CN-121763315-B - Receiver tracking error random model construction method based on ionospheric scintillation index

CN121763315BCN 121763315 BCN121763315 BCN 121763315BCN-121763315-B

Abstract

The invention provides a receiver tracking error random model construction method based on an ionospheric scintillation index, which comprises the steps of constructing a novel receiver tracking error random model suitable for ionospheric scintillation inhibition of a common GNSS receiver by accurate cycle slip detection, trending of SG filtering carrier phase observation values and calculation of scintillation indexes based on 1Hz data of the common GNSS receiver, solving the problem that the positioning precision of the common GNSS receiver is reduced or even unavailable under the ionospheric scintillation condition, and realizing high-precision positioning under the ionospheric scintillation condition.

Inventors

Abudu Haili Yakufu
YAO YIBIN
PENG WENJIE
Xu Chaohan
Fu Leran
Chu Ruitao
SUN BOWEN
LI YANG

Assignees

武汉大学

Dates

Publication Date: 20260508
Application Date: 20260305

Claims (10)

1. The method for constructing the random model of the receiver tracking error based on the ionospheric scintillation index is characterized by comprising the following steps: In the same place and the same time period, acquiring original GNSS observation values at a preset sampling rate by adopting a GNSS receiver respectively, and acquiring ionospheric observation data by adopting a special ionospheric scintillation monitoring receiver; Performing cycle slip detection on an original carrier phase observation value in original GNSS observation values based on TurboEdit algorithm, and outputting a carrier phase observation value in a cycle slip-free period; performing trending treatment on the carrier phase observation value in the cycle slip-free period by adopting a polynomial smoothing method to obtain a trended carrier phase observation value; Calculating a new amplitude flicker index and a new phase flicker index based on carrier-to-noise ratio data of a preset frequency of the GNSS receiver and the carrier phase observed value after trending; For the occurrence cycle slip period, calculating pseudo range and carrier phase observation value variance through a classical receiver tracking error random model based on the scintillation index output by the special ionosphere scintillation receiver, determining the proportional relation between the pseudo range observation value variance and the carrier phase observation value variance under different scintillation intensities, and determining the phase observation value variance in the occurrence cycle slip period by utilizing the proportional relation between the pseudo range variance and the pseudo range and the phase observation value variance in the occurrence cycle slip period calculated by the new amplitude scintillation index; And establishing a flicker variance calculation formula by utilizing the new amplitude flicker index and the phase flicker index for a period without cycle slip, and constructing a receiver tracking error random model suitable for the GNSS receiver so as to obtain a GNSS positioning result under the ionosphere flicker condition.
2. The method for constructing a random model of receiver tracking error based on ionospheric scintillation index as recited in claim 1, wherein performing cycle slip detection on an original carrier phase observation value in an original GNSS observation value based on TurboEdit algorithm, outputting a carrier phase observation value in a cycle slip-free period, comprises: the TurboEdit algorithm comprises a MW combined cycle slip algorithm and a GF combined cycle slip algorithm; the MW combined cycle slip algorithm performs cycle slip detection on the original carrier phase observation value and outputs a wide cycle slip detection value; the GF combined cycle slip algorithm performs cycle slip detection on the original carrier phase observation value and outputs a narrow-term cycle slip detection value; And screening the wide cycle slip detection value and the narrow cycle slip detection value of each epoch respectively, removing the carrier phase observation value corresponding to the epoch with cycle slip occurrence, and reserving the carrier phase observation value in the cycle slip-free period.
3. The method for constructing the random model of the receiver tracking error based on the ionospheric scintillation index according to claim 1, wherein the performing the trending on the carrier phase observations in the period without cycle slip by using a polynomial smoothing method to obtain the trended carrier phase observations comprises: Fitting a plurality of carrier phase observation values in a sliding window to construct a matrix comprising any order polynomial fitting coefficient, any time fitting error and any time carrier phase observation value; And determining the size of the sliding window and the order of the matrix, solving the sliding window and the matrix by a least square algorithm to obtain a fitting coefficient vector, and carrying out weighted filtering on the carrier phase observation values taking the circumference as a unit in the sliding window to obtain the carrier phase observation values after trending.
4. The method of claim 1, wherein calculating new amplitude and phase scintillation indices based on carrier-to-noise ratio data of a GNSS receiver preset frequency and the detritlized carrier phase observations comprises: Acquiring carrier-to-noise ratio data from a RINEX observation file of the GNSS receiver, calculating signal-to-noise ratio data from the carrier-to-noise ratio data, calculating trending signal intensity through the signal-to-noise ratio data, and obtaining a new amplitude flicker index based on the trending signal intensity; And carrying out average value correlation processing on the carrier phase observed value after the trending to obtain a phase flicker index.
5. The method of claim 1, further comprising, after calculating a new amplitude scintillation index and a phase scintillation index based on carrier-to-noise ratio data of a GNSS receiver preset frequency and the detritlized carrier phase observations: Obtaining a delay locked loop tracking error variance by using a single-side bandwidth of a delay locked loop of a GNSS receiver, a C/A chip correlator distance, a delay locked loop pre-detection integration time, signal-to-noise ratio data and an amplitude flicker index; Obtaining a phase lock loop tracking error variance by using a phase lock loop unilateral noise equivalent loop bandwidth, a phase lock loop detection front integration time, signal to noise ratio data, an amplitude flicker index of a GNSS receiver, a frequency spectrum intensity of phase noise at a preset frequency, a phase lock loop order, a loop natural frequency, a power spectrum density slope of a trending phase observation value in a preset frequency range around a signal frequency point and GNSS receiver oscillator noise of the GNSS receiver; Respectively calculating the inverse of the delay locked loop tracking error variance and the phase locked loop tracking error variance to obtain pseudo-range observation value weight and carrier phase observation value weight; and replacing the amplitude flicker indexes in the calculated delay locked loop tracking error variance and the phase locked loop tracking error variance with new amplitude flicker indexes, and determining thermal noise variance components in the new delay locked loop tracking error variance and the phase locked loop tracking error variance.
6. The method for constructing a random model of receiver tracking error based on ionosphere scintillation index according to claim 5, wherein, for a period of occurrence cycle slip, based on scintillation index output by the dedicated ionosphere scintillation receiver, calculating pseudo-range and carrier phase observation value variance through classical receiver tracking error random model, determining a proportional relation between pseudo-range observation value variance and carrier phase observation value variance under different scintillation intensities, determining a phase observation value variance in the period of occurrence cycle slip calculated by using the new amplitude scintillation index, and determining a phase observation value variance in the period of occurrence cycle slip, comprising: calculating a pseudo-range observation value and a carrier phase observation value variance through a classical receiver tracking error random model based on a scintillation index output by a special ionosphere scintillation receiver; under different scintillation intensities, determining the proportional relation between the variance of the pseudo-range observed value and the variance of the carrier phase observed value; And multiplying the pseudo-range variance by the proportional relation between the pseudo-range variance and the phase observation value variance in the occurrence cycle slip period calculated by using the new amplitude flicker index to obtain the phase observation value variance in the occurrence cycle slip period.
7. The method of claim 5, wherein for the period without cycle slip, using the new amplitude scintillation index and the phase scintillation index, establishing a scintillation variance calculation, and constructing a receiver tracking error random model for a GNSS receiver to obtain a GNSS positioning result under ionospheric scintillation conditions, comprises: The phase lock loop tracking error variance includes the thermal noise variance component, a phase flicker noise variance component, and a receiver oscillator noise variance component, wherein the receiver oscillator noise variance component is constant; Establishing a flicker variance calculation formula of the phase flicker noise variance component by the phase flicker index, and constructing a receiver tracking error random model suitable for a GNSS receiver; And performing error estimation by using the receiver tracking error random model to obtain a GNSS positioning result under the ionosphere scintillation condition.
8. A receiver tracking error stochastic model construction system based on ionospheric scintillation indices, comprising: the acquisition module is used for acquiring original GNSS observation values at a preset sampling rate by adopting a GNSS receiver and acquiring ionospheric observation data by adopting a special ionospheric scintillation monitoring receiver in the same place and the same time period; The detection module is used for performing cycle slip detection on the original carrier phase observed value in the original GNSS observed values based on TurboEdit algorithm and outputting the carrier phase observed value in the cycle slip-free period; the processing module is used for carrying out trending processing on the carrier phase observed value in the cycle slip-free period by adopting a polynomial smoothing method to obtain a trended carrier phase observed value; the calculating module is used for calculating a new amplitude flicker index and a new phase flicker index based on carrier-to-noise ratio data of a preset frequency of the GNSS receiver and the carrier phase observed value after trending; The first construction module is used for calculating pseudo-range and carrier phase observation value variance through a classical receiver tracking error random model according to the scintillation index output by the special ionosphere scintillation receiver aiming at the occurrence cycle slip period, determining the proportional relation between the pseudo-range observation value variance and the carrier phase observation value variance under different scintillation intensities, and determining the phase observation value variance in the occurrence cycle slip period by utilizing the proportional relation between the pseudo-range variance and the pseudo-range and the phase observation value variance in the occurrence cycle slip period calculated by the new amplitude scintillation index; The second construction module is used for establishing a flicker variance calculation formula by utilizing the new amplitude flicker index and the phase flicker index aiming at the period without cycle slip, and constructing a receiver tracking error random model suitable for the GNSS receiver so as to obtain a GNSS positioning result under the ionosphere flicker condition.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the ionospheric scintillation index-based receiver tracking error stochastic model construction method of any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the ionospheric scintillation index-based receiver tracking error stochastic model construction method of any one of claims 1 to 7.

Description

Receiver tracking error random model construction method based on ionospheric scintillation index Technical Field The invention relates to the technical field of satellite navigation, in particular to a receiver tracking error random model construction method based on ionosphere scintillation indexes. Background When a radio signal passes through an ionospheric irregular region, the phenomenon in which the amplitude and phase of the signal are disturbed and rapidly fluctuates randomly is called ionospheric scintillation. Ionospheric scintillation is often found in low latitude areas, polar regions and polar regions, and the scintillation intensity is particularly intense within hours after sunset, which not only reduces the quality of global navigation satellite system (Global Navigation SATELLITE SYSTEM, GNSS) observations, but also causes frequent cycle slips and even loss of GNSS signals. When multiple GNSS satellites are simultaneously interfered by severe ionosphere scintillation, navigation positioning may be unavailable in some areas for a period of time, which seriously affects GNSS positioning performance and operation efficiency. In order to identify and judge ionospheric scintillation and the intensity thereof, researchers put forward various scintillation index and intensity division standards, construct corresponding random models, and attempt ionospheric scintillation inhibition research and application. Ionospheric scintillation can be divided into amplitude scintillation and phase scintillation (beacons AND KINTNER, 1999), which are essentially signal diffraction effects, the scintillation intensities of which are defined by the difference in scintillation intensitiesIndex and index ofAnd (5) determining an index. Typically, these indices are directly available from a dedicated ionospheric scintillation monitoring receiver (Ionospheric scintillation monitoring receivers, ISMRs) with a time resolution of 50 Hz. However, ISMRs is not widely deployed in large areas or global GNSS networks due to its ultra-high memory requirements and high price. The literature studies find that the phase flicker indexThe calculation may be based on the raw carrier phase observations during the detritus cycle slip-free period, but the amplitude flicker indexIt needs to be output by ISMRs and cannot be obtained by a normal receiver with a low sampling rate. For this purpose, luo et al (2020) conducted a correlation study to calculate an amplitude flicker index from carrier-to-noise density ratio (C/No) based on 1Hz data of a general geodetic GNSS receiver, and called a new amplitude flicker index based on carrier-to-noise ratioAn index. Statistics show that during flicker, obtained from a common receiverIndex and derived from ISMRsThe correlation coefficient between the indices is generally higher than 0.9 and is therefore usableExponential approximation substitutionThe index enables ionospheric scintillation monitoring based on a common GNSS receiver. For ionospheric scintillation suppression, common stochastic models such as altitude weighting models (Elevation Angle Stochastic, EAS) cannot assign appropriate weights to GNSS observations affected by ionospheric scintillation, since scintillation may affect satellites at any altitude, including satellites at altitudes greater than 50 °. In contrast to the EAS model, luo et al (2022) use GPS data based on IGS (International GNSS SERVICE, IGS) high latitude regional survey stationsThe model is weighted, and the test result shows that,The model performs equally poorly during ionospheric scintillation. With EAS modelsThe receiver tracking error stochastic model (RECEIVER TRACKING Error Stochastic, RTES) can attenuate the effects of ionospheric scintillation on GNSS pinpointing (Conker et al., 2003) compared to the localization of the model during ionospheric scintillation. The model fully considers the influences of different types of flicker and intensities thereof on a receiver delay locked loop (Delay Locked Loop, DLL) and a phase locked loop (Phase Locked Loop, PLL), calculates tracking errors of the receiver DLL and the PLL, can allocate proper weights for GNSS observations during ionospheric flicker, and improves positioning accuracy. Several studies have demonstrated that the RTES model can significantly improve the RTK and PPP positioning accuracy during ionospheric scintillation (Aquino et al., 2009; DA SILVA ET al., 2010). Inspired by the RTES model, luo et al (2023) built an improved RTES model based on a common GNSS receiver, i.e. Impr _rtes model, for the GPS system. Experimental results show that the model remarkably improves GPS single-frequency PPP positioning accuracy compared with an EAS model in the period of ionosphere flickering. Since Impr _RTES model builds the PLL tracking error based on the total electron content rate of change index (Rate Of Total electron content Index, ROTI), rather than based on the phase flicker index calculated from th