6G Multisource-Information-Fusion-Based Indoor Positioning via Gaussian Kernel Density Estimation

With the in-depth development of the Internet of Things (IoT) and the constant discussion of 6G visions, the future development of indoor location-based services (LBSs) in the 6G-enabled IoT is attracting people's attention. In fact, due to the continuous updating of network techniques, the complexity of the indoor environment and the number of connected wireless access points (APs) will increase dramatically, which leads to the diversity of the received signal strength (RSS) on the performance of signal propagation distance estimation, resulting in low positioning accuracy and poor robustness. In response to this problem, this article proposes a multisource information fusion-based indoor positioning approach via Gaussian kernel density estimation. First, the heuristic distribution model is used to establish the mathematical relationship between the RSS from different Wi-Fi APs and the signal propagation distance, and then the Gaussian kernel density estimation approach is applied to estimate the signal propagation distance distribution. Second, the normalized signal propagation distance distribution is set as the basic probability assignment in the Dempster-Shafer (D-S) evidence theory, and then the multisource RSS information is fused according to D-S evidence synthesis rules. Meanwhile, the trust function synthesized by the D-S evidence theory is used to select matching reference points (RPs). Finally, the fuzzy decision algorithm is used to select the ideal matching RPs from the matching RPs for the positioning. Experimental results show that the proposed approach has higher positioning accuracy and stronger positioning robustness compared to existing ones.

Automated summary

This article proposes an indoor positioning approach via Gaussian kernel density estimation for the 6G-enabled IoT environment, which improves positioning accuracy and robustness.