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The purpose of this paper is to relate to the real-time navigation and tracking of pedestrians in a closed environment. To restrain accumulated error of low-cost microelectromechanical system inertial navigation system and adapt to the real-time navigation of pedestrians at different speeds, the authors proposed an improved inertial navigation system (INS)/pedestrian dead reckoning (PDR)/ultra wideband (UWB) integrated positioning method for indoor foot-mounted pedestrians.

This paper proposes a self-adaptive integrated positioning algorithm that can recognize multi-gait and realize a high accurate pedestrian multi-gait indoor positioning. First, the corresponding gait method is used to detect different gaits of pedestrians at different velocities; second, the INS/PDR/UWB integrated system is used to get the positioning information. Thus, the INS/UWB integrated system is used when the pedestrian moves at normal speed; the PDR/UWB integrated system is used when the pedestrian moves at rapid speed. Finally, the adaptive Kalman filter correction method is adopted to modify system errors and improve the positioning performance of integrated system.

The algorithm presented in this paper improves performance of indoor pedestrian integrated positioning system from three aspects: in the view of different pedestrian gaits at different speeds, the zero velocity detection and stride frequency detection are adopted on the integrated positioning system. Further, the accuracy of inertial positioning systems can be improved; the attitude fusion filter is used to obtain the optimal quaternion and improve the accuracy of INS positioning system and PDR positioning system; because of the errors of adaptive integrated positioning system, the adaptive filter is proposed to correct errors and improve integrated positioning accuracy and stability. The adaptive filtering algorithm can effectively restrain the divergence problem caused by outliers. Compared to the KF algorithm, AKF algorithm can better improve the fault tolerance and precision of integrated positioning system.

The INS/PDR/UWB integrated system is built to track pedestrian position and attitude. Finally, an adaptive Kalman filter is used to improve the accuracy and stability of integrated positioning system.

The purpose of this paper is to develop an online smoothing zero-velocity-update (ZUPT) method that helps achieve smooth estimation of human foot motion for the ZUPT-aided inertial pedestrian navigation system.

The smoothing ZUPT is based on a Rauch–Tung–Striebel (RTS) smoother, using a six-state Kalman filter (KF) as the forward filter. The KF acts as an indirect filter, which allows the sensor measurement error and position error to be excluded from the error state vector, so as to reduce the modeling error and computational cost. A threshold-based strategy is exploited to verify the detected ZUPT periods, with the threshold parameter determined by a clustering algorithm. A quantitative index is proposed to give a smoothness estimate of the position data.

Experimental results show that the proposed method can improve the smoothness, robustness, efficiency and accuracy of pedestrian navigation.

Because of the chosen smoothing algorithm, a delay no longer than one gait cycle is introduced. Therefore, the proposed method is suitable for applications with soft real-time constraints.

The paper includes implications for the smooth estimation of most types of pedal locomotion that are achieved by legged motion, by using a sole foot-mounted commercial-grade inertial sensor.

This paper helps realize smooth transitions between swing and stance phases, helps enable continuous correction of navigation errors during the whole gait cycle, helps achieve robust detection of gait phases and, more importantly, requires lower computational cost.

This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.

A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.

The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.

Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

This study aims to design a new low-cost localization platform for estimating the location and orientation of a pedestrian in a building. The micro-electro-mechanical systems (MEMS) sensor error compensation and the algorithm were improved to realize the localization and altitude accuracy.

The platform hardware was designed with common low-performance and inexpensive MEMS sensors, and with a barometric altimeter employed to augment altitude measurement. The inertial navigation system (INS) – extended Kalman filter (EKF) – zero-velocity updating (ZUPT) (INS-EKF-ZUPT [IEZ])-extended methods and pedestrian dead reckoning (PDR) (IEZ + PDR) algorithm were modified and improved with altitude determined by acceleration integration height and pressure altitude. The “AND” logic with acceleration and angular rate data were presented to update the stance phases.

The new platform was tested in real three-dimensional (3D) in-building scenarios, achieved with position errors below 0.5 m for 50-m-long route in corridor and below 0.1 m on stairs. The algorithm is robust enough for both the walking motion and the fast dynamic motion.

The paper presents a new self-developed, integrated platform. The IEZ-extended methods, the modified PDR (IEZ + PDR) algorithm and “AND” logic with acceleration and angular rate data can improve the high localization and altitude accuracy. It is a great support for the increasing 3D location demand in indoor cases for universal application with ordinary sensors.

The aim of this article is to present a PIN (pedestrian inertial navigation) solution that incorporates altitude error correction, which eliminates the altitude error accurately without using external sensors. The main problem of PIN is the accumulation of positioning errors due to the drift caused by the noise in the sensors. Experiment results show that the altitude errors are significant when navigating in multilayer buildings, which always lead to localization to incorrect floors.

The PIN proposed is implemented over an inertial navigation systems (INS) framework and a foot-mounted IMU. The altitude error correction idea is identifying the most probable floor of each horizontal walking motion. To recognize gait types, the walking motion is described with angular rate measured by IMU, and the dynamic time warping algorithm is used to cope with the different dimension samples due to the randomness of walking motion. After gait recognition, the altitude estimated with INS of each horizontal walking is checked for association with one of the existing in a database.

Experiment results show that high accuracy altitude is achieved with altitude errors below 5 centimeters for upstairs and downstairs routes in a five floors building.

The main limitations of the study is the assumption that accuracy floor altitude information is available.

Our PIN system eliminates altitude errors accurately and intelligently, which benefits from the new idea of combination of gait recognition and map-matching. In addition, only one IMU is used which is different from other approach that use external sensors.

Due to the complex mechanism during walking, human gait takes plenty of information reflecting human motion. The method of quantitative measurement of gait makes a profound influence in many fields, such as clinical medicine, biped robot control strategy and so on. The purpose of this paper is to present a gait analysis system based on inertial measurement unit (IMU) and combined with body sensor network (BSN).

The authors placed two wireless inertial nodes on the left and right ankles, so that the acceleration and angular velocity could be obtained from both sides at the same time. By using the kinematical model of the human gait, many methods such as time series analysis, pattern recognition and numerical analysis, are introduced to fuse the inertial data and estimate the sagittal gait parameters.

The gait parameters evaluation gains a practical precision, especially in the gait phase detection and the process of how the two feet cooperate with each other has been analyzed to learn about the mechanism of biped walking.

The gait analysis procedure is off line, so that the system ensures sampling at a high rate.

This gait analysis system can be utilized to measure quantitative gait parameters. Further, the coordination of dual gait pattern is presented. Last but not least, the system can also be put into capturing and analyzing the motion of other parts of the body.

At present, smartphones are embedded with accelerometers, gyroscopes, magnetometers and WiFi sensors. Most researchers have delved into the use of these sensors for localization. However, there are still many problems in reducing fingerprint mismatching and fusing these positioning data. The purpose of this paper is to improve positioning accuracy by reducing fingerprint mismatching and designing a weighted fusion algorithm.

For the problem of magnetic mismatching caused by singularity fingerprint, derivative Euclidean distance uses adjacent fingerprints to eliminate the influence of singularity fingerprint. To improve the positioning accuracy and robustness of the indoor navigation system, a weighted extended Kalman filter uses a weighted factor to fuse multisensor data.

The scenes of the teaching building, study room and office building are selected to collect data to test the algorithm’s performance. Experiments show that the average positioning accuracies of the teaching building, study room and office building are 1.41 m, 1.17 m, and 1.77 m, respectively.

The algorithm proposed in this paper effectively reduces fingerprint mismatching and improve positioning accuracy by adding a weighted factor. It provides a feasible solution for indoor positioning.

The purpose of this paper is to introduce the development of an innovative mobile laser scanning (MLS) method for 3D indoor mapping. The generally accepted and used procedure for this type of mapping is usually performed using static terrestrial laser scanning (TLS) which is high-cost and time-consuming. Compared with conventional TLS, the developed method proposes a new idea with advantages of low-cost, high mobility and time saving on the implementation of a 3D indoor mapping.

This method integrates a low-cost 2D laser scanner with two indoor positioning techniques – ultra-wide band (UWB) and an inertial measurement unit (IMU), to implement a 3D MLS for reality captures from an experimental indoor environment through developed programming algorithms. In addition, a reference experiment by using conventional TLS was also conducted under the same conditions for scan result comparison to validate the feasibility of the developed method.

The findings include: preset UWB system integrated with a low-cost IMU can provide a reliable positioning method for indoor environment; scan results from a portable 2D laser scanner integrated with a motion trajectory from the IMU/UWB positioning approach is able to generate a 3D point cloud based in an indoor environment; and the limitations on hardware, accuracy, automation and the positioning approach are also summarized in this study.

As the main advantage of the developed method is low-cost, it may limit the automation of the method due to the consideration of the cost control. Robotic carriers and higher-performance 2D laser scanners can be applied to realize panoramic and higher-quality scan results for improvements of the method.

Moreover, during the practical application, the UWB system can be disturbed by variances of the indoor environment, which can affect the positioning accuracy in practice. More advanced algorithms are also needed to optimize the automatic data processing for reducing errors caused by manual operations.

The development of this MLS method provides a novel idea that integrates data from heterogeneous systems or sensors to realize a practical aim of indoor mapping, and meanwhile promote the current laser scanning technology to a lower-cost, more flexible, more portable and less time-consuming trend.

Aiding information is frequently adopted to calibrate the errors from inertia-generated trajectories in pedestrian positioning. However, existing calibration methods lack interior connections and unanimity, making it difficult to incorporate multiple sources of aiding information. This paper aims to propose a unanimous anchor-based trajectory calibration framework, which is expandable to encompass different types of anchor information.

The concept of anchors is introduced to represent different types of aiding information, which are, in essence, different constraint conditions on inertia-derived raw trajectories. The foundation of the framework is a particle filter which is implemented based on various particle weight updating strategies using diverse types of anchor information. Herein, three representative anchors are chosen to elaborate and validate the proposed framework, namely, ultra-wide-band (UWB) ranging anchors, iBeacons and the building structure-based virtual anchors.

In the simulations, with the particle reweighting strategies of the proposed framework, the positioning errors can be compensated. In the experimental test in an office building in which three anchors, including one UWB anchor, one iBeacon and one building structure-based virtual anchor are deployed; the final positioning error is decreased from 1.9 to 1.2 m; and the heading error is reduced from about 21° to 7°, respectively.

Herein, an anchor-based unanimous trajectory calibration framework for inertial pedestrian positioning is proposed. This framework is applicable to the schemes with different configurations of the anchors and can be expanded to adopt as much anchor information as possible.

In spite of the vehicle, magnetic field interference can be reduced by some measures and techniques in ammunition design and manufacturing stage, the corruption of the vehicle magnetic field can still reach hundreds to thousands of nanoteslas. Besides, the magnetic field that the ferromagnetic materials generate in response to the strong magnetic field in the vicinity of the body. So, a real-time and accurate vehicle magnetic field calibration method is needed to improve the real-time measurement accuracy of the geomagnetic field for spinning projectiles.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time. In the method, the elliptical model of magnetometer measurement is established to convert the coefficients of hard and soft iron errors into the parameters of the elliptic equation. Then, the parameters are estimated by recursive least square estimator in real-time. Finally, the initial conditions for the estimator are established using prior knowledge method or static calibration method.

Studies show the proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real-time. A turntable experiments indicate that the post-calibration residuals approximate the measurement noise of the magnetometer and the roll accuracy is better than 1°. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers' calibration in real-time.

Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time and the calculation is small. Besides, it does not take up storage space. The proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real time. The algorithm is restricted to biaxial magnetometers’ calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method’s applicability to triaxial magnetometers’ calibration in real-time.