Spacecraft high-dynamic navigation positioning self-checking simulation test equipment and differential positioning self-checking method

Abstract

The invention discloses spacecraft high-dynamic navigation positioning self-inspection simulation test equipment and a differential positioning self-inspection method, belongs to the technical field of spacecraft navigation, is used for self-inspection of navigation signal simulation sources, and mainly comprises a multi-user navigation signal simulation source, a closed-loop detection module, a high-dynamic navigation signal receiver, a management software module and the like. In a differential positioning algorithm of the equipment, the differential positioning self-checking speed and precision in a high dynamic environment can be effectively improved by adopting a dynamic adaptive filtering and multi-path suppression combined strategy, a rapid ambiguity fixing method and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of aerospace testing and experimental verification, and particularly to a high-dynamic simulation test differential positioning self-check technology for spacecraft for self-checking of navigation signal simulation sources. Background Art

[0002] With the increasing requirements for spacecraft navigation function testing, it is necessary not only to achieve absolute positioning and low-dynamic relative positioning, but also to support high-dynamic relative positioning. Before using a navigation signal simulation source to conduct high-dynamic simulation tests on a spacecraft, it is necessary to first self-check the navigation signal simulation source.

[0003] Traditional self-check technologies can support absolute positioning or low-dynamic relative positioning, but have the following deficiencies in a high-dynamic environment: (1) insufficient accuracy, existing self-check methods are difficult to meet the accuracy requirements under high-dynamic conditions, resulting in large positioning errors; (2) low efficiency, the convergence time of existing differential positioning self-check technologies is long, and it is difficult to achieve rapid self-check; (3) limited anti-interference ability, in a high-dynamic environment, multipath effects and signal interference easily lead to positioning failures.

[0004] Therefore, there is an urgent need for a differential positioning technology that can achieve rapid and accurate self-check in a high-dynamic environment to meet the requirements of differential positioning self-check for spacecraft high-dynamic simulation tests. Summary of the Invention

[0005] In view of the fact that the accuracy of the self-check result of the navigation signal simulation source will directly affect the authenticity and data consistency of the test result, the present disclosure provides a high-dynamic simulation test differential positioning self-check technology for spacecraft, and constructs a high-dynamic navigation positioning simulation test device for spacecraft through a multi-user navigation signal simulation source, a closed-loop detection module, a high-dynamic navigation signal receiver, and management software; by using the proposed high-dynamic differential positioning algorithm, a self-check program is written and generated in the management software, and differential positioning calculation and self-check under high-dynamic conditions can be achieved. Thus, the navigation positioning simulation test device is enabled with the ability of high-dynamic differential positioning self-check.

[0006] The high-dynamic navigation positioning self-check simulation test device for spacecraft for self-checking of navigation signal simulation sources provided by the present disclosure mainly includes: A multi-user navigation signal simulation source, which is used to generate high-dynamic simulation test radio frequency signals for multiple users, and the generated signals are divided into 3 paths for output through a splitter device, where 2 paths are used as spacecraft test signals and 1 path is a self-check signal; A closed-loop detection module, which is used to select and combine several paths of user self-check signals output by the multi-user navigation signal simulation source and then input them into the high-dynamic navigation signal receiver; Two high-dynamic navigation signal receivers, serving as the reference station and the mobile station respectively, receive the navigation analog signals corresponding to two users who need differential positioning; among them, the reference station calculates the pseudorange / carrier phase correction amount, generates differential correction data, encapsulates it according to the RTCM protocol and transmits it to the mobile station; the mobile station receives multi-system dual-frequency observation data, fuses the multi-frequency observation values, and filters out satellite signals with high signal-to-noise ratio; the observation data of the two receivers are transmitted to the management software module; The management software module is used to perform position solution self-check through a high-dynamic differential positioning algorithm.

[0007] Furthermore, the closed-loop detection module includes: Several parallel radio frequency switch modules for performing switch selection on several input user self-check signals; Two parallel combining modules for combining the signals output by the radio frequency switch module to form the navigation analog signals of two users who need differential positioning.

[0008] The spacecraft high-dynamic differential positioning method applied to the above self-check simulation device mainly includes the following steps: S1, set the simulation information of the navigation signal simulation source to generate high-dynamic analog test radio frequency signals for multiple users; S2, the self-check signals of multiple users output by the simulation source are selected by the closed-loop detection module and then input into the high-dynamic navigation signal receiver. Among them, the signals received by the two receivers correspond to the navigation analog signals of two users who need differential positioning; S3, the two receivers serve as the reference station and the mobile station respectively, and the data is transmitted to the management software, and position solution self-check is realized through a high-dynamic differential positioning algorithm; S4, initialize the differential positioning algorithm, generate differential correction data, the reference station calculates the pseudorange / carrier phase correction amount, encapsulates it and transmits it to the mobile station; S5, fuse the multi-frequency observation values, the mobile station receives multi-system dual-frequency observation data, and filters out satellite signals with high signal-to-noise ratio; S6, based on the observation data, perform dynamic adaptive filtering and position solution; S7, perform anti-interference processing and ambiguity fixing.

[0009] Furthermore, in the step S5, the multi-system includes one or more of GPS, Beidou, and Galileo.

[0010] Furthermore, the step S6 specifically includes: Construct the state equation , where , [[ID=4]] is , The system state vector at a moment, including position, velocity, acceleration, and ambiguity; is the state transition matrix; represents the process noise, and its covariance matrix is ; Based on the corrected pseudorange and carrier phase, construct the observation equation , where is the observable at moment is the observation matrix, represents the measurement noise, and its covariance matrix is ; Estimate through the innovation vector , estimate through the residual vector , verify the filtering consistency, and estimate through the state error , and continuously adaptively adjust and during the calculation process based on system performance feedback and actual observation data.

[0011] Furthermore, the step S7 includes: Based on the signal-to-noise ratio and carrier phase residual, eliminate abnormal observations, suppress outliers caused by multipath, and improve anti-interference ability; Subsequently, perform ambiguity resolution, adopt the partial ambiguity fixing strategy, and preferentially fix the ambiguities of satellites with elevation angle > 30°; Finally, verify the ambiguity. Through the ratio test, set the threshold > 3 to confirm the fixing reliability.

[0012] Furthermore, in the step S7, the specific method for eliminating abnormal observations based on the signal-to-noise ratio and carrier phase residual and suppressing outliers caused by multipath includes: Based on the linear combination of the pseudorange and carrier phase observations, quantify the multipath effect intensity, and dynamically identify the observations contaminated by multipath. The formula is expressed as:

[0013] where , ]>is the frequency, is the pseudorange, is the carrier phase, is the wavelength; Construct a multi - dimensional quality assessment system with SNR, satellite elevation angle, and cycle slip ratio parameters. Select observations with lower multipath residuals and higher SNR to participate in the solution. Eliminate or weaken spatially correlated errors through double - difference observations, especially for the short - term correlation of multipath errors, and improve relative positioning accuracy. According to the residual detection results and the predicted values of the multipath model, dynamically adjust the weights of the observations at each frequency point, including: reducing the weights of satellites with low elevation angles that are sensitive to multipath, or assigning higher anti - multipath weights to high - frequency signals. Integrate non - Gaussian signal processing methods to separate multipath errors from other noises, and update the covariance matrix parameters in real - time to enhance the stability of the solution.

[0014] Furthermore, the method also includes steps of result storage and post - processing, specifically including: Store the differential positioning results, including position, velocity, and variance; save the carrier phase information for use in the next epoch to ensure data continuity; record SNR information, archive the signal strength data for quality analysis; update the satellite status, mark the fixed status and cycle slip information of the satellites for data processing in subsequent epochs. Release memory and return the status, clean up local variables, and return the final positioning status.

[0015] Compared with the prior art, the beneficial effects of the present disclosure are: ① It proposes a device suitable for high - dynamic navigation and positioning simulation tests of spacecraft, and a differential positioning self - inspection technology for high - dynamic simulation tests of spacecraft, which can improve the speed and accuracy of differential positioning self - inspection in high - dynamic environments; ② It adopts a dynamic adaptive filtering model, an algorithm for adjusting process noise based on carrier acceleration feedback, to optimize the state estimation accuracy under high - dynamic conditions; ③ It adopts a joint strategy for multipath suppression, combines multi - frequency observation difference and carrier phase residual detection, and dynamically constructs an anti - interference weight matrix; ④ It uses a fast ambiguity fixing method, by designing the LAMBDA algorithm, to constrain the ambiguity search space and shorten the convergence time by more than 50%. Brief Description of the Drawings

[0016] By describing the exemplary embodiments of the present disclosure in more detail in conjunction with the drawings, the above - mentioned and other objects, features, and advantages of the present disclosure will become more obvious. Among them, in the exemplary embodiments of the present disclosure, the same reference numerals generally represent the same components.

[0017] Figure 1 It is a structural diagram of an exemplary high - dynamic navigation and positioning self - inspection simulation test device for spacecraft according to the present disclosure; Figure 2 It is an exemplary high - dynamic differential positioning algorithm flow according to the present disclosure. Detailed Description of the Embodiments

[0018] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0019] The present disclosure proposes a device applicable to the high-dynamic navigation and positioning simulation test of spacecraft, which specifically consists of a multi-user navigation signal simulation source, a closed-loop detection module, a high-dynamic navigation signal receiver, and management software; a differential positioning self-check technology for high-dynamic simulation test of spacecraft is proposed, which can improve the speed and accuracy of differential positioning self-check in a high-dynamic environment.

[0020] In an exemplary embodiment, the structure of the high-dynamic navigation and positioning self-check simulation test device for spacecraft is as shown in the appended Figure 1 figures, and it mainly consists of: 1 multi-user navigation signal simulation source, 1 closed-loop detection module, 2 high-dynamic navigation signal receivers, and management software.

[0021] The process of self-check using this device mainly includes: (1) By setting the simulation information of the navigation signal simulation source, high-dynamic simulation test RF signals for multiple users are generated. The signals are divided into 3 paths for output through a shunt control module, where 2 paths are used as spacecraft test signals and 1 path is a self-check signal.

[0022] (2) The closed-loop detection module includes a RF switch module and a combining module. The self-check signals of multiple users are selected by the closed-loop detection module and input into the high-dynamic navigation signal receiver. The signals received by the 2 receivers correspond to the navigation simulation signals of 2 users for which differential positioning is required.

[0023] (3) The 2 receivers are respectively used as a reference station and a mobile station, and the data is transmitted to the management software, and position solution self-check is realized through a high-dynamic differential positioning algorithm. Among them: The reference station calculates the pseudorange / carrier phase correction amount, generates differential correction data, encapsulates it according to the RTCM protocol and transmits it to the mobile station; The mobile station receives multi-system (GPS, Beidou, Galileo) dual-frequency observation data, fuses the multi-frequency observations, and screens satellite signals with high signal-to-noise ratio.

[0024] (4) The management software performs dynamic filtering and position solution based on the observation data: Construct a state equation , where the state vector includes position, velocity, acceleration, and ambiguity; then an observation equation is constructed based on the corrected pseudorange and carrier phase ; Finally, calculate the adaptive adjustment process noise covariance matrix , and dynamically optimize the filtering parameters according to the carrier acceleration.

[0025] Among them, if is too small, the filter has too much trust in the model prediction, and may ignore some dynamic changes in reality, resulting in the filtering result not being able to keep up with the changes of the actual system (the system is prone to divergence). If is too large, the trust of the filter in the model decreases, and it will rely more on the observation data, which may lead to the filtering output being too noisy and reflecting too much measurement noise; through the innovation vector estimate , through the residual vector estimate , verify the filtering consistency, through the state error estimate , and continuously optimize and adjust and during the calculation process based on the system performance feedback and actual observation data using an adaptive algorithm.

[0026] (5) To improve the accuracy and reliability, perform anti-interference processing and ambiguity fixing: Based on the signal-to-noise ratio (SNR) and carrier phase residuals, eliminate abnormal observations, suppress the outliers caused by multipath, and improve the anti-interference ability; Subsequently, perform ambiguity resolution, adopt the partial ambiguity resolution (PAR) strategy, and preferentially fix the ambiguities of satellites with elevation angles > 30°; Finally, verify the ambiguities through the ratio test, set the threshold > 3, and confirm the fixing reliability.

[0027] In this embodiment, by utilizing the multi-frequency characteristics of co-frequency signals such as GPS L1 / L5, Galileo E1 / E5a, and Beidou B1C / B2a, the multipath effect and other error sources are separated through joint modeling, and the impact of the common-mode error on positioning is reduced.

[0028] Based on the linear combination of pseudorange and carrier phase observations , quantify the intensity of the multipath effect and dynamically identify the observations contaminated by multipath. The formula is expressed as:

[0029] Among them , is the pseudorange, is the carrier phase, is the wavelength. A multi-dimensional quality assessment system is constructed by combining parameters such as signal-to-noise ratio, satellite elevation angle, and cycle slip ratio. Observation values with low multipath residuals and high signal-to-noise ratio are preferentially selected for the solution. Spatial correlation errors are eliminated or weakened through double-difference observation values, especially for the short-term correlation of multipath errors, to improve relative positioning accuracy.

[0030] According to the residual detection results and the predicted values of the multipath model, the weights of the observation values of each frequency point are dynamically adjusted. The weights of satellites with low elevation angles, which are sensitive to multipath, can be reduced, or higher anti-multipath weights can be assigned to high-frequency signals (such as GPS L5 / Galileo E5a). Non-Gaussian signal processing methods such as independent component analysis (ICA) are integrated to separate multipath errors from other noises, and the covariance matrix parameters are updated in real time to enhance the stability of the solution.

[0031] In this embodiment, the detailed steps of the differential positioning algorithm executed by the management software are as shown in the appendix Figure 2 and mainly include: (1) Initialization stage of the differential positioning algorithm: Calculate the time difference between the mobile station and the reference station to synchronize the time reference. Calculate the satellite positions based on ephemeris data and the time difference. Calculate the undifferenced residuals of the reference station to generate the residuals of the reference station's observation data. Interpolate and configure the base station information, and interpolate the base station data according to the configuration. Select common satellites and filter the satellites that are visible to both the mobile station and the reference station.

[0032] (2) State update and iteration preparation: Update the state time, and predict the state variables (such as position and velocity) through Kalman filtering (KF). Initialize variables, apply for memory and assign initial values (ambiguity, covariance matrix, etc.). Set the number of iterations, default is 1 time, and an additional 2 iterations are added in the dynamic baseline mode.

[0033] (3) Measurement update, enter the iterative loop for processing. Calculate the undifferenced residuals of the mobile station to generate the observation residuals of the mobile station. Update the double-difference residuals and the measurement matrix, and construct a double-difference observation model by combining the reference station data. Perform KF measurement update to update the state variables (position, ambiguity, etc.).

[0034] (4) Iteration completion detection: Calculate the float solution residuals, calculate the double-difference residuals and noise based on the updated results. Verify the validity of the results, and determine whether it is valid through variance testing. Store the float solution and count, record the ambiguity and the number of valid satellites, and detect whether the number of satellites is sufficient.

[0035] (5) Use the LAMBDA algorithm to calculate the integer ambiguity. Verify the fixed solution. If successful, recalculate the residuals and covariance, and verify the validity. Store the fixed solution. If configured in the hold mode, save the ambiguity information.

[0036] (6) Result storage and post - processing: Store the differential positioning results, including position, velocity, and variance (if the fixed solution is valid, store the fixed solution preferentially). Save the carrier phase information for use in the next epoch to ensure data continuity. Record the SNR information, archive the signal strength data for quality analysis. Update the satellite status, mark the fixed status and cycle slip information of the satellite for data processing in subsequent epochs.

[0037] (7) Winding - up stage: Release the memory and return the status, clear local variables. Return the final positioning status (fixed solution / floating - point number / invalid).

[0038] The device applicable to the simulation test of spacecraft high - dynamic navigation positioning in this embodiment can effectively improve the self - inspection speed and accuracy of differential positioning in a high - dynamic environment; the proposed dynamic adaptive filtering model, based on the process noise adjustment algorithm with carrier acceleration feedback, optimizes the state estimation accuracy under high - dynamic conditions; the proposed multi - path suppression joint strategy, combining multi - frequency observation difference and carrier - phase residual detection, dynamically constructs an anti - interference weight matrix; the proposed fast ambiguity fixing method, by designing the LAMBDA algorithm, constrains the ambiguity search space and shortens the convergence time by more than 50%.

[0039] The above - mentioned technical solutions are only exemplary embodiments of the present invention. For those skilled in the art, based on the disclosed application methods and principles of the present invention, it is very easy to make various types of improvements or deformations, not limited to the methods described in the above - mentioned specific embodiments of the present invention. Therefore, the previously described manner is only preferred and does not have a restrictive meaning.

Claims

1. A high-dynamic navigation and positioning self-check simulation test device for a spacecraft, characterized in that Including: A multi-user navigation signal simulation source, which is used to generate high-dynamic simulation test RF signals for multiple users. The generated signals are divided into three paths through a splitter device, where two paths are used as spacecraft test signals and one path is a self-test signal; A closed-loop detection module, which is used to select and combine several paths of user self-test signals output by the multi-user navigation signal simulation source, and then input them into a high-dynamic navigation signal receiver; Two high-dynamic navigation signal receivers, which are used as a reference station and a mobile station respectively, and receive navigation simulation signals corresponding to two users who need differential positioning. Among them, the reference station calculates the pseudorange / carrier phase correction amount, generates differential correction data, packages and transmits it to the mobile station; the mobile station receives multi-system dual-frequency observation data, fuses multi-frequency observation values, and filters high signal-to-noise ratio satellite signals; the observation data of the two receivers is transmitted to the management software module; A management software module, which is used to perform position solution self-test through a high-dynamic differential positioning algorithm.

2. The device according to claim 1, characterized in that, The closed-loop detection module includes: Several parallel RF switch modules, which are used to perform switch selection on several paths of input user self-test signals; Two parallel combining modules, which are used to combine the signals output by the RF switch module to form navigation simulation signals for two users who need differential positioning.

3. A self-checking method for high-dynamic differential positioning of a spacecraft based on the device described in claim 1 or 2, characterized in that, Including the following steps: S1. Set the simulation information of the navigation signal simulation source to generate high-dynamic simulation test RF signals for multiple users; S2. The self-test signals of multiple users output by the simulation source are selected by the closed-loop detection module and then input into the high-dynamic navigation signal receiver. Among them, the signals received by the two receivers correspond to the navigation simulation signals of two users who need differential positioning; S3. The two receivers are used as a reference station and a mobile station respectively, and the data is transmitted to the management software, and position solution self-test is realized through a high-dynamic differential positioning algorithm; S4. Initialize the differential positioning algorithm, generate differential correction data, and the reference station calculates the pseudorange / carrier phase correction amount, packages and transmits it to the mobile station; S5. Fuse multi-frequency observation values, the mobile station receives multi-system dual-frequency observation data, and filters high signal-to-noise ratio satellite signals; S6. Based on the observation data, perform dynamic adaptive filtering and position solution; S7. Perform anti-interference processing and ambiguity fixing.

4. The method according to claim 3, characterized in that, In step S5, the multi-system includes one or more of GPS, Beidou, and Galileo.

5. The method according to claim 3, characterized in that, Step S6 specifically includes: Construct the state equation , where and are and the system state vectors at moments, including position, velocity, acceleration, and ambiguity; is the state transition matrix; represents the process noise, and its covariance matrix is ; Construct an observation equation based on the corrected pseudorange and carrier phase , where is the observable at time, is the observation matrix, represents the measurement noise, and its covariance matrix is ; Estimated by the innovation vector Estimate , estimated by the residual vector Estimate , verify the filtering consistency, estimated by the state error Estimate , continuously and adaptively adjust and during the calculation process based on the system performance feedback and actual observation data.

6. The method according to claim 3, wherein Step S7 includes: Eliminate abnormal observation values based on the signal-to-noise ratio and carrier phase residuals, suppress the abnormal values caused by multipath, and improve the anti-interference ability; Subsequently, perform ambiguity resolution, adopt a partial ambiguity fixing strategy, and preferentially fix the ambiguities of satellites with an elevation angle > 30°; Finally, verify the ambiguity. Through a ratio test, set a threshold > 3 to confirm the fixing reliability.

7. The method according to claim 6, characterized in that, In step S7, the specific method for eliminating abnormal observation values based on the signal-to-noise ratio and carrier phase residuals and suppressing the abnormal values caused by multipath includes: Linear combination based on pseudorange and carrier phase observations , quantifying the intensity of multipath effects and dynamically identifying observations contaminated by multipath. The formula is expressed as: Among them, , is the frequency, is the pseudorange, is the carrier phase, is the wavelength; Construct a multi-dimensional quality assessment system with parameters such as signal-to-noise ratio, satellite elevation angle, and cycle slip ratio. Select observations with lower multipath residuals and higher signal-to-noise ratio to participate in the solution. Eliminate or weaken spatially correlated errors through double-difference observations, especially targeting the short-term correlation of multipath errors to improve relative positioning accuracy. Dynamically adjust the weights of observations at each frequency point according to the residual detection results and the predicted values of the multipath model, including: reducing the weight of low-elevation satellites sensitive to multipath, or assigning higher anti-multipath weights to high-frequency signals. Integrate non-Gaussian signal processing methods to separate multipath errors from other noises, and update the covariance matrix parameters in real time to enhance the stability of the solution.

8. The method according to any one of claims 3-7, characterized in that It also includes steps for result storage and post-processing, specifically including: Store differential positioning results, including position, velocity, and variance; save carrier phase information for use in the next epoch to ensure data continuity; record SNR information and archive signal strength data for quality analysis; update satellite status, marking the fixed status and cycle slip information of satellites for data processing in subsequent epochs. Release memory and return the status, clear local variables, and return the final positioning status.

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