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| Citation: | WANG Zengfu, YANG Guangyu, and JIN Shuling. A non-myopic and fast resource scheduling algorithm for multi-target tracking of space-based radar considering optimal integrated performance[J]. Journal of Radars, 2024, 13(1): 253–269. doi: 10.12000/JR23162
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A Non-myopic and Fast Resource Scheduling Algorithm for Multi-target Tracking of Space-based Radar Considering Optimal Integrated Performance
doi: 10.12000/JR23162
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Abstract
Appropriate and effective resource scheduling is the key to achieving the best performance for a space-based radar. Considering the resource scheduling problem of multi-target tracking in a space-based radar system, we establish a cost function that considers target threat, tracking accuracy, and Low Probability of Interception (LPI). Considering target uncertainty and constraints of the space-based platform and long-term expected cost, we establish a resource scheduling model based on the Partially Observable Markov Decision Process (POMDP) with multiple constraints. To transform and decompose the resource scheduling problem of multi-target tracking with multiple constraints into multiple unconstrained sub-problems, we use the Lagrangian relaxation method. To deal with the curse of dimensionality caused by the continuous state space, continuous action space and continuous observation space, we use the online POMDP algorithm based on the Monte Carlo Tree Search (MCTS) and partially observable Monte Carlo planning with observation widening algorithm. Finally, a non-myopic and fast resource scheduling algorithm with comprehensive performance indices for multi-target tracking in a space-based radar system is proposed. Simulation results show that the proposed algorithm, when compared with the existing scheduling algorithms, allocates resources more appropriately and shows better performance. -
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References
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- Figure 1. Schematic diagram of the proposed LR-POMCPOW for resource scheduling of space-based radar multi-target tracking
- Figure 2. The process of the MCTS algorithm
- Figure 3. STK-based demonstration for impact of distinct target threat levels on radar resource allocation
- Figure 4. Target threat level results
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Figure 5. Budget ratio (
$\tau/T $ - Figure 6. STK-based demonstration for impact of distinct relative distances on radar resource allocation
- Figure 7. The slant range of targets
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Figure 8. Budget ratio (
$\tau / T $ - Figure 9. STK-based demonstration for multi-target tracking
- Figure 10. Target tracking trajectory
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Figure 11. Budget ratio (
$ \tau / T$ - Figure 12. The average radiation power allocated to each target
- Figure 13. RMSE on position of target1
- Figure 14. RMSE on speed of target1
- Figure 15. Comparison of total average radiation power
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Figure 16. Comparison of the total budget ratio (
$ \tau / T $ - Figure 17. Comparison of the expected cumulative multi-target discount cost