Comprehensive Analysis on Least-Squares Lateration for Indoor Positioning Systems

In pursuit of the accomplishment of certain position estimations of targets in outdoor places, finding the locations of the targets in indoor environments has been a significant topic. Exact position estimations of the objects for indoor places have potentials for the enhancement of several emerging Internet-of-Things (IoT) applications, such as smart manufacturing, smart home, public security, social networks, transportation, traveling, marketing applications, and information services lead to a huge demand on the designing of low-cost and high-accuracy localization and navigation solutions. On the other hand, the global positioning system (GPS) technology designed for outdoor positioning applications, is not suitable to indoor positioning systems. Making exact position detection with GPS is a compelling problem for indoor positioning methods. In this study, received signal strength (RSS)-based least-squares triangulation approach that utilizes existing infrastructure, is proposed. By increasing the number of access points (APs) and using line fitting algorithms to the RSS values, the triangulation method improves the certainty of location estimation. The utilization of the existing infrastructure turns the proposed approach into cheaper when compared to existing localization methods which require expensive components. The proposed least-squares lateration algorithm is compared with pure lateration (PL) in terms of accuracy error under different Gaussian noise parameters for varying number of APs and varying dimensions of the measurement area. Usage of the least-square algorithm with line fitting approaches provides significant performance improvements for all cases when it compared with PL.