An Outlier-Aware Method for UWB Indoor Positioning in NLoS Situations

Liu, Chuan; Cao, Yunkang; Sun, Chen; Shen, Weiming; Li, Xinyu; Gao, Liang

doi:10.1109/cscwd54268.2022.9776125

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…In the indoor environment of GNSS rejection, many scholars have conducted research on different positioning technologies, such as ultra-wide band (UWB) positioning technology [ 7 ], the inertial navigation system (INS) [ 8 ], wireless local area network (WLAN) positioning technology [ 9 ], radio-frequency identification (RFID) positioning technology [ 10 ], and Bluetooth positioning technology [ 11 ]. Existing communication means, including WLAN, RFID, and Bluetooth, are unable to achieve accurate indoor positioning because of easy disruption; UWB has attracted a great deal of interest because of its high temporal resolution, high reliability, and good obstacle-penetration capabilities [ 12 ]. For example, in seamless indoor and outdoor positioning scenes, the authors of [ 13 ] conducted experiments related to multi-sensor fusion positioning.…”

Section: Introductionmentioning

confidence: 99%

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB

Ren

Liu

Dai

et al. 2023

Sensors

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With the widespread development of multiple sensors for UGVs, the multi-source fusion-navigation system, which overcomes the limitations of the use of a single sensor, is becoming increasingly important in the field of autonomous navigation for UGVs. Because federated filtering is not independent between the filter-output quantities, owing to the use of the same state equation in each of the local sensors, a new kinematic and static multi-source fusion-filtering algorithm based on the error-state Kalman filter (ESKF) is proposed in this paper for the positioning-state estimation of UGVs. The algorithm is based on INS/GNSS/UWB multi-source sensors, and the ESKF replaces the traditional Kalman filter in kinematic and static filtering. After constructing the kinematic EKSF based on GNSS/INS and the static ESKF based on UWB/INS, the error-state vector solved by the kinematic ESKF was injected and set to zero. On this basis, the kinematic ESKF filter solution was used as the state vector of the static ESKF for the rest of the static filtering in a sequential form. Finally, the last static ESKF filtering solution was used as the integral filtering solution. Through mathematical simulations and comparative experiments, it is demonstrated that the proposed method converges quickly, and the positioning accuracy of the method was improved by 21.98% and 13.03% compared to the loosely coupled GNSS/INS and the loosely coupled UWB/INS navigation methods, respectively. Furthermore, as shown by the error-variation curves, the main performance of the proposed fusion-filtering method was largely influenced by the accuracy and robustness of the sensors in the kinematic ESKF. Furthermore, the algorithm proposed in this paper demonstrated good generalizability, plug-and-play, and robustness through comparative analysis experiments.

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Section: Introductionmentioning

confidence: 99%

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB

Ren

Liu

Dai

et al. 2023

Sensors

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“…A sizable amount of research has been conducted by using the raw CIR: NLoS detection via Capsule Networks [16], proposing an NLoS-induced outlier-aware positioning method based on multilayer perception [17], signal decomposition by One-Dimensional Wavelet Packet Analysis in conjunction with Convolutional Neural Networks (CNN) [18], Transformer deep learning model [19], combining the Multilayer Perceptron with CNN to reduce calculation complexity [20], overcoming the problem of site-specific models by conducting Long Short-Term Memory training to predict NLoS error magnitude and variance of measurements [21], to name a few of the latest. In addition to ML and deep learning, other methods utilizing the raw CIR are explored: NLoS detection using fuzzy comprehensive evaluation [22], a weighted particle filter based on probability density functions of Line-of-Sight (LoS)/NLoS correlation coefficients [23], and adaptively selecting the optimal anchors based on the channel quality indicators [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Extended Kalman Filter Position Estimation Based on Ultra-Wideband Active-Passive Ranging Protocol

Laadung,

Ulp,

Fjodorov

et al. 2023

IEEE Access

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This paper first presents a comprehensive analysis of Non-Line-of-Sight (NLoS) error cases in the Ultra-Wideband (UWB) Active-Passive Two-Way Ranging (AP-TWR) protocol. Based on this analysis, we then propose the Adaptive Extended Kalman Filter (A-EKF) positioning method, utilizing variances calculated from AP-TWR range estimates, which are adapted based on the distance and intermittency of the range estimates. The proposed method needs no training data, nor any additional information about the environment the system is deployed in and does not yield any additional time delays. Based on experiments conducted in an industrial environment, the results show that the proposed method outperforms standard non-adaptive AP-TWR and active-only Single-Sided Two-Way Ranging (SS-TWR) methods in both stationary and movement tests. The stationary tests show that on average the proposed A-EKF method provides more than three times lower Root-Mean-Square-Error (RMSE) than the next best method (AP-TWR) in 3D positioning, while SS-TWR consistently performs worse by about 0.4 m in the z-axis. Additionally, the movement tests confirm the findings of the stationary tests and show that the challenging propagation conditions of the testing environment cause maximum errors at about 4.5 m for AP-TWR and SS-TWR, whereas the proposed A-EKF managed to mitigate these effects and reduce the error by 9 times, resulting in a maximum error of 0.5 m.

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An Outlier-Aware Method for UWB Indoor Positioning in NLoS Situations

Cited by 2 publications

References 17 publications

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB

Adaptive Extended Kalman Filter Position Estimation Based on Ultra-Wideband Active-Passive Ranging Protocol

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