As a part of Internet-of-Things applications, various smartphone-based indoor location services have considerable commercial value. It is largely agreed that the integration of multiple sensors is the preferable solution for improving the performance of smartphone indoor positioning, thanks to the diversity of built-in sensors. However, heading error remains a challenge for smartphone indoor positioning, especially in complex indoor scenes. This article, therefore, proposes a heading estimation solution to enhance the accuracy and reliability of smartphone indoor positioning. The extended Kalman filter (EKF)-based solution fuses smartphone built-in motion sensors, magnetometers, building map knowledge, and fingerprinting coarse positions from Wi-Fi or Bluetooth. First, the context of pedestrian mobility and scene knowledge is inferred by combining these data. Then, a scene augmentation strategy and magnetic interference online detection method are applied to calibrate the gyro accumulation error and improve the heading estimation accuracy. Additionally, quasistatic and low-dynamic judgments and online calibration are used to mitigate gyro drift. The proposed solution is implemented on a smartphone device and validated in several experiments under natural pedestrian mobility and complex indoor scenarios. Experiments show that the accuracy of heading estimation is improved from 13.1° to 2.0°. The improved heading estimation enhances the accuracy of smartphone indoor positioning from 3.57 to 0.90 m. The proposed solution is applicable to real location-based service scenarios.

Radio fingerprinting positioning is widely used for smartphone location-based services and Internet of Things applications given its high availability and low cost. Radio fingerprinting-based algorithms, however, are subject to the forced matching problem and often yield estimated positions even when a user is actually located outside of the fingerprint region. A positioning solution in multistory buildings should be able to locate positions accurately on the current floor; but these methods may generate unreasonable positioning trajectories, such as irrationally passing through a wall, when fingerprinting positioning is fused the with inertial measurement unit to further improve the accuracy. A radio map must be surveyed dynamically on-the-move, as a dynamic fingerprint, to reduce the time costs and on-site workload. Unlike static fingerprint-based methods, dynamic fingerprinting samples are sparse. Thus, we propose an enhanced indoor positioning solution using spatial context knowledge, extracted from the sparse dynamic fingerprints. In the offline stage of radio map calculation, we extract the dynamic fingerprint features and store them in a spatial features database to reduce the computational time and storage space complexity. The proposed floor detection, region recognition, and path correction algorithms identify the online spatial contexts from the stored spatial features to improve positioning performance. This solution was applied on smartphones combining WiFi and Bluetooth low-energy radio signals in two typical scenarios. The experimental results show that the floor detection accuracy reached 99% while region recognition accuracy reached 90.75%. The positioning path correction method enhances the accuracy of smartphone indoor positioning from 3.27 to 2.56 m.

Ubiquitous positioning is in highly demand in the fields of smartphone location-based services and more widely, in internet of things applications. Many WiFi fingerprinting algorithms have received widespread attention in the industry. Such methods for commercial location-based services however, still experience usability problems. These include low efficiency and high time costs when surveying radio maps. At the same time, heterogeneous radio measurements from different mobile devices may degrade positioning performance. This paper proposes a novel fingerprinting indoor positioning method aiming at breaking through the usability bottleneck in two ways: 1) a dynamic on-the-move surveying method of radio map to enhance the surveying efficiency using a self-developed mobile geo-reference system, and radio map is constructed using much sparser measurements and sparse principle. 2) Correspondingly, a sparse attribute based positioning algorithm was developed. This new positioning algorithm takes account of the heterogeneity of devices, and does not require any manual operations for the calibration of hardware bias, unlike many widely used static fingerprinting methods. The proposed positioning method was implemented on smartphones and verified in typical office indoor environments. The experimental results show that the radio map surveying efficiency was enhanced by 10 times in terms of radio map surveying time. Compared with the widely used static methods, the proposed positioning algorithm has more consistent positioning accuracy even when different devices were used for radio map surveying and positioning. The proposed fingerprinting positioning method is applicable when developing LBS applications (demo attached), while the enhanced usability will improve user experience of positioning performance.

In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.

Cycle slip (CS) is a primary error source in Precise Point Positioning/Inertial Navigation System (PPP/INS) integrated systems. In this study, an INS-aided CS detection and repair method is presented. It utilizes high-precision INS information instead of a pseudorange to remove the satellite–receiver geometric range in the wide-lane (WL) and ionospheric-free (IF) phase combinations and creates an INS-aided WL (WL-INS) model and an INS-aided IF (IF-INS) model. Since INS information is superior to pseudorange, the INS-aided models have high detection accuracy. However, the effectiveness of INS-aided models cannot persist for a long time because of INS accumulation error. To overcome the disturbance of INS error, improved INS-aided models are proposed. This idea takes advantage of the long wavelength of WL combination and tries to fix WL CS. Once it succeeds, the INS error can be evaluated and removed. The proposed method was tested using land vehicle data, in which simulated cycle slips and signal interruption were introduced. The results show that this method can accurately detect and repair different cycle slips between the continuous Global Positioning System (GPS) epoch. When it comes to the cycle slip after a GPS interruption, the method can also accelerate PPP re-convergence, as it is not affected by the inertial accumulation error.