CN114488233A - Global satellite navigation terminal and navigation positioning method thereof - Google Patents

Global satellite navigation terminal and navigation positioning method thereof Download PDF

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CN114488233A
CN114488233A CN202111654776.1A CN202111654776A CN114488233A CN 114488233 A CN114488233 A CN 114488233A CN 202111654776 A CN202111654776 A CN 202111654776A CN 114488233 A CN114488233 A CN 114488233A
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satellite navigation
observation
observation value
ambiguity
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李一鹤
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Aceinna Transducer Systems Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method

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  • Radar, Positioning & Navigation (AREA)
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Abstract

The invention relates to a global satellite navigation terminal and a navigation positioning method thereof. The global satellite navigation terminal comprises a satellite navigation antenna, a first satellite navigation receiver and a second satellite navigation receiver; and a processing module. The processing module performs the following operations: calculating a zero-baseline double-difference observation value based on satellite navigation data of two satellite navigation receivers, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation value; calculating a single-difference observation value based on an observation value in satellite navigation data of a satellite navigation receiver and a correction number acquired from a navigation server, and performing robust adaptive filtering iteration estimation on an adaptive robust parameter vector based on the calculated single-difference observation value and an observation value random noise matrix; searching and fixing the integer ambiguity based on the floating ambiguity in the adaptive robust parameter vector, and re-resolving the adaptive robust parameter vector based on the fixed ambiguity. Thus, high-precision positioning can be realized based on a more reliable observed value random noise model.

Description

Global satellite navigation terminal and navigation positioning method thereof
Technical Field
The invention relates to the field of navigation, in particular to a global satellite navigation terminal and a navigation positioning method thereof.
Background
Real Time Kinematic (RTK) positioning of Global Navigation Satellite System (GNSS) is a low-cost and high-precision positioning technology. The global satellite navigation system includes GPS in the united states, GLONASS in russia, BEIDOU in china, and GALILEO in the european union. Among them, GPS, BEIDOU and GALILEO navigation signals are based on Code Division Multiple Access (CDMA). Therefore, the frequency of the same frequency band signal is the same for different satellite signals. While GLONASS navigation signals are based on Frequency Division Multiple Access (FDMA).
The GNSS RTK positioning technology is a high-precision positioning technology widely applied in the field of current automatic driving. Compared with GNSS differential positioning (DGNSS), RTK also depends on observation data from a base station, and can effectively eliminate ionospheric error, tropospheric error, satellite clock error and receiver clock error; RTK simultaneously adopts carrier observed quantity to carry out position resolving. Starting from pseudo range and carrier observation quantity modeling, solving float solution through linear optimization, then solving ambiguity (ambiguity) by adopting a popular LAMBDA algorithm, and finally obtaining ambiguity fixing solution.
The solution problem of RTK positioning is relatively mature, and if a good receiver is provided in an open environment, the solution of RTK is relatively likely to obtain a fixed solution, so that a considerable centimeter-level precision can be obtained by using an EKF filtering (extended kalman filtering) mode. However, the key problem is that in an urban environment (where GNSS signals are severely occluded), the original observed quantity has obvious errors due to multipath effects by using a low-cost GNSS receiver (also referred to as a satellite navigation receiver herein); in addition, phase observations also suffer from lock loss and cycle slip problems. These problems are exacerbated in low cost GNSS receivers. This is also one of the main reasons why the RTK cannot realize the correct ambiguity fixing in an urban environment, and thus the positioning accuracy is not ideal. However, the GNSS RTK positioning technology is a very critical part of autopilot, and therefore how to effectively monitor whether the autopilot fusion positioning meets the requirements of the current autopilot system is a very important issue. This leads to the concept of GNSS RTK integrity.
Integrity is a criterion used to gauge whether information provided by the entire system can be trusted. The integrity system needs to send alarm information in time to inform the user that the system is wrong when the system sends out wrong information, so as to avoid the information that the user uses the wrong information. A complete GNSS receiver integrity system incorporates false detection identification, exclusion, and availability detection of navigation results. The core step of the integrity system is the calculation of an estimated parameter Protection level (Protection level), which has the following three characteristics. First, when the protection level is less than the alarm Limit (Alert Limit), the positioning result is considered to be available, whereas the system considers the positioning result to be unavailable. Secondly, the protection level is close to the real error as much as possible and slightly larger than the real error, so that the real RTK positioning result can be reflected to the maximum extent by real-time integrity detection. Finally, the computation of the level of protection of a position is usually done based on the least squares or Kalman (Kalman) filter residuals of GNSS positioning, which means that the accuracy of GNSS integrity detection is also strongly dependent on the accuracy of the positioning results and the observed value random noise model. The traditional integrity system obtains the residual error calculation protection level based on the traditional RTK positioning result, and under the condition that urban GNSS signals are seriously shielded, the residual error cannot truly reflect the real positioning error.
Therefore, it is necessary to provide a solution to the above problems.
Disclosure of Invention
The invention aims to provide a global satellite navigation terminal and a navigation positioning method thereof, which use two satellite navigation receivers connected with the same satellite navigation antenna to realize real-time evaluation of the influence of the surrounding environment on the quality of a GNSS observation value through zero-baseline solution, and can establish a more reliable random noise model of the observation value.
To solve the above technical problem, according to an aspect of the present invention, there is provided a global satellite navigation terminal including: a satellite navigation antenna; the first satellite navigation receiver is connected with the satellite navigation antenna and used for receiving satellite navigation data of a first frequency band and a second frequency band, the satellite navigation data comprises ephemeris data and observation values, and the observation values comprise pseudo-range observation values, phase observation values and Doppler observation values; the second satellite navigation receiver is connected with the satellite navigation antenna and used for receiving satellite navigation data of a first frequency band and a second frequency band; the processing module is connected with the first satellite navigation receiver and the second satellite navigation receiver; and the communication module is connected with the processing module. The processing module performs the following operations: calculating a single-point positioning solution based on satellite navigation data of a satellite navigation receiver, sending the single-point positioning solution to a navigation server, and receiving a correction number obtained by the navigation server according to the single-point positioning solution; calculating a zero-baseline double-difference observation value based on satellite navigation data of two satellite navigation receivers, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation value; calculating a single-difference observation value based on an observation value in satellite navigation data of a satellite navigation receiver and a correction number acquired from the navigation server, and performing robust adaptive filtering iteration estimation on an adaptive robust parameter vector based on the calculated single-difference observation value and the observation value random noise matrix, wherein the adaptive robust parameter vector comprises a position, a speed, a receiver clock error and a floating point ambiguity; and searching and fixing the integer ambiguity based on the floating ambiguity in the self-adaptive robust parameter vector obtained by estimation, and re-resolving the self-adaptive robust parameter vector based on the fixed ambiguity.
According to another aspect of the present invention, the present invention provides a navigation positioning method of a global satellite navigation terminal, comprising: a satellite navigation antenna; the first satellite navigation receiver is connected with the satellite navigation antenna and used for receiving satellite navigation data of a first frequency band and a second frequency band; the navigation method comprises the following steps: calculating a single-point positioning solution based on satellite navigation data of a satellite navigation receiver, sending the single-point positioning solution to a navigation server, and receiving a correction number obtained by the navigation server according to the single-point positioning solution; calculating a zero-baseline double-difference observation value based on satellite navigation data of two satellite navigation receivers, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation value; calculating a single-difference observation value based on an observation value in satellite navigation data of a satellite navigation receiver and a correction number acquired from the navigation server, and performing robust adaptive filtering iteration estimation on an adaptive robust parameter vector based on the calculated single-difference observation value and the observation value random noise matrix, wherein the adaptive robust parameter vector comprises a position, a speed, a receiver clock error and a floating point ambiguity; and searching and fixing the integer ambiguity based on the floating ambiguity in the self-adaptive robust parameter vector obtained by estimation, and re-resolving the self-adaptive robust parameter vector based on the fixed ambiguity.
Compared with the prior art, the method has the advantages that two satellite navigation receivers connected with the same satellite navigation antenna are used, real-time evaluation on the influence of the surrounding environment on the GNSS observation value quality (namely observation value random noise) is realized through zero-baseline solution, and a more reliable observation value random noise model can be established.
Drawings
FIG. 1 is a schematic diagram of a global navigation satellite system according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a GNSS terminal in one embodiment of the present invention;
FIG. 3 is a flow diagram illustrating a navigation method of the GNSS terminal of FIG. 2 in one embodiment; and
fig. 4 is a diagram of a normal distribution of the i-th observation residual error induced bias.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
Fig. 1 is a schematic structural diagram of a global satellite navigation system 100 according to an embodiment of the present invention. The global satellite navigation system 100 includes a global satellite navigation terminal device 102 and a navigation server 106.
The navigation terminal apparatus 102 may be plural. The navigation terminal device 102 can be mounted on a motor vehicle, so as to perform high-precision navigation on driving navigation, especially unmanned navigation, of the motor vehicle. The navigation terminal 102 can communicate with the navigation server 106 via a wireless network 104. The wireless network 104 may be a 2G, 3G, 4G or 5G network, or a combination of multiple networks, such as bluetooth +4G, Wifi + internet +5G, etc., and the present invention has no requirement on the specific type of the wireless network 104, as long as it can support stable communication.
Fig. 2 is a schematic structural diagram of a gnss terminal apparatus 102 according to an embodiment of the present invention. The global satellite navigation terminal device 102 includes a satellite navigation antenna 210, a first satellite navigation receiver 220 connected to the satellite navigation antenna 210, a second satellite navigation receiver 221 connected to the satellite navigation antenna 210, a processing module 230 connected to the first satellite navigation receiver 220 and the second satellite navigation receiver 221, and a communication module 240 connected to the processing module 230.
The first satellite navigation receiver 220 receives satellite navigation data in a first frequency band and a second frequency band. And a second satellite navigation receiver 221 that receives satellite navigation data in the first frequency band and the second frequency band. The satellite terminal of the satellite navigation system such as GPS/Beidou/Galileo/GLONASS can simultaneously broadcast signals of different code systems under a plurality of frequency bands. The first frequency band and the second frequency band may be two frequency bands of a plurality of frequency bands supported by GNSS. The satellite navigation data includes satellite navigation ephemeris data (abbreviated as ephemeris data) and satellite navigation observation values (abbreviated as observation values, which are sometimes referred to as observation data, satellite navigation observation data, etc.). The observations include pseudorange observations, phase observations, and doppler observations. The communication module 240 may be a wireless communication module or a wired communication module. The wireless communication module can communicate through mobile communication (such as 5G, 4G and the like), Wifi wireless communication, Bluetooth and other networks. The communication module 240 may communicate with the navigation server 106 via a wireless network 104.
The processing module 230 performs high-precision navigation positioning, that is, real-time kinematic positioning RTK of GNSS, based on the satellite navigation data of the first frequency band and the second frequency band received by the first satellite navigation receiver 220 and the second satellite navigation receiver 221.
Fig. 3 is a flow diagram illustrating a navigation method 300 of the gnss terminal in fig. 2 according to an embodiment. The processing module 230 executes the navigation method 300. As shown in fig. 3, the navigation method 300 includes the following steps.
Step 310, calculating a single-point positioning solution based on the satellite navigation data of one of the satellite navigation receivers, sending the single-point positioning solution to the navigation server 106, and receiving a correction (or RTK correction) obtained by the navigation server 106 according to the single-point positioning solution.
One of the satellite navigation receivers herein may be the first satellite navigation receiver 220 or the second satellite navigation receiver 221. Specifically, a satellite orbit clock error is calculated based on satellite navigation ephemeris data in the satellite navigation data, and a single-point positioning solution is calculated by combining satellite navigation observation data. The global satellite navigation terminal 102 sends the obtained single-point positioning solution to the navigation server 106, and the navigation server 106 provides an RTK correction number according to the obtained single-point positioning solution.
And step 320, calculating a zero-baseline double-difference observation value based on the satellite navigation data of the two satellite navigation receivers 220 and 221, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation value.
Specifically, the calculating a zero-baseline double-difference observation value based on the satellite navigation data of the two satellite navigation receivers includes the following steps:
performing phase observation cycle slip detection and restoration using the phase observation time difference and the doppler observation of the first satellite navigation receiver 220 and the second satellite navigation receiver 221;
and calculating single-difference observation values of the first satellite navigation receiver and the second satellite navigation receiver, selecting a satellite with the highest satellite altitude angle as a reference satellite according to a satellite system to form a zero-baseline double-difference observation value, wherein the zero-baseline double-difference observation value is calculated by using a phase observation value without cycle slip.
More specifically, the zero-baseline double-difference observation is calculated according to equations (1) - (3):
Figure BDA0003447987350000051
Figure BDA0003447987350000052
Figure BDA0003447987350000053
wherein
Figure BDA0003447987350000054
In order to operate as a double-difference operator,
Figure BDA0003447987350000055
is a zero baseline double-difference observation of pseudoranges,
Figure BDA0003447987350000056
is a zero baseline double-difference observation of the phase,
Figure BDA0003447987350000057
the Doppler zero-baseline double-difference observation values are represented, and P, L and D respectively represent pseudo range, phase and Doppler observation values in satellite navigation observation data; λ is the wavelength; subscripts i and j are receiver numbers; subscript f is frequency; the superscript is s, t is the satellite PRN (pseudo random noise) number, N is the ambiguity, and e is the observation error.
Specifically, the random noise matrix R of the observation value is calculated based on the zero-baseline double-difference observation valuekThe method comprises the following steps:
suppose a moving time window t-tn,t]Is internally provided with niIf the observed values are synchronized in terms of noise, the non-differential pseudo-range observed value, the phase observed value and the unit weight variance of the Doppler observed value in the moving time window
Figure BDA0003447987350000061
Respectively as follows:
Figure BDA0003447987350000062
Figure BDA0003447987350000063
Figure BDA0003447987350000064
wherein i represents the satellite navigation system, j represents the observation epoch serial number,
Figure BDA0003447987350000065
error in unit weight for the estimated observations;
then, an altitude-based weighting method is adopted to obtain the initial variance σ of the observation value of the GNSS2：
Figure BDA0003447987350000066
Figure BDA0003447987350000067
Figure BDA0003447987350000068
Wherein s represents the satellite number, σiRepresenting the standard deviation of the noise of the observation of satellite i, EsRepresents the altitude of the satellite s;
finally, the observed value random noise matrix RkIs composed of
Figure BDA0003447987350000069
Wherein k is an observation epoch, RP,k，RD,k，RL,kRespectively, pseudo range, Doppler, random noise matrix of phase observation value. The observation value random noise matrix is the observation value random noise model. The double-frequency satellite navigation receiver based on 2 low-cost satellites can realize real-time evaluation of the surrounding environment on the GNSS observation value quality (observation value random noise) through zero-baseline solution, and is more reliable to establishObserved value random noise model Rk。
And 330, calculating a single-difference observation value based on an observation value in satellite navigation data of a satellite navigation receiver and a correction number acquired from the navigation server, and performing robust adaptive filtering iterative estimation on an adaptive robust parameter vector based on the calculated single-difference observation value and the observation value random noise matrix, wherein the adaptive robust parameter vector comprises a position, a speed, a receiver clock error and a floating point ambiguity.
Specifically, an adaptive robust parameter vector is calculated according to equation (10)
Figure BDA0003447987350000071
Figure BDA0003447987350000072
Wherein
Figure BDA0003447987350000073
Is an adaptive robust parameter vector containing parameters of position, velocity, receiver clock error and ambiguity, HkDesigning a matrix for the observations, LkIn order to be a vector of observations,
Figure BDA0003447987350000074
an equivalence weight matrix which is an observed quantity;
Figure BDA0003447987350000075
in order to predict the state vector(s),
Figure BDA0003447987350000076
a weight matrix that is a prediction state vector; a is more than or equal to 0kThe adaptive factor is less than or equal to 1.
Step 340, searching and fixing integer ambiguities based on floating ambiguity in the estimated adaptive robust parameter vector, and re-resolving the adaptive robust parameter vector based on the fixed ambiguities
Figure BDA0003447987350000077
Specifically, the searching and fixing integer ambiguities based on floating ambiguity in the estimated adaptive robust parameter vector includes:
substituting the floating ambiguity in the self-adaptive robust parameter vector obtained by estimation and a covariance matrix of the floating ambiguity, searching the whole-cycle ambiguity by using an LAMBDA method, verifying whether the ambiguity passes the detection, if the ambiguity passes the detection, continuously checking whether the ambiguity is fixed correctly through a double-frequency combined phase observation value and a ambiguity fixed residual value, and removing the ambiguity with fixed errors.
The adaptive robust parameter vector based on re-solution
Figure BDA0003447987350000078
The position and the velocity in (1) can realize positioning and navigation, so far, the navigation method 300 in the invention can realize RTK navigation positioning.
As described in the background, the navigation method 300 is more accurately evaluated for completeness. The navigation method 300 of the present invention further includes the following steps.
And 350, calculating the residual error of the observation value of the current epoch by using the observation value random noise matrix obtained by calculation and the observation value vector obtained by calculation in the robust adaptive filtering process.
Specifically, the current epoch observed value residual rkComprises the following steps:
Figure BDA0003447987350000079
wherein,
Figure BDA00034479873500000710
variance of observed residual, LkIs an observation vector;
and 360, calculating non-centrality parameters of different parameters according to the distribution of the observed value residual error of the current epoch after the coarse difference is introduced, wherein the different parameters comprise position and speed.
In particular, assume that there is a gross error in the observed values
Figure BDA0003447987350000081
Then L in (11)kThe rewrite of (1) is:
Figure BDA0003447987350000082
Figure BDA0003447987350000087
let us consider introducing gross errors, assume rkIs unbiased rk～N（0,Cr) M is the projection function of the gross error and the observed value, and the residual error rkThe deviation introduced due to the gross error is Δ rkIn conjunction with equation (11), the residual distribution after introducing the gross error can be found as:
Figure BDA0003447987350000083
after normalization, the deviation introduced by the ith observation value residual obeys normal distribution as follows:
Figure BDA0003447987350000084
wherein the residual mean of the observed values
Figure BDA0003447987350000085
As shown in fig. 4, the region designated by α/2 in fig. 4 is collectively referred to as a region where a class I error occurs (i.e., where a zero hypothesis is erroneously rejected), the region designated by β has an occurrence probability of α, the region designated by β has an occurrence probability of a class II error (i.e., where a zero hypothesis is erroneously accepted), and the occurrence probability of β. Thus, the non-centrality parameter δ0The approximation is:
δ0＝N1-α/2+N1-β (16)
wherein N is1-α/2For class I errors, in different positions, N1-βFor the partial position of the II type error, a confidence interval is given to obtain the partial positions of the two types of errors, and then the non-centrality parameters of different parameters are calculated.
Step 370, the minimum detectable gross error is calculated under the current confidence interval based on the non-centrality parameters of the different parameters.
Specifically, the minimum detectable gross error is calculated according to the formula (17) under the current confidence interval based on the non-centrality parameters of different parameters
Figure BDA0003447987350000086
Comprises the following steps:
Figure BDA0003447987350000091
step 380, calculating the protection level of each parameter in the adaptive robust parameter vector based on the minimum detectable gross error.
Specifically, the level of protection for each parameter in the adaptive robust parameter vector is calculated according to equation (18) based on the minimum detectable gross error
Figure BDA0003447987350000092
Figure BDA0003447987350000093
Figure BDA0003447987350000094
Including location, velocity, receiver clock error, and level of protection for ambiguities.
Figure BDA0003447987350000095
Reflecting the effect of gross error (the largest residual value) on each parameter that cannot be detected in existing filtering.
Step 390, comparing the protection level of each parameter obtained by calculation with a preset alarm limit value, and approving the positioning result in the adaptive robust parameter vector when the protection level of each parameter is smaller than the preset alarm limit value, otherwise, not approving the positioning result in the adaptive robust parameter vector and sending an alarm to the user.
According to the method, the GNSS double-satellite navigation receiver is used for realizing the refinement of the random noise model of the real-time navigation satellite observation data, the RTK positioning result precision is improved, and meanwhile, the more accurate position protection level is calculated through the residual error calculated by the double-frequency combined phase observation value and the optimized random noise model, so that the accuracy and the reliability of the integrity system are improved.
The adaptive robust kalman filter is described in detail below.
The robust Kalman filtering is developed on the basis of standard Kalman filtering, focuses on the actual anti-interference performance and reliability of filtering estimation, can correspondingly adjust the weight of observation information, and is more suitable for positioning calculation in a complex dynamic environment. The error equations for the Kalman filtering state model and the observation model are as follows:
Figure BDA0003447987350000096
Figure BDA0003447987350000097
in the above formula, the first and second carbon atoms are,
Figure BDA0003447987350000098
Vkrespectively representing a state prediction residual and an observation residual, and constructing the following extreme value equation in order to control the influence of observation abnormity on filtering estimation:
Figure BDA0003447987350000099
the extreme value is calculated for the state parameter vector, and the obtained robust adaptive filtering solution is:
Figure BDA0003447987350000101
in the formula,
Figure BDA0003447987350000102
an equivalence weight matrix which is an observed quantity; rkFor the computed random noise matrix of the navigation satellite observations from step 7,
Figure BDA0003447987350000103
a weight matrix that is a prediction state vector; a is more than or equal to 0kLess than or equal to 1 is an adaptive factor. From the above formula, it can be seen that when the quantity L is observedkIncluding gross error observed quantity, by an equivalent weight matrix
Figure BDA0003447987350000104
Or equivalent covariance matrix
Figure BDA0003447987350000105
The influence of the observation quantity containing the gross error on the state parameter estimation can be controlled; when the state prediction information has abnormity, the adaptive factor a is usedkThe impact of the predicted state information on the state parameter estimates may be controlled. Thus, the equivalent covariance matrix
Figure BDA0003447987350000106
And an adaptation factor akIs the key to realize the robust adaptive filtering.
In robust adaptive filtering, the weight reduction processing is usually performed on abnormal observation quantity containing gross error, and the equivalent covariance matrix is adopted as follows:
Figure BDA0003447987350000107
in the formula,
Figure BDA0003447987350000108
is a diagonal matrix with elements p on the diagonaliCan be determined by IGG-III three-segment weight function model, i.e.
Figure BDA0003447987350000109
In the formula, k0And k1To verify the threshold, the value is typically k0＝1.0～2.5，k1＝3.5～8.0；
Figure BDA00034479873500001015
For the normalized residual, the calculation method is as follows:
Figure BDA00034479873500001010
in the formula, viIs an observed residual; riA noise variance for the ith observation;
Figure BDA00034479873500001011
is an estimate of the unit weight variance factor.
For the adaptive factor akConstruction is typically based on a three-segment function of the state misfit statistics, namely:
Figure BDA00034479873500001012
in the formula, c0And c1For detection threshold, the value is typically c0＝1.0～1.5，c1＝3.0～8.5；
Figure BDA00034479873500001013
For the state inconsistency statistic, the calculation formula is:
Figure BDA00034479873500001014
in the formula| · | | represents modulo arithmetic; tr represents a trace operation;
Figure BDA0003447987350000111
representing a predicted state;
Figure BDA0003447987350000112
the method is based on the observation information of the current epoch and is solved by least squares, namely:
Figure BDA0003447987350000113
when the robust adaptive filtering is executed, firstly, the motion model information of the carrier is diagnosed, and if the motion model is detected to be abnormal, the adaptive factor a is constructed according to a formula three-segment function formulakAdjusting the covariance matrix of the predicted state; and then diagnosing the observation information of the carrier, and if a gross error exists in a certain observed quantity, constructing a weight scaling factor according to an IGG-III three-section weight function model to adjust the weight of the observed quantity.
As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.
In this document, the terms front, back, upper and lower are used to define the components in the drawings and the positions of the components relative to each other, and are used for clarity and convenience of the technical solution. It is to be understood that the use of the directional terms should not be taken to limit the scope of the claims.
The features of the embodiments and embodiments described herein above may be combined with each other without conflict.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A global satellite navigation terminal, comprising:
a satellite navigation antenna;
the first satellite navigation receiver is connected with the satellite navigation antenna and used for receiving satellite navigation data of a first frequency band and a second frequency band, the satellite navigation data comprises ephemeris data and observation values, and the observation values comprise pseudo-range observation values, phase observation values and Doppler observation values;
the second satellite navigation receiver is connected with the satellite navigation antenna and used for receiving satellite navigation data of a first frequency band and a second frequency band;
the processing module is connected with the first satellite navigation receiver and the second satellite navigation receiver; and
the communication module is connected with the processing module;
wherein the processing module performs the following operations:
calculating a single-point positioning solution based on satellite navigation data of a satellite navigation receiver, sending the single-point positioning solution to a navigation server, and receiving a correction number obtained by the navigation server according to the single-point positioning solution;
calculating a zero-baseline double-difference observation value based on satellite navigation data of two satellite navigation receivers, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation value;
calculating a single-difference observation value based on an observation value in satellite navigation data of a satellite navigation receiver and a correction number acquired from the navigation server, and performing robust adaptive filtering iteration estimation on an adaptive robust parameter vector based on the calculated single-difference observation value and the observation value random noise matrix, wherein the adaptive robust parameter vector comprises a position, a speed, a receiver clock error and a floating point ambiguity;
and searching and fixing the integer ambiguity based on the floating ambiguity in the self-adaptive robust parameter vector obtained by estimation, and re-resolving the self-adaptive robust parameter vector based on the fixed ambiguity.
2. The global satellite navigation terminal of claim 1, wherein computing a single point location solution based on satellite navigation data from a satellite navigation receiver comprises:
and calculating satellite orbit clock error based on satellite navigation ephemeris data of a satellite navigation receiver, and calculating a single-point positioning solution by combining the satellite navigation observation data of the satellite navigation receiver.
3. The global satellite navigation terminal of claim 1, wherein said computing a zero baseline double-difference observation based on satellite navigation data of two satellite navigation receivers comprises:
performing phase observation value cycle slip detection and repair by using the phase observation value time difference and the Doppler observation value of the first satellite navigation receiver and the second satellite navigation receiver;
and calculating single-difference observation values of the first satellite navigation receiver and the second satellite navigation receiver, selecting a satellite with the highest satellite altitude angle as a reference satellite according to a satellite system to form a zero-baseline double-difference observation value, wherein the zero-baseline double-difference observation value is calculated by using a phase observation value without cycle slip.
4. The global satellite navigation terminal of claim 3, wherein the zero baseline double-difference observation is calculated according to equations (1) - (3):
Figure RE-FDA0003584947610000021
Figure RE-FDA0003584947610000022
Figure RE-FDA0003584947610000023
wherein
Figure RE-FDA0003584947610000024
The operator is a double difference operator, and P, L and D respectively represent pseudo range, phase and Doppler observed value in satellite navigation observed data;
Figure RE-FDA0003584947610000025
is a zero baseline double-difference observation of pseudoranges,
Figure RE-FDA0003584947610000026
is a zero baseline double-difference observation of the phase,
Figure RE-FDA0003584947610000027
a zero baseline double difference observation for doppler; λ is the wavelength; subscripts i and j are receiver numbers; subscript f is frequency; the superscript is s, t is the satellite PRN number, N is the ambiguity, and ε is the observed error.
5. The global satellite navigation terminal of claim 1, wherein said computing an observation random noise matrix based on the zero-baseline double-difference observation comprises:
suppose a moving time window t-tn,t]Is internally provided with niAnd if the observed values are synchronous in noise, the unit weight variance of the non-differential pseudo-range observed value, the phase observed value and the Doppler observed value in the moving time window is respectively as follows:
Figure RE-FDA0003584947610000028
Figure RE-FDA0003584947610000029
Figure RE-FDA00035849476100000210
wherein i represents the satellite navigation system, j represents the observation epoch serial number,
Figure RE-FDA00035849476100000211
error in unit weight for the estimated observations;
adopting an altitude-based weighting method to obtain the initial variance of the observation value of the GNSS:
Figure RE-FDA00035849476100000212
Figure RE-FDA0003584947610000031
Figure RE-FDA0003584947610000032
wherein s represents the satellite number, σiRepresenting the standard deviation of the observed noise of satellite i, EsRepresents the altitude of the satellite s;
observed value random noise matrix RkIs composed of
Figure RE-FDA0003584947610000033
Wherein k is an observation epoch, RP,k，RD,k，RL,kRespectively, pseudo range, Doppler, random noise matrix of phase observation value.
6. The global satellite navigation terminal of claim 1, wherein the adaptive robust parameter vector is calculated according to equation (10)
Figure RE-FDA0003584947610000034
Figure RE-FDA0003584947610000035
Wherein
Figure RE-FDA0003584947610000036
Is an adaptive robust parameter vector containing parameters of position, velocity, receiver clock error and ambiguity, HkDesigning a matrix for the observations, LkIn order to be a vector of observations,
Figure RE-FDA0003584947610000037
an equivalence weight matrix which is an observed quantity;
Figure RE-FDA0003584947610000038
in order to predict the state vector(s),
Figure RE-FDA0003584947610000039
a weight matrix that is a prediction state vector; a is more than or equal to 0kLess than or equal to 1 is an adaptive factor.
7. The global satellite navigation terminal of claim 1, wherein said searching for and fixing integer ambiguities based on floating ambiguities in estimated adaptive robust parameter vectors comprises:
substituting the floating ambiguity and the covariance matrix in the self-adaptive robust parameter vector obtained by estimation, searching the integer ambiguity by using an LAMBDA method, verifying whether the ambiguity passes the detection, if the ambiguity passes the detection, continuously checking whether the ambiguity is fixed correctly by using a double-frequency combined phase observation value and a fuzzy fixed residual value, and removing the ambiguity with a fixed error.
8. The global satellite navigation terminal of claim 1, wherein said processing module further performs the following:
calculating the residual error of the observation value of the current epoch by using the observation value random noise matrix obtained by calculation and the observation value vector obtained by calculation in the robust adaptive filtering process;
calculating non-centrality parameters of different parameters according to the distribution of the observed value residual error of the current epoch after the coarse difference is introduced, wherein the different parameters comprise position and speed;
calculating to obtain the minimum detectable gross error under the current confidence interval based on the non-centrality parameters of different parameters;
calculating a protection level of each parameter in the adaptive robust parameter vector based on the minimum detectable gross error;
and comparing the protection level of each parameter obtained by calculation with a preset alarm limit value, and recognizing the positioning result in the adaptive robust parameter vector when the protection level of each parameter is smaller than the preset alarm limit value, or else, not recognizing the positioning result in the adaptive robust parameter vector.
9. The global satellite navigation terminal of claim 8,
residual error r of observed value of current epochkComprises the following steps:
Figure RE-FDA0003584947610000041
wherein,
Figure RE-FDA0003584947610000042
variance of observed residual, LkIs an observation vector;
calculating non-centrality parameters of different parameters according to the distribution of the observed value residual error of the current epoch after the coarse error is introduced comprises the following steps:
assuming that there is a gross difference in the observed values
Figure RE-FDA0003584947610000043
Then L in (11)kThe rewrite of (1) is:
Figure RE-FDA0003584947610000044
Figure RE-FDA0003584947610000045
let us consider introducing gross errors, assume rkIs unbiased rk～N(0,Cr) M is the projection function of the gross error and the observed value, and the residual error rkThe deviation introduced due to the gross error is Δ rkIn conjunction with equation (11), the residual distribution after introducing the gross error can be found as:
Figure RE-FDA0003584947610000046
after normalization, the deviation introduced by the ith observation value residual obeys normal distribution as follows:
Figure RE-FDA0003584947610000051
wherein the residual mean value of the observed values
Figure RE-FDA0003584947610000052
Normalized residual translation values, called centrality parameter, representing a normal distribution, non-centrality parameter δ0The approximation is:
δ0＝N1-α/2+N1-β (16)
wherein N is1-α/2For class I errors, in different positions, N1-βFor the sub-position where the II type error occurs, a confidence interval is given to obtain the sub-position of the two types of errors, and then the non-centrality parameters of different parameters are calculated;
based on the non-centrality parameters of different parameters, the minimum detectable gross error is calculated according to the formula (17) under the current confidence interval
Figure RE-FDA0003584947610000053
Comprises the following steps:
Figure RE-FDA0003584947610000054
computing a level of protection for each parameter in the adaptive robust parameter vector based on the minimum detectable gross error according to equation (18)
Figure RE-FDA0003584947610000055
Figure RE-FDA0003584947610000056
Figure RE-FDA0003584947610000057
Including the level of protection for position, velocity, receiver clock error, and ambiguity.
10. A navigation positioning method of a global satellite navigation terminal, the global satellite navigation terminal comprising: a satellite navigation antenna; the first satellite navigation receiver is connected with the satellite navigation antenna and used for receiving satellite navigation data of a first frequency band and a second frequency band; the navigation method comprises the following steps:
calculating a single-point positioning solution based on satellite navigation data of a satellite navigation receiver, sending the single-point positioning solution to a navigation server, and receiving a correction number obtained by the navigation server according to the single-point positioning solution;
calculating a zero-baseline double-difference observation value based on satellite navigation data of two satellite navigation receivers, and calculating an observation value random noise matrix based on the zero-baseline double-difference observation value;
calculating a single-difference observation value based on an observation value in satellite navigation data of a satellite navigation receiver and a correction number acquired from the navigation server, and performing robust adaptive filtering iteration estimation on an adaptive robust parameter vector based on the calculated single-difference observation value and the observation value random noise matrix, wherein the adaptive robust parameter vector comprises a position, a speed, a receiver clock error and a floating point ambiguity;
and searching and fixing the integer ambiguity based on the floating ambiguity in the self-adaptive robust parameter vector obtained by estimation, and re-resolving the self-adaptive robust parameter vector based on the fixed ambiguity.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115856973A (en) * 2023-02-21 2023-03-28 广州导远电子科技有限公司 GNSS resolving method and device, positioning system, electronic equipment and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115856973A (en) * 2023-02-21 2023-03-28 广州导远电子科技有限公司 GNSS resolving method and device, positioning system, electronic equipment and storage medium

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