The Improved 3D Pedestrian Positioning System Based on Foot-Mounted Inertial Sensor

Autonomous driving relies on reliable localization, but the Global Navigation Satellite System (GNSS) often loses accuracy or becomes unavailable in harsh environments, such as tunnels and dense urban canyons. Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM), which fuses camera and Inertial Measurement Unit (IMU) data, is a key alternative in GNSS-denied scenarios. However most VI-SLAM methods overlook vehicles' inherent periodic motion features, such as zero velocity during stops or gear shifts, and linear motion during cruising. To address this, this paper proposes a visual-inertial localization method integrating these features into a tightly coupled VI-SLAM framework. First, based on IMU data, the Generalized Likelihood Ratio Test is used to detect zero velocity and linear motion. Then, Zero Velocity Update (ZUPT) residuals and Nonholonomic Constraint (NHC) residuals are constructed. These two residuals are jointly optimized with visual residuals, IMU pre-integration residuals, and marginalization residuals within a sliding-window nonlinear backend for Visual-Inertial Odometry. Experiments on self-collected datasets show that compared to the baseline algorithm, both the ZUPT-integrated algorithm and the NHC-integrated algorithm yield higher accuracy, lower error variance, and stronger robustness.

Pedestrian-positioning technology based on the foot-mounted micro inertial measurement unit (MIMU) plays an important role in the field of indoor navigation and has received extensive attention in recent years. However, the positioning accuracy of the inertial-based pedestrian-positioning method is rapidly reduced because of the relatively low measurement accuracy of the measurement sensor. The zero-velocity update (ZUPT) is an error correction method which was proposed to solve the cumulative error because, on a regular basis, the foot is stationary during the ordinary gait; this is intended to reduce the position error growth of the system. However, the traditional ZUPT has poor performance because the time of foot touchdown is short when the pedestrians move faster, which decreases the positioning accuracy. Considering these problems, a forward and reverse calculation method based on the adaptive zero-velocity interval adjustment for the foot-mounted MIMU location method is proposed in this paper. To solve the inaccuracy of the zero-velocity interval detector during fast pedestrian movement where the contact time of the foot on the ground is short, an adaptive zero-velocity interval detection algorithm based on fuzzy logic reasoning is presented in this paper. In addition, to improve the effectiveness of the ZUPT algorithm, forward and reverse multiple solutions are presented. Finally, with the basic principles and derivation process of this method, the MTi-G710 produced by the XSENS company is used to complete the test. The experimental results verify the correctness and applicability of the proposed method.

For indoor pedestrian navigation with a shoe-mounted inertial measurement unit (IMU), the zero velocity update (ZUPT) technique is implemented to constrain the sensors' error. ZUPT uses the fact that a stance phase appears in each step at zero velocity to correct IMU errors periodically. This paper introduces three main contributions we have achieved based on ZUPT. Since correct stance phase detection is critical for the success of applying ZUPT, we have developed a new approach to detect the stance phase of different gait styles, including walking, running and stair climbing. As the extension of ZUPT, we have proposed a new concept called constant velocity update (CUPT) to correct IMU errors on a moving platform with constant velocity, such as elevators or escalators where ZUPT is infeasible. A closed-loop step-wise smoothing algorithm has also been developed to eliminate discontinuities in the trajectory caused by sharp corrections. Experimental results demonstrate the effectiveness of the proposed algorithms.

Pedestrian Inertial Navigation System is of great significance in the fields of smart city services, emergency assistance, home health supervision, etc. However, the wearable Micro Inertial Measurement Unit (MIMU) contains relatively high noise and the error of MIMU accumulates with time. This paper proposes a method of pedestrian inertial navigation assisted by geomagnetic matching positioning. According to the measurement of pedestrian inertial navigation based on zero velocity update (PINS-ZUPT) and the geomagnetic matching positioning, the system model of the PINS-ZUPT/Geomagnetic matching navigation with the Kalman Filtering (KF) working in the zero-velocity interval is established in this paper. The positioning errors and velocity errors are gained from the Iterative Closet Contour Point (ICCP) algorithm of the geomagnetic matching positioning and the zero-velocity information respectively, which can be used as the observation values of KF to obtain the optimal pedestrian trajectory and correct accumulated errors. The experiment shows that the average positioning error of integrated navigation is within 1m under the mileage of 140m, reduced by more than 50% relative to PINS-ZUPT, which verifies the effectiveness of the integrated navigation.

For the reliability of micro inertial measurement unit (MIMU), the failure mode analysis and random vibration test are carried out. Firstly, based on the structure and function analysis of MIMU, the MIMU failure mode, effects and criticality analysis (FMECA) which is carried out to obtain the key failure modes, reliability critical parts and weak parts of MIMU. Secondly, The MIMU vibration step test with a wide-band (15Ηz-2ΚHz) is designed and carried out. It can be concluded that when the environmental stress is overloaded, the MIMU will have sudden failures such as information calculation errors. Under normal stress, gyroscope performance degradation and accelerometer performance degradation are the main failure modes of MIMU. The research can provide reference for MIMU reliability design and evaluation.

With the rapid development of computer technology and communication technology, pedestrian navigation system(PNS) began to enter the real practical stage. PNS is a method of track reckoning based on strapdown inertial navigation system. However, inertial navigation system is prone to error accumulation with time, resulting in low positioning accuracy and practicability. Low cost inertial measurement unit(IMU) device has low accuracy, and the inertial navigation system has accumulated error, and the system is easy to diverge. Apart from those above, civil GPS accuracy is not stable, and poor GPS signal in a multipath environment may lead to errors, and GPS signals can not be used indoors. It is necessary to design a kind of navigation method to adapt low cost IMU. In this paper, it is presented that a foot inertial navigation system which is based on low cost MEMS inertial sensors is presented. According to the characteristics of pedestrian zero-velocity during walking steady state, an inertial navigation method based on Zero Velocity Update (ZUPT) is adopted. This paper uses air pressure, GPS to assist ZUPT method to correcting navigation parameters through Kalman filter. Based on the implementation scheme of pedestrian navigation system proposed in this paper, the steady state of walking were experimentally verified through the actual test data. The experimental results show that the proposed method can provide efficient, reliable and continuous position and navigation services.

With the development of the microelectronic mechanical system (MEMS) inertial measurement units (IMUs), the foot-mounted pedestrian inertial navigation system (PINS) based on the zero velocity update is widely used. The systematic errors of MEMS IMU cause low precision of PINS at the swing phase. However, the biomechanical constraints, which can be used to suppress the divergence of PINS, exist in lower limbs. In this study, three magnetometers and IMUs are fixed on the toe, heel, and shank, respectively. The attitude of each segment is estimated by a MAHONY filter. The gait is defined as four successive phases. For each phase, the velocity and position constraints are constructed based on dynamic geometric constraint and Coriolis theory. Based on the perfect measurement method, the kinematic constraints, which are calculated by inertial data and attitude, are added into the observed equations. The noises are considered additive Gaussian white noise. The linear Kalman filter is set to iteratively estimate the optimal positioning result. Several experiments with different walking velocities and trajectories are performed. The results show that the algorithm, which is proposed in this study, greatly suppresses the velocity and positioning error.

This paper introduces principal of micro inertial measurement unit (MIMU), designs a arrange form of MIMU. According to lots of experiment data, the gyroscope's drift and accelerometer's warp are analyzed, and provides an AR(1) model. After Kalman filtering, the simulation results show that the bias of MIMU reduces to one tenth of the original, and prove this methodological feasible.

Spectrum sensing exploiting polarization has been identified as an effective method for improving detection performance. However, the optimal polarization-based sensing algorithm with an upper bound performance has yet to be developed. The polarization likelihood ratio test (PLRT) is derived using the Neyman–Pearson theorem, resulting in an optimal detector with regard to the likelihood ratio. The best linear polarization processor or filter, i.e., the polarization matched filter (PMF), which processes the observed polarization data to provide the maximum signal-to-noise ratio (SNR) to the output of the filter, is also derived. Although the PLRT and the PMF provide an ideal upper bound on the performance of all detection methods that exploit polarization when likelihood functions or statistic parameters of likelihood functions are exactly known, their performance for realistic blind sensing remains unknown. Using the generalized likelihood ratio test (GLRT) method, we formulate two practical polarization-based detectors, i.e., GLRT based on received electric-field vector (GLRT-EV) and GLRT of the Stokes subvector, according to the statistical distribution of observed data expressed as a received electric-field vector and a Stokes vector, respectively. The two GLRT detectors can serve as a practical bound when all statistical parameters are unknown. Theoretical and numerical simulation results of the detection performance for the proposed detectors are presented. Additionally, these polarization-based detectors were thoroughly tested via a realistic wireless regional area network. Our experimental results show that the GLRT-EV detector exhibits better performance than other polarization-based detectors in terms of sample complexity and computational complexity, which is particularly critical in real-time applications.

Progress has been made enabling expensive, high-end inertial measurement units (IMUs) to be used as tracking sensors. However, the cost of these IMUs is prohibitive to their widespread use, and hence the potential of low-cost IMUs is investigated in this study. A wearable low-cost sensing system consisting of IMUs and ultrasound sensors was developed. Core to this system is an extended Kalman filter (EKF), which provides both zero-velocity updates (ZUPTs) and Heuristic Drift Reduction (HDR). The IMU data was combined with ultrasound range measurements to improve accuracy. When a map of the environment was available, a particle filter was used to impose constraints on the possible user motions. The system was therefore composed of three subsystems: IMUs, ultrasound sensors, and a particle filter. A Vicon motion capture system was used to provide ground truth information, enabling validation of the sensing system. Using only the IMU, the system showed loop misclosure errors of 1% with a maximum error of 4–5% during walking. The addition of the ultrasound sensors resulted in a 15% reduction in the total accumulated error. Lastly, the particle filter was capable of providing noticeable corrections, which could keep the tracking error below 2% after the first few steps.

Zero velocity update (ZUPT) is an inexpensive and effective method for error control of inertial navigation system (INS). Detecting the stopping time interval is important for ZUPT methods. The ZUPT method based on Kalman filter has a higher degree of precision compared with the ZUPT method based on curve fitting and the ZUPT method based on maximum likelihood estimation, but the modeling of Kalman filter is critical for the performance of INS. In this paper, three kinds of zero-velocity detectors and a linear discrete Kalman filter model are applied in ZUPT for INS. Simulation results show that the three kinds of zero-velocity detectors are effective, and the ZUPT method based on the linear discrete Kalman filter model makes the positioning accuracy of INS significantly improved.

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

In the paper, the low-frequency part of the random-drift error on micro inertial measurement unit (MIMU) is modeled. In order to solve the problem of high-frequency interference and ensure the original correlation of the samples, the wavelet soft-threshold method is used to remove high-frequency noise and the autoregressive integrated moving average model (ARIMA), which is more complex than the first-order Markov, is used to describe the slow-drift of the random-drift error. Finally, the model is applied to Kalman filter to smooth, filter and predict the MIMU measurement data. The experimental results show that the model has good dynamic tracking performance, the model better reflects the drift process of MIMU random error, and improves the measurement accuracy of micro-inertial devices.

Integration is a current trend in navigation system research. MIMU (Micro Inertial Measurement Unit) is compact, cheap, low-power, and highly reliable. BD (BeiDou System) is a satellite navigation system with our own independent intellectual property right. Integrating MIMU with BD can lessen MIMU's error accumulation and BD's low robustness, thus making MIMU/BD an ideal integrated navigation system. However, MIMU/BD has poor heading error observability. A three-axis magnetometer (TAM) can measure earth's magnetic field to obtain carrier heading. This paper focuses on improving heading accuracy of MIMU/BD integrated navigation system with TAM aided. TAMs are used to calculate the heading angle. Calibrating TAMs using a RLS algorithm with a forgetting factor is introduced. An integrated navigation algorithm based on Kalman Filter is used. Vehicle experiments show that this method efficiently improves the heading accuracy.

In this paper, an algorithm of 3-D single observer passive location for fixed emitters based on the extended kalman particle filter (EPF) using the azimuth angle and its changing rate is proposed. EPF is a method of particle filter (PF) using the extended Kalman filter (EKF) as the importance function. The method obtains target emitter's position by the principle of kinematic geometry. The raw location results are rectified and smoothed by EPF algorithm to realize fast and high precision passive target's location. Simulation results show that the performance of algorithm based on EPF is better than EKF's has faster and steady convergence speed, better filtering effect and better location precision.