Attention‐Based MAPPO for Large‐Scale Sensor Scheduling in Multisource Localisation

ABSTRACT Large‐scale sensor scheduling for multisource localisation is a critical technology in wireless communication control and navigation systems. Most existing heuristic algorithms face challenges in adapting to large‐scale sensor systems. To overcome this limitation, we utilise the self‐learning capabilities of deep reinforcement learning (DRL) to enable multisource localisation. This paper proposes a large‐scale sensor scheduling algorithm based on the multiagent proximal policy optimisation (LSS‐MAPPO) framework. We develop a multisource localisation model based on time difference of arrival (TDOA) and design a reward function grounded in the Cramér–Rao lower bound (CRLB). Our approach integrates multihead attention layers into MAPPO to improve the performance of the algorithm. In large‐scale sensor scheduling systems, multihead attention mechanisms can effectively handle the high‐dimensional state space associated with multisource localisation in multiagent environments. Experimental results under different environments show that LSS‐MAPPO improves localisation accuracy compared to the baseline in large‐scale sensor scheduling. Notably, it maintains robust performance under partial observability, addressing critical gaps in large‐scale dynamic sensor scheduling.