CN117647830B - Random model construction method suitable for GNSS chip positioning in complex urban environment - Google Patents
Random model construction method suitable for GNSS chip positioning in complex urban environment Download PDFInfo
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Abstract
The invention provides a random model construction method suitable for positioning GNSS chips in complex urban environments, which comprises the following steps: step S1, acquiring GNSS original observation data by using a terminal; step S2, calculating multipath error values, altitude angles and carrier-to-noise ratios of all satellites; step S3, before three-dimensional surface fitting, constraint is applied to the weight coefficient; step S4, parameter fitting is carried out on the constructed three-dimensional function model, and parameters of the altitude angle model and the carrier-to-noise ratio model are fitted: s5, respectively bringing parameters of the carrier-to-noise ratio model and the altitude angle model into a three-dimensional function model, and performing three-dimensional surface fitting to determine weight parameters of the altitude angle model and the carrier-to-noise ratio model; and S6, constructing a complete joint fixed weight random model. The invention enables the random model to more accurately reflect the current environment shielding and diffraction situation, effectively weakens the multipath effect, and can improve the reliability and stability of the positioning of the low-cost terminal in the complex environment by adopting the random model in the positioning.
Description
Technical Field
The invention relates to the technical field of satellite navigation and positioning, in particular to a random model construction method suitable for positioning a GNSS chip in a complex urban environment.
Background
With the popularization of smart phones, the requirements of mass users on the accuracy, stability and reliability of location information services provided by the smart phones are higher and higher, and many smart phones equipped with multi-constellation multi-frequency GNSS chips are also appeared on the market. In 2016, google corporation announced that an API for acquiring an original observed value is opened on the Android nougat7.0 system, and a user can acquire pseudo-range, carrier phase, doppler and carrier-to-noise ratio data through fields in a specific class, so that conditions are provided for developing a high-precision positioning algorithm based on a mobile phone chip and evaluating the quality of observed data. The application program for acquiring the GNSS data of the mobile phone is also sequentially introduced, for example, rinexON of Geo++ RINEX Logger, FLAMINGO team of GNSSLogger, geo ++ GmbH company of Google company, and can directly output the observation file in RINEX format.
Compared with a geodetic receiver, the acquisition of the intelligent mobile phone reduces the quality of observed data by low-cost hardware and complex application environment carried by the intelligent mobile phone, and makes multipath errors a main error source for mobile phone positioning. The method for restraining the multipath error can be divided into external assistance and data processing, the external assistance mainly adopts a fisheye camera and a three-dimensional map as tools, the multipath error is calculated in a ray tracing mode, and then the multipath error is directly corrected into an observed value, and the method can accurately calculate the multipath error correction value but has higher cost and is difficult to realize.
In the aspect of data processing, the data processing can be divided into function model correction and random model construction, the representation methods of the function model comprise sun-day filtering, a semi-day sphere graph method and the like, the method considers the multipath error periodicity caused by satellite motion characteristics, the calculated multipath error value can be directly corrected to the next period, and the index reflecting signal quality is adopted to construct a random model so as to weaken multipath effect, thus the method is easy to operate and can cope with the change of the observation environment, and the positioning performance of the urban environment mobile phone can be effectively improved, but the method is limited by the change of the observation environment.
In a conventional measurement receiver, there is a significant correlation between the received satellite signal quality and the satellite altitude, and it is generally considered that satellites with higher altitudes are less affected by atmospheric delays and noise than satellites with lower altitudes. How to construct a function based on satellite altitude and observation noise as a random model on the basis; the carrier-to-noise ratio refers to the ratio between the received satellite signal strength and the background noise strength, and is one of the important factors affecting the positioning performance of the GNSS, so a higher carrier-to-noise ratio means stronger signal and less noise, which helps to improve the positioning accuracy and reliability.
Disclosure of Invention
In the prior art, the problems that a mobile phone repeatedly observes data for a plurality of times in an observation environment, calculates multipath error values of different frequency points, counts together with a satellite altitude angle and a carrier-to-noise ratio, and fully characterizes the data quality with a single evaluation index are solved; meanwhile, how to fit model parameters to different satellite systems and different frequencies respectively, a random model reflecting the current environment shielding condition is adaptively constructed without depending on experience constants, so that the mobile phone positioning accuracy is improved, the portability is good, and the mobile phone positioning method is suitable for the problems of various complex environments.
In order to solve the problems, the invention designs a random model construction method suitable for positioning GNSS chips in complex urban environments.
A random model construction method suitable for positioning GNSS chips in complex urban environments is characterized by comprising the following steps:
step S1, repeatedly acquiring GNSS original observation data by using a terminal for a plurality of times in a complex urban environment, deriving pseudo-range, carrier phase and carrier-to-noise ratio data, calculating a multi-path error by utilizing the combination of the pseudo-range and the carrier phase, and then downloading satellite data to calculate a satellite altitude angle;
Step S2, carrying out corresponding statistics on the carrier-to-noise ratio output by each satellite obtained in the step S1, the multipath error value calculated by the satellite and the satellite altitude angle calculated by the satellite;
S3, constructing a carrier-to-noise ratio model and a satellite altitude model, applying constraint to weight coefficients of the carrier-to-noise ratio model and the satellite altitude model in a range of 0 to 1, distributing equal weights to two indexes of the carrier-to-noise ratio model and the satellite altitude model before iteration in order to ensure the rationality of the fitting process, and setting initial weights to be 0.5;
s4, multiplying the carrier-to-noise ratio model and the altitude angle model by weight coefficients respectively and adding the weight coefficients, initially constructing a combined fixed-weight random model, and then performing parameter fitting, wherein the parameters of the altitude angle random model and the carrier-to-noise ratio random model are respectively fitted by adopting a Levenberg-Marquardt algorithm in consideration of the altitude nonlinear characteristic of the combined fixed-weight random model;
s5, carrying out multipath error statistics on the carrier-to-noise ratio random model and the altitude angle random model according to 0.25dB-Hz and 0.5 degree intervals, carrying out three-dimensional surface fitting according to the three-dimensional function model which is a nonlinear surface to determine the weight parameters of the two indexes of the carrier-to-noise ratio random model and the altitude angle random model, wherein the parameters of the altitude angle random model and the carrier-to-noise ratio random model obtained in the step S4 are brought into the three-dimensional function model;
step S6, based on the step 2, the two indexes of the satellite altitude angle and the carrier-to-noise ratio are both incorporated into the random model for joint weighting, the corresponding weight coefficients are multiplied before the altitude angle random model and the carrier-to-noise ratio random model respectively, then the weight coefficients are added, and the extracted multipath errors are used as errors in measurement to form a three-dimensional function model: Wherein sigma is the error in measurement, namely the multipath error; v, c, a, b is a constant, and fitting is required for different systems and different frequencies; w s、we is the weight of the carrier-to-noise ratio and the altitude model respectively, and is also the parameter to be fitted; C/N 0 is the carrier-to-noise ratio observation value, and ele is the satellite altitude; and based on the step 5, performing three-dimensional surface fitting by utilizing corresponding statistical points of the multipath error, the satellite altitude angle and the carrier-to-noise ratio to establish the correlation between the carrier-to-noise ratio and the altitude angle and the multipath error, and still adopting a Levenberg-Marquardt algorithm to find a group of model parameters capable of minimizing the distance between the data point and the surface in the fitting process, obtaining weight coefficients w s and w e, and constructing a complete combined weighting random model. Preferably, the carrier-to-noise ratio in the step S1 refers to a ratio between the received satellite signal strength and the background noise strength; the carrier-to-noise ratio is a factor affecting GNSS positioning performance, and a high carrier-to-noise ratio stabilizes the signal, does not delay transmission, and does not generate noise.
Preferably, in the step S2, firstly, a combination calculation of the dual-frequency pseudo-range and the carrier phase originally observed by the terminal is performed to calculate a multipath error, and the implementation method is as follows:
When satellite double-frequency observations are available, the multipath error can be obtained by combining the carrier phase and the pseudo-range observations, and the two-frequency multipath error is expressed as:
Wherein MP i and MP j are multipath errors for i and j frequencies, respectively; p i,Pj is the pseudorange observations for i, j frequencies, respectively; carrier phase observations at i, j frequencies, respectively; /(I) Wherein f i、fj is two band frequencies respectively.
Preferably, the carrier-to-noise ratio weighting random model constructed in the step 4 is as follows:
V and c are constants, and calibration is carried out according to the used receiver, different systems and different frequencies;
The calculation of the altitude angle stochastic model of the step 4 uses a sine function model:
σ2 2＝a2+b2/sin2(ele)
Wherein a and b are constants; ele is satellite altitude, C/N 0 is carrier-to-noise ratio observation, v, C, a, b initial values are respectively set to 0, 1, 0 and 1, and the expression is respectively expressed And σ 2 2＝a2+b2/sin2 (ele) solve for the optimal parameters v, c, a, b using the Levenberg-Marquardt algorithm.
Preferably, the altitude angle in step S4 is less affected by the atmospheric delay and noise at the high satellite than at the low satellite, and a function based on the satellite altitude angle and the observed noise is constructed as a random model according to the satellite altitude angle model.
Preferably, the observation noise is generated by factors of a receiver circuit and an antenna, the observation noise is influenced by carrier power and noise power, and a fixed weight random model of carrier-to-noise ratio is used for measuring signal precision in a function of the observation noise, and in general, the larger the carrier-to-noise ratio is, the higher the observation value precision is.
Preferably, the carrier-to-noise ratio parameter value is calculated by firstly calculating the update direction of the gradient and the Hessian matrix according to the gradient of the objective function under the current parameter value and the Hessian matrix, updating the parameter value by combining the step factor, recalculating the objective function value according to the updated parameter value, accepting the update if the objective function value is reduced, otherwise, adjusting the step factor to recalculate the update direction.
Preferably, in the step S6, the corresponding weight coefficients are multiplied before the altitude angle random model and the carrier-to-noise ratio random model, and then added, and the extracted multipath error is used as the error in measurement to form a three-dimensional function model, where the expression is as follows:
Wherein sigma is the error in measurement, namely the multipath error; v, c, a, b is a constant, and fitting is required for different systems and different frequencies; w s、we is the weight of the carrier-to-noise ratio and the altitude model respectively, and is also the parameter to be fitted; C/N 0 is the carrier-to-noise ratio observation value, and ele is the satellite altitude; substituting v, c, a, b into the joint weighting random model.
Preferably, in the step S6, the weight parameters of the two indexes of the satellite altitude angle random model and the carrier-to-noise ratio random model are determined according to three-dimensional surface fitting, the satellite altitude angle and the carrier-to-noise ratio are both incorporated into the random model for joint weight determination, the weight coefficients are multiplied before the satellite altitude angle random model and the carrier-to-noise ratio random model respectively, then the weight coefficients are added, the extracted multipath errors are used as errors in measurement to form a three-dimensional function model, model parameters are fitted respectively for different satellite systems and different frequencies, and the complete joint weight random model replaces the traditional altitude angle model to be introduced into the single-point positioning process, so that accurate weight determination of the GNSS observation values in the complex environment is realized.
For the prior art, the technical scheme of the application has the following advantages and effects:
1. The invention relates to a random model construction method suitable for positioning GNSS chips in complex urban environments, which utilizes a modeling method while considering a height angle and a carrier-to-noise ratio, avoids the abnormal situation of the carrier-to-noise ratio caused by low-cost linear polarized antennas, and simultaneously takes multipath errors as error fitting model parameters in measurement, so that the random model can more accurately reflect the current environment shielding and diffraction situation, effectively weakens multipath effects, has strong self-adaptive capacity, and can improve the reliability and stability of positioning of a low-cost terminal in complex environments in the positioning process.
2. The random model construction method suitable for positioning the GNSS chip in the complex urban environment improves the capacity of the low-cost GNSS chip for inhibiting multipath errors by means of data processing rather than hardware upgrading, and provides a set of perfect and feasible algorithm basis for realizing high-precision positioning of the consumer mobile terminal in the complex urban environment.
The foregoing is only a summary of the application, and is used for better understanding of the technical means of the application, so that the technical means may be carried out according to the disclosure, and for better understanding of the above and other objects, features and advantages of the application, the following detailed description of the preferred embodiments of the application will be given with reference to the accompanying drawings.
The above and other objects, advantages and features of the present application will become more apparent to those skilled in the art from the following detailed description of the specific embodiments of the present application when taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a method for constructing a stochastic model for GNSS chip positioning in a complex urban environment according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. In the following description, specific details such as specific configurations and components are provided merely to facilitate a thorough understanding of embodiments of the application. It will therefore be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the application. In addition, descriptions of well-known functions and constructions are omitted in the embodiments for clarity and conciseness. It should be appreciated that reference throughout this specification to "one embodiment" or "this embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the "one embodiment" or "this embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the present application may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
The term "and/or" is used herein to describe only one association relationship that describes an associated object, and means that there may be three relationships, e.g., A and/or B, and that there may be A alone, B alone, and both A and B, and the term "/and" is used herein to describe another association relationship, and means that there may be two relationships, e.g., A/and B, and that there may be A alone, and both A and B alone. In addition, the character "/" herein generally indicates that the front-rear association object is an "or" relationship.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprise," "include," or any other variation thereof, are intended to cover a non-exclusive inclusion.
Example 1
The embodiment mainly describes a random model construction method suitable for positioning GNSS chips in a complex urban environment, as shown in FIG. 1.
A random model construction method suitable for positioning GNSS chips in complex urban environments is characterized by comprising the following steps:
step S1, repeatedly acquiring GNSS original observation data by using a terminal for a plurality of times in a complex urban environment, deriving pseudo-range, carrier phase and carrier-to-noise ratio data, calculating a multi-path error by utilizing the combination of the pseudo-range and the carrier phase, and then downloading satellite data to calculate a satellite altitude angle;
Step S2, carrying out corresponding statistics on the carrier-to-noise ratio output by each satellite obtained in the step S1, the multipath error value calculated by the satellite and the satellite altitude angle calculated by the satellite;
S3, constructing a carrier-to-noise ratio model and a satellite altitude model, applying constraint to weight coefficients of the carrier-to-noise ratio model and the satellite altitude model in a range of 0 to 1, distributing equal weights to two indexes of the carrier-to-noise ratio model and the satellite altitude model before iteration in order to ensure the rationality of the fitting process, and setting initial weights to be 0.5;
s4, multiplying the carrier-to-noise ratio model and the altitude angle model by weight coefficients respectively and adding the weight coefficients, initially constructing a combined fixed-weight random model, and then performing parameter fitting, wherein the parameters of the altitude angle random model and the carrier-to-noise ratio random model are respectively fitted by adopting a Levenberg-Marquardt algorithm in consideration of the altitude nonlinear characteristic of the combined fixed-weight random model;
s5, carrying out multipath error statistics on the carrier-to-noise ratio random model and the altitude angle random model according to 0.25dB-Hz and 0.5 degree intervals, carrying out three-dimensional surface fitting according to the three-dimensional function model which is a nonlinear surface to determine the weight parameters of the two indexes of the carrier-to-noise ratio random model and the altitude angle random model, wherein the parameters of the altitude angle random model and the carrier-to-noise ratio random model obtained in the step S4 are brought into the three-dimensional function model;
step S6, based on the step 2, the two indexes of the satellite altitude angle and the carrier-to-noise ratio are both incorporated into the random model for joint weighting, the corresponding weight coefficients are multiplied before the altitude angle random model and the carrier-to-noise ratio random model respectively, then the weight coefficients are added, and the extracted multipath errors are used as errors in measurement to form a three-dimensional function model: Wherein sigma is the error in measurement, namely the multipath error; v, c, a, b is a constant, and fitting is required for different systems and different frequencies; w s、we is the weight of the carrier-to-noise ratio and the altitude model respectively, and is also the parameter to be fitted; C/N 0 is the carrier-to-noise ratio observation value, and ele is the satellite altitude; and based on the step 5, performing three-dimensional surface fitting by utilizing corresponding statistical points of the multipath error, the satellite altitude angle and the carrier-to-noise ratio to establish the correlation between the carrier-to-noise ratio and the altitude angle and the multipath error, and still adopting a Levenberg-Marquardt algorithm to find a group of model parameters capable of minimizing the distance between the data point and the surface in the fitting process, obtaining weight coefficients w s and w e, and constructing a complete combined weighting random model.
Further, the carrier-to-noise ratio in the step S1 refers to the ratio between the received satellite signal strength and the background noise strength; the carrier-to-noise ratio is a factor affecting GNSS positioning performance, and a high carrier-to-noise ratio stabilizes the signal, does not delay transmission, and does not generate noise.
In step S2, the multipath error is calculated by combining the dual-frequency pseudo-range and the carrier phase originally observed by the terminal, and the implementation method is as follows:
When satellite double-frequency observations are available, the multipath error can be obtained by combining the carrier phase and the pseudo-range observations, and the two-frequency multipath error is expressed as:
Wherein MP i and MP j are multipath errors for i and j frequencies, respectively; p i,Pj is the pseudorange observations for i, j frequencies, respectively; carrier phase observations at i, j frequencies, respectively; /(I) Wherein f i、fj is two band frequencies respectively.
Further, the carrier-to-noise ratio weighting random model constructed in the step 4 is as follows:
V and c are constants, and calibration is carried out according to the used receiver, different systems and different frequencies;
The calculation of the altitude angle stochastic model of the step 4 uses a sine function model:
σ2 2＝a2+b2/sin2(ele)
Wherein a and b are constants; ele is the satellite altitude, C/N 0 is the carrier-to-noise ratio observation, v, C,
A. the initial values of b are set to 0, 1, respectively, for the expressionsAnd σ 2 2＝a2+b2/sin2 (ele) solve for the optimal parameters v, c, a, b using the Levenberg-Marquardt algorithm.
Further, the influence of the atmospheric delay and noise on the high altitude angle of the step S4 is smaller than that of the low altitude angle satellite, and a function based on the satellite altitude angle and the observation noise is constructed as a random model according to the satellite altitude angle model.
Further, the observation noise is generated by factors of a receiver circuit and an antenna, the observation noise is influenced by carrier power and noise power, and a fixed weight random model of carrier-to-noise ratio is used for measuring signal precision in a function of the observation noise, and in general, the larger the carrier-to-noise ratio is, the higher the observation value precision is.
Further, the carrier-to-noise ratio parameter value is calculated, firstly, according to the gradient of the objective function under the current parameter value and under the Hessian matrix, the update direction is calculated for the gradient and the Hessian matrix, the step factor is combined to update the parameter value, the objective function value is recalculated according to the updated parameter value, if the objective function value is reduced, the update is accepted, and otherwise, the step factor is adjusted to recalculate the update direction.
Further, in the step S6, the corresponding weight coefficients are multiplied before the altitude angle random model and the carrier-to-noise ratio random model, and then added, and the extracted multipath error is used as the error in measurement to form a three-dimensional function model, and the expression is as follows:
Wherein sigma is the error in measurement, namely the multipath error; v, c, a, b is a constant, and fitting is required for different systems and different frequencies; w s、we is the weight of the carrier-to-noise ratio and the altitude model respectively, and is also the parameter to be fitted; C/N 0 is the carrier-to-noise ratio observation value, and ele is the satellite altitude; substituting v, c, a, b into the joint weighting random model.
Further, in step S6, the weight parameters of the two indexes of the satellite altitude angle random model and the carrier-to-noise ratio random model are determined according to the three-dimensional surface fitting, the satellite altitude angle and the carrier-to-noise ratio are both incorporated into the random model for joint weight determination, the weight coefficients are multiplied before the satellite altitude angle random model and the carrier-to-noise ratio random model respectively, then the weight coefficients are added, the extracted multipath errors are used as errors in measurement to form a three-dimensional function model, model parameters are fitted respectively for different satellite systems and different frequencies, and the complete joint weight random model replaces the traditional altitude angle model to be introduced into the single-point positioning process, so that accurate weight determination of the GNSS observation values in the complex environment is realized.
The invention provides a model self-adaptive construction method for positioning an urban environment GNSS chip, which simultaneously considers a height angle and a carrier-to-noise ratio in a modeling method, avoids the abnormal situation of the carrier-to-noise ratio caused by a low-cost linear polarized antenna, and simultaneously takes a multipath error as an error fitting model parameter in measurement, so that a random model can more accurately reflect the current environment shielding and diffraction situation, effectively weakens multipath effect, has very light self-adaptive capacity, and can improve the reliability and stability of positioning a low-cost terminal in a complex environment in the positioning process.
Example 2
The embodiment is mainly based on the above embodiment 1, and mainly describes the operation steps of a random model construction method suitable for positioning a GNSS chip in a complex urban environment, as shown in fig. 1, and the specific operation steps are as follows:
Step S1, repeatedly acquiring GNSS original observation data for a plurality of times by using a terminal under a specific environment, and calculating multipath errors by using the combination of pseudo-range and carrier phase of original observation;
Step S2, correspondingly counting the multipath error values and the altitude angles of the satellites obtained in the step S1 and the output carrier-to-noise ratio;
and step S3, before three-dimensional surface fitting, restraining the weight coefficient in the range of 0 to 1 in order to meet the normalized characteristic of the weight coefficient. In order to ensure the rationality of the fitting process, equal weights are distributed to the two indexes before iteration, and the initial weights are set to be 0.5;
Step S4, carrying out parameter fitting on a preliminarily constructed three-dimensional function model, taking into consideration the highly nonlinear characteristic of a combined fixed-weight random model, firstly adopting a Levenberg-Marquardt algorithm to fit parameters of a high-angle model and a carrier-to-noise ratio model respectively, and setting proper initial values before fitting;
S5, carrying out multipath error statistics on the carrier-to-noise ratio and the height angle according to intervals of 0.25dB-Hz and 0.5 DEG respectively, bringing the parameters obtained in the step S4 into a three-dimensional function model, and carrying out three-dimensional surface fitting to determine the weight parameters of the two indexes of the height angle and the carrier-to-noise ratio according to the model as a nonlinear surface;
and S6, constructing a complete combined weight-fixing random model based on the weight parameters obtained in the step S5, and introducing the model to the single-point positioning process instead of the traditional altitude model to realize accurate weight fixing of the GNSS observation value in the complex environment.
The method improves the capacity of the low-cost GNSS chip for inhibiting multipath errors by means of data processing rather than hardware upgrading, and provides a set of perfect and feasible algorithm basis for realizing high-precision positioning of the consumer mobile terminal in a complex urban environment.
The above description is only of the preferred embodiments of the present invention and it is not intended to limit the scope of the present invention, but various modifications and variations can be made by those skilled in the art. Variations, modifications, substitutions, integration and parameter changes may be made to these embodiments by conventional means or may be made to achieve the same functionality within the spirit and principles of the present invention without departing from such principles and spirit of the invention.
Claims (8)
1. A random model construction method suitable for positioning GNSS chips in complex urban environments is characterized by comprising the following steps:
step S1, repeatedly acquiring GNSS original observation data by using a terminal for a plurality of times in a complex urban environment, deriving pseudo-range, carrier phase and carrier-to-noise ratio data, calculating a multi-path error by utilizing the combination of the pseudo-range and the carrier phase, and then downloading satellite data to calculate a satellite altitude angle;
Step S2, carrying out corresponding statistics on the carrier-to-noise ratio output by each satellite obtained in the step S1, the multipath error value calculated by the satellite and the satellite altitude angle calculated by the satellite;
S3, constructing a carrier-to-noise ratio model and a satellite altitude model, applying constraint to weight coefficients of the carrier-to-noise ratio model and the satellite altitude model in a range of 0 to 1, distributing equal weights to two indexes of the carrier-to-noise ratio model and the satellite altitude model before iteration in order to ensure the rationality of the fitting process, and setting initial weights to be 0.5;
s4, multiplying the carrier-to-noise ratio model and the altitude angle model by weight coefficients respectively and adding the weight coefficients, initially constructing a combined fixed-weight random model, and then performing parameter fitting, wherein the parameters of the altitude angle random model and the carrier-to-noise ratio random model are respectively fitted by adopting a Levenberg-Marquardt algorithm in consideration of the altitude nonlinear characteristic of the combined fixed-weight random model;
s5, carrying out multipath error statistics on the carrier-to-noise ratio random model and the altitude angle random model according to 0.25dB-Hz and 0.5 degree intervals, carrying out three-dimensional surface fitting according to the three-dimensional function model which is a nonlinear surface to determine the weight parameters of the two indexes of the carrier-to-noise ratio random model and the altitude angle random model, wherein the parameters of the altitude angle random model and the carrier-to-noise ratio random model obtained in the step S4 are brought into the three-dimensional function model;
step S6, based on the step 2, the two indexes of the satellite altitude angle and the carrier-to-noise ratio are both incorporated into the random model for joint weighting, the corresponding weight coefficients are multiplied before the altitude angle random model and the carrier-to-noise ratio random model respectively, then the weight coefficients are added, and the extracted multipath errors are used as errors in measurement to form a three-dimensional function model: Wherein sigma is the error in measurement, namely the multipath error; v, c, a, b is a constant, and fitting is required for different systems and different frequencies; w s、we is the weight of the carrier-to-noise ratio and the altitude model respectively, and is also the parameter to be fitted; C/N 0 is the carrier-to-noise ratio observation value, and ele is the satellite altitude; and based on the step 5, performing three-dimensional surface fitting by utilizing corresponding statistical points of the multipath error, the satellite altitude angle and the carrier-to-noise ratio to establish the correlation between the carrier-to-noise ratio and the altitude angle and the multipath error, and still adopting a Levenberg-Marquardt algorithm to find a group of model parameters capable of minimizing the distance between the data point and the surface in the fitting process, obtaining weight coefficients w s and w e, and constructing a complete combined weighting random model.
2. The method for constructing a random model suitable for positioning a GNSS chip in a complex urban environment according to claim 1, wherein the carrier-to-noise ratio in the step S1 refers to the ratio between the received satellite signal strength and the background noise strength; the carrier-to-noise ratio is a factor affecting GNSS positioning performance, and a high carrier-to-noise ratio stabilizes the signal, does not delay transmission, and does not generate noise.
3. The method for constructing the random model suitable for positioning the GNSS chip in the complex urban environment according to claim 1, wherein in the step S2, firstly, the dual-frequency pseudo-range and the carrier phase originally observed by the terminal are combined to calculate the multipath error, and the implementation method is as follows:
When satellite double-frequency observations are available, the multipath error can be obtained by combining the carrier phase and the pseudo-range observations, and the two-frequency multipath error is expressed as:
Wherein MP i and MP j are multipath errors for i and j frequencies, respectively; p i,Pj is the pseudorange observations for i, j frequencies, respectively; carrier phase observations at i, j frequencies, respectively; /(I) Wherein f i、fj is two band frequencies respectively.
4. The method for constructing the random model suitable for positioning the GNSS chip in the complex urban environment according to claim 1, wherein the carrier-to-noise ratio weighting random model constructed in the step 4 is as follows:
V and c are constants, and calibration is carried out according to the used receiver, different systems and different frequencies;
The calculation of the altitude angle stochastic model of the step 4 uses a sine function model:
σ2 2＝a2+b2/sin2(ele)
Wherein a and b are constants; ele is satellite altitude, C/N 0 is carrier-to-noise ratio observation, v, C, a, b initial values are respectively set to 0, 1, 0 and 1, and the expression is respectively expressed And σ 2 2＝a2+b2/sin2 (ele) solve for the optimal parameters v, c, a, b using the Levenberg-Marquardt algorithm.
5. The method for constructing the random model suitable for the positioning of the GNSS chip in the complex urban environment according to claim 1, wherein the influence of the atmospheric delay and the noise on the high altitude angle of the step S4 is smaller than that on the low altitude angle satellite, and the function based on the satellite altitude angle and the observation noise is constructed as the random model according to the satellite altitude angle model.
6. The method for constructing a random model suitable for positioning GNSS chips in a complex urban environment according to claim 5, wherein the observation noise is generated by factors of a receiver circuit and an antenna, the observation noise is influenced by carrier power and noise power, and a weighted random model of carrier-to-noise ratio is used for measuring signal precision in a function of the observation noise, and generally, the larger the carrier-to-noise ratio is, the higher the observation value precision is.
7. The method for constructing the stochastic model for positioning the GNSS chip in the complex urban environment according to claim 6, wherein the carrier-to-noise ratio parameter value is calculated by firstly calculating the update direction of the gradient and the Hessian matrix according to the gradient of the objective function under the current parameter value and the Hessian matrix, updating the parameter value by combining the step factor, recalculating the objective function value according to the updated parameter value, accepting the update if the objective function value is reduced, and otherwise, adjusting the step factor to recalculate the update direction.
8. The method for constructing the random model suitable for the GNSS chip positioning in the complex urban environment according to claim 1, wherein in the step S6, the weight parameters of two indexes of the satellite altitude angle random model and the carrier-to-noise ratio random model are determined according to three-dimensional surface fitting, the satellite altitude angle and the carrier-to-noise ratio are both incorporated into the random model for joint weight determination, the weight coefficients are multiplied before the satellite altitude angle random model and the carrier-to-noise ratio random model respectively, then the weight coefficients are added, the extracted multipath errors are used as errors in measurement to form a three-dimensional function model, the model parameters are fitted respectively aiming at different satellite systems and different frequencies, and the complete joint weight random model replaces the traditional altitude angle model to be introduced into the single-point positioning process, so that the accurate weight determination of the GNSS observation value in the complex environment is realized.
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