Indoor Localization Algorithm and Research Based on Improved Tuna Swarm-Optimized Particle Filter

This paper proposes an indoor positioning method that enhances the particle filter through an improved tuna swarm optimization algorithm to address the challenges of low precision and inadequate target tracking in complex indoor environments. By incorporating the improved tuna optimization algorithm, the particle degradation issue in the particle filter is eliminated, resulting in improved algorithm runtime and reduced processing time. Experimental results demonstrate that the optimized particle filter, employing the improved tuna swarm optimization, achieves an average error of approximately 5.8 cm, surpassing the traditional particle filter algorithm's performance of 19 cm. Furthermore, the computational workload is reduced by nearly 94 %, enhancing positioning accuracy.