Online IoT Indoor Localization Evolution: Enhanced Sparse Random Fourier Features With Multikernel Strategy

Indoor positioning within the Internet of Things (IoT) has become critically important, with accuracy and robustness being essential for its practical applications. However, the complexity of real-world disturbances, including both Gaussian and non-Gaussian noise, poses significant challenges to existing methods. Kernel adaptive filtering (KAF) has proven effective in addressing these challenges, yet traditional KAF approaches face issues with growing structural complexity and increasing memory demands. To address these drawbacks, this study introduces an enhanced sparsification technique within the random Fourier features (RFFs) framework. The improvement lies in adopting the generalized Gaussian distribution (GGD) to be better suited for real-world noise, leading to the development of the ESCGKAF algorithm—a robust solution for high-precision indoor positioning in online scenarios. The algorithm leverages the half-quadratic (HQ) optimization on the kernel risk-sensitive loss (KRSL) cost function, which is further refined through the conjugate gradient (CG) method, to achieve a superior performance in noisy environments. The proposed approach permits a more generalized representation of random features, surpassing traditional Gaussian-based RFF methods in terms of adaptability and robustness. To further enhance performance in complex scenarios, MESCGKAF algorithm is proposed by introducing a multikernel strategy. The effectiveness of the proposed algorithms is validated in two real-world scenarios, showing marked performance improvement.