CN115327584A - GNSS observation value weighting method, terminal and medium integrating multiple indexes - Google Patents
GNSS observation value weighting method, terminal and medium integrating multiple indexes Download PDFInfo
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Abstract
The invention discloses a GNSS observation value weighting method, a terminal and a medium integrating multiple indexes, wherein the method comprises the following steps: acquiring a current GNSS observation value; calculating a carrier-to-noise ratio nominal value based on the altitude angle observation value of the current GNSS observation value; calculating to obtain a PLD nominal value based on the carrier-to-noise ratio nominal value; calculating to obtain a carrier-to-noise ratio threshold value based on the altitude angle observation value of the current GNSS observation value; calculating to obtain a PLD threshold value based on the carrier-to-noise ratio nominal value; acquiring a carrier-to-noise ratio observation value and a PLD observation value of a current GNSS observation value, subtracting the carrier-to-noise ratio observation value from a carrier-to-noise ratio nominal value, and subtracting the PLD observation value from the PLD nominal value; and comparing the two difference values with corresponding threshold values respectively, and finally determining a combined weighting index and weight. The accuracy of the weighting is effectively improved, and then the positioning accuracy and reliability are improved, which is very necessary and beneficial for realizing the positioning with high accuracy and high reliability in the complex environment nowadays.
Description
Technical Field
The invention relates to the technical field of positioning, in particular to a method, a terminal and a medium for weighting GNSS observation values by integrating multiple indexes.
Background
In many industries such as surveying and mapping, smart agriculture and automatic driving, the positioning accuracy of the current GNSS (global navigation satellite system) can not effectively meet the requirements of high accuracy and high reliability. In view of the current state of high-precision GNSS positioning technology, GNSS is easily affected by objective conditions, which makes it difficult to meet application requirements in aspects of stability, reliability, safety, cost, etc. For example, in the sheltering or strong multipath environments such as tree shadows, buildings, glass curtain walls and elevated buildings, GNSS signals are prone to losing lock or frequent cycle slips, and accordingly positioning accuracy and reliability are reduced. The quality of the GNSS observation quality and whether the influence of the abnormal observation is effectively detected, weakened or eliminated directly determine the reliability and accuracy of the positioning result, so that the GNSS observation quality must be reliably detected and the observation weight must be determined when high-precision positioning is performed.
In view of the above problems, a large number of researchers have been conducting research. At present, the following methods are mainly used:
(1) Random model based on altitude angle. The random model is determined by using a satellite altitude angle, the satellite altitude angle has no influence on an observed value, but the influence of an atmospheric delay error on a signal propagation path on the quality of the observed value is reduced along with the rise of the altitude angle, and secondly, the influence of a multipath error is related to the satellite altitude angle.
(2) A stochastic model based on carrier-to-noise ratio. The stochastic model is a stochastic model determined by utilizing a carrier-to-noise ratio and is used for estimating the variance of the observed value, and the stochastic model can effectively weaken the influence of multipath effect and diffraction error.
(3) The elevation angle is combined with a carrier-to-noise ratio stochastic model. In some environments, the positioning accuracy may not be ideal using a single stochastic model for positioning. Therefore, the scholars establish an integrated weight-determining model based on satellite altitude and carrier-to-noise ratio.
The above methods have the following problems:
(1) in an open environment, the higher the altitude angle, the better the observation quality, but in a complex environment, the GNSS observation quality is affected by factors such as shading, reflection, and diffraction, and the positive correlation between the altitude angle and the observation quality does not hold. At this time, the index cannot accurately indicate the observation quality.
(2) The output of the carrier-to-noise ratio has certain time delay, and the estimated smooth period of the time delay can reach 1 second; some receiver manufacturers also filter the carrier-to-noise ratio output value, so that the carrier-to-noise ratio cannot completely and accurately reflect the quality of the real-time observed value; also, when the carrier-to-noise ratio is low, it is likely that cycle slip does not occur because the elevation angle is low. Therefore, certain misjudgment exists in the quality evaluation of the observed value based on the carrier-to-noise ratio.
(3) And the elevation angle combined carrier-to-noise ratio random model utilizes the actual satellite signal intensity to constrain a weighting model based on the satellite elevation angle. However, in some cases, neither elevation angle nor carrier-to-noise ratio are effective indicators of observation quality. Moreover, if the weight determination of each index is incorrect, the reliability and accuracy of the positioning result will be affected.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a comprehensive multi-index GNSS observation value weight determining method, a terminal and a medium, so as to solve the problems of poor effect and low positioning precision of the conventional weight determining method.
In a first aspect, a method for weighting GNSS observation values by integrating multiple indexes is provided, including:
s1: acquiring a current GNSS observation value;
s2: calculating a carrier-to-noise ratio nominal value based on the altitude angle observation value of the current GNSS observation value;
s3: calculating to obtain a PLD nominal value based on the carrier-to-noise ratio nominal value;
s4: calculating altitude angle observation value based on current GNSS observation value to obtain carrier-to-noise ratio threshold SNR STD ；
S5: PLD threshold value PLD is obtained based on carrier-to-noise ratio nominal value calculation STD ；
S6: obtaining a carrier-to-noise ratio observed value and a PLD observed value of the current GNSS observed value, and subtracting the carrier-to-noise ratio observed value from a carrier-to-noise ratio nominal value to obtain delta SNR And obtaining delta by subtracting the PLD observed value from the PLD nominal value PLD ；
S7: if Δ SNR ≤2×SNR STD And Δ PLD ≤2×PLD STD Weighting by using a height angle empirical random model;
if Δ SNR ≤2×SNR STD And Δ PLD >2×PLD STD Weighting by using an altitude angle combined carrier-to-noise ratio random model;
if Δ SNR >2×SNR STD And Δ PLD ≤2×PLD STD Then, using the elevation angle to jointly determine the weight of the PLD random model;
if Δ SNR >2×SNR STD And Δ PLD >2×PLD STD The weight of the GNSS observation is set to zero.
Further, the step S1 further includes rejecting GNSS observations whose PLD is less than 6 or whose PLD is greater than 100.
Further, the step S2 specifically includes:
and obtaining the altitude angle observation value of the current GNSS observation value, and calculating by using a carrier-to-noise ratio template function based on the altitude angle to obtain a carrier-to-noise ratio nominal value.
Further, the step S3 specifically includes:
and acquiring a nominal value of the carrier-to-noise ratio, and calculating by using a PLD template function based on the carrier-to-noise ratio to obtain the PLD nominal value.
Further, the step S4 specifically includes:
obtaining an altitude angle observation value of the current GNSS observation value, and calculating by utilizing a carrier-to-noise ratio precision template function based on an altitude angle to obtain a carrier-to-noise ratio threshold SNR STD 。
Further, the step S5 specifically includes:
obtaining a nominal value of the carrier-to-noise ratio, and calculating by utilizing a PLD (programmable logic device) precision template function based on the carrier-to-noise ratio to obtain a PLD (programmable logic device) threshold value PLD STD . Further, in step S7, the altitude empirical stochastic model is expressed as follows:
σ Ele ＝c 2 +d 2 /sin 2 (Ele)
in the formula, σ Ele The variance of the observed value is determined for the empirical stochastic model of the elevation angle, c and d are constants, and Ele is the observed value of the elevation angle; the elevation angle joint carrier-to-noise ratio stochastic model is represented as follows:
σ all ＝a×σ SNR +b×σ Ele
in the formula, σ all Indicating altitude angle unionVariance, sigma, of observed values determined by a carrier-to-noise ratio stochastic model SNR The variance of the observation value determined by the empirical random model of the carrier-to-noise ratio, and a and b are the weight of the variance of the observation value determined by the corresponding random model respectively;
the carrier-to-noise ratio empirical stochastic model is expressed as follows:
in the formula, B i Tracking loop bandwidth, λ, for carrier phase i Is the carrier wavelength, C/N 0 The carrier-to-noise ratio observed value of the current GNSS observed value is alpha, which is a constant, and delta is the difference between the carrier-to-noise ratio observed value and a theoretical value;
the elevation angle joint PLD stochastic model is expressed as follows:
σ all ′＝e×σ PLD +f×σ Ele
in the formula, σ all ' expressing variance of observed values, σ, of elevation angle joint PLD random model PLD And e and f are respectively the observed value variance weights determined by the corresponding random models.
Further, the PLD random model is obtained by the following method:
acquiring and processing a GNSS observation value to obtain PLD and carrier phase single difference residual sequences of all epochs of each satellite;
and (3) representing the carrier phase precision by using the standard deviation calculated by the carrier phase single-difference residual sequence, constructing an observation value precision estimation formula according to the relation between the PLD and the carrier phase precision, and further constructing a weight-fixing function based on the PLD to obtain a PLD random model.
Further, after determining the weight of the GNSS observation, the method further includes:
s8: performing first adjustment on the GNSS observation value by using the following formula, thereby obtaining residual error information of the observation value;
v＝Ax-l
in the formula, v is an observed value residual error vector, A is a design matrix, x is an unknown parameter vector, and l is an observed vector;
s9: calculating an robust coefficient of the GNSS observation value by using an IGG III model, and re-weighting the robust coefficient; wherein igiii model re-weighting is represented as follows:
in the formula, p i Is a weight of the GNSS observations,
is the coefficient of tolerance, k 1 、k 2 Is a constant number, k 1 The value range is 1.5-2.0 2 The value range is 3.0-8.5; v. of i The ith element of the observation value residual vector is, and sigma is a variance factor;
σ＝med(|v i |)/0.6745
wherein med (| · |) is a function of median;
s10: if the robust coefficient is converged, performing final adjustment; otherwise, repeating the steps S8-S9 after updating the weight matrix until the robust coefficient is converged.
In a second aspect, an electronic terminal is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the integrated multi-index GNSS observation weighting method as described above when executing the computer program.
In a third aspect, a computer readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the integrated multi-index GNSS observation weighting method as described above.
Advantageous effects
The invention provides a comprehensive multi-index GNSS observation value weighting method, a terminal and a medium, which introduce PLD as an observation value quality evaluation index for the first time and make up the defect of observation value quality evaluation by using a height angle and a carrier-to-noise ratio at present; a comprehensive random model is established by utilizing PLD, an altitude angle and a carrier-to-noise ratio for the first time, and a prior weighting method based on indexes such as the altitude angle, the carrier-to-noise ratio and the like is optimized; the comprehensive random model based on the PLD, the altitude angle and the carrier-to-noise ratio is supplemented by using a robust estimation method for the first time. The PLD is not influenced by atmospheric delay errors and an observation station environment, is only related to a satellite tracking state, does not have a time delay phenomenon, can effectively indicate cycle slip, and is related to a periodic variation part of a carrier phase double-difference residual error. Considering that the comprehensive stochastic model can perform relatively accurate prior weighting, but still cannot completely indicate the quality of the observed value, the robust estimation method is provided for detecting gross errors, so that the defects of the current stochastic model can be effectively overcome, the positioning accuracy and reliability are effectively improved, and the method is very necessary and beneficial to realizing high-accuracy and high-reliability positioning in a complex environment nowadays.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a GNSS observation weighting method with multiple indicators integrated according to an embodiment of the present invention
Fig. 2 (a) and (B) are respectively fitted curves of height angle and carrier-to-noise ratio at a GPS L1 frequency point and a BDS B1 frequency point according to an embodiment of the present invention;
fig. 3 (a) and (B) are fitting curves of the download noise ratio of the GPS L1 frequency point and the BDS B1 frequency point provided in the embodiment of the present invention and the PLD, respectively;
fig. 4 (a) and (B) are fitting curves of elevation angle and carrier-to-noise ratio accuracy at the GPS L1 frequency point and the BDS B1 frequency point, respectively, according to an embodiment of the present invention;
fig. 5 (a) and (B) are fitting curves of the download noise ratio of the GPS L1 frequency point and the BDS B1 frequency point and the PLD precision, respectively, according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
First, referring to PLD, a phase lock indication value PLD is a numerical index for indicating a tracking state of a signal, which is calculated from output signals of an in-phase branch (I branch) and an in-quadrature branch (Q branch) of a phase-locked loop.
I i (t)＝aD(t)cos(ω e t+θ e )
Q i (t)＝aD(t)sin(ω e t+θ e )
Wherein, I i An in-phase signal at time i; q i Is the quadrature signal at time i; a is the signal amplitude; d (t) is a data code modulated on a carrier wave; omega e Is the carrier frequency difference between the input signal and the local replica signal; theta e Is the initial phase difference between the input signal and the local replica signal.
Where k is the result of the kth coherent integration, and M is the time of one coherent integration.
Based on this, as shown in fig. 1, an embodiment of the present invention provides a method for weighting GNSS observation values based on multiple indicators, including:
s1: acquiring a current GNSS observation value, and rejecting the GNSS observation value with the PLD smaller than 6 or the PLD larger than 100; if the PLD is not in the interval [6, 100], the cycle slip is likely to occur, and therefore, the cycle slip occurrence probability can be reduced by eliminating this data.
S2: and calculating to obtain a carrier-to-noise ratio nominal value based on the altitude angle observation value of the current GNSS observation value. The method specifically comprises the following steps:
and obtaining the altitude angle observation value of the current GNSS observation value, and calculating by using a carrier-to-noise ratio template function based on the altitude angle to obtain a carrier-to-noise ratio nominal value.
In this embodiment, a carrier-to-noise ratio template function based on an altitude angle is established by using GNSS observation data acquired in an open environment for about 72 hours. The establishment process comprises the following steps: and drawing a scatter diagram by taking the altitude angle data as an X axis and the carrier-to-noise ratio data as a Y axis, and then carrying out curve fitting on the scatter diagram by using a curve fitting tool to obtain carrier-to-noise ratio template functions corresponding to the GPS L1 frequency point and the BDS B1 frequency point.
In the following embodiments, a GPS L1 frequency point and a BDS B1 frequency point are taken as examples for explanation. Fig. 2 (a) and fig. 2 (B) show carrier-to-noise ratios corresponding to different altitude angles of GPS L1 and BDS B1 frequency points and corresponding fitting curves, where a scattered point represents an actual observed value of the carrier-to-noise ratio corresponding to different altitude angles in an open environment, and a solid line represents a fitting result, and it can be seen from the figure that both the fitting curves can relatively truly reflect a functional relationship between the altitude angle and the carrier-to-noise ratio of the two frequency points, and therefore, by using the altitude-based carrier-to-noise ratio template function, a nominal value of the carrier-to-noise ratio corresponding to the altitude angle of the current GNSS observed value can be calculated.
The carrier-to-noise ratio template functions of the GPS L1 and BDS B1 frequency points based on the altitude angle are respectively as follows:
Template_SNR G ＝7.159×Ele 0.2569 +29.98
Template_SNR C ＝-49.13×Ele -0.1082 +81.2
in the formula, emplate _ SNR represents a nominal value of a carrier-to-noise ratio corresponding to the current altitude angle observation value, subscripts G and C represent GPS and BDS satellite systems, respectively, and Ele is an altitude angle observation value. The carrier-to-noise ratio nominal values corresponding to different altitude angles of the two frequency points of the GPS L1 and the BDS B1 can be calculated through the formula.
S3: and calculating to obtain a PLD nominal value based on the carrier-to-noise ratio nominal value. The method specifically comprises the following steps:
and acquiring a nominal value of the carrier-to-noise ratio, and calculating by using a PLD template function based on the carrier-to-noise ratio to obtain the PLD nominal value.
The building process of the PLD template function based on the carrier-to-noise ratio is consistent with the building process of the PLD template function based on the altitude angle, and a scatter diagram is drawn by taking carrier-to-noise ratio data as an X axis and PLD data as a Y axis. Fig. 3 (a) and fig. 3 (B) show PLDs and corresponding fitting curves corresponding to different carrier-to-noise ratios of GPS L1 and BDS B1 frequency points, wherein scattered points and solid lines represent the PLDs and fitting results corresponding to the different carrier-to-noise ratios, respectively.
The PLD template functions of the GPS L1 and BDS B1 frequency points based on the carrier-to-noise ratio are respectively as follows:
Template_PLD G ＝140.2+44.17×cos(SNR G ×0.09244)+174.6×sin(SNR G ×0.09244)-40.28×cos(2×SNR G ×0.09244)+23×sin(2×SNR G ×0.09244)
in the formula, template _ PLD represents the PLD nominal value corresponding to the current carrier-to-noise ratio, subscripts G and C represent the GPS and BDS satellite systems, respectively, and SNR is the carrier-to-noise ratio nominal value. The nominal values of PLD corresponding to different carrier-to-noise ratios of GPS L1 and BDS B1 frequency points can be respectively obtained through the above formula.
S4: calculating altitude angle observation value based on current GNSS observation value to obtain carrier-to-noise ratio threshold SNR STD . The method specifically comprises the following steps:
obtaining an altitude angle observation value of the current GNSS observation value, and calculating by utilizing a carrier-to-noise ratio precision template function based on an altitude angle to obtain a carrier-to-noise ratio threshold SNR STD 。
Because the carrier-to-noise ratio data corresponding to different elevation angles are not determined values but fluctuate within a certain range, the carrier-to-noise ratio standard deviation corresponding to different elevation angles is obtained as the carrier-to-noise ratio accuracy index (namely, the carrier-to-noise ratio threshold value) corresponding to different elevation angles.
Fig. 4 (a) and fig. 4 (B) show carrier-to-noise ratio accuracies and corresponding fitting curves corresponding to different altitude angles of GPS L1 and BDS B1 frequency points, where the scattered points and the solid lines represent the carrier-to-noise ratio accuracies and fitting results corresponding to different altitude angles, respectively. As can be seen from the figure, the fitting curve can accurately reflect the functional relationship between the altitude angle and the carrier-to-noise ratio precision of each frequency point, so that the carrier-to-noise ratio threshold SNR corresponding to the altitude angle observation value of the current GNSS observation value can be calculated and obtained by utilizing the altitude angle-based carrier-to-noise ratio precision template function STD 。
The carrier-to-noise ratio precision template functions of the GPS L1 frequency point and the BDS B1 frequency point based on the altitude angle are respectively as follows:
wherein SNR is STD Representing a carrier-to-noise ratio threshold value corresponding to the current altitude angle observation value, namely carrier-to-noise ratio precision, subscripts G and C respectively represent a GPS satellite system and a BDS satellite system, and Ele is an altitude angle observation value. The carrier-to-noise ratio accuracy corresponding to different altitude angles of two frequency points of GPS L1 and BDS B1 can be respectively obtained through the above formula.
S5: PLD threshold value PLD is obtained through calculation based on carrier-to-noise ratio nominal value STD . The method specifically comprises the following steps:
similarly, a carrier-to-noise ratio (carrier-to-noise ratio) -based PLD accuracy (i.e., PLD threshold) template can be established using the relationship between the carrier-to-noise ratio and the PLD accuracyA function. Fig. 5 (a) and 5 (B) show PLD accuracies and corresponding fitting curves corresponding to different carrier-to-noise ratios of GPS L1 and BDS B1 frequency points, where a scattered point and a solid line respectively represent the PLD accuracies and fitting results corresponding to the different carrier-to-noise ratios. As can be seen from the figure, the fitting curve in the figure can more accurately reflect the functional relationship between the carrier-to-noise ratio and the PLD precision, so that the PLD precision template function based on the carrier-to-noise ratio can be used for calculating the PLD threshold value PLD corresponding to the carrier-to-noise ratio nominal value of the current GNSS observation value STD 。
The PLD precision template functions of the GPS L1 and BDS B1 frequency points based on the carrier-to-noise ratio are respectively as follows:
in the formula, PLD STD Representing the PLD threshold value corresponding to the current carrier-to-noise ratio, namely PLD precision, subscripts G and C represent GPS and BDS satellite systems respectively, and SNR is the nominal value of the carrier-to-noise ratio. Through the above formula, the PLD accuracy corresponding to different carrier-to-noise ratios of GPS L1 and BDS B1 frequency points can be respectively obtained.
S6: obtaining a carrier-to-noise ratio observed value and a PLD observed value of the current GNSS observed value, and subtracting the carrier-to-noise ratio observed value from a carrier-to-noise ratio nominal value to obtain delta SNR And obtaining delta by subtracting the PLD observed value from the PLD nominal value PLD 。
S7: and comparing the two difference values with corresponding threshold values respectively, and finally determining the combined weighting index and the weight of each index.
The method specifically comprises the following steps:
s71: if Δ SNR ≤2×SNR STD And Δ PLD ≤2×PLD STD The altitude empirical stochastic model is used for weighting.
The empirical stochastic model of elevation angle is represented as follows:
σ Ele ＝c 2 +d 2 /sin 2 (Ele)
in the formula, σ Ele The variance of the observed value is determined for the empirical stochastic model of the elevation angle, c and d are constants, and Ele is the observed value of the elevation angle; in the embodiment, the empirical values of c and d are both 3mm;
s72: if Δ SNR ≤2×SN STD And Δ PLD >2×PLD STD At the moment, the PLD is considered to be abnormal, and the quality of an observed value cannot be accurately indicated, so that the elevation angle combined carrier-to-noise ratio random model is used for weighting;
the elevation angle joint carrier-to-noise ratio stochastic model is expressed as follows:
σ all ＝a×σ SNR +b×σ Ele
in the formula, σ all Representing the variance, sigma, of the observed values determined by the elevation angle joint carrier-to-noise ratio random model SNR The variance of the observation value determined by the empirical random model of the carrier-to-noise ratio, and a and b are the weight of the variance of the observation value determined by the corresponding random model respectively;
the carrier-to-noise ratio empirical stochastic model is expressed as follows:
in the formula, B i Tracking the loop bandwidth (in Hz), λ, for the carrier phase i Is the carrier wavelength (in m), C/N 0 The carrier-to-noise ratio observed value (the unit is dB & Hz) of the current GNSS observed value is alpha, 2 can be taken, and delta is the difference between the carrier-to-noise ratio observed value and a theoretical value;
in view of the differences between the GPS and BDS satellite systems, the weights a and b of the two systems, GPS and BDS, respectively, are determined using a principal component analysis method. The weights a and b for the two systems, GPS and BDS, obtained using principal component analysis are given in the table below:
s73: if Δ SNR >2×SNR STD And Δ PLD ≤2×PLD STD At this time, it is assumed that there is a difference in carrier-to-noise ratioOften, the quality of an observed value cannot be accurately indicated, so that the elevation angle is combined with the PLD random model for weighting;
the elevation angle joint PLD stochastic model is expressed as follows:
σ all ′＝e×σ PLD +f×σ Ele
in the formula, σ all ' expressing variance of observed values, σ, of elevation angle joint PLD random model PLD And e and f are respectively the observed value variance weights determined by the corresponding random models.
The PLD-based random models of GPS L1 and BDS B1 frequency points are respectively as follows:
σ G ＝0.001224×PLD 0.5031
σ C ＝-0.04731×PLD -0.1916 +0.03482
wherein sigma is an observed value variance, and subscripts G and C represent a GPS satellite system and a BDS satellite system respectively;
likewise, the weights e and f of the two systems, GPS and BDS, respectively, were determined using the principal component analysis method, resulting in the following table:
s74: if Δ SNR >2×SNR STD And Δ PLD >2×PLD STD And if the three indexes are not consistent, the visual observation value is abnormal, and the weight of the GNSS observation value is set to be zero.
When the weighting is performed by the stochastic model, the weighting is the reciprocal of the variance of the observed value.
The PLD random model is obtained by the following method:
acquiring and processing a GNSS observation value to obtain PLD and carrier phase single difference residual sequences of all epochs of each satellite;
and (3) representing the carrier phase precision by using the standard deviation calculated by the carrier phase single-difference residual sequence, performing linear fitting according to the relation between the PLD and the carrier phase precision, constructing an observation value precision estimation formula, converting the standard deviation into a variance, and constructing a fixed weight function based on the PLD to obtain the PLD random model.
In the steps S1 to S7, a comprehensive random model before test is constructed in consideration of PLD, elevation angle, and carrier-to-noise ratio, and in consideration of that the comprehensive random model can perform relatively accurate prior weighting but cannot completely indicate the quality of an observed value, in some embodiments of the present invention, a robust estimation method is further improved on the basis of the comprehensive random model before test to detect gross errors, so as to effectively make up for the deficiency of the current random model.
Specifically, after determining the weight of the GNSS observation value by the foregoing method, the method further includes:
s8: performing first adjustment on various GNSS observations (such as GPS and BDS observations) by using the following formula, thereby obtaining residual information of various observations;
v＝Ax-l
in the formula, v is an observed value residual error vector; a is a design matrix, and x is an unknown parameter vector; l is an observation vector which comprises a carrier phase and a pseudo-range observation value;
s9: calculating the robust coefficients of various GNSS observation values by using an IGG III model, and re-weighting the robust coefficients; wherein igiii model re-weighting is represented as follows:
in the formula, p i Is a weight of the GNSS observations,
is the coefficient of tolerance, k 1 、k 2 Is a constant number, k 1 The value range is 1.5-2.0 2 The value range is 3.0-8.5; v. of i The ith element of the observation residual vector can be understood as the residual of the ith observation; sigma is a variance factor;
σ＝med(|v i |)/0.6745
wherein med (| · |) is a median function;
s10: if the robust coefficient is converged, performing final adjustment; otherwise, after updating the weight matrix, repeating the steps S8-S9 until the robust coefficient is converged.
The error tolerance is performed to weaken and eliminate the gross error influence, so that the problem of unreasonable weight assignment in the random model before the test can be solved, and the positioning result is further improved. The first adjustment is to obtain a residual error, the adjustment in the cycle is performed after the weight is adjusted according to the robust, because the weight is adjusted by the robust, but the robust effect is not yet applied to the estimated value, and the re-adjustment is equivalent to obtaining the estimated value after the robust. And judging which interval the variance of the current observation value is positioned in by using the IGGIII model, if the variance is overlarge, reducing the weight by using a formula given in the IGGIII model, and if the variance meets the requirement, keeping the original weight.
The embodiment of the invention also provides an electronic terminal, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the steps of the comprehensive multi-index GNSS observation value weighting method when executing the computer program.
The electronic terminal further comprises: and the communication interface is used for communicating with external equipment and carrying out data interactive transmission.
The memory may include high speed RAM memory, and may also include a non-volatile defibrillator, such as at least one disk memory.
If the memory, the processor and the communication interface are implemented independently, the memory, the processor and the communication interface may be connected to each other via a bus and perform communication with each other. The bus may be an industry standard architecture bus, a peripheral device interconnect bus, an extended industry standard architecture bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
Optionally, in a specific implementation, if the memory, the processor, and the communication interface are integrated on a chip, the memory, the processor, that is, the communication interface may complete communication with each other through the internal interface.
The specific implementation process of each step refers to the explanation of the foregoing method.
It should be understood that in the embodiments of the present invention, the Processor may be a Central Processing Unit (CPU), and the Processor may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may include both read-only memory and random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information.
Embodiments of the present invention further provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for weighting GNSS observation values with multiple indicators as described above is implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as methods, systems, computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. This application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar contents in other embodiments may be referred to for the contents which are not described in detail in some embodiments.
In order to verify the positioning performance of the GNSS observation value weighting method utilizing the comprehensive multi-index, firstly, a GNSS antenna is placed in typical environments of open space, tree shade shielding, building shielding and glass curtain wall shielding to acquire GNSS observation data, and the data acquisition frequency is 1Hz; and then, performing double-system single-frequency point (GPS L1 and BDS B1) relative positioning calculation by using a random model based on a single index, two indexes and comprehensive multiple indexes. The results of the experiments in different environments are shown in the following table.
Static relative positioning result statistical table in open environment
Statistical table of static relative positioning results of tree shade environment
Building shelters from environment relative positioning result statistical table
Statistical table for relative positioning result of glass curtain wall shielding environment
According to experimental results, in terms of fixed solution ratio, the elevation angle + PLD + carrier-to-noise ratio + robust random model, the elevation angle + PLD random model and the elevation angle + PLD + carrier-to-noise ratio random model are both 100% and are 6.7% -99.5% higher than other six random models. And on the STD and RMS in the three directions of E, N and U, the height angle + PLD + carrier-to-noise ratio + robust random model is 0.10-24.41 cm smaller than that of other random models. In the above results, the altitude angle + carrier-to-noise ratio random model (empirical) fixed solution ratios are all less than 1%, and it is considered as unreliable that the data volume is too small when the STD and RMS in three directions are counted by using the fixed solution results, and therefore, when the positioning accuracy and reliability in three directions of various random models are analyzed, the random models are not compared with the random models.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A GNSS observation value weighting method integrating multiple indexes is characterized by comprising the following steps:
s1: acquiring a current GNSS observation value;
s2: calculating a carrier-to-noise ratio nominal value based on the altitude angle observation value of the current GNSS observation value;
s3: calculating to obtain a PLD nominal value based on the carrier-to-noise ratio nominal value;
s4: calculating altitude angle observation value based on current GNSS observation value to obtain carrier-to-noise ratio threshold SNR STD ；
S5: PLD threshold value PLD is obtained through calculation based on carrier-to-noise ratio nominal value STD ；
S6: obtaining a carrier-to-noise ratio observed value and a PLD observed value of the current GNSS observed value, and subtracting the carrier-to-noise ratio observed value from a carrier-to-noise ratio nominal value to obtain delta SNR And obtaining delta by subtracting the PLD observed value from the PLD nominal value PLD ；
S7: if Δ SNR ≤2×SNR STD And Δ PLD ≤2×PLD STD Weighting by using a height angle empirical random model;
if Δ SNR ≤2×SNR STD And Δ PLD >2×PLD STD Weighting by using an altitude angle combined carrier-to-noise ratio random model;
if Δ SNR >2×SNR STD And Δ PLD ≤2×PLD STD Then, using the elevation angle to combine with the PLD random model to decide the weight;
if Δ SNR >2×SNR STD And Δ PLD >2×PLD STD The weight of the GNSS observation is set to zero.
2. The method for integrated multi-index GNSS observation weighting according to claim 1, wherein the step S2 specifically includes:
and obtaining an altitude angle observation value of the current GNSS observation value, and calculating by using a carrier-to-noise ratio template function based on an altitude angle to obtain a carrier-to-noise ratio nominal value.
3. The method for weighting GNSS observations according to claim 1, wherein the step S3 comprises:
and acquiring a nominal value of the carrier-to-noise ratio, and calculating by using a PLD template function based on the carrier-to-noise ratio to obtain the PLD nominal value.
4. The method for weighting GNSS observations according to claim 1, wherein the step S4 specifically comprises:
obtaining an altitude angle observation value of the current GNSS observation value, and calculating to obtain a carrier-to-noise ratio threshold SNR by utilizing a carrier-to-noise ratio precision template function based on the altitude angle STD 。
5. The method for weighting GNSS observations according to claim 1, wherein the step S5 specifically comprises:
obtaining a nominal value of the carrier-to-noise ratio, and calculating by utilizing a PLD precision template function based on the carrier-to-noise ratio to obtain a PLD threshold value PLD STD 。
6. The method of claim 1, wherein in step S7, the altitude empirical stochastic model is expressed as follows:
σ Ele ＝c 2 +d 2 /sin 2 (Ele)
in the formula, σ Ele The variance of the observed value determined for the altitude angle empirical random model, c and d are constants, and Ele is the altitude angle observed value;
the elevation angle joint carrier-to-noise ratio stochastic model is expressed as follows:
σ all ＝a×σ SNR +b×σ Ele
in the formula, σ all Representing the variance, sigma, of the observed values determined by the elevation angle joint carrier-to-noise ratio random model SNR The variance of the observed value determined for the empirical random model of the carrier-to-noise ratio is determined, and a and b are the weights of the variance of the observed value determined for the corresponding random model respectively;
the carrier-to-noise ratio empirical stochastic model is expressed as follows:
in the formula, B i Tracking loop bandwidth, λ, for carrier phase i Is the carrier wavelength, C/N 0 The carrier-to-noise ratio observed value of the current GNSS observed value is alpha, which is a constant, and delta is the difference between the carrier-to-noise ratio observed value and a theoretical value;
the elevation angle joint PLD stochastic model is expressed as follows:
σ all ′＝e×σ PLD +f×σ Ele
in the formula, σ all ' expressing variance of observed values, σ, of elevation angle joint PLD random model PLD And e and f are respectively the observed value variance weights determined by the corresponding random models.
7. The integrated multi-index GNSS observation weighting method according to claim 6, wherein the PLD stochastic model is obtained by:
acquiring and processing a GNSS observation value to obtain PLD and carrier phase single difference residual sequences of all epochs of each satellite;
and (3) representing the carrier phase precision by using the standard deviation calculated by the carrier phase single-difference residual sequence, constructing an observation value precision estimation formula according to the relation between the PLD and the carrier phase precision, and further constructing a weighting function based on the PLD to obtain a PLD random model.
8. The integrated multi-index GNSS observation weighting method according to any one of claims 1 to 7, further comprising, after determining the weights of the GNSS observations:
s8: performing adjustment on the GNSS observation value by using the following formula, thereby obtaining residual error information of the observation value;
v＝Ax-l
in the formula, v is an observed value residual error vector, A is a design matrix, x is an unknown parameter vector, and l is an observed vector;
s9: calculating an robust coefficient of the GNSS observation value by using an IGG III model, and re-weighting the robust coefficient; wherein igiii model re-weighting is represented as follows:
in the formula, p i Is a weight of the GNSS observations,
is the coefficient of tolerance, k 1 、k 2 Is a constant, v i The ith element of the observation value residual vector is, and sigma is a variance factor;
σ＝med(|v i |)/0.6745
wherein med (| · |) is a median function;
s10: if the robust coefficient is converged, performing final adjustment; otherwise, repeating the steps S8-S9 after updating the weight matrix until the robust coefficient is converged.
9. An electronic terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 8 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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