Adaptive Particle Filter-Neural Network Fusion for Cooperative Localization of Multi-UAV Systems in GNSS-Denied Indoor Environments

Accurate autonomous navigation of unmanned aerial vehicles (UAVs) in complex indoor environments where satellite signals are denied remains a critical challenge. Conventional state estimation methods, such as particle filters, often suffer from particle degeneracy and high computational costs, limiting their robustness and real-time applicability. Here, we introduce an adaptive particle filter-neural network (PF-NN) fusion framework that achieves high-fidelity cooperative localization for multi-UAV systems. Our approach integrates a lightweight neural network that optimizes particle weight allocation by learning from motion consistency, thereby mitigating sample impoverishment. This is coupled with an adaptive resampling strategy that dynamically adjusts the particle population based on the effective sample size, balancing computational load with estimation accuracy. By fusing ultra-wideband (UWB) inter-vehicle ranging with visual landmark observations, the system leverages both global and local constraints to achieve robust state estimation. In simulations involving six UAVs in a complex indoor setting, our algorithm demonstrated superior performance, achieving an average root-mean-square error (RMSE) of 0.437 m. This work provides a robust and efficient solution for multi-UAV cooperative localization, paving the way for reliable autonomous operations in GNSS-denied scenarios such as search-and-rescue and industrial inspection.