Linear IMM-SMF for Multi-UAV Cooperative Target Localization in Low-Altitude Economy

The low-altitude economy (LAE) has become a key application field for unmanned aerial vehicles (UAVs), where cooperative target localization is crucial for logistics, infrastructure inspection and emergency response. However, UAV-based localization in LAE faces dual challenges: unknown or maneuvering target motions, and non-Gaussian unknown-but-bounded (UBB) noise from electromagnetic interference and complex airflows. To address these problems, a linear interacting multi-model-set membership filter (IMM-SMF) is proposed for multi-UAV cooperative target localization. This method integrates the IMM framework for adaptive tracking of maneuvering targets, adopts SMF to robustly handle UBB noise, and uses a sequential fusion strategy to fuse multi-UAV observation data. A simple linear model ensures computational efficiency, which is vital for real-time LAE applications. Theoretical analysis verifies the method’s strong robustness to UBB noise in maneuvering target tracking, and comparative experiments confirm its superiority in localization accuracy and stability over traditional single-model or single-UAV methods. This work provides a reliable and efficient localization solution for UAV cooperative systems in the LAE.