Zero Velocity Interval Research Articles

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array.

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%.

A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial Sensors

A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial Sensors

Nonlinear Control of Magnetically Coupled Rodless Cylinder Position Servo System

Magnetically coupled rodless cylinders are widely used in the coordinate positioning of mechanical arms, electrostatic paintings, and other industrial applications. However, they exhibit strong nonlinear characteristics, which lead to low servo control accuracy. In this study, a mass-flow equation through the valve port was derived to improve the control performance, considering the characteristics of the dynamics and throttle-hole flow. Subsequently, a friction model combining static, viscous, and Coulomb friction with a zero-velocity interval was proposed. In addition, energy and dynamic models were set for the experimental investigation of the magnetically coupled rodless cylinder. A nonlinear mathematical model for the position of the magnetically coupled rodless cylinder was proposed. An incremental PID controller was designed for the magnetically coupled rodless cylinder to control this system, and the PID parameters were adjusted online using RBF neural network. The response results of the PID parameters based on the RBF neural network were compared with those of the traditional incremental PID control, which proved the superiority of the optimization control algorithm of the incremental PID parameters based on the RBF neural network servo control system. The experimental results of this model were compared with the simulation results. The average error between the established model and the actual system was 0.005175054 (m), which was approximately 2.588% of the total travel length, demonstrating the accuracy of the theoretical model.

An adaptive threshold method with error correction for pedestrian inertial navigation system

As an effective method for reducing accumulated errors in pedestrian navigation systems based on foot-mounted inertial measurement unit, the zero velocity update is widely used in global navigation satellite system signal rejection environments that require accurate zero velocity detection. However, the detection accuracy of the classical fixed threshold method is not satisfactory under complex gaits. In this paper, an adaptive threshold method with error correction is proposed to increase the detection accuracy without adding sensors. In this method, an adaptive threshold model based on gait intensity, which can dynamically adjust the zero velocity detection threshold under complex gaits, is established. Afterward, error correction is suggested to identify and correct misjudgments in zero velocity detection by analyzing the zero velocity interval in one gait cycle. We build an actual pedestrian inertial navigation system to evaluate the proposed method. The experimental results demonstrate that the average zero velocity detection accuracy of the proposed method under complex gaits of different people is 98.67%, and the end-to-end positioning error is about 0.53% of the total distance during long-distance movement.

Zero-Velocity Detection Method Based on Ultrasonic Ranging Assistance

The accumulated error in inertial pedestrian navigation can be transplanted through the zero-velocity correction algorithm, and the zero-velocity detection algorithm at this stage has the problems of single threshold, easy to miss detection and false detection, large calculation amount and large calculation delay. In order to solve these problems, an ultrasonic ranging auxiliary zero-velocity detection method is proposed. The foot spacing is measured in real time by an ultrasonic sensor placed on both feet, and then a time constraint is added to the Generalized Likelihood Ratio (GLRT) method using the time relationship between the shortest foot spacing and the zero-velocity interval. Experiments show that the accuracy of the proposed algorithm is better than that of the single threshold method under different gait states, and the average accuracy is above 95%. And due to the characteristics of high ultrasonic short-distance accuracy and small size, this method has simple calculation and small delay, which has practical application value.

Free-walking: Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system (INS), which is frequently used in emergency rescue operations and other situations, has the benefits of not relying on infrastructure, high positioning frequency, and strong real-time performance. However, the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time. This paper aims to enhance the accuracy of zero-velocity interval (ZVI) detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet. Aiming at the observational noise problem of low-cost inertial sensors, we utilize a denoising autoencoder to automatically eliminate the inherent noise. Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error, we propose a sample-level ZVI detection algorithm based on the U-Net neural network, which effectively solves the problem of mislabeling caused by sliding windows. Aiming at the problem that Zero-Velocity Update (ZUPT) cannot suppress heading and altitude error, we propose a bipedal INS method based on the equation constraint and ellipsoid constraint, which uses foot-to-foot distance as a new observation to correct heading and altitude error. We conduct extensive and well-designed experiments to evaluate the performance of the proposed method. The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

Pitch angle sliding variance test method based on Mahony filter for zero-velocity detection

The threshold judgment method is a comparatively simple and classic zero-velocity detection (ZVDT) method for zero-velocity update. To solve the problem with the existing threshold judgment method, where the ZVDT effect is critically diminished by sensor noise, motion state changes, and other factors, this paper proposes a pitch angle sliding variance test (PSVT) method based on Mahony filtering. First, a Mahony filtering algorithm is designed to merge and de-noise the original foot acceleration and angular velocity information to provide high-precision pitch angle information. Subsequently, based on the significant periodicity, isolation, and characteristics of foot pitch angle variation during pedestrian movement, a PSVT method is proposed for pedestrian ZVDT. Experimental results show that for the motions of different pedestrians in different scenes, the average single-step misjudgment rate in the zero-velocity interval of the proposed method is less than 10% overall. When compared with the generalized likelihood ratio test, the ZVDT effect is improved by approximately 30%. The method effectively improves the applicability and accuracy of ZVDT for pedestrian movement, and will be significant in improving the applicability, navigational accuracy, and practical use of pedestrian autonomous navigation systems.

New Zero-Velocity Detector for Low-Cost Inertial Pedestrian Navigation

A new Zero Velocity Interval (ZVI) detector is proposed and investigated in this article to augment the computation of a pedestrian’s position. In a low-cost pedestrian navigation system, the position of a pedestrian can be computed by using inertial navigation algorithms. The low-cost pedestrian navigation system employs an inertial measurement unit (IMU) comprised of accelerometer and gyroscope sensors to record acceleration and attitude rate. These measurements, when integrated mathematically, yield velocity and position. Similarly, attitude rate changes to attitude. The algorithm will then be able to figure out the changes in pedestrian position with the proper attitude. However, due to its low-cost nature, these sensors are built in such a way that their measurements are easily corrupted by noise, and once integrated mathematically, the measurement error grows exponentially. Zero Velocity Update (ZUPT) algorithm is frequently used to limit these errors. It works by detecting the ZVI that occur when the foot is on the ground. Assuming that the foot on the ground should have zero velocity, any remaining velocity measurements detected during this period are considered an error and are fed back to the navigation algorithm for correction. To detect the ZVI, a few commonly used detectors, namely Angular Rate Energy (ARE), Acceleration Moving Variance (MV), Acceleration Magnitude (MAG), and Generalized Likehood Ratio Test (GLRT) were revisited. These detectors were tested with real-world datasets of walking pedestrians. Then, a new detector, the Angular Rate Moving Variance (ARMV) detector, is proposed, and its performance is compared to that of the existing detectors.

Symmetrical-Net: Adaptive Zero Velocity Detection for ZUPT-Aided Pedestrian Navigation System

Inertial navigation system (INS) is a practical method for indoor pedestrian navigation without pre-installation of infrastructure. Based on the fundamentals of human bipedal motion, zero velocity update (ZUPT) is a pervasive approach to tackle the accumulated error of inertial measurement units (IMU). While zero velocity detection plays a vital role in the algorithm, existing fixed-threshold methods to pick these pseudo-measurements of error-state Kalman Filter (ESKF) have the doubtful capability to fit various individuals in different motions. To address this issue, we propose the Symmetrical-Net leveraging deep Recurrent Convolutional Neural Networks (RCNNs) to detect the zero velocity interval adaptively. Additionally, two RCNNs are constructed in the symmetrical framework considering bidirectional IMU readings, which further improves the performance of the model. A comprehensive evaluation containing 87 different trajectories from 27 individuals has been conducted. The results show that the detection accuracy is up to 99.5% and 96.5% on the training and validation sets, respectively. It is verified that the precise and robust Symmetrical-Net can be a viable approach for the ZUPT-aided INS system.

An Adaptive Zero-Velocity Interval Detector Using Instep-Mounted Inertial Measurement Unit

The zero-velocity update (ZUPT) technique plays a key role in foot-mounted inertial navigation system, which can minimize the drift errors of low-cost inertial measurement unit (IMU) by resetting the velocity to zero when the foot is stationary. However, it is difficult to detect the zero-velocity interval (ZVI) in mixed locomotion patterns. This article presents a novel ZVI detector based on adaptive simulated energy consumption (SEC) curve. It can identify the ZVIs of various locomotion patterns, such as normal walking, fast walking, running, going upstairs, and downstairs. The SEC curve is generated based on the signal processing approach using the interlaced peak property of the IMU-measured angular rate and acceleration waveforms. Another benefit of the proposed method is that the IMU can be mounted on the instep without restrictions of orientation and position, which improves flexibility and convenience. Experiment results show that the proposed algorithm performs better than three other widely used methods in mixed gait patterns.

An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

The specific objective of this study is to propose a low‐cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT‐aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT‐aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real‐world scenarios.

Lower Limb Model Based Inertial Indoor Pedestrian Navigation System for Walking and Running

Foot-mounted inertial navigation systems are suitable in GNSS denied environment for special tasks such as fire rescue, soldier, etc. These tasks are often accompanied by vigorous movements such as running and jumping, which is a challenge for the Zero Velocity Update (ZUPT) based inertial navigation system. As the speed increases, the inertial sensor drift increases, while the zero-velocity interval shortens, making the gait detection more difficult and resulting in a limited error correction. And as the motion intensifies, the stability of the system decreases. A multi-node inertial pedestrian navigation system based on the lower limb model constraint is proposed in this paper. Our first contribution in this paper is studying a simplified adaptive zero-bias estimation algorithm. The changes in velocity and angular velocity between two steps are used to estimate and compensate for the drift of accelerometers and gyroscopes. Our second contribution is proposing a novel gait segmentation method by lower limb motion statistics feature. A hybrid detection model of dual IMU is conducted to improve the accuracy of gait recognition. After that, the maximum distance inequality constraint is studied to correct the position error when the still phase is not available in vigorous movement. The final contribution is studying the chi-square fault detection method to improve the stability of KF. Multiple experiments prove that the proposed multi-node inertial pedestrian navigation system is effective in dealing with various challenging in walking, running, and hybrid motion pedestrian inertial navigation problems which could achieve less than 0.3% average distance error.

A Novel Adaptive Zero-Velocity Detector for Inertial Pedestrian Navigation Based on Optimal Interval Estimation

In the foot-mounted inertial pedestrian navigation system, the zero-velocity update (ZUPT) algorithm is an efficient way to bound the inertial error propagation. Therefore, a reliable and accurate zero-velocity detector (ZVD) that adapts to all kinds of locomotion and scenarios plays a vital role in achieving high-precision and long-term pedestrian navigation. The classical threshold-based ZVDs are susceptible to failures during dynamic locomotion due to the fixed threshold. Recent machine-learning-based ZVDs need a huge amount of data to support the model training and their generalization is limited in new testing scenarios. In this paper, we propose a novel adaptive ZVD using the optimal interval estimation. Two filters are used to process the angular rate, aiming at determining a gait cycle. In a gait cycle, the acceleration is mapped to the search space by a special convex function. Based on the features of the data in the search space, a zero-velocity benchmark is calculated for the following interval estimation. The zero-velocity benchmark and the hierarchical iterative search are used to estimate the optimal zero-velocity interval (ZVI). The experiments demonstrate the effectiveness and adaptability of this novel ZVD.

A zero-velocity detection method with transformation on generalized likelihood ratio statistical curve

MEMS-SINS (Micro-Electro-Mechanical System – Strapdown Inertial navigation System) is always used for pedestrian navigation. But MEMS-SINS cannot fulfill navigation task independently for its low precision gyros and accelerometers, thus ZUPT (Zero-Velocity-Updating) should be introduced. ZUPT uses zeros-velocity when the foot contacting the ground as observation information aiding MEMS-SINS. Zero-velocity detection is a key and difficult technology. Currently, zero-velocity detection methods use the output of accelerometers or (and) gyros from MEMS-IMU (Inertial Measurement Unit) as measurement and compare those measurement with given threshold to judge zero-velocity constraints occur or not. Due to the diversity of pedestrian gaits and low signal-to-noise ratio of MEMS-IMU data, zero-velocity detection methods based on threshold detection own inherent shortcomings such as poor environmental adaptability and inconvenient usage. To overcome these, in this paper ideas using threshold detection are discarded, characteristics of pedestrians gaits is detailedly analyzed and two characters – zero-velocity interval must be located at the valley bottom of the generalized likelihood ratio statistical curves but the bottom of the curve is not necessarily zero-velocity interval – are concluded, thus a novel detecting method is designed. With proposed method, transformations including translation and symmetrical flip on generalized likelihood ratio statistical curves of MEMS-IMU are data are used to detect true zero-velocity interval and discard interfering curve bottoms. Theoretical analysis and experimental results show that the proposed method can detect zero-velocity occurring effectively without any given threshold and detecting rate on different groups of people and different velocity is 100%.

Research on the Forward and Reverse Calculation Based on the Adaptive Zero-Velocity Interval Adjustment for the Foot-Mounted Inertial Pedestrian-Positioning System.

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.

Zero velocity interval detection based on a continuous hidden Markov model in micro inertial pedestrian navigation

Shoe-mounted pedestrian navigation systems based on micro inertial sensors rely on zero velocity updates to correct their positioning errors in time, which effectively makes determining the zero velocity interval play a key role during normal walking. However, as walking gaits are complicated, and vary from person to person, it is difficult to detect walking gaits with a fixed threshold method. This paper proposes a pedestrian gait classification method based on a hidden Markov model. Pedestrian gait data are collected with a micro inertial measurement unit installed at the instep. On the basis of analyzing the characteristics of the pedestrian walk, a single direction angular rate gyro output is used to classify gait features. The angular rate data are modeled into a univariate Gaussian mixture model with three components, and a four-state left–right continuous hidden Markov model (CHMM) is designed to classify the normal walking gait. The model parameters are trained and optimized using the Baum–Welch algorithm and then the sliding window Viterbi algorithm is used to decode the gait. Walking data are collected through eight subjects walking along the same route at three different speeds; the leave-one-subject-out cross validation method is conducted to test the model. Experimental results show that the proposed algorithm can accurately detect different walking gaits of zero velocity interval. The location experiment shows that the precision of CHMM-based pedestrian navigation improved by 40% when compared to the angular rate threshold method.

Pedestrian navigation system using MEMS sensors for heading drift and altitude error correction

PurposeThis 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.Design/methodology/approachA 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.FindingsThe 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.Originality/valueBesides 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.

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors.

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance.