RF-CPO-SVR algorithm for indoor localization based on wifi fingerprints

Abstract With the rapid development of wireless communication technology, WiFi indoor positioning has become an important method for achieving indoor localization. Achieving high accuracy in WiFi positioning is a challenging issue. To enhance the accuracy of positioning systems, this paper proposes a WiFi indoor positioning algorithm that uses the random forest (RF) algorithm for access point (AP) selection and the Crested Porcupine Optimizer (CPO) algorithm to optimize support vector regression (SVR), referred to as RF-CPO-SVR. The RF algorithm selects APs by evaluating the feature importance of each AP, reducing the negative impact of redundant and unstable APs on the performance of the positioning system. After AP selection, the CPO algorithm is used to optimize the hyperparameters of SVR, further improving the system’s performance. Comprehensive tests of the proposed RF-CPO-SVR algorithm were conducted on public datasets, and the results show that 90% of the positioning accuracy is within 4 m, with an average positioning error of 2.1082 m. Experimental results demonstrate that the RF-CPO-SVR algorithm outperforms traditional positioning methods and existing classical optimization algorithms, improving positioning accuracy by 23.5%, 27.4%, and 24.7% compared to particle swarm optimization-SVR, GA-SVR, and K nearest neighbors, respectively.