Phased-Array Radar Task Scheduling Method for Hypersonic-Glide Vehicles

Meng, Fanqing; Tian, Kangsheng

doi:10.1109/access.2020.3043338

Cited by 7 publications

(9 citation statements)

References 18 publications

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“…Assuming that the cross-sectional area of HGV is 0.01 m 2 , and the false alarm probability is 10 −6 , the following two benchmark models are selected for comparison. The first model is a radar task scheduling algorithm based on the particle swarm-annealing algorithm proposed by Meng and Tian [32], which is marked as Method 1. The second model is a sensor network deployment optimisation algorithm based on modified differential evolution proposed by Cao et al [33], which is marked as Method 2.…”

Section: Simulationmentioning

confidence: 99%

A search method for a hypersonic gliding vehicle based on early warning information guidance

Zhang¹,

Jia-jun²,

Li³

et al. 2022

IET Radar Sonar & Navi

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The high speed and high manoeuvrability of a hypersonic gliding vehicle (HGV) pose a severe challenge to the existing early warning detection system. Optimising the search resources of a phased array radar to improve the HGV detecting efficiency has become a practical problem. In this study, a HGV search method based on a hybrid optimisation algorithm is proposed with the early warning information guidance as a priori condition. Firstly, the HGV priority judgement model is established, and the priority quantification equations for height, velocity and distance are designed. Secondly, the search parameters of the radar are optimised with the maximum cumulative detection probability, the shortest average discovery time and the highest priority level as the objective functions, and a radar search model is established. Finally, to overcome the problem that particle swarm optimisation (PSO) is easy to fall into local optimal solution, a hybrid optimisation algorithm based on differential evolution (DE) and PSO is proposed. The performance of the proposed method is verified in two simulation scenarios, and the results show that the proposed method outperforms existing mainstream search methods and can reasonably allocate search resources according to the priority of HGV.

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Section: Simulationmentioning

confidence: 99%

A search method for a hypersonic gliding vehicle based on early warning information guidance

Zhang¹,

Jia-jun²,

Li³

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…HGV is an aircraft with a speed above Mach 5 and flying in near space [1]. Compared with a conventional ballistic missile, HGV has stronger maneuverability and can strike any target in the world within two hours [2]. It has changed the traditional combat style and has significant military application value, which has attracted the great attention of various countries [3,4].…”

Section: Introductionmentioning

confidence: 99%

Motion State Recognition and Trajectory Prediction of Hypersonic Glide Vehicle Based on Deep Learning

Zhang¹,

Jia-jun²,

Li³

et al. 2022

IEEE Access

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“…The adaptive dwell scheduling algorithms can be further divided into the ones based on the heuristic method [5][6][7][8][9][10][11][12][13][14] and the ones based on the intelligent method [15][16][17][18][19][20][21][22][23][24][25][26][27]. A dwell scheduling algorithm based on time pointer is proposed in ref.…”

Section: Introductionmentioning

confidence: 99%

“…All the dwell scheduling algorithms based on the intelligent algorithm in refs. [15][16][17][18][19][20][21][22][23][24][25] treat dwell tasks as nonpreemptive tasks. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…[9,10] can shorten the bias between the actual execution time and the desired execution time, but dwell tasks are more likely to be dropped. Secondly, although algorithms based on intelligent algorithms [15][16][17][18][19][20][21][22][23][24][25][26][27] can achieve different criteria of scheduling, they are not real-time dwell scheduling algorithms, which means that they do not apply to the actual radar systems. Last but not the least, most algorithms treat dwell tasks as non-preemptive tasks, which limited the performance of the dwell scheduling algorithm [5][6][7][8][9][10][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy

Cheng

Heng

2022

IET Radar Sonar & Navi

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The effective dwell scheduling algorithm must be studied to make full use of the radar system's limited resources. In this study, a real‐time dwell scheduling in phased array radar is investigated, where the optimisation problem model that takes the importance, urgency and desired execution time criteria into consideration is built. A real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy is proposed to solve this problem. Based on the scheduled task pointed by the time pointer, when it is not interleaved with the previously scheduled tasks, two previously interleaved blocks are backtracked, and the actual execution time of tasks in these two blocks are adjusted. In order to guarantee real‐time scheduling, the concept of the virtual dwell task is introduced to reduce the dimension of the original execution time optimisation problem first, then it is converted into two convex optimisation problems and their analytical solutions are deduced. Simulation results demonstrate that the proposed algorithm can realise real‐time dwell scheduling, and it has the lowest Task Drop Ratio, the highest Hit Value Ratio, Time Utilisation Ratio and low Average Time Shifting Ration compared with conventional and up‐to‐date algorithms.

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Phased-Array Radar Task Scheduling Method for Hypersonic-Glide Vehicles

Cited by 7 publications

References 18 publications

A search method for a hypersonic gliding vehicle based on early warning information guidance

A search method for a hypersonic gliding vehicle based on early warning information guidance

Motion State Recognition and Trajectory Prediction of Hypersonic Glide Vehicle Based on Deep Learning

Real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy

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