An Optimization Method for Indoor Pseudolites Anchor Layout Based on MG-MOPSO

To address the challenge of optimizing the layout of pseudolite anchor points in complex indoor environments with significant occlusions, this paper proposes a multi-objective particle swarm optimization algorithm (MG-MOPSO). The algorithm leverages a minimum geometric dilution of precision (GDOP) configuration to optimize anchor deployment, aiming to meet the high-precision requirements of indoor pseudolite positioning systems. Experimental results show that compared to the standard MOPSO, MG-MOPSO improves the convergence speed of two objective functions by 21.43% and 25.81%, respectively, and enhances optimization accuracy by 29.41% and 10%. Compared to the non-dominated sorting genetic algorithm II (NSGA-II), the convergence speed increases by 33.33% and 36.99%, while optimization accuracy improves by 36.84% and 29.41%. Moreover, MG-MOPSO outperforms both standard MOPSO and NSGA-II in terms of the Pareto front’s convergence and diversity, with improvements of 16.8% and 14.7%, respectively. Additionally, significant reductions are observed in average positioning error, maximum positioning error, and standard deviation across multiple test points. These results validate the effectiveness of the minimum-GDOP-based initialization and segmented weighting strategy, demonstrating the superior performance and broad applicability of the proposed MG-MOPSO algorithm in optimizing pseudolite layouts under complex indoor occlusion conditions.