Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution
Efficient radar resource allocation is a fundamental yet computationally challenging problem, as optimal solutions typically require iterative optimization with high complexity. Motivated by the need for real-time scheduling, robust generalization, and low data dependency, this paper proposes a novel paradigm that leverages large language model (LLM)-guided evolutionary search (AlphaEvolve) to autonomously discover a closed-form power allocation solution for multi-target tracking. The approach encodes high-dimensional radar states into physically inspired features, then evolves a compact and interpretable scoring function, which is transformed to feasible power allocations via a determini...
| Authors | Zhenkang Hou, Wenqiang Pu, Junkun Yan et al. |
|---|---|
| Primary category | eess.SP |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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