprises the steps of
[t c10 …t cj0 …t cm0 ]＝I 1n ·(D*T 0 )，[t c1f …t cjf …t cmf ]＝I 1n ·(D*T f )
The "target-sensor target channel-detection period" pairing is expressed as
(T j ,S i ,[t TjSi0 t TjSif ])。
4. The multi-sensor collaborative detection mission planning method under the constraint of claim 1, wherein in the coverage determination step, it is determined whether a detection period allocated to each target completely covers a desired detection period of the corresponding target, and for a jth target, a percentage of coverage of the detection period allocated to the target to the desired detection period is
5. The multi-sensor collaborative detection task planning method according to claim 1, wherein in the coverage determination step, a transition period duration of two sensor target channel handover targets is subtracted from a termination detection time of the target given by a collaborative detection task scheme, a desired start detection time of the target is updated, and a j-th target is provided with
t ej0 ＝t TjSif -t TjΔ ，
t TjΔ When representing that two sensor target channels are used for detecting the same target relay, two sensors are allowed to be used in the process of target handoverThe method comprises the steps that a plurality of sensor target channels detect transition period duration of the target at the same time;
supplementing the new collaborative detection task scheme into the collaborative detection task scheme formed before, and for the jth target, the new collaborative detection task scheme can be expressed as
(T j ,S 1 ,[t TjS10 t TjS1f ],S 2 ,[t TjS20 t TjS2f ],…)。
6. A multi-sensor collaborative detection mission planning system under multiple constraints, comprising:
and an information acquisition module: acquiring the number of targets supporting task planning, the total number of target channels of each sensor, the starting detection time and the ending detection time of each sensor target channel to each target, defining the starting detection time to the ending detection time of each sensor target channel to each target as a detectable period, and defining the starting detection time to the ending detection time to each target as a desired detection period;
and (3) establishing a model module: establishing a multi-constraint optimization mathematical model of task planning, wherein the multi-constraint optimization mathematical model comprises an optimization variable, constraint conditions and an optimization target;
the task generation module: solving a multi-constraint optimization mathematical model to obtain a pairing relation between a target and a target channel of a sensor, inquiring a detectable period of the allocated sensor target channel to a corresponding target, and taking an intersection of the detectable period of the target and a desired detection period of the target as a detection period which is optimally allocated to the target at one time to form a collaborative detection task scheme represented by 'the target-sensor target channel-detection period' pairing;
and a coverage judging module: judging whether the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets, if so, indicating that task planning is completed, otherwise, updating a multi-constraint optimization mathematical model, subtracting the transition time period of the two sensor target channel handover targets from the ending detection time of the targets in the collaborative detection task scheme for the targets of which the detection time periods do not completely cover the expected detection time periods, updating the expected starting detection time of the targets, reconstructing the multi-constraint optimization mathematical model, and solving until the detection time periods allocated to the targets completely cover the expected detection time periods of the corresponding targets;
in the modeling module, the optimization variable is 'target-sensor target channel' pairing;
constraints include two types of sensor performance constraints and detection task constraints, wherein:
sensor performance constraints include:
no more than one target is allocated to one target channel of each sensor;
the target allocated to one target channel of each sensor cannot be the target which cannot be detected by the sensor;
the probing task constraints include:
the sensor target channels are distributed to each target for detection;
the initial detection time of each target is smaller than or equal to the expected initial detection time of the target;
the detection of each target should be continuous;
the optimization target is that the detectable time period of the allocated sensor target channel to the target is covered with the expected detection time period of the corresponding target to the greatest extent by designing the pairing of the target and the sensor target channel;
the optimization variable "target-sensor target channel" pairing is expressed as an n x m dimensional matrix D,
its element d ij =0 or 1, d ij =1 represents that the ith sensor target channel detects the jth target, d ij =0 represents that the ith sensor target channel does not detect the jth target;
the constraint that no more than one target is allocated to one target channel of each sensor is expressed as
D·I m1 -I n1 ≤0 n1
I m1 For m-dimensional unit column vectors, I n1 Is n-dimensional unit column vector, 0 n1 Is an n-dimensional zero column vector;
the constraint that the target allocated to one target channel of each sensor cannot be the target that the sensor cannot detect is expressed as
-D*T 0 ≤0 nm
Operator represents multiplication of elements of the same row and column of two same-dimensional matrices or vectors, 0 nm Is an n x m dimensional zero matrix;
the constraint of "assigning sensor target channels to targets" is expressed as
I 1n ·D-I 1m ＝0 1m
I 1n For n-dimensional unit row vectors, I 1m For m-dimensional unit row vector, 0 1m Is an m-dimensional zero line vector;
the constraint that the initial detection time of each target is less than or equal to the expected initial detection time of the target is expressed as I 1n ·(D*T 0 )-T e0 ≤0 1m
The constraint "detection of objects should be continuous" is denoted as T e0 -I 1n ·(D*T f )＜0 1m ，T sf *(D·I m1 )-(D*T 0 )·I m1 ＜0 n1
T sf For an n-dimensional column vector, if the ith sensor target channel has been assigned a probe task, then T sf Is the ith element t of (2) sif The end detection moment representing the detection period of the ith sensor target channel, t if no detection task is allocated sif =0; the optimization objective is expressed as j= { [ I 1n ·(D*T f )-T e0 ]//[T ef -T e0 ]}·I m1
Operator// represents the division of elements of the same row and column of two co-dimensional matrices or vectors;
the multi-constraint optimization mathematical model is expressed as
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010167175.7A CN111415073B (en) | 2020-03-11 | 2020-03-11 | Multi-sensor collaborative detection task planning method, system and medium under multi-constraint |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010167175.7A CN111415073B (en) | 2020-03-11 | 2020-03-11 | Multi-sensor collaborative detection task planning method, system and medium under multi-constraint |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111415073A CN111415073A (en) | 2020-07-14 |
| CN111415073B true CN111415073B (en) | 2023-07-04 |
Family
ID=71491056
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010167175.7A Active CN111415073B (en) | 2020-03-11 | 2020-03-11 | Multi-sensor collaborative detection task planning method, system and medium under multi-constraint |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111415073B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344473B (en) * | 2021-08-04 | 2021-11-02 | 北京邮电大学 | Optimization method and device for ocean target cooperative detection device |
| CN115320887B (en) * | 2022-05-17 | 2023-05-23 | 北京航天飞行控制中心 | Method and device for determining flight control task model, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103336885A (en) * | 2013-06-03 | 2013-10-02 | 北京航空航天大学 | Method for solving weapon-target assignment problem based on differential evolution algorithm |
| JP2014190959A (en) * | 2013-03-28 | 2014-10-06 | Mitsubishi Electric Corp | Target tracking device |
| CN108229719A (en) * | 2017-11-29 | 2018-06-29 | 合肥工业大学 | Unmanned plane formation task distributes the Multipurpose Optimal Method and device with trajectory planning |
| CN108288107A (en) * | 2017-07-03 | 2018-07-17 | 上海机电工程研究所 | Multi-sensor cooperation detection mission planing method |
| CN109116349A (en) * | 2018-07-26 | 2019-01-01 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Multi-sensor cooperation tracks combined optimization decision-making technique |
-
2020
- 2020-03-11 CN CN202010167175.7A patent/CN111415073B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014190959A (en) * | 2013-03-28 | 2014-10-06 | Mitsubishi Electric Corp | Target tracking device |
| CN103336885A (en) * | 2013-06-03 | 2013-10-02 | 北京航空航天大学 | Method for solving weapon-target assignment problem based on differential evolution algorithm |
| CN108288107A (en) * | 2017-07-03 | 2018-07-17 | 上海机电工程研究所 | Multi-sensor cooperation detection mission planing method |
| CN108229719A (en) * | 2017-11-29 | 2018-06-29 | 合肥工业大学 | Unmanned plane formation task distributes the Multipurpose Optimal Method and device with trajectory planning |
| CN109116349A (en) * | 2018-07-26 | 2019-01-01 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Multi-sensor cooperation tracks combined optimization decision-making technique |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111415073A (en) | 2020-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111415073B (en) | Multi-sensor collaborative detection task planning method, system and medium under multi-constraint | |
| CN112131786B (en) | Target detection and distribution method and device based on multi-agent reinforcement learning | |
| CN109189094B (en) | Resource scheduling method for hybrid formation of multiple human-machines and multiple unmanned aerial vehicles | |
| CN110275531B (en) | Obstacle trajectory prediction method and device and unmanned equipment | |
| US20050223295A1 (en) | Method for the creation of sequences for testing software | |
| CN107016212B (en) | Intention analysis method based on dynamic Bayesian network | |
| CN113867295A (en) | Manufacturing workshop AGV dynamic scheduling method, system, equipment and storage medium based on digital twinning | |
| CN112801290A (en) | Multi-agent deep reinforcement learning method, system and application | |
| CN109034670A (en) | Satellite on-orbit activity planning method and system | |
| CN112200319A (en) | Rule reasoning method and system for achieving unmanned vehicle navigation obstacle avoidance | |
| CN115496201A (en) | Train accurate parking control method based on deep reinforcement learning | |
| CN115236997B (en) | Predictive control method, apparatus, device, and computer-readable storage medium | |
| CN114567560B (en) | Edge node dynamic resource allocation method based on generation of countermeasure imitation learning | |
| CN114872730A (en) | Vehicle driving track prediction method and device, automobile and storage medium | |
| CN114103938A (en) | Method, device and storage medium for controlling longitudinal movement of vehicle | |
| CN111775952A (en) | Automatic driving method, device and storage medium | |
| US20190025803A1 (en) | Computer system and control method | |
| CN116572993A (en) | Intelligent vehicle risk sensitive sequential behavior decision method, device and equipment | |
| CN113253684B (en) | Multi-AGV (automatic guided vehicle) scheduling method and device based on graph convolution neural network and electronic equipment | |
| CN113627646A (en) | Path planning method, device, equipment and medium based on neural network | |
| CN113501007A (en) | Path trajectory planning method, device and terminal based on automatic driving | |
| CN115114976B (en) | Training method, device, equipment and storage medium of pretightening distance prediction model | |
| CN111582553A (en) | Information and firepower integrated task planning method | |
| CN115360772B (en) | Active safety correction control method, system, equipment and storage medium for power system | |
| CN115598985B (en) | Training method and device of feedback controller, electronic equipment and medium |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: No. 1333-1 Zhongchun Road, Minhang District, Shanghai, 201109 Applicant after: SHANGHAI INSTITUTE OF ELECTROMECHANICAL ENGINEERING Address before: No. 3888, Yuanjiang Road, Minhang District, Shanghai, 201100 Applicant before: SHANGHAI INSTITUTE OF ELECTROMECHANICAL ENGINEERING |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |