Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

Wu, Dongjin; Xia, Lan; Geng, Jijun

doi:10.3390/s18061970

Cited by 27 publications

(51 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Seo and Laine [ 38 ] in their approach determine the device orientation and then count the steps, which enables dynamic changes in the way of holding the device without any significant error in the step detection. Wu et al [ 39 ] introduced a heading estimation method based on a robust adaptive Kalman filtering, which incorporates measurements from the accelerometer, gyroscope, and magnetometer. Moreover, they integrated a model to limit outliers in the measurement data and to resist negative effects of state model disturbances.…”

Section: Solution Background and Related Workmentioning

confidence: 99%

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Opiela

Galčík

2020

Sensors

View full text Add to dashboard Cite

Indoor positioning systems for smartphones are often based on Pedestrian Dead Reckoning, which computes the current position from the previously estimated location. Noisy sensor measurements, inaccurate step length estimations, faulty direction detections, and a demand on the real-time calculation introduce the error which is suppressed using a map model and a Bayesian filtering. The main focus of this paper is on grid-based implementations of Bayes filters as an alternative to commonly used Kalman and particle filters. Our previous work regarding grid-based filters is elaborated and enriched with convolution mask calculations. More advanced implementations, the centroid grid filter, and the advanced point-mass filter are introduced. These implementations are analyzed and compared using different configurations on the same raw sensor recordings. The evaluation is performed on three sets of experiments: a custom simple path in faculty building in Slovakia, and on datasets from IPIN competitions from a shopping mall in France, 2018 and a research institute in Italy, 2019. Evaluation results suggests that proposed methods are qualified alternatives to the particle filter. Advantages, drawbacks and proper configurations of these filters are discussed in this paper.

show abstract

Section: Solution Background and Related Workmentioning

confidence: 99%

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Opiela

Galčík

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Zhao et al [15] improved gradient descent algorithm to reduce the heading drift. To obtain a better heading estimation, extended Kalman filtering (EKF) [31]- [33] and complementary filters (CF) [34]- [36] are used to fuse inertial sensors and magnetometer readings, and reduce sensor noise. In References [37], [38], smartphone-based heading estimation algorithms required users to hold a smartphone in a fixed mode as long as possible.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Wang

Luo

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Accurate heading estimation is the foundation of numerous applications, including augmented reality, pedestrian dead reckoning, and human-computer interactions. While magnetometer is a key source of heading information, the poor accuracy of consumer-grade hardware coupled with the pervasive magnetic disturbances makes accurate heading estimation a challenging issue. Heading error is one of the main error sources of pedestrian dead reckoning. To reduce the heading error and enhance robustness, we proposed a novel heading estimation method based on Spatial Transformer Networks (STNs) and Long Short-Term Memory (LSTM), termed DeepHeading, which uses sensors embedded in a smartphone without any historical training data or dedicated infrastructure. We automatically annotate heading data based on map matching, and augment heading data based on device attitude. We leverage the STNs to align the device coordinate system and the navigation coordinate system, allow an unconstrained use of smartphones. Based on the characteristics of pedestrian heading continuity, we designed a hierarchical LSTM-basedSeq2Seq model to estimate the walking heading of the pedestrian. We conducted well-designed experiments to evaluate the performance of deepheading and compared it with the state-of-the-art heading estimation algorithms. The experimental results on real-world demonstrated that deepheading outperformed the compared heading estimation algorithms and achieved promising estimation accuracy with a median heading error of 4.52 • , mean heading error of 6.07 • and heading error of 9.18 • at the confidence of 80% when a pedestrian is walking in indoor environments with magnetic field disturbances. The proposed method is high-efficiency and easy to integrate with various mobile applications. INDEX TERMS Indoor positioning, heading estimation, pedestrian dead reckoning, deep learning.

show abstract

“…In the GNSS field, the signal-to-noise ratio (SNR), elevation angle, posterior observation residual, the estimated variance matrix of float ambiguity, and the bootstrapping AR success rate or ratio value are usually used as the alternative information for selecting the optimal ambiguity subset [42]. Some scholars have successfully applied the robust Kalman filter to GNSS positioning and integrated navigation [43,44,45]. Most previous studies on PL positioning have focused on the outdoor simulated code-based meter-level positioning system and are thus not convincing [46,47,48].…”

Section: Introductionmentioning

confidence: 99%

Reliable Indoor Pseudolite Positioning Based on a Robust Estimation and Partial Ambiguity Resolution Method

Huang

Zhang

et al. 2019

Sensors

View full text Add to dashboard Cite

The unscented Kalman filter (UKF) can effectively reduce the linearized model error and the dependence on initial coordinate values for indoor pseudolite (PL) positioning unlike the extended Kalman filter (EKF). However, PL observations are prone to various abnormalities because the indoor environment is usually complex. Standard UKF (SUKF) lacks resistance to frequent abnormal observations. This inadequacy brings difficulty in guaranteeing the accuracy and reliability of indoor PL positioning, especially for phase-based high-precision positioning. In this type of positioning, the ambiguity resolution (AR) will be difficult to achieve in the presence of abnormal observations. In this study, a robust UKF (RUKF) and partial AR (PAR) algorithm are introduced and applied in indoor PL positioning. First, the UKF is used for parameter estimation. Then, the anomaly recognition statistics and optimal ambiguity subset of PAR are constructed on the basis of the posterior residuals. The IGGIII scheme is adopted to weaken the influence of abnormal observation, and the PAR strategy is conducted in case of failure of the conventional PL-AR. The superiority of our proposed algorithm is validated using the measured indoor PL data for code-based differential PL (DPL) and phase-based real-time kinematic (RTK) positioning modes. Numerical results indicate that the positioning accuracy of RUKF-based indoor DPL is higher with a decimeter-level improvement compared that of the SUKF, especially in the presence of large gross errors. In terms of high-precision RTK positioning, RUKF can correctly identify centimeter-level anomalous observations and obtain a corresponding positioning accuracy improvement compared with the SUKF. When relatively large gross errors exist, the conventional method cannot easily realize PL-AR. By contrast, the combination of RUKF and the PAR algorithm can achieve PL-AR for the selected ambiguity subset successfully and can improve the positioning accuracy and reliability significantly. In summary, our proposed algorithm has certain resistance ability for abnormal observations. The indoor PL positioning of this algorithm outperforms that of the conventional method. Thus, the algorithm has some practical application value, especially for kinematic positioning.

show abstract

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering

Cited by 27 publications

References 35 publications

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones

Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM

Reliable Indoor Pseudolite Positioning Based on a Robust Estimation and Partial Ambiguity Resolution Method

Contact Info

Product

Resources

About