An improved selective correction adaptive particle filter-based cooperative navigation algorithm for UAV swarms in GNSS-denied areas

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Abstract
In Global Navigation Satellite System (GNSS)-denied environments, positioning accuracy is critical for unmanned aerial vehicle (UAV) swarms. A particle filter-based cooperative localization algorithm is proposed to improve UAV positioning accuracy. To address the issues of particle impoverishment and loss of diversity inherent in traditional particle filters, a Selective Correction Adaptive Particle Filter (SCA-PF) with an enhanced resampling strategy is introduced. First, the algorithm incorporates a threshold-based resampling improvement to mitigate particle impoverishment common in conventional particle filters. Second, an adaptive threshold feedback strategy, based on the residual variation rate, dynamically adjusts the weight threshold to more effectively select high-weight particles. Additionally, a firefly-inspired attraction mechanism is applied to fine-tune neighboring particles, thereby improving convergence and robustness under complex noise conditions. Simulation results demonstrate that the adaptive threshold strategy increases localization accuracy by an additional 10% over fixed-threshold methods. The proposed SCA-PF enhances cooperative navigation accuracy by approximately 15% compared to other resampling-based algorithms.
Funding Information
  • National Natural Science Foundation of China (U2341201, 62201326, 62571216)
  • Research Project of Quancheng Laboratory,China under Grant (QCL20250303)
  • Young Talent of Lifting engineering for Science and Technology in Shandong, China.

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