Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Abstract

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

“…The term d Si denotes the spatial distance of L Ei to all other L E which are initially selected. Equation (4) shows that the inverse proportional of d Si is taken which implies that the closer the two location candidates are the higher the weight is and vice versa. So, we consider the Euclidean distance of location candidates from the user sample, as well as, the spatial closeness of location candidates.…”

“…However, infrastructural changes involving ferromagnetic materials like iron, nickle, etc., make changes to the magnetic field. For example, installing an elevator in a building would cause a huge magnetic change in the proximity of the elevator [4].…”

Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines

“…The Internet of Things and pervasive computing technology have continually progressed since their inception [1,2]. At present, the Internet of Things technology has become increasingly used in a variety of fields.…”

A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

“…The problem of navigation in urban areas can be mitigated by integrating GNSS with the Inertial Navigation System (INS) [3,4]. Nevertheless, since GNSS signals are not available indoors, various navigation methods relying on different technologies, such as proximity, triangulation, fingerprinting, and dead reckoning, have been proposed [5]. Since the dead reckoning method requires little or no infrastructure to be using a knowledge-based method to model human locomotion, and the adopted sensors of this system are continuously calibrated when GNSS signals are available [28].…”