CN110009164A - Based on resource conversion and complementary multi-functional radar network mission planning method - Google Patents
Based on resource conversion and complementary multi-functional radar network mission planning method Download PDFInfo
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Abstract
The invention discloses a kind of based on resource conversion and complementary multi-functional radar network mission planning method, this method is first by by the task abstract modeling of multi-functional radar network, then using the sum of be delayed loss and compression loss as objective function, the Optimized model for constructing a multi-functional radar network utilization of resources, obtains optimal solution finally by the algorithm being nested based on interior point method and coordinate descent；The present invention considers the conversion and complementary relationship between the flexibility and multi-functional radar network system resource of mission duration, when efficiently solving more multi-functional radar network task quantity and mission duration presence conflict, task caused by resource allocation is unbalanced cannot excellent effect the problem of completing, improve the efficiency of multi-functional radar network execution task.
Description
Technical field
The invention belongs to Radar Technology fields, and in particular to a kind of based on resource conversion and complementary multi-functional radar network
Mission planning method.
Background technique
Multifunction radar is integrated into many kinds of radar antenna function in one antenna aperature, and it is shared to realize aperture.It has
There is the ability of execution multiple-task (tracking, interference, detection, communication etc.).Compared with multifunction radar of singly standing, multi-functional networking thunder
Improve the performance of system information acquisition and the effect of task completion significantly up to the diversity that can use radar nodal spatial distribution
Rate, therefore multi-functional radar network suffers from wide development space in civil and military field.
The mission planning technology of multi-functional radar network refers to providing time limited in radar network system, power etc.
Source is reasonably distributed to execute different tasks, to further increase the efficiency of task completion, the loss for keeping system total is minimum.
But the complexity of environment, the factors such as uncertainty of task self attributes can all influence the quality of task completion.Therefore a conjunction
The mission planning method of reason, excellent effect is vital to the performance boost of multi-functional radar network.
The mission planning method of multi-functional radar network is one of the hot spot of the outer field of radar research of Now Domestic, has been had
Many disclosed articles study it.For example, document " Task selection and scheduling in
Multifunction multichannel radars, 2017 IEEE Radar Conference (RadarConf), 2017:
In 0969-0974 ", task is arranged on a timeline, by minimizing the method for delay loss come planning tasks；Document " band
The phased-array radar real-time task scheduling method of time window, firepower and command and control, 2016,41:70-74 " proposes a kind of base
In the mission planning method of task itself working method.However, the above-mentioned document being mentioned to all be built upon task it is lasting when
Between be fixed on the basis of, but in fact, the duration of task is variable.In addition, when the above method all only accounts for
Between resource without considering other dimension resources, even more have ignored the mutual inversion of phases between different resource in task implementation procedure
With supplement.From the point of view of the document published, the multi-functional radar network mission planning method based on resource conversion and complementation is also
There is not research.
Summary of the invention
It is a kind of based on resource conversion and complementation the purpose of the present invention is aiming at the problems existing in the prior art, providing
Multi-functional radar network mission planning method, solve existing mission planning method as resource allocation it is unreasonable caused by task
Loss ratio it is high and cannot excellent effect completion task the problem of.
The technical scheme is that a kind of based on resource conversion and complementary multi-functional radar network mission planning side
Method, comprising the following steps:
S1, multi-functional radar network task model is established；
S2, delay loss of the task on time dimension and the compression loss in power dimension are calculated separately；
S3, will delay loss and compression loss sum as objective function, it is excellent to construct multi-functional radar network mission planning
Change model；
S4, the optimal executive mode of each task is calculated using the interior point method and coordinate descent that are nested；
S5, judge whether task executes；If so, output job start time and task execution power；If it is not, then abandoning
Task n:
S6, judge whether all tasks terminate；If it is not, the then position according to next task time window on radar time shaft
It sets and the time started is adjusted, return step S4；If so, the planning that ends task.
Further, in the step S1, multi-functional radar network task model is established, task is specially abstracted into square
Shape block, the length of rectangle are defined as the duration of task, and the width of rectangle is defined as the execution power of task, sets the beginning of task
At the beginning of time, time window and deadline.
Further, in the step S2, delay loss of the calculating task on time dimension, specially task continues
Time moves in time window, and when the end time of task exceeding the deadline of task time window, task is damaged by delay
Consumption；When the end time of task exceeding the given threshold of time window deadline, task is dropped, and is generated and is abandoned loss, from
And delay loss of the task on time dimension is expressed as
Wherein, xnIndicate the two-valued variable of task execution situation, λnThe delay loss factor of expression task n,Expression task
At the beginning of n,The duration of expression task n,Indicate the deadline of time window, DnThe discarding of expression task n is damaged
Consumption.
Further, in the step S2, compression loss of the calculating task in power dimension, specially in the task of execution
When, increase the execution power of task, task is compressed, generates compression loss；When the execution power of task reaches on power
In limited time, task is dropped, and is generated and is abandoned loss, so that compression loss of the task in power dimension is expressed as
Wherein, μnThe compressed coefficient of expression task n, PnThe execution power of expression task n,Expression task n's initially holds
Row power.
Further, it in the step S3, using the sum of delay loss and compression loss as objective function, is embodied as
Wherein, N indicates the total task number for needing to plan.
Further, in the step S3, multi-functional radar network mission planning Optimized model is constructed, is embodied as
xn={ 0,1 }
Wherein,At the beginning of indicating time window,The upper limit of the power of expression task n, EnWhen indicating execution task n
The energy of consumption.
Further, in the step S4, it is optimal that each task is calculated using the interior point method and coordinate descent being nested
Executive mode, specifically include it is following step by step:
S401, objective function in step S3 is divided into N number of subfunction and, construction do not consider abandon Optimized model；
S402, the penalty function that subfunction is constructed using interior point method；
S403, the optimal solution that penalty function is solved using coordinate descent.
Further, in the step S401, objective function in step S3 is divided into the form of N number of subfunction sum, often
A subfunction is denoted as f (Xn), it is expressed as
Wherein,Here XnIndicate a two-dimentional column vector, the element of the vector is respectively task n
The execution timeWith the execution power of task n, and f (Xn) indicate to XnThe operation of middle element；；
The Optimized model abandoned is not considered according to subfunction construction, is expressed as
Further, in the step S402, the constraint adjusting of Optimized model in step S401 is standardized,
It is expressed as
Subfunction f (X is constructed using interior point methodn) penalty functionIt is expressed as
Wherein,Indicate penalty factor sequence.
Further, in the step S403, according to initial pointTo penalty functionIn each component constantly force
Closely, i.e.,
Wherein,WithBe illustrated respectively at the beginning of the task n obtained after iteration j in coordinate descent and
Execute power；
Until meeting termination condition
Obtain an optimal solution of subfunction；
It is constantly iterated, kth time iteration adjustment initial point and penalty factor areDirectly
To meeting the condition of convergence
Wherein, c is degradation factor, obtains subfunction f (Xn) optimal solution.
The beneficial effects of the present invention are: the present invention is by by the task abstract modeling of multi-functional radar network, then to prolong
When loss and compression loss and be objective function, construct a multi-functional radar network utilization of resources Optimized model, finally
Optimal solution is obtained by the algorithm being nested based on interior point method and coordinate descent；The present invention considers the spirit of mission duration
Conversion and complementary relationship between active and multi-functional radar network system resource efficiently solve multi-functional radar network task
When quantity is more and the mission duration has conflict, task caused by resource allocation is unbalanced cannot the completion of excellent effect the problem of, mention
High multi-functional radar network executes the efficiency of task.
Detailed description of the invention
Fig. 1 is the multi-functional radar network mission planning method flow signal of the invention based on resource conversion and complementation
Figure；
Fig. 2 is the task basic structure schematic diagram in the embodiment of the present invention；
Fig. 3 is that system in the embodiment of the present invention is averaged total losses schematic diagram；
Fig. 4 is that task in the embodiment of the present invention is averaged loss ratio schematic diagram；
Fig. 5 is the schematic diagram of task quantity performed in different time period in the embodiment of the present invention；
Fig. 6 is the average variance schematic diagram of power used in the case where completing same task said conditions in the embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
In order to facilitate the description contents of the present invention, following term is explained first:
Term 1: job start time
Job start timeRefer to the time that task n starts to be performed.
Term 2: mission duration
Mission durationRefer to the physical length of time required for execution task n.
Term 3: time window
Time window is a period, refers to the actual duration of the task n energy before and after the task expected duration
Mobile effective range.
Term 4: power is executed
Execute power PnRefer to power required for completion task n.
As shown in Figure 1, being of the invention based on resource conversion and complementary multi-functional radar network mission planning method stream
Journey schematic diagram；The invention proposes a kind of based on resource conversion and complementary multi-functional radar network mission planning method, including
Following steps:
S1, multi-functional radar network task model is established；
S2, delay loss of the task on time dimension and the compression loss in power dimension are calculated separately；
S3, will delay loss and compression loss sum as objective function, it is excellent to construct multi-functional radar network mission planning
Change model；
S4, the optimal executive mode of each task is calculated using the interior point method and coordinate descent that are nested；
S5, judge whether task executes；If so, output job start time and task execution power；If it is not, then abandoning
Task n:
S6, judge whether all tasks terminate；If it is not, the then position according to next task time window on radar time shaft
It sets and the time started is adjusted, return step S4；If so, the planning that ends task.
The present invention analyzes the task attribute of multi-functional radar network and is abstracted as task model, then on a timeline to prolong
When loss and compression loss and be objective function, construct a multi-functional radar network utilization of resources optimization problem, finally
Solve the optimal executive mode of each task.This method is efficiently solved in mission planning and is made due to multi dimensional resource conflict
At the high problem of task loss ratio, promote multi-functional radar network operational performance.
In step sl, as shown in Fig. 2, task is abstracted into rectangular block, the serial number n of task, wherein rectangle by the present invention
Length be defined as duration of task nThe width of rectangle is defined as the execution power P of task nn, at the beginning of setting task
Between beAt the beginning of time window and deadline is respectivelyWith
In step s 2, the duration of task nIn time windowIt is inside transportable, but works as task n
End timeHave exceeded the deadline of task time windowThen task n will be lost by delayDelay
Loss is defined as the direct proportion function for the task n end time exceeding its time window deadline numerical value, it may be assumed that
Wherein, λnFor the delay loss factor of task n.
But once numerical value of the end time of task n beyond time window deadline has been more than the value of a setting, example
Such as 3/4ths of time window length, i.e.,So task n just has to abandon, and produces
D is lost in a raw discardingn。
The present invention passes through one two-valued variable x of settingnFor indicating the executive condition of task: if task n is performed, xn
=1, it is dropped then xn=0, so that delay of the task on time dimension is lostIt is expressed as
Wherein, xnIndicate the two-valued variable of task execution situation, λnThe delay loss factor of expression task n,Expression task
At the beginning of n,The duration of expression task n,Indicate the deadline of time window, DnThe discarding of expression task n is damaged
Consumption, the value range of task n are as follows:
For task n, the delay loss on time dimension is not always existing；Since the duration of task can
To be moved in time window, when task n is moved to a certain position, and the end time is less than or equal to the cut-off of its time window
Between, i.e.,When, task is just not exposed to delay loss.
Task immoderate delay on a timeline, can waste a large amount of time, also task loss ratio can be made to increase.It holds
One task of row, needs to consume certain ENERGY En, it is also desirable to distribute an initial powerTo each task, power with
There is a relationship between timeHere PnIt is the execution power of task,It is the actual duration of task.
It, can be by suitably increasing the execution power P of task n in order to reduce its delay lossn, to shorten its duration.This side
Formula is known as the compression of task.Task n can generate compression loss by compressionCompression loss is defined as the execution power of task n
PnQuadratic function, i.e.,
Wherein, μnFor the compressed coefficient of task n.
It is contemplated that actual conditions, the execution power P of task nnIt unconfined cannot increase, i.e., task n be deposited
In a upper limit of the powerOnce the execution power of task n has reached its upper limit of the power, then the task must just abandon, and produce
Raw discarding loss, thus the compression loss by task in power dimensionIt is expressed as
It is similar with delay loss, compression loss be also not always it is existing, when the execution power of the n of task be exactly its initially
Power, i.e.,When, task n is just not exposed to compression loss.
In step s3, for task n, its executive mode has delay, compression, delay compression to combine.Multi-functional group
Net radar has N number of task needs to be planned, will delay loss and compression loss sum as objective function, available N number of
The expression formula for total losses of being engaged in, i.e.,
The process of mission planning is just to solve for the optimal Starting Executing Time of each taskOptimal use power PnAnd
Determine whether certain task n is performed, so that the process that total losses minimize, therefore according to the multi-functional networking of objective function
Radar Task plan optimization model, i.e.,
xn={ 0,1 }
Wherein, n=1,2 ..., N.
In step s 4, the present invention calculates the optimal execution of each task using the interior point method and coordinate descent being nested
Mode, specifically include it is following step by step:
S401, objective function in step S3 is divided into N number of subfunction and, construction do not consider abandon Optimized model；
Due to independently of each other, objective function in step S3 to be divided into the shape of N number of subfunction sum between each task
Formula, each subfunction are denoted as f (Xn), it is expressed as
Wherein,What subfunction indicated is to execute the total loss of the task when not abandoning task n.
The Optimized model abandoned is not considered according to subfunction construction, is expressed as
S402, the penalty function that subfunction is constructed using interior point method；
The constraint adjusting of Optimized model in step S401 is standardized, is expressed as
Subfunction f (X is constructed using interior point methodn) penalty functionIt is expressed as
Wherein,Indicate that penalty factor sequence, characteristic are strictly monotone decreasing and level off to zero.
S403, the optimal solution that penalty function is solved using coordinate descent.
The above-mentioned penalty function constructed has been a unconfined problem, its value and the number of iterations k has relationship；This hair
The bright optimal solution that kth time iteration is found out with coordinate descent, by given initial pointTo penalty functionIn each component constantly approach, i.e.,
Until meeting termination condition
Just stop approaching the optimal solution of k iteration, optimal solution at this time regards an extreme point of atom function asWherein, ε1Allowable error in indicates coordinate descent method, can value be ε1=10-3。
It is constantly iterated, kth time iteration adjustment initial point and penalty factor areC is
Degradation factor can use c ∈ (0.1,0.5), until meeting the condition of convergence, i.e.,
Then iteration terminates, extreme point at this timeIt is exactly subfunction f (Xn) optimal solution, wherein ε2Table
Show the allowable error of interior point method, can value be ε2=10-4。
In step s 5, the present invention sets the condition that task n is identified discarding, including following 3:
1, numerical value of the end time of task n beyond its time window deadline has been more than the value of a setting；
2, the execution power of task n has reached its upper limit of the power；
3, task n loss function f (Xn) minimum value be greater than its abandon loss Dn。
Above three condition is compared, judges whether task executes；If task n is executed, job start time is exportedWith
Task execution power Pn；If task n is not executed, it is discarded into task n.
In step s 6, the present invention needs to judge whether all tasks terminate；
If all tasks are not over, due to task may delayed execution, lead to the time window portion of next task
Point be occupied, it is therefore desirable to judge the position of next task time window, and to next task time window at the beginning of do
It adjusts out, then turns to step S4；
If all tasks are over, end task planning, obtains mission planning scheme.
In order to verify to planing method of the invention, the present invention makees the emulation experiment carried out on Matlab2016
It further illustrates.
Simulating scenes: assuming that multi-functional radar network has N=100 task to be planned, at the beginning of task time window
Obedience is uniformly distributed U (0,100)；The deadline of time window is naturally bigger than time started, when time window deadline obeys
Between add U (8,12) at the beginning of window；Initial powerIt obeys U (10,15), ENERGY EnIt obeys U (80,120), the upper limit of the power
It is set asBe delayed loss factor λnWith compression loss coefficient μnU (10,15) and U (0.08,0.4) are obeyed respectively；
Abandon loss DnIt obeys U (150,200).The result of emulation is delayed and compression, is only delayed and only compresses these three method performances
Comparison, simulation process have used 1000 monte carlo methods.
Fig. 3 is the total average loss of multi-functional radar network system.Total loss of three kinds of methods all can be with number of tasks
Amount increases and increases, but lower than other two methods based on the total losses for being delayed and being lost method.This is because the time
There is conversion complementary relationships between resource and power resource.Too late at the beginning of task n, delay loss becomes very
When big, the execution power of task n can be increased suitably to make up for lost time, and then reduce total losses.Other two kinds of sides
Method does not have conversion and supplement between resource, can only constantly postpone or compression duty, so that total losses become larger.
Fig. 4 is the schematic diagram that the loss ratio of task changes as number of tasks changes.With increasing for number of tasks, task
Loss ratio increased, but always lower than other both of which with the task loss ratio of compression method based on being delayed.This is
Because task executes task by way of being delayed or compressing and combine, this allows for the condition of the discarding of the task in step 6
It will not meet easily, the loss ratio of task is relatively low.And in addition the mode of two ways planning tasks is single, abandon condition compared with
Easily meet, task loss ratio is also just relatively high.
Fig. 5 is at the same time in section, and three kinds of methods complete the contrast schematic diagram of task quantity, and Fig. 6 is to complete to appoint
In the case that quantity of being engaged in is certain, the schematic diagram of the variance of power service condition.This shows the task based on delay and compression method
Planning mode is relative to the planning mode based on delay and based on the planning mode of compression, on time dimension and power dimension
On have more preferably performance.
Specific embodiment through the invention can be seen that the present invention and can be very good to realize more than the task quantity and appoint
It the case where window segment of being engaged in overlapping, makes rational planning for the task of multi-functional radar network, reduces task loss ratio.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.This field
Those of ordinary skill disclosed the technical disclosures can make according to the present invention and various not depart from the other each of essence of the invention
The specific variations and combinations of kind, these variations and combinations are still within the scope of the present invention.
Claims (10)
1. a kind of based on resource conversion and complementary multi-functional radar network mission planning method, which is characterized in that including following
Step:
S1, multi-functional radar network task model is established；
S2, delay loss of the task on time dimension and the compression loss in power dimension are calculated separately；
S3, will delay loss and compression loss sum as objective function, construct multi-functional radar network mission planning optimization mould
Type；
S4, the optimal executive mode of each task is calculated using the interior point method and coordinate descent that are nested；
S5, judge whether task executes；If so, output job start time and task execution power；If it is not, then abandoning task
N:
S6, judge whether all tasks terminate；If it is not, the then position pair according to next task time window on radar time shaft
Time started is adjusted, return step S4；If so, the planning that ends task.
2. as described in claim 1 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S1, establishes multi-functional radar network task model, specially
Task is abstracted into rectangular block, the length of rectangle is defined as the duration of task, and the width of rectangle is defined as the execution of task
Power, at the beginning of setting task, at the beginning of time window and deadline.
3. as claimed in claim 2 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S2, delay loss of the calculating task on time dimension, specially
The duration of task moves in time window, when the end time of task exceeding the deadline of task time window,
Task is lost by delay；When the end time of task exceeding the given threshold of time window deadline, task is dropped, and is produced
It is raw to abandon loss, so that delay loss of the task on time dimension is expressed as
Wherein, xnIndicate the two-valued variable of task execution situation, λnThe delay loss factor of expression task n,Expression task n's opens
Begin the time,The duration of expression task n,Indicate the deadline of time window, DnThe discarding of expression task n is lost.
4. as claimed in claim 3 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S2, compression loss of the calculating task in power dimension, specially
In the task of execution, increase the execution power of task, task is compressed, generates compression loss；When the execution of task
When power reaches the upper limit of the power, task is dropped, and is generated and is abandoned loss, thus the compression loss table by task in power dimension
It is shown as
Wherein, μnThe compressed coefficient of expression task n, PnThe execution power of expression task n, P0 nThe original execution function of expression task n
Rate.
5. as claimed in claim 4 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S3, using the sum of delay loss and compression loss as objective function, is embodied as
Wherein, N indicates the total task number for needing to plan.
6. as claimed in claim 5 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S3, constructs multi-functional radar network mission planning Optimized model, be embodied as
xn={ 0,1 }
Wherein,At the beginning of indicating time window,The upper limit of the power of expression task n, EnIt indicates to consume when execution task n
Energy.
7. as claimed in claim 6 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S4, the optimal executive mode of each task is calculated using the interior point method and coordinate descent that are nested, has
Body include it is following step by step:
S401, objective function in step S3 is divided into N number of subfunction and, construction do not consider abandon Optimized model；
S402, the penalty function that subfunction is constructed using interior point method；
S403, the optimal solution that penalty function is solved using coordinate descent.
8. as claimed in claim 7 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S401, objective function in step S3 is divided into the form of N number of subfunction sum, each subfunction is denoted as f
(Xn), it is expressed as
Wherein,Here XnIndicate a two-dimentional column vector, the element of the vector is respectively the execution of task n
TimeWith the execution power of task n, and f (Xn) indicate to XnThe operation of middle element；
The Optimized model abandoned is not considered according to subfunction construction, is expressed as
9. as claimed in claim 8 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S402, the constraint adjusting of Optimized model in step S401 is standardized, is expressed as
Subfunction f (X is constructed using interior point methodn) penalty functionIt is expressed as
Wherein,Indicate penalty factor sequence.
10. as claimed in claim 9 based on resource conversion and complementary multi-functional radar network mission planning method, feature
It is, in the step S403, according to initial pointTo penalty functionIn each component constantly approach, i.e.,
Wherein,WithIt is illustrated respectively at the beginning of the task n obtained after iteration j in coordinate descent and executes function
Rate；
Until meeting termination condition
Obtain an optimal solution of subfunction；
It is constantly iterated, kth time iteration adjustment initial point and penalty factor areUntil full
The sufficient condition of convergence
Wherein, c is degradation factor, obtains subfunction f (Xn) optimal solution.
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