Error model and simulation for multisource fusion indoor positioning

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

VISEL: A visual and magnetic fusion‐based large‐scale indoor localization system with improved high‐precision semantic maps

“…SLAC 37 fuses the IMU sensor with fingerprints and maps the target to a coarse region via stacked denoising autoencoders to execute the fine‐grained localization, which simultaneously localizes the user and calibration‐free with heterogeneous devices. In the wireless radio localization, magnetic‐based fusion algorithm requires PDR trajectory to enhance the discrimination of local features, Ai et al 15 and He et al 37 calculate the user's posture, walking behavior, and step frequency based on IMU and magnetic. The calculation of the step length varies from person to person and cannot be accurately estimated, VMag proposes the step length optimization, which is based on the walking posture of the user and path information to calculate the step length closest to each user.…”

“…14 Multisource fusion localization has become the mainstream method in largescale indoor scenes. [15][16][17] The fusion localization based on vision and magnetic 18 has aroused much interest in large indoor sites, 19 due to the easy-to-deploy without infrastructure support while has a low localization error.…”

“…Single signal‐based localization algorithms, however, suffer from signal fingerprint ambiguities and long‐term maintenance of the database, 13 the localization error in large‐scale indoor scenes is relatively large 14 . Multisource fusion localization has become the mainstream method in large‐scale indoor scenes 15–17 . The fusion localization based on vision and magnetic 18 has aroused much interest in large indoor sites, 19 due to the easy‐to‐deploy without infrastructure support while has a low localization error.…”