An Indoor Localization Method Based on Cauchy Inverse Whale Optimization Algorithm

With the rapid development of 5g technology, the era of interconnection has come. At the same time, more and more software and hardware acquire location information through sensors, so the accuracy of location information becomes more and more important. How to improve the indoor node positioning accuracy has become a research hotspot in the field of wireless sensor. Aiming at the problem of poor positioning accuracy and large error fluctuation of traditional indoor nodes, relevant scholars propose to use swarm intelligence optimization algorithm to optimize the relevant positioning method, which improves the accuracy of indoor positioning. But there are still some shortcomings, such as slow convergence speed, easy to fall into local optimal value and so on. This paper uses the Cauchy inverse whale optimization algorithm to optimize the environmental impact factor and propagation factor of the indoor environment signal attenuation model in the wireless fingerprint positioning method, so as to reduce the impact of environment and obstacles on the positioning accuracy. This algorithm can improve the convergence speed of the algorithm in the initial stage, improve the overall performance of the algorithm, and improve the accuracy of indoor positioning. The experimental results show that the Cauchy inverse whale optimization algorithm has certain advantages in iterative speed, optimization speed and indoor positioning accuracy.