Foot-Mounted Pedestrian Navigation Method by Comparing ADR and Modified ZUPT Based on MEMS IMU Array

Using an MEMS Inertial Measurement Unit (MEMS IMU) array mounted on foot is a feasible approach to improve the pedestrian tracking accuracy for the pedestrian navigation system (PNS). Based on the in-house developed IMU array, the paper proposes a new integrated framework that combines adaptive deck reckoning (ADR) with the modified zero velocity update (ZUPT). In the proposed ADR algorithm, the IMUs with large drift errors on the array are selected and removed according to the step length and the track angle computed by each IMU on the array. Then, by using the step length and the track angle of each step computed by remaining IMUs, the foot position extracted from the traditional ZUPT model is estimated on the basis of least squares (LS) so as to improve the traveled distance calculation accuracy. Compared with the traditional IMU array fusion method based on a maximum likelihood estimator (MLE) when it is used in the PNS, which is approximately taking the mean value of array readings, the proposed method is equivalent to adaptively fusing the array readings and thus improves the pedestrian tracking accuracy. To compare the proposed method with MLE, two different types of walking tracks are designed. The 161 m straight line experiments show that the end position by ADR/modified ZUPT method is much closer to the one of the reference trajectory compared with the MLE in repeated walks, and the closed-loop tracks about 300 m show that the positioning error with respect to the total traveled distance is less than 0.6% (1σ), which is higher than 1% (1σ) of MLE.

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Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽

10.3390/s19020364 ◽

2019 ◽

Vol 19 (2) ◽

pp. 364 ◽

Cited By ~ 5

Author(s):

Ming Xia ◽

Chundi Xiu ◽

Dongkai Yang ◽

Li Wang

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Navigation System ◽

Low Cost ◽

Ultrasonic Sensor ◽

Measurement Unit ◽

Positioning Error ◽

Process Noise ◽

Pedestrian Navigation ◽

Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

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Infrastructure-Free Indoor Pedestrian Tracking with Smartphone Acoustic-Based Enhancement

Sensors ◽

10.3390/s19112458 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2458 ◽

Cited By ~ 1

Author(s):

Chao Liu ◽

Sining Jiang ◽

Shuo Zhao ◽

Zhongwen Guo

Keyword(s):

Doppler Effect ◽

Location Estimation ◽

Location Based Services ◽

Estimation Accuracy ◽

Measurement Unit ◽

Average Error ◽

Pedestrian Tracking ◽

Tracking Accuracy ◽

Acoustic Frequency ◽

Indoor Pedestrian Tracking

Indoor pedestrian tracking has been identified as a key technology for indoor location-based services such as emergency locating, advertising, and gaming. However, existing smartphone-based approaches to pedestrian tracking in indoor environments have various limitations including a high cost of infrastructure constructing, labor-intensive fingerprint collection, and a vulnerability to moving obstacles. Moreover, our empirical study reveals that the accuracy of indoor locations estimated by a smartphone Inertial Measurement Unit (IMU) decreases severely when the pedestrian is arbitrarily wandering with an unstable speed. To improve the indoor tracking performance by enhancing the location estimation accuracy, we exploit smartphone-based acoustic techniques and propose an infrastructure-free indoor pedestrian tracking approach, called iIPT. The novelty of iIPT lies in the pedestrian speed reliability metric, which characterizes the reliability of the pedestrian speed provided by the smartphone IMU, and in a speed enhancing method, where we adjust a relatively less reliable pedestrian speed to the more reliable speed of a passing by “enhancer” based on the acoustic Doppler effect. iIPT thus changes the encountered pedestrians from an“obstacle” into an “enhancer.” Extensive real-world experiments in indoor scenarios have been conducted to verify the feasibility of realizing the acoustic Doppler effect between smartphones and to identify the applicable acoustic frequency range and transmission distance while reducing battery consumption. The experiment results demonstrate that iIPT can largely improve the tracking accuracy and decrease the average error compared with a conventional IMU-based method.

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Biomechanics of Trail Running Performance: Quantification of Spatio-Temporal Parameters by Using Low Cost Sensors in Ecological Conditions

Applied Sciences ◽

10.3390/app11052093 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2093

Author(s):

Noé Perrotin ◽

Nicolas Gardan ◽

Arnaud Lesprillier ◽

Clément Le Goff ◽

Jean-Marc Seigneur ◽

...

Keyword(s):

Heart Rate ◽

Low Cost ◽

Step Length ◽

Measurement Unit ◽

Ecological Conditions ◽

Sports Science ◽

Ground Contact Time ◽

Spatio Temporal ◽

Performance Results ◽

Trail Running

The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10km. The three sensors used were (i) a Stryd sensor (Stryd Inc. Boulder CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.

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Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0038 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Svenja Ipsen ◽

Sven Böttger ◽

Holger Schwegmann ◽

Floris Ernst

Keyword(s):

Target Tracking ◽

Real Time ◽

Template Matching ◽

Image Data ◽

Distinct Advantage ◽

Reference Trajectory ◽

Data Streaming ◽

Tracking Accuracy ◽

Time Data ◽

4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

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Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation

Symmetry ◽

10.3390/sym13020266 ◽

2021 ◽

Vol 13 (2) ◽

pp. 266 ◽

Cited By ~ 1

Author(s):

Yifeng Wang ◽

Zhijiang Zhang ◽

Ning Zhang ◽

Dan Zeng

Keyword(s):

Object Tracking ◽

Multiple Object Tracking ◽

Tracking Accuracy ◽

Feature Maps ◽

Backbone Networks ◽

Multiple Object ◽

The Arts ◽

Training Stage ◽

The One ◽

Baseline State

The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs).

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Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018

Advances in Atmospheric Sciences ◽

10.1007/s00376-021-0136-9 ◽

2021 ◽

Author(s):

Xiaoyi Shen ◽

Chang-Qing Ke ◽

Bin Cheng ◽

Wentao Xia ◽

Mengmeng Li ◽

...

Keyword(s):

Sea Ice ◽

Ice Cover ◽

Reanalysis Data ◽

Mean Value ◽

Ocean Model ◽

North Coast ◽

Average Value ◽

The North ◽

Wind Sea ◽

The One

AbstractIn August 2018, a remarkable polynya was observed off the north coast of Greenland, a perennial ice zone where thick sea ice cover persists. In order to investigate the formation process of this polynya, satellite observations, a coupled ice-ocean model, ocean profiling data, and atmosphere reanalysis data were applied. We found that the thinnest sea ice cover in August since 1978 (mean value of 1.1 m, compared to the average value of 2.8 m during 1978–2017) and the modest southerly wind caused by a positive North Atlantic Oscillation (mean value of 0.82, compared to the climatological value of −0.02) were responsible for the formation and maintenance of this polynya. The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind. Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.

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A pedestrian navigation system based on MEMS inertial measurement unit

2016 35th Chinese Control Conference (CCC) ◽

10.1109/chicc.2016.7554183 ◽

2016 ◽

Cited By ~ 3

Author(s):

Xiaochun Tian ◽

Jiabin Chen ◽

Yongqiang Han ◽

Jianyu Shang ◽

Nan Li

Keyword(s):

Navigation System ◽

Inertial Measurement Unit ◽

Measurement Unit ◽

Inertial Measurement ◽

Pedestrian Navigation

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MEMS Based SINS/OD Filter for Land Vehicles’ Applications

Mathematical Problems in Engineering ◽

10.1155/2017/1691320 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Huisheng Liu ◽

Zengcai Wang ◽

Susu Fang ◽

Chao Li

Keyword(s):

Navigation System ◽

Velocity Measurement ◽

Microelectromechanical Systems ◽

Low Cost ◽

Detection Algorithm ◽

Measurement Unit ◽

Integrated Navigation ◽

Integrated Navigation System ◽

Land Vehicles ◽

Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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Calculation of Nonlinear Systems Under Narrow Band Excitation Using Equivalent Linearization and Path Continuation

10.1115/gt2021-58437 ◽

2021 ◽

Author(s):

Alwin Förster ◽

Lars Panning-von Scheidt

Keyword(s):

Nonlinear Systems ◽

Narrow Band ◽

Gaussian White Noise ◽

Random Excitation ◽

Mean Value ◽

Equivalent Linearization ◽

Efficient Procedure ◽

Stochastic Excitations ◽

Wide Range ◽

The One

Abstract Turbomachines experience a wide range of different types of excitation during operation. On the structural mechanics side, periodic or even harmonic excitations are usually assumed. For this type of excitation there are a variety of methods, both for linear and nonlinear systems. Stochastic excitation, whether in the form of Gaussian white noise or narrow band excitation, is rarely considered. As in the deterministic case, the calculations of the vibrational behavior due to stochastic excitations are even more complicated by nonlinearities, which can either be unintentionally present in the system or can be used intentionally for vibration mitigation. Regardless the origin of the nonlinearity, there are some methods in the literature, which are suitable for the calculation of the vibration response of nonlinear systems under random excitation. In this paper, the method of equivalent linearization is used to determine a linear equivalent system, whose response can be calculated instead of the one of the nonlinear system. The method is applied to different multi-degree of freedom nonlinear systems that experience narrow band random excitation, including an academic turbine blade model. In order to identify multiple and possibly ambiguous solutions, an efficient procedure is shown to integrate the mentioned method into a path continuation scheme. With this approach, it is possible to track jump phenomena or the influence of parameter variations even in case of narrow band excitation. The results of the performed calculations are the stochastic moments, i.e. mean value and variance.

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Implications of the Regional Earthquake Likelihood Models test of earthquake forecasts in California

Research in Geophysics ◽

10.4081/rg.2012.e10 ◽

2012 ◽

Vol 2 (2) ◽

pp. 10 ◽

Cited By ~ 2

Author(s):

Michael Karl Sachs ◽

Ya-Ting Lee ◽

Donald Turcotte ◽

James R. Holliday ◽

John B. Rundle

Keyword(s):

Conditional Probability ◽

Mean Value ◽

Test Area ◽

Test Period ◽

Future Earthquake ◽

The Mean ◽

The One ◽

Earthquake Likelihood Models ◽

Regional Earthquake

The Regional Earthquake Likelihood Models (RELM) test was the first competitive comparison of prospective earthquake forecasts. The test was carried out over 5 years from 1 January 2006 to 31 December 2010 over a region that included all of California. The test area was divided into 7682 0.1°x0.1° spatial cells. Each submitted forecast gave the predicted numbers of earthquakes Nemi larger than M=4.95 in 0.1 magnitude bins for each cell. In this paper we present a method that separates the forecast of the number of test earthquakes from the forecast of their locations. We first obtain the number Nem of forecast earthquakes in magnitude bin m. We then determine the conditional probability λemi=Nemi/Nem that an earthquake in magnitude bin m will occur in cell i. The summation of λemi over all 7682 cells is unity. A random (no skill) forecast gives equal values of λemi for all spatial cells and magnitude bins. The skill of a forecast, in terms of the location of the earthquakes, is measured by the success in assigning large values of λemi to the cells in which earthquakes occur and low values of λemi to the cells where earthquakes do not occur. Thirty-one test earthquakes occurred in 27 different combinations of spatial cells i and magnitude bins m, we had the highest value of λemi for that mi cell. We evaluate the performance of eleven submitted forecasts in two ways. First, we determine the number of mi cells for which the forecast λemi was the largest, the best forecast is the one with the highest number. Second, we determine the mean value of λemi for the 27 mi cells for each forecast. The best forecast has the highest mean value of λemi. The success of a forecast during the test period is dependent on the allocation of the probabilities λemi between the mi cells, since the sum over the mi cells is unity. We illustrate the forecast distributions of λemi and discuss their differences. We conclude that the RELM test was successful in illustrating the choices required when a forecast of the location of a future earthquake is made.

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