CN-122017905-A - Beidou multi-frequency non-differential non-combination PPP-RTK cooperative optimization method and system

CN122017905ACN 122017905 ACN122017905 ACN 122017905ACN-122017905-A

Abstract

The present disclosure provides a collaborative optimization method and system for Beidou multi-frequency non-differential non-combination PPP-RTK, electronic equipment, storage medium and program product, so as to solve the problems of isolated error processing, ambiguity fixation and error state disconnection, wherein the method comprises the steps of constructing a coupling model of ionosphere delay, troposphere wet components and multipath errors based on Beidou multi-frequency observation signals, and performing real-time calculation to obtain an error priori estimated value; the method comprises the steps of injecting an error prior estimated value into a search space of a ambiguity fixing algorithm to optimize a search boundary and realize quick ambiguity fixing, carrying out coarse difference detection on observed data based on DIA, distributing solution weights for different data according to coarse difference severity, carrying out weighted positioning solution by utilizing the solution weights to obtain a positioning result, and feeding back residual errors generated by positioning solution to a solution process of a coupling model to update model parameters so as to realize closed-loop optimization of a collaborative mechanism. The method and the device can realize quick positioning and improve positioning precision.

Inventors

YANG WEIBIN
MAI ZHIMIN
LI BAILIN
LUO DONGHONG
Lin Shaoze
LIU TIANGE
HE CHUNXIA
YANG JIEMIN

Assignees

中国联合网络通信集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260210

Claims (10)

1. A collaborative optimization method of Beidou multi-frequency non-differential non-combination PPP-RTK is characterized by comprising the following steps: Based on the Beidou multi-frequency observation signals, constructing a coupling model of ionosphere delay, troposphere wet components and multipath errors, and performing real-time calculation to obtain an error priori estimated value; injecting the error prior estimated value into a search space of an ambiguity fixing algorithm to optimize a search boundary and realize quick ambiguity fixing; Performing coarse detection on the observed data based on the data integrity analysis DIA, and distributing resolving weights for different data according to the severity of the coarse detection; Weighting and positioning calculation is carried out by utilizing the calculation weight, and a positioning result is obtained; and feeding back the residual error generated by the positioning calculation to the calculation process of the coupling model so as to update model parameters and realize closed-loop optimization of a cooperative mechanism.
2. The method of claim 1, wherein constructing a model of coupling ionospheric delay, tropospheric wet components and multipath errors and performing real-time resolution comprises: Based on the difference between the observed values of the code phase and the carrier phase of the Beidou B1C, B a and the Beidou B3I frequency points, a coupling observation equation comprising ionospheric delay, tropospheric wet components, multipath errors and ambiguity parameters is established; and carrying out real-time recursive calculation on the coupling observation equation by adopting Kalman filtering so as to estimate the coupling coefficient and the prior value of the ionospheric delay, the tropospheric wet component and the multipath error.
3. The method of claim 2, wherein the coupling observation equation is: ; Wherein P B1C is the observed value of the code phase of the B1C frequency point, and L B2a 、L B3I is the observed value of the carrier phase of the B2a frequency point and the B3I frequency point respectively; The method comprises the steps of determining the geometrical distance between a receiver and a satellite, determining ionospheric delay of each frequency point, determining T w as a tropospheric wet component, determining M f as multipath error of each frequency point, determining N f λ f as an ambiguity term of each frequency point, determining N f as an ambiguity parameter, determining lambda f as carrier wavelength, determining f=B1C/B2a/B3I, determining epsilon p and epsilon L as code phase and carrier phase observation noise respectively, and setting ionospheric delay coefficients aiming at a frequency point B2a as preset optimization coefficients.
4. A method according to claim 3, wherein the kalman filtered state vector comprises ionospheric delay, tropospheric wet components and multipath error parameters, the initial variance matrix of which is set separately according to the characteristics of each error source.
5. The method of claim 1, wherein said injecting the error a priori estimate into a search space of an ambiguity fixing algorithm to optimize search boundaries and achieve fast ambiguity fixing comprises: reducing the ambiguity searching range related to ionospheric delay and multipath errors in an LAMBDA algorithm by utilizing the error priori estimated value; And searching the ambiguity candidate values in the optimized search space, and verifying the validity of the candidate values through residual error detection.
6. The method of claim 1, wherein the gross error detection of observed data based on the data integrity analysis DIA and assigning a solution weight to different data according to gross error severity comprises: Calculating residual statistics of the observed data by adopting a card method test method, and comparing the residual statistics with a preset critical value to judge a gross error; and grading the data judged to be coarse according to the ratio r=T/T0 of the residual statistics T to the preset critical value T0: if 1<r is less than or equal to alpha, judging that the difference is slight, and giving a first weight value w1; if r > alpha, judging that the difference is serious, and giving a second weight value w2; Wherein w1> w2, and alpha is a preset coefficient greater than 1; Data not determined to be a coarse difference is given a standard weight value w0, where w0> w1.
7. A co-optimization system for a beidou multi-frequency non-differential non-combination PPP-RTK, characterized in that the system comprises: the modeling module is arranged for constructing a coupling model of ionosphere delay, troposphere wet components and multipath errors based on the Beidou multi-frequency observation signals and performing real-time calculation to obtain an error priori estimated value; The ambiguity fixing module is used for injecting the error prior estimated value into a search space of an ambiguity fixing algorithm so as to optimize a search boundary and realize quick ambiguity fixing; The distribution module is used for carrying out rough detection on the observed data based on the data integrity analysis DIA and distributing resolving weights for different data according to the severity of the rough detection; the positioning calculation module is used for carrying out weighted positioning calculation by utilizing the calculation weight to obtain a positioning result; And the feedback module is used for feeding back residual errors generated by positioning calculation to the calculation process of the coupling model so as to update model parameters and realize closed-loop optimization of a cooperative mechanism.
8. An electronic device, comprising: at least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores one or more computer programs executable by the at least one processor to enable the at least one processor to perform the co-optimization method of the beidou multi-frequency non-differential non-combination PPP-RTK of any one of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a co-optimization method of a beidou multi-frequency non-differential non-combination PPP-RTK according to any of claims 1-6.
10. A computer program product comprising computer readable code, or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, performs a co-optimization method of the beidou multi-frequency non-differential non-combination PPP-RTK according to any one of claims 1-6.

Description

Beidou multi-frequency non-differential non-combination PPP-RTK cooperative optimization method and system Technical Field The disclosure relates to the technical field of satellite navigation positioning, in particular to a Beidou multi-frequency non-differential non-combination PPP-RTK collaborative optimization method and system, electronic equipment, a computer readable storage medium and a computer program product. Background The PPP-RTK technology can provide high-precision and rapid convergence positioning service in the global scope for users by integrating the advantages of precise single-point positioning (PPP) and real-time dynamic positioning (RTK), and is a research hot spot in the current satellite navigation field. Under the support of Beidou multi-frequency systems (such as B1C, B a and B3I), the PPP-RTK technology obtains richer observation information and stronger error processing potential. However, existing beidou PPP-RTK technology implementations typically employ a "step-and-process" architecture. Specifically, first, separate compensation is generally performed by using a model or a filtering method independent of each other for main error sources such as ionospheric delay, tropospheric delay (in particular, wet component), and multipath effect. For example, ionospheric errors are corrected using a Klobuchar or Global Ionospheric Map (GIM) model, tropospheric errors are compensated using a Saastamoinen model and a mapping function, and multipath errors are suppressed by a sliding average or signal-to-noise ratio based method. The processing mode ignores the strong coupling and interaction influence existing between error sources under a complex observation environment (such as an urban canyon), so that the compensation deviation of one error can interfere the correction effect of the other error, even the precision offset is generated, and the further improvement of the final positioning precision is limited. Secondly, in the link of ambiguity fixing (Ambiguity Resolution), the classical LAMBDA (class-squares Ambiguity Decorrelation Adjustment) algorithm is widely adopted. When searching and fixing integer ambiguity, the algorithm mainly relies on the geometric structure and general statistical characteristics of an observation equation to construct a search space, and the algorithm cannot be effectively integrated with and utilizes the error priori information with high precision estimated in real time under the current observation condition. When a significant unmodeled error or residual error exists in an observation environment, the ambiguity search space is abnormally enlarged, so that the search efficiency is low, the convergence time required by the ambiguity fixing is prolonged, and the urgent requirements of 'second level' and even 'instantaneous' high-precision positioning by the application of automatic driving, real-time deformation monitoring and the like are difficult to meet. Furthermore, in terms of data quality control, a "probe-cull" strategy based on data integrity analysis (DATA INTEGRITY ANALYSIS, DIA) is commonly employed. Firstly, detecting a Gross Error (Gross Error) or an abnormal value in observed data through statistical test (such as chi-square test), and then directly removing the data judged to be abnormal from a subsequent resolving flow. This "one-shot" static processing mode fails to distinguish the severity of gross errors (e.g., short-term light interference from continuous heavy deviations), resulting in a large amount of observed data that still contains valid information being discarded, resulting in low data utilization (typically less than 70%), wasting valuable observed resources, and possibly indirectly compromising the accuracy and reliability of the positioning results due to reduced number of available satellites or poor observation geometry. In summary, in the prior art, the defects of three links including isolated error processing, fixed ambiguity and disjointed error state and stiff quality control mechanism are associated with each other, so that the upper performance limit of the beidou PPP-RTK technology in a complex scene is restricted. Therefore, a synergistic optimization method capable of modeling through-error, fixing ambiguity, and controlling the whole process is needed to systematically solve the above-mentioned problems. Disclosure of Invention The method aims at least solving the problems of isolated error processing, fixed ambiguity, disjointed error state and rigid quality control mechanism in the prior art. The disclosure provides a collaborative optimization method and system for Beidou multi-frequency non-differential non-combination PPP-RTK, electronic equipment, a computer readable storage medium and a computer program product. The collaborative linkage and closed-loop optimization of three links of error correction, ambiguity fixing and quality control are realized by establishing an error coupling model, injecting