# BDS code bias periodical mitigation by low-pass filtering and its applications in precise positioning

- Xin Li
^{1}Email author, - Keke Zhang
^{1}, - Yongqiang Yuan
^{1}, - Xiaohong Zhang
^{1}and - Xingxing Li
^{1}

**16**:2

https://doi.org/10.1186/s41445-018-0012-9

© The Author(s) 2018

**Received: **15 September 2017

**Accepted: **31 January 2018

**Published: **26 February 2018

## Abstract

The code-phase divergences, which are minimal for GPS, GLONASS, and Galileo satellites, are commonly found in BeiDou Navigation Satellite System (BDS) Geostationary Orbit (GEO), Inclined GeoSynchronous Orbit (IGSO) and Medium Earth Orbit (MEO) satellites. Several precise positioning applications which use code observations are severely affected by these code biases. We present an analysis of code bias based on multipath (MP) combination observations. To mitigate the effect of BDS code bias on precise positioning, we proposed a periodical correction method using a low-pass filter for BDS GEO, IGSO and MEO satellites. The auto-correlation of MP series over long periods is analyzed to obtain the periods of the dominant repeating components for three types of BDS satellites. The periods of the dominant daily repeating components are close to 86,160 s for BDS GEO and IGSO satellites while 603,120 s for MEO satellites. The zero phase-shift low-pass filter was used to extract the low-frequency components of MP series and then low-frequency components are applied to mitigate the code bias periodically. The developed correction methods can make a more remarkable improvement for the accuracy of MP series, compared to the current elevation-dependent correction models. Data sets collected at 50 Global Navigation Satellite System (GNSS) ground stations including 15 of the International GNSS Monitoring and Assessment System (iGMAS) and 35 of the Multi-GNSS Experiment (MGEX) stations are employed for this study. To analyze the influence of code bias on precise positioning and validate the effectiveness of the correction methods, some applications such as single point positioning (SPP), wide-lane (WL) ambiguity analysis and Uncalibrated Phase Delays (UPDs) estimation are conducted. After applying the proposal correction method to the code observations, SPP solutions outperform the uncorrected ones in term of positioning accuracy. The positioning accuracy decreased by 0.28 and 0.1 m in the north and east components and the improvements are more significant for the U components decreased by 0.42 m. In addition, the systematic variations of Melbourne-Wübbena (MW) combination are greatly removed and the convergence time of the MW series are decreased. Moreover, significant improvement is also achieved in terms of the usage rate and residuals of UPDs estimation.

## Keywords

## Introduction

The Chinese BeiDou Navigation Satellite System (abbreviated as BDS, referred to as COMPASS previously) is currently providing continuous positioning, navigation, and timing (PNT) services to most areas of the Asian-Pacific. As of June 2017, BDS consists of 14 satellites providing PNT services, three satellites in medium earth orbits (MEO), six satellites in inclined geostationary orbits (IGSO), and five satellites in geostationary orbits (GEO) (CSNO 2013, Li et al. 2015). These satellites are all transmitting triple-frequency signals centered at B1(1561.098 MHz), B2 (1207.14 MHz), and B3 (1268.52 MHz).

It is well known that GNSS signals are affected by various bias such as Inter-frequency bias, code bias and inter-system bias (Montenbruck et al. 2013; Zhao et al. 2013; Li et al. 2015; Geng et al. 2016; Liu et al. 2017). When it comes to the characteristics of BDS signals, the BDS code bias was mentioned firstly by Hauschild et al. (2012). The same systematic variations were also detected by Perello Gisbert et al. (2012) and more details were presented by Montenbruck et al. (2012, 2013). In their publications, such code-phase divergences (code bias variations) have been characterized as orbit type-, frequency-, and elevation-dependent. Since these code bias variations put code-phase divergences of more than 1 m, they can severely affect precise applications using code observations. Zhang et al. (2017) analyzed the originate characteristic of code bias in BDS MW-defined combination and concluded that the origin of errors may from transmitting satellite sides for IGSO and MEO satellites, and the biases for GEO satellites originate from both ground- and satellite sides, mainly from ground multipath. Wanninger and Beer (2015) proposed an elevation-dependent correction model for BDS IGSO and MEO satellites, which is an important contribution to the current BDS constellation. However, observations from two 10-day intervals for modeling these variations are rare. The long-term stability of the systematic biases could not be identified conclusively from such a limited data set. Guo et al. (2016) also proposed another modified correction model for BDS IGSO and MEO satellites considering the stochastic model. Results indicate that PPP solutions with the improved model outperform the traditional one (Wanninger and Beer 2015) in terms of positioning accuracy and convergence speed. Wang et al. (2015) proposed a correction method for GEO satellites by correcting the observables with a low-frequency multipath of the previous day, which mitigate the multipath of GEO satellites. Lou et al. (2016) presented an assessment of code bias variations in BDS triple-frequency signals and the ambiguity resolutions, which further illustrated its influence on the ambiguity resolution. Wang et al. (2017) achieved BDS/GPS PPP ambiguity resolution after the code bias correction of IGSO/MEO satellites and results confirmed that the correction of the code bias will improve the fixing rate of undifferenced BDS ambiguity resolution. At present, most studies about code bias are limited on BDS IGSO and MEO satellite since it is hard to use elevation-dependent correction model to correct the code bias of GEO satellites which stay relatively static position to observer. The traditional elevation-dependent model parameters were presented for different groups of satellites and ignored the differences of the bias among the different satellites, which also decrease the correction effect.

In this paper, we proposed periodical correction method to mitigate the code bias for each GEO, IGSO and MEO satellite. The auto-correlation of MP series over long periods is analyzed to obtain the periods of the dominant repeating components for three types of BDS satellites. Then, the low-frequency component of multipath (MP) combination series is extracted using a zero phase-shift low-pass filter and applied to help minimize this error based on the repetition of code bias. Meanwhile, the multipath from ground stations is also mitigated. Finally, its PNT applications such as Single Point Positioning (SPP), wide-lane (WL) ambiguity and Uncalibrated Phase Delays (UPDs) estimation are presented to validate the correction methods.

### Observation data sets

All observation data sets used in this study were collected from the International Global Navigation Satellite System (GNSS) Monitoring and Assessment System (iGMAS, Chen et al. 2015) and Multi-GNSS Experiment (MGEX, Montenbruck et al. 2014) stations. As of June 2017, the MGEX offers a global network of approximately 80 stations of GNSS receivers capable to receive BDS signals. Most of these stations are equipped with Trimble NetR9 receivers providing triple-frequency BDS observations. Only a few stations are equipped with other receiver types of receivers, such as Septentrio POLARX4/ POLARX4TR/ASTERX3 and Leica GR25 receivers, providing dual-frequency observations on frequencies B1 and B2.

## Methods

### Analysis of MP time series

#### MP combination

*i*,

*j*and

*k*are used to denote different frequencies and

*MP*

_{ i }is the MP combination on frequency

*i*; P and

*ϕ*represent code observations and carrier phase observations; is the wavelength of the specific signal;

*B*refers to the ambiguity of phase observations and hardware delays. Usually, the multipath of phase observations is ignored. The subtraction of the mean value from the measurements removes the phase ambiguities and combined hardware delay, which contains integer characteristic if no cycle slip happens. The linear factors are selected to cancel out the ionospheric and tropospheric delays and all geometric contributions (clocks, orbits, and antenna movements). The resulting time series of MP combination of BDS satellites mainly reflect multipath effects, tracking noise and code bias of the code observations.

### Correlation analysis for BDS MP series

The geometry between BDS satellites and a specific receiver-reflector location repeats every few days, which may lead to the same pattern in code bias for consecutive days. Taking GEO satellites as an example, the elevation angles of GEO satellites fluctuate degrees because of various perturbations such as solar radiation, daily variations (Zhang et al. 2013) and repeat every sidereal day. At the same time, similar fluctuation can be found for MP series and there is significant consistency for MP series between the adjacent 2 days (Wang et al. 2015). It has been confirmed that the repetition of satellite geometry is about a sidereal day for GEO and IGSO satellites, but approximately seven sidereal days for MEO satellites (Ye et al. 2015). To determine the optimal lag and analyze the correlation of code bias between different days for three types of BDS satellites, an auto-correlation analysis was performed on the 10-day period low-frequency components of MP time series for each GEO and IGSO satellite, while 30-day period for each MEO satellite.

A zero phase-shift low-pass filter was used to extract the low-frequency components of MP series. With the high-frequency components separated, the low-frequency components can reflect the characteristic of systematic variations more clearly. The filter with window sizes of 4 h are employed in our experiment to obtain the low-frequency components.

Peak auto-correlation values and corresponding lags for GEO, IGSO and MEO satellites at station CUT0

Satellites | MP1 | MP2 | MP3 | |||
---|---|---|---|---|---|---|

Auto-correlation | Lag(s) | Auto-correlation | Lag(s) | Auto-correlation | Lag(s) | |

C01 | 0.8390 | 86,190 | 0.8469 | 86,190 | 0.7307 | 86,070 |

C02 | 0.7226 | 86,400 | 0.8225 | 86,210 | 0.6786 | 86,400 |

C03 | 0.8480 | 86,190 | 0.8496 | 86,190 | 0.5803 | 86,310 |

C04 | 0.7745 | 86,250 | 0.7376 | 86,280 | 0.8213 | 86,310 |

C06 | 0.7966 | 86,220 | 0.7938 | 86,310 | 0.7936 | 86,220 |

C07 | 0.8214 | 86,220 | 0.8124 | 86,190 | 0.8306 | 86,160 |

C08 | 0.5936 | 86,340 | 0.7045 | 86,070 | 0.6211 | 86,220 |

C09 | 0.6824 | 86,160 | 0.7419 | 86,190 | 0.7967 | 86,160 |

C10 | 0.7047 | 86,160 | 0.7928 | 86,160 | 0.7711 | 86,160 |

C11 | 0.6478 | 603,120 | 0.6575 | 603,120 | 0.5820 | 603,120 |

C12 | 0.6721 | 603,120 | 0.6756 | 603,120 | 0.6741 | 603,120 |

C14 | 0.7191 | 603,120 | 0.7284 | 603,120 | 0.7094 | 603,120 |

### Code bias periodical mitigation

The RMS values of MP series for BDS satellites before and after the code bias correction at station CUT0

Satellites | Uncorrected | Corrected | ||||
---|---|---|---|---|---|---|

B1 | B2 | B3 | B1 | B2 | B3 | |

C01 | 0.458 | 0.296 | 0.21 | 0.295 | 0.226 | 0.203 |

C02 | 0.364 | 0.244 | 0.207 | 0.322 | 0.229 | 0.203 |

C03 | 0.574 | 0.306 | 0.191 | 0.227 | 0.22 | 0.181 |

C04 | 0.772 | 0.5222 | 0.351 | 0.366 | 0.244 | 0.214 |

C06 | 0.538 | 0.398 | 0.378 | 0.436 | 0.32 | 0.293 |

C07 | 0.699 | 0.396 | 0.387 | 0.642 | 0.341 | 0.335 |

C08 | 0.552 | 0.431 | 0.37 | 0.542 | 0.394 | 0.345 |

C09 | 0.571 | 0.386 | 0.371 | 0.477 | 0.319 | 0.284 |

C10 | 0.525 | 0.375 | 0.376 | 0.484 | 0.337 | 0.333 |

C11 | 0.716 | 0.621 | 0.556 | 0.619 | 0.541 | 0.522 |

C12 | 0.746 | 0.693 | 0.597 | 0.664 | 0.601 | 0.533 |

C14 | 0.789 | 0.686 | 0.613 | 0.695 | 0.623 | 0.556 |

## Results

### SPP result

To test the validation of proposed correction methods, single point positioning (SPP) was conducted since the code observations were used to obtain the positioning results. The processing strategies for SPP are as summarized:

In the first scheme, the original code observations are used without the code bias correction. In the second scheme, the code biases of BDS satellites are corrected by periodical correction method using low-pass filter. For brevity, “Uncorrected” and “Corrected” are used to denote the above-mentioned two different schemes throughout the rest of this article, if there is no additional explanation.

RMS of coordinate bias of 10 stations before and after the code bias correction (unit: m)

Uncorrected | Corrected | |||||
---|---|---|---|---|---|---|

E | N | U | E | N | U | |

CUT0 | 1.33 | 2.57 | 6.5 | 1.15 | 2.11 | 5.32 |

JFNG | 1.08 | 1.93 | 5.09 | 0.89 | 1.84 | 4.58 |

GMSD | 0.96 | 2.02 | 3.85 | 0.91 | 1.97 | 3.37 |

SIN1 | 0.58 | 1.21 | 1.82 | 0.53 | 1.17 | 1.70 |

SEYG | 1.34 | 3.43 | 4.95 | 1.38 | 3.14 | 4.81 |

WROC | 5.00 | 2.91 | 5.30 | 4.98 | 2.39 | 4.87 |

MAYG | 0.99 | 2.25 | 4.26 | 0.78 | 1.73 | 3.90 |

REDU | 1.24 | 2.24 | 4.45 | 1.22 | 2.99 | 4.23 |

REUN | 1.46 | 1.79 | 5.26 | 1.36 | 1.53 | 4.96 |

KOUR | 1.75 | 1.85 | 3.60 | 1.56 | 1.53 | 3.20 |

The average RMS of coordinate bias before and after the code bias correction (unit: m)

RMS(m) | Uncorrected | Corrected | Improvements |
---|---|---|---|

EAST | 1.57 | 1.47 | 0.10 |

NORTH | 3.72 | 3.44 | 0.28 |

UP | 4.50 | 4.09 | 0.42 |

### MW combination

The so-called Melbourne-Wübbena (MW, Melbourne 1985; Wübbena 1985) linear combination, was derived from B1/B2 dual-frequency code and carrier phase measurements. The geometrical terms, such as geometry range, clocks and tropospheric delay, and first-order ionospheric delay, are eliminated for MW combinations. The residual terms refer to observable noise, multipath, and the WL ambiguity with combined hardware delays. For BDS satellites, the code bias also existed in the MW combination and will lead to the WL ambiguity far away from an integer.

In order to compare the convergence time for BDS WL ambiguity before and after the code bias correction, we split the observational data of GMSD station into several arcs and compute the averaged undifferenced MW values of every arc. If the number of the averaged undifferenced MW values for a satellite, which is less than the convergence criterion, is more than nine-tenths of the number of data parts, is regarded to be convergent. Figure 11 shows the convergence time of the MW value for BDS satellites. The original and corrected results are shown by blue and red bars, respectively. As can be seen, the convergence time of BDS undifferenced MW values ranges from 4 h to more than 10 h, which is much longer than that of GPS undifferenced MW values (usually less than 30 min). After the correction methods are employed, the convergence time of BDS undifferenced MW values is less 45 min.

### UPD estimation

In the process of ambiguity resolution, we usually fix the WL ambiguities firstly, and then the narrow-lane (NL) ambiguities derived from the ionosphere-free ambiguity and fixed WL ambiguity can be resolved to integer values. However, due to the code bias of BDS observations, the float wide-lane ambiguity derived from MW observations may be far away from the expected value and lead to incorrect ambiguity resolution. To verify the effectiveness of our new correction method, the UPD estimation was applied before and after the code bias correction. The UPDs estimation method proposed by Li et al. (2013) was applied to generate the BDS WL UPD products. Datasets collected from 15 iGMAS stations and 10 MGEX stations located at Asia pacific region were used to generate the WL UPDs on B1 and B3 frequencies.

## Discussion and conclusions

The code bias has been detected from observations of BDS satellites which have been verified to be orbit type-, frequency-, and elevation-dependent. Since these systematic biases can result in code-phase divergences of more than 1 m, they can severely affect precise applications using code observations.

A new correction method using low-pass filter is conducted to mitigate the code bias of BDS satellites. An auto-correlation is performed for each BDS satellite and demonstrate that the optimal lag of MP series for GEO and IGSO satellites is 86,160 s and 603,120 s for MEO satellites, respectively. The low-frequency component of the follow-up epoch extracted by low-pass filtering is applied to minimize the code bias of BDS observation data and finally improves the accuracy of the MP time series. The RMS of original MP series on B1, B2 and B3 frequencies are 0.65 m, 0.53 m and 0.34 m, respectively, after the code bias correction, the RMS decreases to 0.5 m, 0.35 m and 0.3 m, respectively, with an improvement of 23.1%, 33.9% and 11.8%. Comparing the traditional model correction, there are obvious improvements using our models, the improvements are 8.9%, 11.7% and 13.5% on B1, B2 and B3 frequencies, respectively.

SPP tests were conducted to verify the effectiveness of the correction methods first. With the code bias corrected, the accuracy of SPP results is improved, the improvements are more significant for the vertical component decreased by 0.42 m, and by 0.28 and 0.1 m in north and east direction. For MW combination, after applying the code bias correction, the systematic variations have been greatly removed, and the resulting MW series run much more stable and closer to the zero. Furthermore, the convergence time of BDS undifferenced MW values is less 45 min, which is much shorter than that of original undifferenced MW values (more than 10 h). Moreover, with the observations corrected, the average usage rate of UPDs estimation is increased from 86.2 to 98.75%. And the distribution of residuals is much closer to a normal distribution than that of the uncorrected ones. The RMS of BDS WL residuals is 0.0983 cycles while the residuals is 0.059 cycles with the code bias corrected. The correction of the BDS code bias can also shorten the convergence time of PPP, and has the potential to improve the BDS ionosphere-free ambiguity resolution, which need to be studied in future.

## Declarations

### Acknowledgements

We are very grateful to MGEX and iGMAS for providing data of BDS satellites.

### Funding

There is no funding for this research.

### Authors’ contributions

XL wrote this paper and analyzed the results of the experiments; XZ and XL proposed the initial idea and revised this manuscript; KZ and YY analyzed the results of the experiments and revised this manuscript. All authors read and approved the final manuscript.

### Competing interests

The authors declare that they have no competing interests.

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## Authors’ Affiliations

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