Accurate indoor localization in nonline-of-sight (NLOS) environments remains a critical challenge for resource-constrained internet of things (IoT) systems, particularly under sparse anchor deployments. This article proposes a geometry-constrained NLOS error mitigation (GC-NEM) algorithm that synergistically integrates received signal strength and angle of arrival measurements. Unlike existing methods requiring explicit line-of-sight/NLOS identification or prior error statistics, GC-NEM dynamically calibrates NLOS biases through adaptive error weighting coefficients bounded by geometric feasibility constraints. By formulating the localization problem as a quadratic optimization task with equality constraints, the proposed algorithm achieves submeter accuracy using only two anchor nodes. To validate the proposed algorithm, a wireless positioning system was established in a practical indoor environment. Experimental results indicate that the proposed GC-NEM algorithm improves localization accuracy by 16.4% compared to conventional NLOS error mitigation algorithms.

Indoor location-based service is emerging as the crucial application of the Internet of Things, which promotes the advance of relevant technology in the indoor scenario. Several positioning algorithms are proposed for different indoor configurations in recent years. The fingerprint-based indoor positioning algorithm has drawn much attention because of the good positioning performance without additional hardware. However, the false fingerprint matching frequently incurs due to the complexity of the indoor positioning environment and affects the positioning accuracy. In this article, a principal component analysis (PCA)-assisted indoor positioning algorithm based on the adaptive hierarchical clustering algorithm (PAHC) is proposed, which can improve the positioning accuracy through aggregating the reference points (RPs) and conducting the cluster-based PCA (C-PCA) features extraction. More specifically, a clustering termination method is proposed to obtain reasonable RPs clusters adaptively according to the preset RPs. A two-stage fingerprint matching algorithm is proposed based on the C-PCA to further increase the difference between similar RPs and thus improving the positioning accuracy. To verify the proposed algorithm, an indoor wireless system is established in the practical indoor scenario. The experimental results indicate that the proposed PAHC algorithm can increase the positioning accuracy by 9.3% compared with the conventional fingerprint algorithm.