GNSS instantaneous speed measurement method and device based on least square filtering estimation

Abstract

The invention provides a GNSS instantaneous speed measurement method and device based on least square filtering estimation, and the method comprises the steps: constructing an observation equation and a state equation needed by an estimator, the observation equation comprises a pseudo-range and carrier phase observation equation, and the speed and acceleration parameters of a carrier are added; according to the state equation, carrier motion is modeled into uniformly accelerated motion, acceleration, receiver clock correction and zenith troposphere wet delay are respectively set as a random walk process, and ambiguity is set as a random constant process; performing dimension expansion on the state vector dimension of the normal equation, and eliminating the state component of the previous epoch through a parameter elimination method to realize state prediction; and superposing a prediction result with a normal equation formed by the current epoch observation equation, and carrying out measurement updating to obtain an instantaneous speed estimation value of the current epoch. According to the invention, single-station real-time speed measurement is realized based on broadcast ephemeris, and compared with traditional TDCP speed measurement (average speed) and Doppler speed measurement (large noise), the method has higher precision and reaches centimeter level in the horizontal direction.

Description

Technical Field

[0001] The present invention belongs to the field of global satellite navigation systems, and particularly relates to GNSS speed measurement, and provides a technical solution for realizing single-station high-precision instantaneous speed measurement based on broadcast ephemeris. Background Art

[0002] Speed is one of the important parameters describing the motion state of a carrier, and has wide applications in fields such as unmanned driving, inertial navigation, and automatic control [1]. GNSS has become a commonly used technology for speed measurement due to its advantages of continuity, all-weather, and wide coverage. Common GNSS speed measurement methods include position differential speed measurement, Doppler speed measurement, TDCP speed measurement. In addition, there is also precise point positioning speed measurement [2-3].

[0003] The accuracy of position differential speed measurement depends on the accuracy of the position result, and the speed measurement result is the average speed. There will be significant differences in accuracy among different speed measurement results obtained based on SPP, RTK, and PPP. The speed measurement accuracy obtained from the SPP result is only at the dm / s level; the RTK position differential speed measurement accuracy is mm / s [4]; the PPP result position differential accuracy can reach mm / s. PPP speed measurement requires high-precision real-time orbit and clock offset products [5], and there are certain difficulties in real-time speed measurement. The accuracy of Doppler observation speed measurement is at the cm / s level [6].

[0004] Many researchers have conducted relevant research on TDCP speed measurement [7-8], and the precision can reach mm / s. In order to further improve the speed measurement accuracy and suppress the adverse effects of gross error observations, Reference [9] applied the RANSAC algorithm to the quality control of the TDCP algorithm, and there was a certain improvement in the results. Reference

[10] compared the solution results of precise ephemeris and broadcast ephemeris, and it is also feasible to use the broadcast ephemeris to solve the speed. Even when using the broadcast ephemeris, the speed measurement accuracy is sufficient to meet the requirements, and using precise orbit clock offset products does not improve the speed accuracy.

[0005] References

[0006] [1] Geng Tao, Ding Zhihui, Xie Xin, et al. Evaluation of the Speed Measurement Accuracy of Multi-Frequency Multi-GNSS Based on Carrier Phase Differencing [J]. Journal of Wuhan University (Information Science Edition), 2023, 48(02): 206-213.

[0007]

[23] Newspaper (Information Science Edition), 2023, 48(02): 206-213.

[0008] [2] Tu R. Fast determination of displacement by PPP velocity estimation [J]. Geophysical Journal International, 2014, 196(3): 1397-1401.

[0009] [3] Wang Xingxing. Research on Velocity Measurement Model by Fusing GNSS Phase and Doppler Observations [D]. University of Chinese Academy of Sciences (National Time Service Center, Chinese Academy of Sciences), 2020.

[0010]

[0011] [4] Jin Shaofei. Research and Implementation of Beidou Precise Relative Positioning and Velocity Measurement Algorithms [D]. China University of Mining and Technology, 2020.

[0012] [5] Ma Xiangtai, Zhong Shiming, Zhang Jie, et al. Analysis of BDS / GPS Doppler Velocity Measurement and Dynamic PPP Velocity Measurement Accuracy [J]. Journal of Geodesy and Geodynamics, 2021, 41(01): 34 - 38.

[0013]

[0014] [6] Liu Hui. Research on GNSS Buoy Doppler Velocity Wave Measurement Technology [D]. Shandong University of Science and Technology, 2020.

[0015] [7] VAN GRAAS F, SOLOVIEV A. Precise Velocity Estimation Using a Stand - Alone GPS Receiver [J]. Navigation, 2004, 51(4): 283 - 292.

[0016] [8] Ding W, Wang J. Precise Velocity Estimation with a Stand - Alone GPS Receiver [J]. Journal of Navigation, 2011, 64(2): 311 - 325.

[0017] [9] Zhu Z, Vinande E, Pontious J, et al. A robust multi - constellation time - differenced carrier phase solution [J]. GPS Solutions, 2022, 26(1)

[0018]

[10] Colosimo G, Crespi M, Mazzoni A. Real-time GPS seismology with a stand-alone receiver: A preliminary feasibility demonstration[J]. Journal of Geophysical Research: Solid Earth, 2011,

[0019] 116(B11) Summary of the Invention

[0020] As known from the prior art, TDCP speed measurement and Doppler speed measurement can achieve single-station real-time speed measurement based on broadcast ephemeris. Although TDCP speed measurement has high accuracy, its result is the average speed. When the dynamics of the carrier is strong, even if the sampling rate is 1 Hz, the difference between the average speed and the instantaneous speed is relatively large; although Doppler can measure the instantaneous speed, the noise of the Doppler observation value is large, so the speed measurement accuracy is poor and cannot meet the requirements of higher accuracy. The present invention proposes a GNSS instantaneous speed measurement method based on least squares filtering and estimation, and realizes high-precision speed estimation by adding state constraints in the PPP model.

[0021] The technical solution of the present invention provides a GNSS instantaneous speed measurement method based on least squares filtering and estimation, including the following steps:

[0022] Step 1, construct the observation equation and state equation required by the estimator. The observation equation includes the pseudorange and carrier phase observation equations, and additional velocity and acceleration parameters of the carrier are added; the state equation models the carrier motion as a uniformly accelerated motion, and sets the acceleration, receiver clock error, and zenith tropospheric wet delay as random walk processes, and the ambiguity is set as a random constant process;

[0023] Step 2, expand the dimension of the state vector of the normal equation, and eliminate the state components of the previous epoch by the parameter elimination method to achieve state prediction;

[0024] Step 3, superimpose the prediction result obtained in Step 2 and the normal equation formed by the observation equation of the current epoch for measurement update to obtain the instantaneous speed estimation value of the current epoch.

[0025] Moreover, in Step 1, the state vector of the observation equation includes position parameters, velocity parameters, acceleration parameters, receiver clock error, zenith tropospheric delay, and ambiguity parameters.

[0026] Moreover, the position parameters, receiver clock error, zenith tropospheric delay, and ambiguity parameters are used as filtering signals, and the velocity and acceleration parameters are used as estimation signals.

[0027] Moreover, step 2 includes:

[0028] Step 2.1, merging the state vectors of the current epoch and the previous epoch by expanding the normal equation;

[0029] Step 2.2, eliminating the state components of the previous epoch by the parameter elimination method and retaining the predicted state of the current epoch.

[0030] Moreover, step 3 includes:

[0031] Step 3.1, regarding the state prediction result as a virtual observation equation;

[0032] Step 3.2, superimposing the virtual observation equation and the actual observation equation of the current epoch, and solving to obtain the optimal state estimation value of the current epoch.

[0033] Moreover, single - station real - time speed measurement is realized based on broadcast ephemeris.

[0034] Moreover, it is used in the fields of unmanned driving, inertial navigation or automatic control.

[0035] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the GNSS instantaneous speed measurement method based on least - squares filtering extrapolation as described above is implemented.

[0036] On the other hand, the present invention also provides a non - transitory computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the GNSS instantaneous speed measurement method based on least - squares filtering extrapolation as described above is implemented.

[0037] On the other hand, the present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the GNSS instantaneous speed measurement method based on least - squares filtering extrapolation as described above is implemented.

[0038] The present invention can realize real - time instantaneous speed measurement based on broadcast ephemeris, and provide high - precision speed parameters for fields such as unmanned driving, inertial navigation, and automatic control. Compared with the existing technical means, the advantages and technical effects of the present invention are as follows:

[0039] 1. When the state of the carrier changes greatly, the average speed measured by TDCP cannot truly reflect the state of the carrier. The present invention can realize the measurement of the instantaneous speed of the carrier, so as to more accurately describe the state of the carrier;

[0040] 2. It has higher accuracy than Doppler velocity measurement. Although Doppler can measure instantaneous velocity, the Doppler observation value has a large noise. The main thing used in the present invention is the carrier phase observation value, which has higher accuracy. Therefore, it can achieve higher-accuracy velocity measurement. Description of the Drawings

[0041] Figure 1 It is a schematic diagram of the dimension expansion of the normal equation in the embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the elimination and transposition of the normal equation in the embodiment of the present invention;

[0043] Figure 3 It is a schematic diagram of the superposition of the normal equation in the embodiment of the present invention;

[0044] Figure 4 It is a vehicle driving trajectory diagram in the embodiment of the present invention;

[0045] Figure 5 It is a road environment and equipment installation diagram in the embodiment of the present invention;

[0046] Figure 6 It is a comparison diagram of vehicle speed measurement results in the embodiment of the present invention;

[0047] Figure 7 It is a comparison diagram of the differences in vehicle speed measurement results in the embodiment of the present invention;

[0048] Figure 8 It is a diagram of the difference in instantaneous velocity between LSQ-V and Doppler-V of a vehicle in the embodiment of the present invention.

[0049] Figure 9 It is a flowchart of the method in the embodiment of the present invention. Detailed Embodiment

[0050] The following will further explain the concept, specific structure and technical effects generated by the present invention in combination with the drawings and embodiments, so as to fully understand the purpose, features and effects of the present invention.

[0051] See Figure 9 , a GNSS instantaneous velocity measurement method based on least squares filtering and estimation provided by the embodiment of the present invention includes the following steps:

[0052] Step 1, construct the observation equation and state equation required by the estimator.

[0053] The embodiment further provides a preferred implementation manner of Step 1, including the following sub-steps:

[0054] Step 1.1: Add the velocity and acceleration parameters of the carrier to the pseudorange and carrier phase observation equations, and set the coefficients to 0. Since the position parameters, clock bias, tropospheric delay, and ambiguity parameters are all actual parameters and have prior information, they are filtering signals. For the velocity and acceleration parameters that do not directly appear in the observation equations, they are called predicted signals.

[0055] Abbreviate the observation equations corresponding to the pseudorange and carrier as:

[0056] Z(k) = H k X(k) + Δ(k) (1)

[0057] In the formula, k is used to identify the observation epoch, and H k is the design matrix of the k-th epoch, Z(k) represents the observation vector of the k-th epoch, Δ(k) represents the noise vector. In addition, the state vector X(k) is:

[0058] X(k) = [X Y Z V x V y V z a x a y a z d t T N1 N2…N n T (2)

[0059] In the formula, (X, Y, Z), (V x , V y , V z ), (a x , a y , a z ) are the position, velocity, and acceleration parameters in sequence; d t is the receiver clock bias, T is the zenith tropospheric delay, and (N1, N2, …, N n ) are the ambiguity parameters of n satellites observed in the current epoch.

[0060] Step 1.2: Consider the carrier as a uniformly accelerated motion, and regard the acceleration as a random walk process. Set the corresponding power spectral density according to the type of the carrier. The receiver clock bias and the remaining zenith tropospheric wet delay are also regarded as random walk processes, and set the corresponding power spectral density according to experience respectively. The ambiguity is regarded as a random constant process.

[0061] Express the state equation as:

[0062] X(k) = Φ k,k-1 X(k - 1) + w(k - 1) (3)

[0063] ​where X(k) and X(k - 1) are state vectors of adjacent epochs (the k-th epoch and the (k - 1)-th epoch), Φ k,k-1 is the state transition matrix, and w(k - 1) is the noise term.

[0064] Step 2: Increase the dimension of the state vector of the normal equation, and then eliminate the state components of the previous epoch through the parameter elimination method to achieve state prediction.

[0065] The embodiment further provides a preferred implementation manner of Step 2, including the following sub-steps:

[0066] Step 2.1: To achieve the prediction of the next epoch, it is necessary to add a state equation to the normal equation composed of the observation equations of the current epoch, which is specifically achieved by expanding the dimension of the normal equation. The states of the current epoch and the previous epoch will be included in the new state vector.

[0067] Denote as the state estimate value of the previous epoch and the corresponding variance-covariance information respectively. For the initial solution, is the prior information of the state.

[0068] Rewrite the state equation (3) as:

[0069] 0 = Φ k,k-1 X(k - 1) - X(k) + w(k - 1) (4)

[0070] The above equation can be regarded as a virtual observation equation, and its corresponding weight matrix is Regarding the prior information It can also be rewritten as:

[0071]

[0072] It can also be regarded as a virtual observation equation, where is the error included in the prior information The weight matrix corresponding to this virtual observation equation is Write equations (4) and (5) in matrix form as:

[0073]

[0074] In the formula, I represents the identity matrix.

[0075] The error equation is:

[0076]

[0077] Using the least squares solution, the formed normal equation is:

[0078]

[0079] In the formula, is the transpose of the state transition matrix Φ k,k-1 , are the least-squares estimates of X(k - 1) and X(k) respectively, represents the inverse matrix of the variance of the noise term w(k - 1).

[0080] It can be seen by observation that the normal equation (8) realizes the expansion of the state dimension by adding the state equation, so that the normal equation contains the state parameters of the previous epoch and the current epoch at the same time. The schematic diagram of the dimension expansion is as Figure 1 shown. Figure 1 In [the figure], the two yellow frames on the left represent the normal equations of the previous epoch. The square yellow frame represents the coefficient matrix of the normal equation, and the narrow rectangular yellow frame represents the constant vector of the normal equation. It can be seen from equations (7) and (8) that the state parameters after dimension expansion increase from the original X(k - 1) to X(k). Therefore, the blue frame part represents the increased part. Figure 1 The part to the left and above the blue dotted line in the right figure indicates that it will be eliminated in the next step.

[0081] Step 2.2: The previous epoch X(k - 1) is output at the end of epoch k - 1, while the predicted X(k, k - 1) needs to be passed to the next epoch as a virtual observation value. Therefore, it is necessary to eliminate the X(k - 1) parameter in equation (8) and only retain X(k, k - 1). After the value of the parameter in the current epoch is eliminated, its corresponding predicted value in the next epoch will move to the position of the eliminated parameter for measurement update in the next epoch. The schematic diagram of parameter elimination and transposition is as Figure 2 shown. In the left figure, ①, ②, ③, ④, ⑤, ⑥ represent the remaining parts after the normal equation parameters are eliminated. The right figure shows that ① and ⑤ remain unchanged, and ②, ③, ④, ⑥ are moved to the corresponding positions in turn to ensure that the dimension of the state parameter is consistent with Figure 1 the initial state in [the figure].

[0082] Step 3: Measurement update is achieved by superimposing the normal equation formed by the normal equation after parameter elimination and transposition in the previous epoch and the observation equation in the current epoch.

[0083] The embodiment further provides a preferred implementation manner of step 3, including the following sub-steps:

[0084] Step 3.1: Regard the result of state prediction as a virtual observation equation:

[0085]

[0086] In the formula, Δ X (k, k - 1) is the least-squares estimate obtained from the above equation (8) The included error.

[0087] Combining Equation (1) with the above Equation (9) and writing it in matrix form gives:

[0088]

[0089]

[0090] where is the variance of the error Δ X (k,k - 1), and D Δ (k) is the variance of the observation error Δ(k).

[0091] Equation (10) is rewritten in the form of an error equation as:

[0092]

[0093] The formed normal equation is:

[0094]

[0095] where is the inverse matrix of D Δ (k), and the variance matrix of the current historical state update result is: as:

[0096]

[0097] Step 3.2: Solve the normal equation (13) to obtain the result of this epoch. The form of Equation (13) is the superposition of the normal equation after state prediction and the normal equation formed by actual observations. As Figure 3 shown, the left figure is the result after eliminating and shifting terms in the normal equation in Figure 2 . In practice, the mainly eliminated are time-varying parameters such as coordinate parameters, etc. ②, ③, ④, ⑥ represent the relevant information of the time-varying parameters in the normal equation, and ①, ⑤ mainly represent the relevant information of the time-invariant parameters in the normal equation such as ambiguity parameters, etc. The right figure represents the normal equation formed by the actual observation equation. Since the dimensions of the two equations are the same, the corresponding numerical values are directly added during superposition. After obtaining the optimal estimated value of the current epoch state through Equation (13), based on Equation (13), enter the state prediction of the next k + 1 epoch, perform the dimension expansion and elimination of the normal equation according to Step 2, and perform the superposition of the normal equation of the current k + 1 epoch according to the subsequent Step 3, and so on, until all epochs are solved in sequence.

[0098] To facilitate the understanding of the technical effects of the present invention, a vehicle experiment was conducted in a certain urban area. The receiver type was PANDAPD51A, and multi-frequency data of the BDS / GPS dual system was collected. The data from 8:43 - 8:55 (GPST) was selected for dynamic speed measurement analysis. The driving mileage in 12 minutes was approximately 5 km, and the driving trajectory was as Figure 4 shown. The road environment and equipment installation were as Figure 5 shown.

[0099] The data from 8:43 - 8:55 (GPST) was solved, and the speed measurement accuracies of three speed measurement methods in the road environment were analyzed. RTK-V represents the average speed calculated by RTK reference result position difference, TDCP-V represents the average speed calculated by carrier phase epoch difference, LSQ-V is the GNSS instantaneous speed measurement method proposed in this patent based on least squares filtering and estimation, and Doppler-V represents Doppler speed measurement. The speed measurement results were as Figure 6 shown, Figure 6 In the provided curve graph, the abscissa is the epoch, with the unit of second (s), and the ordinate is the speed in the NEU direction, with the unit of meter per second (m / s).

[0100] From Figure 6 it can be seen that the maximum speed of the vehicle is about 14 m / s. Generally speaking, the three speed measurement results are basically consistent with the RTK result, and the Doppler speed measurement fluctuates greatly in the elevation direction.

[0101] The instantaneous speeds of Doppler-V and LSQ-V were converted to average speeds by taking the average of adjacent epochs. RTK-V is the average speed calculated by RTK reference result position difference. Taking RTK-V as the reference value, the differences between TDCP-V, the averaged Doppler-V, and LSQ-V relative to RTK-V were compared, as Figure 7 shown, Figure 7 in which the abscissa is the epoch, with the unit of second (s), and the ordinate is the speed in the NEU direction, with the unit of meter per second (m / s). The statistical results are shown in Table 1.

[0102] Table 1 Statistical differences in vehicle speed measurement results

[0103]

[0104] From Figure 7As can be seen from Table 1, the average speed TDCP-V measured by TDCP is basically the same as the result RTK-V of RTK position differential. LSQ-V is slightly worse, while the accuracy of Doppler-V in the elevation direction is relatively poor. Taking RTK-V as a reference and comparing the differences between other speed measurement results and RTK-V, the difference of TDCP-V in the horizontal direction is less than 1 cm / s, and the difference in the elevation direction is at the centimeter level; the differences of LSQ-V and Doppler-V in the horizontal direction are both at the centimeter level, LSQ-V is better than Doppler-V, and the difference of Doppler-V in the elevation direction is at the decimeter level. Figure 8 The error histogram of Figure 8 reflects the difference between LSQ-V and Doppler-V at two instants. It can be seen that they are basically the same in the horizontal direction, and the difference basically follows a normal distribution. The difference in the elevation direction is relatively large and the distribution is not concentrated.

[0105] From the above results, it can be analyzed that the average speed measured by TDCP has the highest accuracy. In the vehicle experiment, the least squares filtered PPP speed measurement is better than the Doppler speed measurement in both the horizontal and elevation directions. The difference in the horizontal direction is small, but the difference in the elevation direction is large. The main reason is that the Doppler value itself reflects relative motion and is sensitive to dynamic capture. The dynamicity in the horizontal direction is better, so the speed measurement result is relatively accurate. When the vehicle is driving, the change in the elevation direction itself is small under stable conditions. In addition, errors such as multipath effects in the road environment may have a greater impact on the Doppler observation value, so the result is relatively poor.

[0106] In specific implementation, the method proposed by the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. The system device for implementing the method, such as a computer-readable storage medium storing the corresponding computer program of the technical solution of the present invention and a computer device including running the corresponding computer program, should also be within the protection scope of the present invention.

[0107] The following embodiments describe an electronic device for establishing a GNSS instantaneous speed measurement method based on least squares filtered estimation provided by the present invention. The electronic device for establishing a GNSS instantaneous speed measurement method based on least squares filtered estimation described below can be correspondingly referred to the GNSS instantaneous speed measurement method based on least squares filtered estimation described above.

[0108] The electronic device may include: a processor, a communications interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus. The processor may call the logic instructions in the memory to execute the GNSS instantaneous velocity measurement method based on least squares filtering and estimation, mainly including the software processing part in the above steps.

[0109] In addition, when the logic instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0110] On the other hand, an embodiment of the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the software processing part in the GNSS instantaneous velocity measurement method based on least squares filtering and estimation provided by the above-mentioned various methods.

[0111] On yet another aspect, an embodiment of the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the software processing part in the GNSS instantaneous velocity measurement method based on least squares filtering and estimation provided by the above-mentioned various methods.

[0112] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0113] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A GNSS instantaneous velocity measurement method based on least squares filtering extrapolation, characterized in that: Including the following steps: Step 1: Construct the observation equation and state equation required by the estimator. The observation equation includes pseudorange and carrier phase observation equations, and additional velocity and acceleration parameters of the carrier. The state equation models the carrier motion as a uniformly accelerated motion, and sets the acceleration, receiver clock bias, and zenith tropospheric wet delay as random walk processes, and the ambiguity as a random constant process. Step 2: Expand the dimension of the state vector of the normal equation, and eliminate the state components of the previous epoch through the parameter elimination method to achieve state prediction. Step 3: Superimpose the prediction result obtained in Step 2 with the normal equation formed by the observation equation of the current epoch, perform measurement update, and obtain the instantaneous velocity estimate value of the current epoch.

2. The GNSS instantaneous velocity measurement method based on least squares filtering extrapolation according to claim 1, wherein: In Step 1, the state vector of the observation equation includes position parameters, velocity parameters, acceleration parameters, receiver clock bias, zenith tropospheric delay, and ambiguity parameters.

3. A GNSS instantaneous velocity measurement method based on least squares filtering and estimation according to claim 2, characterized in that: The position parameters, receiver clock bias, zenith tropospheric delay, and ambiguity parameters are used as filtering signals, and the velocity and acceleration parameters are used as extrapolation signals.

4. A GNSS instantaneous velocity measurement method based on least squares filtering extrapolation according to claim 1, characterized in that, Step 2 includes: Step 2.1: Merge the state vectors of the current epoch and the previous epoch through normal equation dimension expansion. Step 2.2: Eliminate the state components of the previous epoch through the parameter elimination method, and retain the predicted state of the current epoch.

5. A GNSS instantaneous velocity measurement method based on least squares filtering extrapolation according to claim 3, characterized in that, Step 3 includes: Step 3.1: Regard the state prediction result as a virtual observation equation. Step 3.2: Superimpose the virtual observation equation with the actual observation equation of the current epoch, and solve to obtain the optimal state estimate value of the current epoch.

6. A GNSS instantaneous velocity measurement method based on least squares filtering extrapolation according to claim 1, characterized in that: Realize single-station real-time velocity measurement based on broadcast ephemeris.

7. A GNSS instantaneous velocity measurement method based on least squares filtering extrapolation according to claim 1, characterized in that: It is used in the fields of unmanned driving, inertial navigation, or automatic control.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: When the processor executes the program, it realizes the GNSS instantaneous velocity measurement method based on least squares filtering and extrapolation as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it realizes the GNSS instantaneous velocity measurement method based on least squares filtering and extrapolation as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by the processor, it realizes the GNSS instantaneous velocity measurement method based on least squares filtering and extrapolation as described in any one of claims 1 to 7.

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