A New Indoor Positioning Method Reducing the Errors Due to NLOS Environments Under Sparse Sensor Deployment Conditions

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.