CN112907143B - DAR real-time self-adaptive beam resident scheduling method based on online dynamic template - Google Patents
DAR real-time self-adaptive beam resident scheduling method based on online dynamic template Download PDFInfo
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Abstract
The invention belongs to the field of radar system resource management, and particularly relates to a method for adaptive beam resident scheduling of a digital array radar by using a pulse interleaving technology. The invention provides a real-time digital array radar self-adaptive beam resident scheduling method based on an on-line dynamic template, wherein the on-line dynamic template is introduced to realize pulse interleaving analysis in DAR. The beam resident scheduling algorithm provided by the invention can effectively reduce the task loss rate, improve the realization value rate and the system time utilization rate of the system and realize real-time beam resident scheduling by means of the online dynamic template.
Description
Technical Field
The invention belongs to the field of radar system resource management, and particularly relates to an algorithm of adaptive beam resident scheduling of a digital array radar by applying a pulse interleaving technology.
Background
Digital array radar (digital array radar, DAR) is a phased array radar that transmits and receives beams by digital beam forming techniques. Compared with the traditional phased array radar, the digital array radar has the advantages of larger dynamic range, faster scanning speed, easier control and the like, and is therefore widely focused and studied (see document: wu Manqing: digital array radar and progress thereof, national institute of electronic science, 2008,12, (06), pp.401-405, shang Xiao: shang Jun, peng Yingning: digital array radar parallel signal processing algorithm and implementation, information and electronic engineering, 2009,7, (04), pp.294-299). The efficient beam-resident scheduling algorithm is a key to fully exploiting the performance of digital array radars.
The existing radar beam resident Scheduling algorithm can be divided into a beam resident Scheduling algorithm based ON a Template method (see literature: shih, c.s., golalakrishan, s., ganti, p., et al. The technology-based Real-time dwell Scheduling with energy constraint, the 9th IEEE Real-Time and Embedded Technology and Applications Symposium, toronto, ON, canada, may 2003, pp.19-27, shih, C.S., gopalakrishenan, S., ganti, P., et al: scheduling Real-time dwells using tasks with synthetic periods,24th IEEE Real-Time Systems Symposium, canun, mexico, december 2003, pp.210-219, tang Ting, he Zishu, cheng Ting: an adaptive radar dwell Scheduling algorithm based ON The Template method, signal processing, 2010,26, (07), pp.998-1002) and adaptive beam dwell Scheduling algorithms (see literature: zeng Guang, lu Jian, hu Weidong: multifunctional phased array adaptive Scheduling algorithm research, modern radar, 2004,26, (06), pp.14-18, lu Jian, hu Weidong, yu Wenxian: multifunctional phased array Real-time radar dwell adaptive Scheduling algorithm, 2005,27 and electronic radar system 732, (1984, pp.998-1002), and electronic phased array system, yu Wenxian, (1984, 527, 528-1002), and multiple phased array Real-time phased array dwell Scheduling algorithms (see literature: zeng Guang, 5225, hu Weidong, 5256). Since templates are typically designed offline, the template-based beam-resident scheduling algorithm lacks adaptation to the actual radar operating environment, and the adaptive beam-resident scheduling algorithm is considered a more efficient scheduling algorithm. The adaptive beam-resident scheduling algorithm can be further divided into an algorithm based on scheduling interval analysis and an algorithm based on time pointer analysis. The algorithm proposed by the literature (once-light, lu Jian, hu Weidong: multifunctional phased array radar adaptive scheduling algorithm research, modern radar, 2004,26, (06), pp.14-18) is an algorithm based on scheduling interval analysis, wherein resident task requests sequentially select respective optimal actual execution moments according to the priority order of working modes. In literature (Lu Jian, hu Weidong, yu Wenxian: adaptive scheduling algorithm for real-time parking of a multifunctional phased array radar, system engineering and electronics technology, 2005, (12), pp.1981-1987), the optimal execution time of each parking task request is obtained by a quadratic programming method. In the beam residence scheduling process based on time pointer analysis, for each moment pointed by the time pointer, an algorithm selects the task most suitable to be scheduled and executed at the moment (see documents Lu Jian, hu Weidong and Yu Wenxian: multifunctional phased array radar real-time task scheduling research, electronic report, 2006,34, (04) pp.732-736), wherein the task comprehensive priority considers the deadline and working mode priority of the task at the same time. Due to the introduction of the time pointer, the task priority is changed along with the change of analysis time, and the adaptive beam resident scheduling algorithm based on the time pointer analysis has dynamic characteristics, so that the adaptive beam resident scheduling algorithm based on the time pointer analysis has better performance compared with the adaptive beam resident scheduling algorithm based on the scheduling interval analysis.
The beam dwell scheduling algorithm above was designed for phased array radar. In digital array radar, receive beams of different tasks may be received simultaneously due to digital beam forming techniques. Thus, in the digital array radar beam dwell scheduling process, pulse interleaving with overlapping reception periods can be performed (see literature: cheng, t., he, z.s., li, h.y.: adaptive dwell scheduling for digital array radar based ON online pulse interleaving, chinese Journal of Electronics,2009,18, (3), pp.574-578, zhao Hongtao, cheng Ting, he Zishu: digital array radar beam dwell scheduling interval analysis algorithms, information & electronics engineering, 2011,9, (01), pp.17-21, meng, d., wu, y, zhang, q., et al, an Adaptive Dwell Scheduling Algorithm for Digital Array Radar Based ON Pulse Interleaving,2nd International Conference ON Advances in Management Engineering and Information Technology (AMEIT 2017), shanghai, china, april 2017, pp.314-319, zhang, h.w., xie, j.w., zhang, z.j., et al, pulse interleaving scheduling algorithm for digital array radar, journal of Systems Engineering and Electronics,2018,29, (1), pp.67-73, zhang, h., xie, j., ge, j., et al, hybrid particle swarm optimization algorithm based ON entropy theory for solving DAR scheduling problem, tsinghua Science and Technlogy,2019,24, (03), pp.282-290, lu, x, che, t., peng, h., et al, novel Adaptive Dwell Scheduling Algorithm for Digital Array Radar based ON Pulse Interleaving,2019 22th International Conference ON Information Fusion (fuon), ottawa, cant, 201y, jul, jy, 1-8. Although pulse interleaving techniques have been applied to phased arrays in the literature (Cheng, T, he, z.s., tang, T.: novel radar dwell scheduling algorithm based on pulse interleaving, journal of Systems Engineering and Electronics,2009,20, (2), pp.247-253, zhang, h., xie, j., ge, j., et al.: task Interleaving Scheduling for Phased Array Radar in Multi-Target Tracking,2018IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE), wuhan, china, roust 2018, pp.347-350, zhang, h.w., xie, j.w., ge, j., et al.: A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar, s European Journal of Operational Research,2019,272, (3), pp.868-878, zhang, h.w., xie, j.w., zha, z.j.,: task Scheduling of Phased Array Radar Based on Hybrid Adaptive Genetic Algorithm, acta Armamentarii,2017,38, (9), pp.1761-1770), pulse interleaving is not used in digital arrays, but is fully utilized in the radar. Literature (Cheng, t., he, z.s., li, h.y.: adaptive dwell scheduling for digital array radar based on online pulse interleaving, chinese Journal of Electronics,2009,18, (3), pp.574-578) introduces a pulse interleaving technique based on time pointer analysis, wherein only tasks with the same pulse repetition period and number are allowed to be interleaved. The same problems exist with algorithms that introduce pulse interleaving techniques based on scheduling interval analysis (see documents: zhao Hongtao, cheng Ting, he Zishu: digital array radar beam dwell scheduling interval analysis algorithm, information and electronics engineering, 2011,9, (01), pp.17-21). Document (Meng, d., wu, y., zhang, q., et al: an Adaptive Dwell Scheduling Algorithm for Digital Array Radar Based on Pulse Interleaving,2nd International Conference on Advances in Management Engineering and Information Technology (AMEIT 2017), shanghai, china, april 2017, pp.314-319) implements DAR search and tracking tasks while imaging sparse aperture cognitive ISAR for fine tracking tasks, where interleaving between different pulse repetition periods and times tasks is achieved by time interval vectors and energy state vectors. The beam dwell scheduling algorithm proposed in literature (Zhang, h.w., xie, j.w., zhang, z.j., et al: pulse interleaving scheduling algorithm for digital array radar, journal of Systems Engineering and Electronics,2018,29, (1), pp.67-73) implements pulse interleaving in DAR by analyzing whether scheduling tasks meet time and energy constraints at each pointing moment, but the dwell task model in this literature is simplified to a single pulse task model. Literature (Zhang, h., xie, j., ge, j., et al hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem, tsinghua Science and Technlogy,2019,24, (03), pp. 282-290) proposes an intelligent beam-resident scheduling algorithm for DAR, but suffers from the same disadvantage as the previous beam-resident scheduling algorithm, i.e., a single-pulse task model, and furthermore, the intelligent beam-resident scheduling algorithm cannot guarantee real-time scheduling. Literature (Lu, x., cheng, t., peng, h., et al: novel Adaptive Dwell Scheduling Algorithm for Digital Array Radar based ON Pulse Interleaving,2019 22th International Conference ON Information Fusion (FUSION). Ottawa, ON, canada, july 2019, pp.1-8) proposes a DAR beam dwell scheduling algorithm based ON scheduling interval analysis, where tasks with different pulse repetition periods and pulse repetition times are allowed to interleave.
Although DAR beam dwell scheduling has achieved some degree of phased success, problems remain to be solved. First, some algorithms are based on an unreasonable task model, where the radar task model contains only one pulse repetition period, as in the literature (Zhang, h.w., xie, j.w., zhang, z.j., et al: pulse interleaving scheduling algorithm for digital array radar, journal of Systems Engineering and Electronics,2018,29, (1), pp.67-73, zhang, h., xie, j., ge, j., et al, hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem, tsinghua Science and Technlogy,2019,24, (03), pp.282-290). Second, the algorithm-introduced pulse interleaving algorithm is not generic and only allows interleaving of the dwells with the same pulse repetition period and number, as in documents (Cheng, t., he, z.s., li, h.y.: adaptive dwell scheduling for digital array radar based on online pulse interleaving, chinese Journal of Electronics,2009,18, (3), pp.574-578, zhao Hongtao, cheng Ting, he Zishu: digital array radar beam dwells scheduling interval analysis algorithm, information and electronics engineering, 2011,9, (01), pp. 17-21). Third, none of the above algorithms take into account the real-time nature of beam dwell scheduling.
Based on the problems, the invention provides a real-time digital array radar self-adaptive beam resident scheduling method based on an online dynamic template. The invention introduces an online dynamic template to realize pulse interleaving analysis in DAR, the pulse interleaving method has generality, radar resident tasks with different pulse repetition periods and times are allowed to be interleaved, and the pulse interleaving analysis process is visual and simple. The pulse interleaving technology is combined with the adaptive beam resident scheduling algorithm based on time pointer analysis, so that a new adaptive beam resident scheduling algorithm is formed. The algorithm can realize beam resident scheduling of resident tasks with multiple pulse repetition periods, so that the algorithm can be used in an actual radar system, and the beam resident scheduling algorithm provided by the invention can realize real-time beam resident scheduling by means of an online dynamic template. The beam resident scheduling algorithm provided by the invention is a perfect combination of resident templates and adaptive scheduling.
Disclosure of Invention
The invention provides a real-time digital array radar self-adaptive beam resident scheduling method based on an online dynamic template, which comprises the following specific contents:
assume that the current scheduling interval is [ t ] 0 ,t 0 +L SI ]There are N resident tasks t= { T 1 ,T 2 ,…,T N Application scheduling execution, where t 0 L is the starting time of the current scheduling interval SI For the duration of one scheduling interval, the beam-resident task model is T i ＝{rt i ,l i ,w i ,Pt i ,tx i ,tw i ,tr i ,M i ,pri i -wherein rt i To expect execution time, l i Is a time window, w i For working mode priority, pt i To transmit power tx i 、tw i And tr i The transmission period, the waiting period and the receiving period in each pulse repetition period are respectively. pri (pri) i For pulse repetition period M i For the number of pulse repetitions. Then the digital array radar adaptive beam dwell scheduling algorithm based on the online dynamic template includes the following steps:
1. initializing a dynamic template Tp: { tp, deltat, t 0 +L SI ,Sx,Sr,E}，
tp＝t 0 (4)
Where Tp is the starting time, t, of the template Tp 0 +L SI Let Δt be the end time of Tp and be the time unit length in Tp. n is n tp For the length of the template, satisfy n tp ＝(t 0 +L SI Tp) Δt. Sx describes the case where all scheduled task transmission periods occupy time slots within Tp. Sr describes the case where all scheduled task reception periods occupy time slots within Tp. E represents the energy consumption state of each time slot in Tp, where E 0 (j)＝E(t 0 )e -△t·j/τ ,j∈1,2,...,n tp ，E(t 0 ) At t for the system 0 The energy consumption at the moment, τ, is the backoff parameter. i=0.
2. Deleting the latest executable instant rt i +l i Tasks smaller than tp, the number of deleted tasks is recorded as n i ，i＝i+n i 。
3. For earliest execution time rt i -l i The tasks smaller than tp calculate the corresponding comprehensive priorities according to the formula (5), and the tasks with the largest comprehensive priorities are selected and marked as T, assuming M tasks are all selected.
sw i ＝[η·Np i +(M+2-η)·Nd i ]/(M+1) (5)
Wherein Np i And Nd i Respectively task T i In the current executable task set, the obtained sequence numbers are sequenced according to the working mode priority and the deadline, and eta is an adjustable parameter.Then analyze whether T can be scheduled to be executed at the current tp. First, the amount of change in Sx, sr, and E that would be caused if T were scheduled at the current time is calculated, including Δsx, Δsr, and Δe:
in formula (8), deltaE k (j) The energy change in the jth time slot caused by the kth pulse repetition period in the task T in the representative template can be referred to as a specific calculation formula (9). Then judging whether the time constraint and the energy constraint can be satisfied according to the formulas (10) - (13):
max(Sx+△Sx)≤1 (10)
max(Sx+△Sr)≤1 (11)
max(Sr+△Sx)≤1 (12)
max(E+△E)≤E th (13)
4. if the inequality is satisfied, T may be scheduled to execute at tp, and the parameters are updated as shown in equations (14) - (18):
△tp＝tx (14)
Sx＝Sx+△Sx (15)
Sr＝Sr+△Sr (16)
E＝E+△E (17)
i＝i+1 (18)
where tx is the transmit period length of task T. Then the element other than 0 in Sr is reset to 1. If T cannot be scheduled for execution at time tp, Δtp= Δt.
5. Updating parameters in Tp:
tp＝tp+△tp (19)
6. if tp>t 0 +L SI Or i>And N, ending the analysis of the scheduling interval, otherwise, turning to step 2.
Principle of the invention
The invention realizes a general pulse interleaving method which allows overlapping of receiving periods by introducing an online dynamic template, and combines the method with a beam resident scheduling algorithm based on time pointer analysis to obtain a DAR real-time beam resident scheduling algorithm. The principle thereof is explained as follows:
assume that the current scheduling interval t 0 ,t 0 +L SI ]There are N resident task requests, denoted as t= [ T ] 1 ,T 2 ,…,T N ]. In the scheduling process, two principles of priority and deadline should be fully considered, namely, tasks with higher working mode priority and tasks with earlier deadline should be scheduled preferentially. To implement the above principle, given the comprehensive priority based on the working mode priority and deadline, the beam dwell scheduling optimization problem in one scheduling interval can be modeled as:
wherein N is 1 ，N 2 And N 3 The number of scheduled tasks, deferred tasks, and deleted tasks, respectively. Obviously there is n=n 1 +N 2 +N 3 The method comprises the steps of carrying out a first treatment on the surface of the In the scheduling process, due to limited system resources, the following constraint conditions exist: first, tasks should be scheduled for execution before the latest executable time, as reflected in the first constraint; secondly, the emission periods of different tasks cannot be overlapped, and the emission periods are reflected by a second constraint condition; thirdly, the transmitting period of each task cannot conflict with the receiving periods of other tasks, and the transmitting period is reflected by a third constraint condition; fourth, the lengthy firing period due to interleaving may lead to radar transmitter damage, and thus energy constraints need to be met, reflected by a fourth constraint.
For the energy consumed by the system at time t, where P (x) is the transmit power function, τ is the back-off parameter, E th Is an energy threshold. The last two constraint conditions are used for judging that the unscheduled task is a delayed task or a deleted task.
In the conventional beam resident scheduling algorithm based on time pointer analysis, for the analysis time pointed by each time pointer, the executable task with the highest comprehensive priority is selected as the task to be currently scheduled for execution. The time pointer will then slide forward in units of dwell time, which is the product of the pulse repetition period and the number corresponding to the task. As shown in fig. 1, the time pointer tp points to the current time t 1 After scheduling the task with the highest comprehensive priority among the currently executable tasks, the time pointer slides to t 2 A similar process is then repeated.
Since the time pointer slides according to the residence time of the scheduled task, it is obvious from fig. 1 that the pulse interleaving is not considered in the conventional algorithm, and thus the waiting period and the receiving period are not fully utilized, based on which the present invention introduces a pulse interleaving method in the conventional algorithm that considers that the receiving period can overlap. To achieve an efficient pulse interleaving analysis, an online dynamic template Tp is introduced here. As shown in the figure2, when shown at t 1 After task 1 is scheduled to be executed, the sliding length of tp is modified to be tx 1 . The template tp= { Tp, [ delta ] t, t is now utilized 0 +L SI Sx, sr, E } describes the remaining resources of the current scheduling interval. When task 1 is scheduled, its transmit and receive periods will occupy a portion of the time gap. Here we denote the occupied time slot with element 1, element 0 denotes that the time slot is free, and Sx and Sr denote the transmit period and receive period occupancy, respectively. Taking fig. 2 as an example, sx and Sr in Tp after task 1 is scheduled can be expressed as:
Sx＝[0,0,0,1,0,0,0,1,0,0,0,1,0,…0] (24)
Sr＝[0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,…,0] (25)
if task 2 can be scheduled at current tp, it will cause changes in Sx and Sr, recorded as Δsx and Δsr. To test whether scheduling the task would cause a conflict between the emission period and the emission period, it is only necessary to determine whether equation (10) can be satisfied; to test whether scheduling the task would cause a conflict between the receive period and the occupied transmit period of task 2, it is only necessary to determine whether equation (11) holds; to test whether scheduling the task would cause a conflict between the transmit period and the occupied receive period of task 2, it is only necessary to determine whether equation (12) holds. And meanwhile, judging whether the energy constraint condition can be met or not by using the formula (13). If the above conditions are satisfied, it is indicated that task 2 can be scheduled to be executed at this time, and the time pointer tp is set to tx 2 The start time of Tp increases accordingly, and thus the Tp length becomes shorter. The occupancy of the transmitting period and the receiving period should be changed correspondingly, and Sx and Sr should be updated correspondingly
Sx＝[0,0,1,1,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,…0] (26)
Sr＝[1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,…,0] (27)
It can be seen that the template length and the lengths of Sx and Sr vary accordingly during the scheduling process. This is why Tp is named an online dynamic template. Based on this, the invention adopts formulas (10) - (13) to judge whether the online interleaving is successful; and when the interleaving is successful, updating the template parameters by adopting formulas (14) - (22), so that the method is perfectly combined with the traditional method based on time pointer analysis, and the adaptive beam residence scheduling of the DAR is realized.
Drawings
Fig. 1 is a schematic diagram of a conventional adaptive beam-resident scheduling based on time pointer analysis.
FIG. 2 is a schematic diagram of an online dynamic template.
FIG. 3 is a graph showing the task loss rate of the algorithm of the present invention compared to the prior art algorithm.
FIG. 4 is a comparison of the realized value rate of the algorithm of the present invention with the prior algorithm.
FIG. 5 is a generalized time utilization comparison of the algorithm of the present invention with a prior art algorithm.
FIG. 6 is a time-consuming comparison of the algorithm of the present invention with a prior art algorithm.
Detailed Description
In the simulation scene, the existence verification task, the precise tracking task, the common tracking task, the horizon searching task and the airspace searching task are considered. The ratio of the number of the precise tracking targets to the number of the common tracking targets is 1:4, and specific parameters of the radar task are given in table 1. The simulation time length is 12s, L SI ＝50ms，E th ＝20J，τ＝200ms,△t＝0.5ms。
Table 1 radar residence task parameter table
For the purpose of comprehensively evaluating the algorithm performance, a Task Drop Rate (TDR), an implementation Value rate (HVR), and a generalized time utilization rate (Generalized time utilization Ratio, GTUR) are adopted as evaluation indexes, and the definitions of the three are as follows:
TDR＝N drop /N total
wherein N is drop To lose the number of tasks, N total The total number of tasks scheduled for the request.
Wherein N is scheduled Is the number of scheduled tasks.
In DAR, the receiving periods can be overlapped, and the actual receiving period duration is smaller than
Thus, GTUR may be greater than 1.
In addition, to examine whether the resident scheduling has real-time performance, the time consumed by the algorithm can be calculated by t sum /(N monte N SI ) Calculation, wherein t sum To be at N monte Total time spent in sub-monte carlo simulation, and N SI The total number of scheduling intervals is for each monte carlo simulation.
The algorithm provided by the invention is adopted to realize the DAR beam resident scheduling. Its performance was compared with algorithm a (see literature: cheng, t., he, z.s., li, h.y.: adaptive dwell scheduling for digital array radar based ON online pulse interleaving, chinese Journal of Electronics,2009,18, (3), pp.574-578) and algorithm B (see literature: lu, x., cheng, t., peng, h., et al.: novel Adaptive Dwell Scheduling Algorithm for Digital Array Radar based ON Pulse Interleaving,2019 22th International Conference ON Information Fusion (fusia). Ottawa, ON, canada, july 2019, pp.1-8), where algorithm a and algorithm B were the earliest and latest radar beam dwell scheduling algorithms, respectively, that take into account multiple pulse repetition periods. The following results are statistical results of 100 monte carlo simulations.
Fig. 3 shows the task loss rate comparison results for three algorithms. The three algorithms all introduce pulse interleaving with overlapping receiving periods, when the target number exceeds 50, the TDR of the algorithm A starts to rise, when the target number exceeds 40, the TDR of the algorithm B starts to rise, and when the target number reaches 80, the algorithm provided by the invention starts to lose tasks. This is because algorithm a requires exactly the same pulse repetition period and number of tasks to be interleaved. Although tasks with different pulse repetition periods and numbers may be interleaved in algorithm B, algorithm B schedules using a method based on scheduling interval analysis. The algorithm provided by the invention performs scheduling based on time pointer analysis and can realize the interleaving among tasks with different pulse repetition intervals and numbers through an online dynamic template.
Fig. 4 shows the implementation value rate comparison results for three algorithms. When all tasks are successfully scheduled, the HVR value is 1. When the target number exceeds 50, the HVR of algorithm a begins to drop; when the target number exceeds 40, the HVR of algorithm B begins to drop. The algorithm proposed by the present invention has the highest HVR and the HVR begins to drop when the target number reaches 80.
Fig. 5 shows generalized time utilization comparisons for three algorithms. When all tasks are successfully scheduled, the GTUR grows linearly. GTUR for algorithm a and algorithm B begins to drop as the target number increases to 50 and 40, respectively. The reason for this is that the search task is discarded due to the presence of too many trace tasks, which occupy more time resources than the trace tasks, so the GTUR starts to drop. In the algorithm proposed by the present invention, DAR is saturated when the number of tasks reaches 100. The algorithm provided by the invention has the highest GTUR.
Fig. 6 shows the scheduling time consumption comparison results of the three algorithms. The algorithm B consumes more time than the other two algorithms, and the algorithm B does not have real-time property. Compared with the algorithm A, the algorithm provided by the invention consumes more time in the scheduling process, but has little growth and real-time performance.
In summary, compared with the existing method, the beam resident scheduling algorithm provided by the invention can effectively reduce the task loss rate in the scheduling process, and obtain higher realization value rate and generalized time utilization rate. Meanwhile, the algorithm provided by the invention can realize real-time beam resident scheduling, and is a DAR beam resident scheduling method with practical value.
Claims (1)
1. A DAR real-time self-adaptive beam resident scheduling method based on an online dynamic template comprises the following steps:
assume that the current scheduling interval is [ t ] 0 ,t 0 +L SI ]There are N resident tasks t= { T 1 ,T 2 ,···,T N Application scheduling execution, where t 0 L is the starting time of the current scheduling interval SI For the duration of one scheduling interval, the beam-resident task model is T i ＝{rt i ,l i ,w i ,Pt i ,tx i ,tw i ,tr i ,M i ,pri i -wherein rt i To expect execution time, l i Is a time window, w i For working mode priority, pt i To transmit power tx i 、tw i And tr i Respectively a transmitting period, a waiting period and a receiving period in each pulse repetition period; pri (pri) i For pulse repetition period M i The number of pulse repetition is the number; then the digital array radar adaptive beam dwell scheduling algorithm based on the online dynamic template includes the following steps:
step 1, initializing a dynamic template Tp: { tp, Δt, t 0 +L SI ,Sx,Sr,E}，
tp＝t 0 (4)
Where Tp is the starting time, t, of the template Tp 0 +L SI At the end time of Tp, Δt is the time unit length in Tp; n is n tp For the length of the template, satisfy n tp ＝(t 0 +L SI Tp)/Δt; sx describes the situation that all scheduled task emission periods occupy time slots in Tp; sr describes the case where all scheduled task reception periods occupy time slots within Tp; e represents the energy consumption state of each time slot in Tp, where E 0 (j)＝E(t 0 )e -Δt·j/τ ,j∈1,2,...,n tp ，E(t 0 ) At t for the system 0 The energy consumption at the moment, tau is a rollback parameter; i=0;
step 2, deleting the latest executable time rt i +l i Tasks smaller than tp, the number of deleted tasks is recorded as n i ，i＝i+n i ；
Step 3, for earliest execution time rt i -l i Calculating corresponding comprehensive priorities of tasks smaller than tp according to a formula (5), and assuming M tasks, selecting the task with the largest comprehensive priority, and marking the task as T;
sw i ＝[η·Np i +(M+2-η)·Nd i ]/(M+1) (5)
wherein Np i And Nd i Respectively task T i In the current executable task set, according to the working mode priority and the sequence number obtained by sequencing in a deadline, eta is an adjustable parameter; then analyzing whether T can be scheduled to be executed at the current tp; first, the amount of change in Sx, sr, and E that would be caused if T were scheduled at the current time is calculated, including Δsx, Δsr, and Δe:
in formula (8), ΔE k (j) Representing the energy change in the jth time slot caused by the kth pulse repetition period in the task T in the template, wherein the specific calculation formula can refer to formula (9); then judging whether the time constraint and the energy constraint can be satisfied according to the formulas (10) - (13):
max(Sx+ΔSx)≤1 (10)
max(Sx+ΔSr)≤1 (11)
max(Sr+ΔSx)≤1 (12)
max(E+ΔE)≤E th (13)
step 4, if the inequality is satisfied, T may be scheduled to be executed at tp, and the parameters are updated according to equations (14) - (18):
Δtp＝tx (14)
Sx＝Sx+ΔSx (15)
Sr＝Sr+ΔSr (16)
E＝E+ΔE (17)
i＝i+1 (18)
wherein tx is the transmission period length of the task T; then resetting elements other than 0 in Sr to 1; if T cannot be scheduled for execution at time tp, Δtp=Δt;
step 5, updating parameters in Tp:
tp＝tp+Δtp (19)
step 6, if tp > t 0 +L SI Or i > N, ending the analysis of the scheduling interval, otherwise turning to step 2.
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