An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan, Qigao; Zhang, Hai; Sun, Yougang; Zhu, Yixin; Zhuang, Xiangpeng; Jia, Jie; Peng-song, Zhang

doi:10.3390/s18051404

Cited by 27 publications

(23 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For better comparison, ground truth and tracking trajectories applying all mentioned methods (the proposed method and three comparative ones) are drawn in Figure 9. It is clearly seen that, when applying the proposed method, the experiment results were far closer to the ground truth, while the results when applying methods of IMU [51], IMU/ex-TOA [23] and IMU-OEKF [52] were drifting away as time accumulates. The result remained similar at the very beginning; however, the gap became larger over time.…”

Section: Practical Use Case In 3d Scenariomentioning

confidence: 77%

“…From above analysis, our proposed method had significantly higher accuracy, as well as little drift problem. Besides, compared with IMU/ex-TOA method [23] and IMU-OEKF method [52], our method did not need external anchors and had higher tracking accuracy, thus is more suitable for wearable motion tracking applications.…”

Section: Practical Use Case In 3d Scenariomentioning

confidence: 98%

“…For simplicity, and to separate it from our proposed method, we denoted it as "IMU/ex-TOA" method. (3) Optimal Enhanced Kalman Filter (OEKF)-based method in [52] was applied. The cumulative errors in attitude and velocity were corrected using the attitude fusion filtering algorithm and ZUPT, respectively.…”

Section: Practical Use Case In 3d Scenariomentioning

confidence: 99%

See 2 more Smart Citations

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Zhang

et al. 2019

Electronics

View full text Add to dashboard Cite

Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér-Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

show abstract

Section: Practical Use Case In 3d Scenariomentioning

confidence: 77%

Section: Practical Use Case In 3d Scenariomentioning

confidence: 98%

See 1 more Smart Citation

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Zhang

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

“…However, the acceleration and angular rate information both include random noises, which result in integral cumulated errors in velocity and heading [1]. Fortunately, a zero velocity detection update (ZUPT) technique was proposed to aid the inertial navigation system (INS) [2][3][4], antennas. The host modulates multiple navigation signals, and each of navigation signal is transmitted by a pseudolite antenna.…”

Section: Introductionmentioning

confidence: 99%

RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments

Zhu

Wang

Cao

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.

show abstract

“…Strapdown inertial navigation methods assisted by ZUPT use accelerometers and gyroscopes to calculate the navigation parameters of the human feet by a SINS algorithm, and ZUPT algorithm is used to suppress the accumulation of navigation errors when human feet are in static gait phases. The method is based on the fact that when a MEMS-IMU is installed on the foot of a pedestrian, the zero-velocity at the moment of the foot being on the ground is taken as an observation, and the zero-velocity and SINS information are fused to obtain the modified information of the MEMS-SINS, and sequentially to improve the accuracy of the pedestrian navigation system [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance

Zhou

Yang

et al. 2020

Sensors

View full text Add to dashboard Cite

In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.

show abstract

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Cited by 27 publications

References 37 publications

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments

Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance

Contact Info

Product

Resources

About