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Performance analysis of indoor pseudolite positioning based on the unscented Kalman filter

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Abstract

The indoor pseudolite (PL) code-based solution is not sufficiently precise, and conducting prolonged static carrier phase observations is impractical due to the static PL geometric distribution. Thus, a high-precision indoor PL positioning, which is generally based on the known point initialization (KPI) method and adopts the extended Kalman filter (EKF), is used to obtain the PL float ambiguity solution. However, the first-order linear truncation error of the EKF cannot be neglected because the indoor space is small, and its performance is quite dependent on the accuracy of the initial coordinates in the KPI. In this study, a well-suited nonlinear parameter estimation method, which is expected to have high precision and low dependence on the initial coordinate values, namely the unscented Kalman filter (UKF), is introduced and applied in PL positioning. Based on the relative positioning model and the KPI method, the positioning performance of the EKF and UKF under code-based differential pseudolite (DPL) positioning and phase-based real-time kinematic (RTK) positioning modes is compared and analyzed. Numerical results indicate that the computational efficiencies of the EKF and UKF are of the same level, though the former is slightly superior to the latter. In terms of the DPL positioning results, the precision of the UKF is higher with a decimeter-level improvement compared with that of the EKF. The dependence on the accuracy of the initial coordinates of UKF is reduced to some extent, which makes it a convenient technique, especially in the PL ambiguity resolution of the RTK positioning with fast convergence speed. The UKF outperforms the EKF and is more practicable in indoor PL positioning. The UKF can also achieve a decimeter-level positioning precision in DPL and a centimeter-level positioning precision in RTK.

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Abbreviations

AR:

Ambiguity resolution

DD:

Double differenced

DGPS:

Differential GPS positioning

DPL:

Differential pseudolite

EKF:

Extended Kalman filter

GDOP:

Geometric dilution of precision

GNSS:

Global navigation satellite system

GPS:

Global positioning system

ICB:

Initial coordinate bias

KPI:

Known point initialization

PNC:

Process noise of coordinates

PL:

Pseudolite

PL-AR:

Pseudolite ambiguity resolution

RTK:

Real-time kinematic positioning

SD:

Single differenced

SPP:

Single point positioning

STD:

Standard deviation

UKF:

Unscented Kalman filter

USRP:

Universal software radio peripheral

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Acknowledgements

This work was supported by the China Postdoctoral Science Foundation (No. 2018M633441); the Fundamental Research Funds for the Central Universities, Chang’an University, 2018 (No. 300102268102); and the Programs of the National Natural Science Foundation of China (41790445, 41774025, 41604001, and 41731066).

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Authors and Affiliations

  1. College of Geology Engineering and Geomatics, Chang’an University, Xi’an, 710054, Shanxi, China

    Xin Li, Guanwen Huang & Qin Zhang

  2. School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China

    Peng Zhang, Jiming Guo & Yinzhi Zhao

  3. College of Geomatics, Xi’an University of Science and Technology, Xi’an, 710054, China

    Qingzhi Zhao

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  1. Xin Li
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Correspondence to Peng Zhang.

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Li, X., Zhang, P., Huang, G. et al. Performance analysis of indoor pseudolite positioning based on the unscented Kalman filter. GPS Solut 23, 79 (2019). https://doi.org/10.1007/s10291-019-0870-y

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