Error model and simulation for multisource fusion indoor positioning. Issue 3 (28th November 2021)

• Record Type: Journal Article
• Authors: Ai, Haojun; Tao, Jingjie; Ai, Shan; Xu, Tianshui; Li, Ning; Tang, Kaifeng; Zhang, Sheng; Yang, Yuhong; Li, Shengchen
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 3(2022)
• Page Start: 2219
• Page End: 2241
• Publication Date: 2021-11-28
• Subjects: error model -- fingerprint -- indoor positioning simulation system -- PDR; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22771
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single‐source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system. To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed error model provides a solution for optimizing the deployment of positioning sources.