A hybrid ambiguity-fixing real-time orbit determination method and device based on inter-satellite ranging

Abstract

The application discloses a hybrid ambiguity fixing real-time orbit determination method and device based on inter-satellite ranging, and belongs to the technical field of satellite navigation and precise orbit determination. The method fuses on-board GNSS observation data and inter-satellite ranging data, constructs a joint filtering model, uses extended Kalman filtering for state estimation, introduces inter-satellite ranging constraints to enhance ambiguity covariance, adjusts the covariance structure through a dynamic weighting factor, and improves the success rate of integer ambiguity fixing. The LAMBDA algorithm is used to realize fast ambiguity fixing, and the orbit state is re-estimated. The application effectively solves the problems of slow ambiguity fixing and poor stability of the traditional PPP-AR method in a low-orbit high-dynamic environment, realizes faster convergence, higher precision and stronger robustness of real-time orbit determination, and is suitable for low-orbit constellation formation control, remote sensing and navigation enhancement and other applications.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of satellite navigation and precise orbit determination, and particularly relates to a hybrid ambiguity fixed real-time orbit determination method and device based on inter-satellite ranging. BACKGROUND

[0002] Low Earth Orbit (LEO) satellite Real-Time Precise Orbit Determination (RT-POD) is a core technology to support key missions such as high-precision remote sensing, constellation networking, and space-based timing. Currently, the mainstream method in this field relies on carrier phase and pseudorange observation data obtained by a satellite-borne GNSS receiver, combined with real-time precise ephemeris and clock error products, to achieve orbit and clock error estimation of LEO satellites using Precise Point Positioning (PPP) algorithm, in order to achieve centimeter-level orbit determination accuracy. To further improve the stability and convergence speed of the solution, Ambiguity Resolved Precise Point Positioning (PPP-AR) technology is introduced to improve the estimation performance by fixing the carrier phase integer ambiguity. However, the existing PPP-AR method has obvious limitations: first, ambiguity fixing relies heavily on long observation time to build a stable ambiguity covariance, which takes a long time to initialize; second, the method is highly sensitive to observation geometry, ionospheric delay, multipath effect and other error sources, and under the conditions of high dynamic operation of LEO satellites, frequent signal blocking and switching, the failure rate of ambiguity fixing is high, which seriously affects the real-time and continuity of orbit determination.

[0003] In addition, most of the existing orbit determination techniques rely only on GNSS single observation source, which is easily affected by ionospheric disturbances, signal interruptions and multipath interference in complex space environments, resulting in large fluctuations in orbit accuracy and insufficient robustness, making it difficult to meet the demand for continuous high-precision orbit determination. Although the gradually popularized inter-satellite links (such as laser and microwave ranging systems) in low-orbit constellation can provide independent ranging data at centimeter or even millimeter level, and bring high-precision geometric constraints to orbit determination, the existing methods have not fully utilized inter-satellite ranging data to assist ambiguity resolution, and have not effectively integrated multi-source observation information to improve overall orbit determination performance.

[0004] Therefore, it is urgent to develop a new type of orbit determination method that can integrate inter-satellite ranging and GNSS observations to enhance ambiguity fixing capability, improve convergence speed and positioning accuracy, and ensure stable, continuous and high-precision orbit determination of LEO satellites in complex task environments. SUMMARY

[0005] To solve the above technical problems, the application provides a hybrid ambiguity fixed real-time orbit determination method and device based on inter-satellite ranging, which aims to enhance ambiguity covariance constraints, improve ambiguity fixing success rate, and improve the accuracy, convergence speed and robustness of orbit calculation to meet the needs of complex orbit control and high-precision formation applications.

[0006] To achieve the above purpose, the technical solution adopted by the application is as follows:

[0007] A hybrid ambiguity fixed real-time orbit determination method based on inter-satellite ranging, the method comprising:

[0008] Step 1: synchronously collecting space-borne GNSS multi-frequency carrier phase, pseudorange observation data and inter-satellite ranging observation values between low-orbit satellites, constructing ionosphere-free combined observations and performing time alignment processing;

[0009] Step 2: establishing a state vector containing satellite position, velocity, receiver clock bias and integer ambiguity, constructing a GNSS ionosphere-free combined observation model and an inter-satellite ranging observation model;

[0010] Step 3: constructing an indirect ambiguity covariance based on the error characteristics of the inter-satellite ranging observation model, dynamically weighting and fusing it with the GNSS ambiguity covariance to generate a hybrid ambiguity covariance matrix;

[0011] Step 4: based on the hybrid ambiguity covariance matrix, using the LAMBDA algorithm to fix the integer ambiguity, combining the GNSS ionosphere-free combined observation model to re-estimate and verify the orbit state of the low-orbit satellite, and outputting the real-time orbit result.

[0012] In another aspect, the application provides a hybrid ambiguity fixed real-time orbit determination device based on inter-satellite ranging, comprising:

[0013] The acquisition module is configured to synchronously collect space-borne GNSS multi-frequency carrier phase, pseudorange observation data and inter-satellite ranging observation values between low-orbit satellites, construct ionosphere-free combined observations and perform time alignment processing;

[0014] The construction module is configured to establish a state vector containing satellite position, velocity, receiver clock bias and integer ambiguity, construct a GNSS ionosphere-free combined observation model and an inter-satellite ranging observation model;

[0015] The fusion module is configured to construct an indirect ambiguity covariance based on the error characteristics of the inter-satellite ranging observation model, dynamically weighting and fusing it with the GNSS ambiguity covariance to generate a hybrid ambiguity covariance matrix;

[0016] The output module is configured to fix the integer ambiguity based on the hybrid ambiguity covariance matrix using the LAMBDA algorithm, combine the GNSS ionosphere-free combined observation model to re-estimate and verify the orbit state of the low-orbit satellite, and output the real-time orbit result.

[0017] In a third aspect, the present application provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned mixed ambiguity fixed real-time orbit determination method based on inter-satellite ranging.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, enable the processor to implement the aforementioned mixed ambiguity fixed real-time orbit determination method based on inter-satellite ranging.

[0019] The present application has the following beneficial effects:

[0020] The present application significantly improves the performance of real-time orbit determination of low-orbit satellites by fusing inter-satellite ranging and GNSS observation data. Firstly, the introduction of inter-satellite ranging constraints effectively enhances the ambiguity covariance structure, greatly improves the fixing success rate and fixing speed of the integer ambiguity, and shortens the convergence time from more than 10 minutes of the traditional PPP-AR method to within 3 minutes. Secondly, the joint filtering model fully utilizes the high precision and strong geometric constraints of inter-satellite ranging, reduces the orbit positioning error from the order of 10 centimeters to about 5 centimeters, and significantly improves the orbit determination accuracy. Thirdly, the multi-source fusion mechanism enhances the system's tolerance to abnormal situations such as GNSS signal interruption and ionospheric disturbance, ensuring the continuity and robustness of orbit determination in high dynamic and multi-interference environments. The present application is suitable for low-orbit constellation formation flight, remote sensing and real-time navigation enhancement tasks, and has good engineering application prospects. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A flowchart of the mixed ambiguity fixed real-time orbit determination method based on inter-satellite ranging of the present application;

[0022] Figure 2a An orbit determination accuracy curve of the prior art method;

[0023] Figure 2b An orbit determination accuracy curve based on the method of the present application. DETAILED DESCRIPTION

[0024] The present application will be further described below in conjunction with the drawings and examples.

[0025] As shown in the drawings, Figure 1 The present application proposes a mixed ambiguity fixed real-time orbit determination method based on inter-satellite ranging enhancement, fuses on-board GNSS and inter-satellite ranging (ISL) observation information, constructs a joint filtering model, and improves the ambiguity fixing success rate and orbit resolution accuracy through covariance enhancement and dynamic weighting. The overall process is divided into the following four steps:

[0026] Step 1: Synchronous acquisition and preprocessing of multi-source observation data; Synchronous acquisition of satellite-borne GNSS multi-frequency carrier phase, pseudorange observation data and inter-satellite ranging observation values between low-orbit satellites, construction of ionosphere-free combined observations and time alignment processing; including:

[0027] (1) Collecting multi-frequency carrier phase and pseudorange observation data from satellite-borne GNSS receivers, wherein represents GNSS frequency points, such as L1 / L2 or B1 / B2;

[0028] (2) Construction of ionosphere-free combined observations:

[0029] ,

[0030] ,

[0031] wherein represents GNSS frequency points, and subscripts 1 and 2 are used to distinguish different frequency points, such as L1 / L2 or B1 / B2, and represent carrier and pseudorange observations of ionosphere-free combinations, respectively.

[0032] (3) Synchronous acquisition of inter-satellite ranging observation values , wherein i and j are different numbered low-orbit satellites;

[0033] (4) Time alignment of GNSS and ISL data, unified time scale (such as BDST), standardized processing of different data formats, and ensuring synchronization and availability when entering the filtering model.

[0034] Step 2: State vector modeling and observation model construction; Establishing a state vector containing satellite position, velocity, receiver clock error and integer ambiguity, constructing GNSS ionosphere-free combined observation model and inter-satellite ranging observation model; including:

[0035] (1) Construction of extended Kalman filter (EKF) system, state vector definition as follows:

[0036] ,

[0037] wherein:

[0038] : Position of low-orbit satellite i;

[0039] : Velocity of low-orbit satellite i;

[0040] : Satellite-borne receiver clock error;

[0041] : GNSS observation related integer ambiguity vector.

[0042] (2) Construct GNSS observation model (no ionosphere combination), adopt different frequency GNSS carrier phase and pseudorange observation value to carry on linear combination, eliminate ionosphere delay first order influence, form no ionosphere combination carrier phase observation value and pseudorange observation value:

[0043] ,

[0044] ,

[0045] Wherein:

[0046] : GNSS satellite-low earth orbit satellite i satellite receiver geometric distance;

[0047] : GNSS satellite clock error;

[0048] , : carrier, pseudorange observation noise respectively;

[0049] : GNSS carrier wavelength;

[0050] : GNSS observation related integer ambiguity vector.

[0051] (3) Construct inter-satellite ranging observation model ρ, establish observation equation based on the geometric distance between two low earth orbit satellites and inter-satellite clock difference, wherein the geometric distance is the Euclidean distance between the current positions of two satellites;

[0052] ,

[0053] Wherein:

[0054] : Euclidean distance between low earth orbit satellite i and j;

[0055] : inter-satellite clock difference of low earth orbit satellite i and j;

[0056] : inter-satellite ranging error term.

[0057] Step 3: Ambiguity covariance enhancement and fusion modeling; based on the error characteristics of the inter-satellite ranging observation model, construct indirect covariance between ambiguities, fuse it with GNSS ambiguity covariance, and generate hybrid ambiguity covariance matrix; including:

[0058] This invention introduces inter-satellite ranging constraints to enhance ambiguity covariance, constructs a hybrid ambiguity covariance matrix, and improves the fixation success rate.

[0059] ,

[0060] in:

[0061] Ambiguity variance estimation under traditional GNSS observations;

[0062] : Indirect covariance of ambiguity constructed from the propagation of inter-satellite ranging errors;

[0063] ∈[0,1]: Dynamic weighting factor, adjusted in real time based on inter-satellite ranging accuracy and residual consistency, can be defined as:

[0064] ,

[0065] in:

[0066] : Standard deviation (estimated value) of inter-satellite ranging error;

[0067] Standard deviation (estimated value) of GNSS observation error.

[0068] Step 4: Ambiguity Fixation and Orbit State Estimation; Based on the aforementioned hybrid ambiguity covariance matrix, the LAMBDA algorithm is used to fix integer ambiguities. Combined with the aforementioned GNSS ionospheric-free combined observation model, the orbit state of the low-Earth orbit satellite is re-estimated and verified, outputting real-time orbit results. This includes:

[0069] (1) Using the above-mentioned mixed covariance to construct the integer ambiguity fixed condition, the LAMBDA (Least-squares AM Biguity Decorrelation Adjustment) algorithm is used to solve for the optimal integer ambiguity vector. ;

[0070] (2) Substitute the optimal integer ambiguity back into the spaceborne GNSS observation equations to obtain the orbital state vector of the low-Earth orbit satellite. , and Perform a re-estimation and run a residual consistency test;

[0071] (3) If the test passes, output the trajectory estimation result and covariance for use by the attitude control system, formation control or payload mission, to ensure real-time acquisition of trajectory information with centimeter-level accuracy. If the test fails, retain the floating-point solution.

[0072] Figure 2a and Figure 2b The accuracy effect diagram of real-time orbit determination based on the conventional algorithm and the method of the application is given, it can be seen that the three-dimensional real-time orbit determination accuracy of the conventional algorithm is about 11.0 cm, and the three-dimensional real-time orbit determination accuracy of the application is about 5.5 cm, which effectively improves the orbit determination accuracy.

[0073] In another aspect, the application provides a hybrid ambiguity fixed real-time orbit determination device based on inter-satellite ranging, which comprises various modules capable of realizing various steps of the foregoing method, specifically comprising:

[0074] The acquisition module is used for synchronously acquiring the satellite-borne GNSS multi-frequency carrier phase, pseudorange observation data and inter-satellite ranging observation values between low-orbit satellites, constructing ionosphere-free combined observations and performing time alignment processing.

[0075] The construction module is used for establishing a state vector containing satellite position, velocity, receiver clock error and integer ambiguity, constructing a GNSS ionosphere-free combined observation model and an inter-satellite ranging observation model.

[0076] The fusion module is used for constructing an indirect ambiguity covariance based on the error characteristics of the inter-satellite ranging observation model, dynamically weighting and fusing it with the GNSS ambiguity covariance to generate a hybrid ambiguity covariance matrix.

[0077] The output module is used for fixing the integer ambiguity by using the LAMBDA algorithm based on the hybrid ambiguity covariance matrix, re-estimating and checking the orbit state of the low-orbit satellite in combination with the GNSS ionosphere-free combined observation model, and outputting the real-time orbit result.

[0078] In a third aspect, the application provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the foregoing hybrid ambiguity fixed real-time orbit determination method based on inter-satellite ranging.

[0079] In a fourth aspect, the application provides a computer-readable storage medium having stored executable instructions, which, when executed by a processor, can enable the processor to implement the foregoing hybrid ambiguity fixed real-time orbit determination method based on inter-satellite ranging.

[0080] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the application, and it should be understood that the above description is only for specific embodiments of the application and is not intended to limit the application, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A hybrid ambiguity-fixed real-time orbit determination method based on inter-satellite ranging, characterized in that, The method comprises: Step 1: synchronously collecting spaceborne GNSS multi-frequency carrier phase, pseudorange observation data and inter-satellite ranging observation values between low-orbit satellites, constructing ionosphere-free combined observations and performing time alignment processing; Step 2: establishing a state vector comprising satellite position, velocity, receiver clock error and integer ambiguity, constructing a GNSS ionosphere-free combined observation model and an inter-satellite ranging observation model; Step 3: constructing an indirect ambiguity covariance based on the error characteristics of the inter-satellite ranging observation model, dynamically weighting and fusing the ambiguity covariance with the GNSS ambiguity covariance to generate a hybrid ambiguity covariance matrix; Step 4: fixing the integer ambiguity based on the hybrid ambiguity covariance matrix, re-estimating and checking the orbit state of the low-orbit satellite in combination with the GNSS ionosphere-free combined observation model, and outputting real-time orbit results.

2. The hybrid ambiguity-fixed real-time orbit determination method based on inter-satellite ranging according to claim 1, characterized in that, In the step 1, the time alignment processing specifically comprises: unifying the GNSS multi-frequency carrier phase, pseudorange observation data and inter-satellite ranging data to the BDST or GPST time scale system.

3. The hybrid ambiguity-fixed real-time orbit determination method based on inter-satellite ranging according to claim 1, wherein, In the step 2, the construction of the GNSS ionosphere-free combined observation model comprises: linearly combining GNSS carrier phase and pseudorange observation values of different frequencies to eliminate the influence of the first-order ionospheric delay, and forming ionosphere-free combined carrier phase and pseudorange observation values; and the construction of the inter-satellite ranging observation model comprises: establishing an observation equation based on the geometric distance between two low-orbit satellites and the inter-satellite clock error, wherein the geometric distance is the Euclidean distance between the current positions of the two satellites.

4. The hybrid ambiguity-fixed real-time orbit determination method based on inter-satellite ranging according to claim 1, wherein, In the step 3, the hybrid ambiguity covariance matrix is a weighted sum of the GNSS ambiguity covariance and the indirect ambiguity covariance.

5. The hybrid ambiguity-fixed real-time orbit determination method based on inter-satellite ranging according to claim 4, characterized in that, The weighted sum is realized by applying a dynamic weighting factor to the indirect ambiguity covariance, and the dynamic weighting factor is adjusted in real time according to the ratio of the standard deviation of the inter-satellite ranging observation error to the standard deviation of the GNSS observation error.

6. The hybrid ambiguity-fixed real-time orbit determination method based on inter-satellite ranging according to claim 1, characterized in that, In the step 4, the optimal integer ambiguity vector is searched and determined by using the LAMBDA algorithm based on the hybrid ambiguity covariance matrix; and the optimal integer ambiguity vector is back-substituted into the GNSS ionosphere-free combined observation equation as a known value; Based on the back-substituted observation equation, the position, velocity and receiver clock error parameters of the low-orbit satellite are re-estimated.

7. The hybrid ambiguity-fixed real-time orbit determination method based on inter-satellite ranging according to claim 6, characterized in that, The step 4 further comprises, after re-estimating the orbit state of the low-orbit satellite, performing residual consistency checking, and if the checking passes, outputting the final real-time orbit determination result; If not, keep the floating-point solution or use the fixed solution of the last epoch.

8. A hybrid ambiguity-fixed real-time orbit determination device based on inter-satellite ranging, characterized in that, It comprises: A collection module for synchronously collecting spaceborne GNSS multi-frequency carrier phase, pseudorange observation data and inter-satellite ranging observation values between low-orbit satellites, constructing ionosphere-free combined observations and performing time alignment processing; A construction module for establishing a state vector comprising satellite position, velocity, receiver clock error and integer ambiguity, constructing a GNSS ionosphere-free combined observation model and an inter-satellite ranging observation model; a fusion module configured to construct an indirect ambiguity covariance matrix based on error characteristics of the inter-satellite ranging observation model, and dynamically weight and fuse the ambiguity covariance matrix with a GNSS ambiguity covariance matrix to generate a hybrid ambiguity covariance matrix; an output module configured to fix integer ambiguities based on the hybrid ambiguity covariance matrix using a LAMBDA algorithm, and re-estimate and verify the orbit state of the low earth orbit satellite using the GNSS ionosphere-free combination observation model, and output real-time orbit results.

9. An electronic device, comprising: comprising: one or more processors; a memory configured to store one or more programs; wherein the one or more programs, when executed by the one or more processors, enable the one or more processors to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, a non-transitory computer-readable medium having stored thereon executable instructions that, when executed by a processor, enable the processor to implement the method of any one of claims 1-7.

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