GNSS (Global Navigation Satellite System) dynamic positioning method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses a GNSS (Global Navigation Satellite System) dynamic positioning method, device and equipment and a storage medium, and relates to the technical field of navigation positioning, and the method comprises the following steps: determining an estimation state vector of a signal receiving end in a previous epoch and an estimation state vector of a current epoch based on GNSS observation data; multiplying the estimated state vector of the current epoch by a preset constraint matrix to obtain a constraint parameter, comparing the constraint parameter with a preset constraint condition threshold, and if the constraint parameter of the current epoch is greater than the preset constraint condition threshold and the constraint parameters of at most n continuous epochs are greater than the preset constraint condition threshold, constructing a virtual observation equation, and calculating to obtain a prediction state vector of the signal receiving end in the current epoch, and outputting a positioning result. According to the method, a possible output abnormal result is solved by utilizing extended Kalman filtering iteration in a complex environment, and dynamic constraint condition adjustment is realized by introducing inequality constraints.

Description

Technical Field

[0001] The present application relates to the field of navigation and positioning technology, and in particular to a GNSS dynamic positioning method, device, equipment and storage medium. Background Art

[0002] The global navigation satellite system (GNSS) real-time kinematic (RTK) precision positioning technology has been widely used in many fields such as geographic surveying and mapping, transportation and disaster monitoring. It can achieve centimeter-level high-precision positioning based on carrier phase observation through the differential method.

[0003] However, in complex environments such as urban canyons, forests and mountains, factors such as signal multipath effects, non-line-of-sight (NLOS) reception and occlusion will seriously affect the positioning accuracy and reliability of GNSS-RTK. In these environments, there are frequent signal reflections, refractions and occlusions, which will introduce large positioning errors and thus reduce the positioning performance of the GNSS system.

[0004] The GNSS-RTK positioning technology in related technologies mainly relies on equation constraints and reduces observation errors through functional model compensation and random model compensation. Although it improves the positioning accuracy to a certain extent, it is still difficult to completely eliminate the unmodeled errors in complex environments. These residual errors will still have a significant impact on the positioning results.

[0005] In order to solve the GNSS positioning problem in complex environments, multi-sensor fusion technology has been gradually introduced. By combining external auxiliary information such as inertial measurement unit (IMU), laser radar (LiDAR) and visual sensors, the GNSS positioning performance can be significantly enhanced. Although these methods have performed well in improving positioning accuracy, their complex implementation process, high cost and low efficiency limit their widespread promotion in practical applications.

[0006] Therefore, there is currently a lack of a positioning method that can improve positioning accuracy in complex environments. Summary of the invention

[0007] The embodiments of the present application provide a GNSS dynamic positioning method, apparatus, device and storage medium to solve the defect that the modeling error cannot be completely eliminated only by equation constraints in the related art. The constraint conditions in the related art remain unchanged during the positioning process. If the positioning result of the current epoch changes after multiple consecutive epochs, the original constraint items will lead to erroneous positioning results. The present application introduces constraint conditions and judges the processing method of the estimated state vector according to the comparison results of the constraint parameters of multiple consecutive epochs and the constraint condition thresholds. According to different situations, the method of calculating the positioning result can be adaptively adjusted to ensure the accuracy of the positioning result. The technical solution is as follows:

[0008] In a first aspect, an embodiment of the present application provides a GNSS dynamic positioning method, which is applied to a GNSS signal receiving end, comprising:

[0009] Determining an estimated state vector of the signal receiving end at a previous epoch adjacent to the current epoch based on GNSS observation data acquired at each preset time interval;

[0010] Determining an estimated state vector of the signal receiving end at a current epoch based on a Kalman filter algorithm and the estimated state vector of the previous epoch;

[0011] Multiplying the estimated state vector of the current epoch by a preset constraint matrix to obtain constraint parameters, and comparing the constraint parameters of the current epoch with a preset constraint condition threshold;

[0012] If the constraint parameter of the current epoch is greater than the preset constraint threshold, and the constraint parameters of at most n consecutive epochs up to the current epoch are greater than the preset constraint threshold, then a virtual observation equation containing a virtual observation error is constructed based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint threshold; wherein n is an integer greater than 1;

[0013] Based on the Kalman filter algorithm and the virtual observation equation, the predicted state vector of the signal receiving end in the current epoch is calculated, and the positioning result of the signal receiving end in the current epoch is obtained based on the predicted state vector.

[0014] In an optional solution of the first aspect, after multiplying the estimated state vector of the current epoch by a preset constraint matrix to obtain a constraint parameter, and comparing the constraint parameter of the current epoch with a preset constraint condition threshold, the method further includes:

[0015] Accumulate the number of times that the constraint parameter of multiple consecutive epochs is greater than the preset constraint condition threshold, and obtain the positioning result count corresponding to each epoch;

[0016] If the constraint parameter of the current epoch is greater than the preset constraint condition threshold, the estimated state vector of the current epoch is determined to be an abnormal positioning result, the value of the positioning result count of the previous epoch adjacent to the current epoch is increased by 1, and the positioning result count of the current epoch is output;

[0017] If the constraint parameter of the current epoch is less than or equal to the preset constraint condition threshold, it is determined that the estimated state vector of the current epoch is not an abnormal positioning result, the value of the positioning result count of the previous epoch adjacent to the current epoch is set to 0, and the positioning result count of the current epoch is output.

[0018] In another optional solution of the first aspect, after accumulating the number of times that the constraint parameter of a plurality of consecutive epochs is greater than the preset constraint condition threshold and obtaining the positioning result count corresponding to each epoch, the method further includes:

[0019] If the constraint parameter of the current epoch is greater than the preset constraint condition threshold, and the positioning result count of the current epoch is greater than the positioning result count threshold, the estimated state vector of the current epoch is input into the extended Kalman filter, and the predicted state vector of the signal receiving end in the current epoch is obtained through the extended Kalman filter, and the positioning result of the signal receiving end in the current epoch is obtained based on the predicted state vector.

[0020] In another optional solution of the first aspect, after accumulating the number of times that the constraint parameter of a plurality of consecutive epochs is greater than the preset constraint condition threshold and obtaining the positioning result count corresponding to each epoch, the method further includes:

[0021] If the constraint parameter of the current epoch is less than or equal to the preset constraint condition threshold, the estimated state vector of the current epoch is input into the extended Kalman filter, the predicted state vector of the signal receiving end in the current epoch is obtained through the extended Kalman filter, and the positioning result of the signal receiving end in the current epoch is obtained based on the predicted state vector.

[0022] In another optional solution of the first aspect, the process of determining the estimated state vector of the signal receiving end at a current epoch and determining the estimated state vector of the signal receiving end at a previous epoch adjacent to the current epoch includes:

[0023] Multiple types of error models are established to construct pseudorange observation equations of the signal receiving end; wherein the multiple types of error models include a multipath error model, a non-line-of-sight error model, and a tropospheric delay error model.

[0024] A positioning equation is constructed based on the pseudorange observation equation, and an estimated state vector of a corresponding epoch is calculated based on Kalman filtering and the positioning equation.

[0025] In a second aspect, an embodiment of the present application further provides a GNSS dynamic positioning device, comprising:

[0026] A data processing unit, configured to determine an estimated state vector of the signal receiving end at a previous epoch adjacent to the current epoch based on GNSS observation data acquired at each preset time interval;

[0027] The data processing unit is further used to determine the estimated state vector of the signal receiving end at the current epoch according to the Kalman filter algorithm and the estimated state vector of the previous epoch;

[0028] A constraint unit, configured to multiply the estimated state vector of the current epoch by a preset constraint matrix to obtain a constraint parameter, and compare the constraint parameter of the current epoch with a preset constraint condition threshold;

[0029] If the constraint parameter of the current epoch obtained by the constraint unit is greater than the preset constraint condition threshold, and the constraint parameters of n consecutive epochs until the current epoch are greater than the preset constraint condition threshold, the constraint unit constructs a virtual observation equation containing a virtual observation error based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint condition threshold; wherein n is an integer greater than 1, and n is less than the positioning result counting threshold;

[0030] A positioning unit is used to calculate the predicted state vector of the signal receiving end at the current epoch according to the Kalman filter algorithm and the virtual observation equation, and obtain the positioning result of the signal receiving end at the current epoch based on the predicted state vector.

[0031] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect is implemented.

[0032] In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiment of the present application or any one of the implementations of the first aspect.

[0033] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0034] A GNSS dynamic positioning method, apparatus, device and storage medium provided in an embodiment of the present application take into account that abnormal positioning results will be output when using an extended Kalman filter for iterative solution in a complex terrain environment due to the existence of unmodeled errors. By setting a preset constraint condition threshold and judging the constraint parameters and preset constraint condition threshold of multiple consecutive epochs, dynamic constraint condition adjustment is achieved. Only when the number of consecutive epochs in which the constraint parameters of the current epoch are greater than the preset constraint condition threshold is less than or equal to n, the inequality constraints of the constraint parameters and the preset constraint condition threshold are applied to correct the positioning model.

[0035] Furthermore, by introducing a preset constraint matrix and multiplying it with the estimated state vector of the current epoch to obtain the corresponding constraint parameters, by comparing the constraint parameters with the preset constraint condition threshold and the comparison results of the constraint parameters of multiple consecutive epochs with the preset constraint condition threshold, a virtual observation equation containing virtual observation errors is constructed based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint condition threshold, thereby preventing erroneous positioning and quickly responding to changes in positioning results of consecutive epochs, which is conducive to conveniently improving positioning accuracy without increasing the amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a flowchart of a GNSS dynamic positioning method according to an embodiment of the present application;

[0038] Figure 2 It is a structural schematic diagram of a GNSS dynamic positioning device provided in an embodiment of the present application;

[0039] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] The terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0042] It should be noted that the terms "first\second" involved in the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that "first\second" can be interchanged with a specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those described or illustrated herein.

[0043] It should be noted that among the related technologies, GNSS positioning technology based on inequality constraints has gradually attracted attention. Introducing inequality constraints in GNSS positioning, that is, introducing more flexible constraints, can more effectively deal with unmodeled errors in complex environments. However, in the current GNSS field, the application research of inequality constraints is still relatively limited.

[0044] It is understandable that the relevant technologies have significant limitations when dealing with GNSS-RTK positioning in complex environments, such as low positioning accuracy, high implementation complexity and high cost. The existing methods of optimizing data processing by introducing inequality constraints have defects such as potential over-constraints and complex calculations. Therefore, the application of inequality constraints is limited.

[0045] The present application is described in detail below with reference to specific embodiments.

[0046] Next, combine Figure 1 , taking the GNSS signal receiving end executing the GNSS dynamic positioning method as an example, the GNSS dynamic positioning method provided by the embodiment of the present application is introduced. For details, please refer to Figure 1 , Figure 1 A schematic diagram of a GNSS dynamic positioning method provided in an embodiment of the present application is shown, and the method comprises the following steps:

[0047] S101, determining an estimated state vector of the signal receiving end in a previous epoch adjacent to a current epoch based on GNSS observation data acquired at each preset time interval.

[0048] S102, determining an estimated state vector of the signal receiving end in a current epoch based on a Kalman filter algorithm and the estimated state vector of the previous epoch.

[0049] S103, multiplying the estimated state vector of the current epoch by a preset constraint matrix to obtain constraint parameters, and comparing the constraint parameters of the current epoch with a preset constraint condition threshold.

[0050] If the constraint parameter of the current epoch is greater than the preset constraint condition threshold, and the constraint parameters of at most n consecutive epochs up to the current epoch are greater than the preset constraint condition threshold, then S104 is executed to construct a virtual observation equation containing a virtual observation error based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint condition threshold; wherein n is an integer greater than 1;

[0051] S105, based on the Kalman filter algorithm and the virtual observation equation, calculate and obtain the predicted state vector of the signal receiving end in the current epoch, and obtain the positioning result of the signal receiving end in the current epoch based on the predicted state vector.

[0052] In some embodiments, in S101, GNSS observation data can be collected by a GNSS receiver, and the sampling interval is set to 5 seconds and the observation time is set to 24 hours. The received signal should include signals from multiple satellite systems (such as GPS and BDS) to ensure the diversity and reliability of the data.

[0053] Specifically, the GNSS observation data may include, for example, but is not limited to: pseudorange, carrier phase, and deionospheric (also known as semi-combination) composed of pseudorange and carrier.

[0054] Specifically, the GNSS observation data can be preprocessed by including but not limited to at least one of the following processing: detecting and repairing single-frequency receiver clock jumps in historical observations, detecting and eliminating gross errors in historical observations, detecting and repairing cycle slips in historical observations, etc.

[0055] In some embodiments, the process of determining the estimated state vector of the signal receiving end at the current epoch and determining the estimated state vector of the signal receiving end at the previous epoch adjacent to the current epoch in S101 and S102 includes:

[0056] Multiple types of error models are established to construct pseudorange observation equations of the signal receiving end; wherein the multiple types of error models include a multipath error model, a non-line-of-sight error model, and a tropospheric delay error model.

[0057] A positioning equation is constructed based on the pseudorange observation equation, and an estimated state vector of a corresponding epoch is calculated based on Kalman filtering and the positioning equation.

[0058] Specifically, the multipath error model: The multipath error model is corrected by the difference between the signal reflection path and the straight path, and the formula is as follows:

[0059]

[0060] Among them, M i is the multipath error of the ith satellite, a j 、f j ,φ j are amplitude, frequency and phase respectively.

[0061] Non-line-of-sight error model: The non-line-of-sight error model models the signal occlusion situation, and the formula is as follows:

[0062]

[0063] Among them, NLOS is the non-line-of-sight error, b i 、c i , d are model parameters and distance respectively.

[0064] Tropospheric delay error model: The tropospheric delay error model is corrected by actual observation data, and the formula is as follows:

[0065] T d =Z d sin(e);

[0066] Among them, T d is the tropospheric delay error, Z d is the tropospheric zenith delay, and e is the satellite elevation angle.

[0067] In some embodiments, constructing a positioning equation based on a pseudorange observation equation to obtain an estimated state vector includes:

[0068] Taking pseudorange single point positioning as an example, the pseudorange can be directly obtained by multiplying the speed of light by the signal propagation time. The calculation formula is:

[0069]

[0070] Among them, represents pseudorange, c represents the speed of light, t R is the time when the signal receiving end receives the data, t S The time at which data is sent to the satellite.

[0071] Specifically, considering the ionospheric error and tropospheric error in the signal propagation process, the expression of pseudorange can be further expressed as:

[0072]

[0073] Among them, δ T represents the tropospheric error, δ I represents the ionospheric error, and is the clock error, P represents the geometric distance between the satellite and the receiver, which can be expressed as:

[0074]

[0075] Substituting into the pseudorange calculation formula, we can get:

[0076]

[0077] The above nonlinear formula is linearized by Taylor series expansion:

[0078]

[0079] By simplifying the coefficients, the above formula can be simplified to:

[0080]

[0081] Where B = [b x b y b z 1], The above formula can be written as a general adjustment format of L=BX+Δ; it can be solved iteratively by least squares and other methods.

[0082] Using Kalman filtering for initial value estimation, the above formula can be transformed as follows:

[0083]

[0084] Among them, X k is the estimated state vector of the current epoch k, Φ k,k-1 is the state transition matrix from epoch k-1 to epoch k, is the estimated state vector of the previous epoch k-1, W k is the state noise of the kth epoch.

[0085] Further, step S103 is performed to introduce an adaptive inequality constraint using a dynamic penalty function to improve the positioning effect, wherein the inequality formula is as follows:

[0086] Ψ k X k ≤G k ;

[0087] Among them, k is the preset constraint matrix, G k is the preset constraint threshold, X k is the estimated state vector of the current epoch. Generally speaking, the preset constraint threshold is a constant vector. The preset constraint matrix and the preset constraint threshold can be set according to actual conditions.

[0088] Among them, the constraint parameter obtained by multiplying the estimated state vector and the preset constraint matrix is ​​Ψ k X k .

[0089] In some embodiments, in S103, after comparing the constraint parameter of the current epoch with the preset constraint condition threshold, the number of times that the constraint parameter of multiple consecutive epochs is greater than the preset constraint condition threshold is accumulated to obtain the positioning result count corresponding to each epoch, and the formula is applied:

[0090]

[0091] The superscript i indicates the number of times the inequality constraint is activated, C k Indicates the number of consecutive abnormal positioning, that is, the positioning result count. The positioning solution at epoch k is classified as an abnormal positioning result, and the positioning result count increases by 1; on the contrary, if Ψ k X i k ≤G k , indicating that the positioning is normal, and the positioning result count is reset to 0. Abnormal positioning occurs randomly in consecutive epochs and affects the continuous convergence of positioning results and displacement calculation accumulation between consecutive epochs.

[0092] Specifically, if the constraint parameter of the current epoch is greater than the preset constraint condition threshold, step S106 is executed:

[0093] Determine that the estimated state vector of the current epoch is an abnormal positioning result, increase the value of the positioning result count of the previous epoch adjacent to the current epoch by 1, and output the positioning result count of the current epoch.

[0094] Otherwise, if the constraint parameter of the current epoch is less than or equal to the preset constraint condition threshold, the step S107 is executed:

[0095] Determine that the estimated state vector of the current epoch is not an abnormal positioning result, set the value of the positioning result count of the previous epoch adjacent to the current epoch to 0, and output the positioning result count of the current epoch.

[0096] By the above method, the inequality constraint can be obtained by the counter, that is, the positioning result count C k Adaptive shutdown, that is, Ψ k X i k ≤G k When , the positioning result estimation can be obtained by the extended Kalman filter method Otherwise, inequality constraints are required.

[0097] In some embodiments, by comparing the positioning result count Ck The comparison result with the positioning result counting threshold n determines whether to introduce an inequality constraint, that is, And C k ≤n, then introduce the inequality constraint and execute steps S104 and S105. And C k >n, then execute steps S108 and S109 to obtain the estimated positioning result by using the extended Kalman filter method.

[0098] Among them, n is an integer greater than 1, and the value of n can be adjusted according to the specific environment. For application scenarios with high environmental complexity, the value of n can be reduced to increase the frequency of changes in inequality constraints. For application scenarios with low environmental complexity, the value of n can be increased to avoid the increase in computational complexity caused by frequent judgment of constraint conditions.

[0099] It can be understood that it is through the comparison results of the constraint parameters of continuous epochs with the preset constraint condition thresholds that it is possible to sensitively detect that the positioning results of a certain section of continuous epochs are abnormal, and then add given inequality constraints in the continuous epochs to eliminate erroneous positioning caused by factors such as environmental factors that cause signal disappearance or failure. If the constraint parameter in a certain epoch is less than or equal to the preset constraint condition threshold, it indicates that the signal has been restored. At this time, the constraint conditions can be sensitively adjusted to avoid erroneous positioning results caused by excessive constraints.

[0100] In some embodiments, after S106, if the constraint parameter of the current epoch is greater than the preset constraint condition threshold, and the positioning result count of the current epoch is greater than the positioning result count threshold, that is, And C k ≤n, that is, at most n consecutive epochs have constraint parameters that are greater than the preset constraint condition threshold, then steps S104 and S105 are executed, including:

[0101] S104 includes:

[0102] Based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint condition threshold, a virtual observation equation including a virtual observation error is constructed. The virtual observation error is denoted as M k , the virtual observation equation can be established as:

[0103] G k =Ψ k X k +M k .

[0104] S105, including:

[0105] Based on the Kalman filter algorithm and the virtual observation equation, the predicted state vector of the signal receiving end at the current epoch is calculated, and the positioning result of the signal receiving end at the current epoch is obtained based on the predicted state vector. The virtual observation equation is combined with L=BX+Δ to obtain the augmented equation:

[0106]

[0107] make

[0108] The recursive solution under the augmented matrix can be solved by Kalman filtering:

[0109]

[0110] Therefore, when And C k When ≤n, Based on the calculated predicted state vector It is not difficult to calculate the positioning result of the signal receiving end at the current epoch.

[0111] In some embodiments, when And C k >n, indicating that the positioning results in n consecutive epochs have been judged as abnormal. At this time, sufficient information has been accumulated to show that there are obvious problems with the current model or state estimation. This means that these abnormal results have provided sufficient feedback to the system, making it unnecessary to add additional constraints to correct the model and avoid over-constraint.

[0112] In some embodiments, when Ψ k X i k ≤G k When , the positioning result is judged to be normal, and the positioning result can also be obtained by the extended Kalman filter.

[0113] In some embodiments, when Ψ k X i k ≤G k when, or, when And C k >n, the positioning result can be obtained by the extended Kalman filter, and steps S108-S109 are executed:

[0114] S108, inputting the estimated state vector of the current epoch into an extended Kalman filter;

[0115] S109, obtaining a predicted state vector of the signal receiving end at the current epoch through the extended Kalman filter, and obtaining a positioning result of the signal receiving end at the current epoch based on the predicted state vector.

[0116] In some embodiments, the predicted state vector of the current epoch can be obtained by using an extended Kalman filter: and the corresponding variance-covariance matrix

[0117]

[0118] in, Denotes the variance-covariance matrix of the prediction parameters, D k represents the variance-covariance matrix of the k-th epoch observations, represents the variance-covariance matrix of the estimated parameters at the kth epoch, Represents the variance-covariance matrix of the state noise.

[0119] The calculated predicted state vector of the current epoch can be expressed as:

[0120]

[0121] Based on the calculated predicted state vector It is not difficult to calculate the positioning result of the signal receiving end at the current epoch.

[0122] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0123] See next Figure 2 , is a schematic diagram of the structure of a GNSS dynamic positioning device provided by an exemplary embodiment of the present application. The device can be implemented as all or part of a terminal through software, hardware, or a combination of both, and can also be integrated on a server as an independent module. The GNSS dynamic positioning device in the embodiment of the present application can be applied to a terminal or a cloud. The device 20 includes a data processing unit 201, a constraint unit 202, and a positioning unit 203, wherein:

[0124] The data processing unit 201 is used to determine the estimated state vector of the signal receiving end in the previous epoch adjacent to the current epoch according to the GNSS observation data obtained at each preset time interval;

[0125] The data processing unit 201 is further configured to determine the estimated state vector of the signal receiving end at the current epoch according to the Kalman filter algorithm and the estimated state vector of the previous epoch;

[0126] The constraint unit 202 is used to multiply the estimated state vector of the current epoch by a preset constraint matrix to obtain a constraint parameter, and compare the constraint parameter of the current epoch with a preset constraint condition threshold;

[0127] If the constraint parameter of the current epoch obtained by comparison by the constraint unit 202 is greater than the preset constraint condition threshold, and the constraint parameters of at most n consecutive epochs up to the current epoch are greater than the preset constraint condition threshold, then the constraint unit constructs a virtual observation equation containing a virtual observation error based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint condition threshold; wherein n is an integer greater than 1;

[0128] The positioning unit 203 is used to calculate the predicted state vector of the signal receiving end at the current epoch according to the Kalman filter algorithm and the virtual observation equation, and obtain the positioning result of the signal receiving end at the current epoch based on the predicted state vector.

[0129] It should be noted that when the device 20 provided in the above embodiment executes the GNSS dynamic positioning method, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the GNSS dynamic positioning method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0130] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the program.

[0131] See also Figure 3 , which is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0132] like Figure 3 As shown, the electronic device 300 includes: a processor 301 and a memory 302 .

[0133] In the embodiment of the present application, the processor 301 is the control center of the computer system, which can be a processor of a physical machine or a processor of a virtual machine. The processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0134] The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also called a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state.

[0135] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the method in the embodiment of the present application.

[0136] In some embodiments, the electronic device 300 further includes: a peripheral device interface 303 and at least one peripheral device 304. The processor 301, the memory 302 and the peripheral device interface 303 can be connected via a bus or a signal line. Each peripheral device 304 can be connected to the peripheral device interface 303 via a bus, a signal line or a circuit board. Specifically, the peripheral device 304 includes: a display screen, a camera and an audio circuit. The peripheral device interface 303 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302.

[0137] In some embodiments of the present application, the processor 301, the memory 302, and the peripheral device interface 303 are integrated on the same chip or circuit board; in some other embodiments of the present application, any one or two of the processor 301, the memory 302, and the peripheral device interface 303 can be implemented on a separate chip or circuit board. This embodiment of the present application does not specifically limit this.

[0138] The electronic device structure block diagram shown in the embodiment of the present application does not constitute a limitation on the electronic device 300. The electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0139] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method of any of the above embodiments are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0140] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for 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 embodiment.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A GNSS dynamic positioning method, characterized in that: Applied to GNSS signal receiving end, including: Determining an estimated state vector of the signal receiving end at a previous epoch adjacent to the current epoch based on GNSS observation data acquired at each preset time interval; Determining an estimated state vector of the signal receiving end at a current epoch based on a Kalman filter algorithm and the estimated state vector of the previous epoch; Multiplying the estimated state vector of the current epoch by a preset constraint matrix to obtain constraint parameters, and comparing the constraint parameters of the current epoch with a preset constraint condition threshold; If the constraint parameter of the current epoch is greater than the preset constraint threshold, and the constraint parameters of at most n consecutive epochs up to the current epoch are greater than the preset constraint threshold, then a virtual observation equation containing a virtual observation error is constructed based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint threshold; wherein n is an integer greater than 1; Based on the Kalman filter algorithm and the virtual observation equation, the predicted state vector of the signal receiving end in the current epoch is calculated, and the positioning result of the signal receiving end in the current epoch is obtained based on the predicted state vector.

2. A GNSS dynamic positioning method according to claim 1, characterized in that: After multiplying the estimated state vector of the current epoch by a preset constraint matrix to obtain constraint parameters and comparing the constraint parameters of the current epoch with a preset constraint condition threshold, the method further includes: Accumulate the number of times that the constraint parameter of multiple consecutive epochs is greater than the preset constraint condition threshold, and obtain the positioning result count corresponding to each epoch; If the constraint parameter of the current epoch is greater than the preset constraint condition threshold, the estimated state vector of the current epoch is determined to be an abnormal positioning result, the value of the positioning result count of the previous epoch adjacent to the current epoch is increased by 1, and the positioning result count of the current epoch is output; If the constraint parameter of the current epoch is less than or equal to the preset constraint condition threshold, it is determined that the estimated state vector of the current epoch is not an abnormal positioning result, the value of the positioning result count of the previous epoch adjacent to the current epoch is set to 0, and the positioning result count of the current epoch is output.

3. A GNSS dynamic positioning method according to claim 2, characterized in that: After accumulating the number of times that the constraint parameter of a plurality of consecutive epochs is greater than the preset constraint condition threshold and obtaining the positioning result count corresponding to each epoch, the method further includes: If the constraint parameter of the current epoch is greater than the preset constraint condition threshold, and the positioning result count of the current epoch is greater than the positioning result count threshold, the estimated state vector of the current epoch is input into the extended Kalman filter, and the predicted state vector of the signal receiving end in the current epoch is obtained through the extended Kalman filter, and the positioning result of the signal receiving end in the current epoch is obtained based on the predicted state vector.

4. A GNSS dynamic positioning method according to claim 2, characterized in that: After accumulating the number of times that the constraint parameter of a plurality of consecutive epochs is greater than the preset constraint condition threshold and obtaining the positioning result count corresponding to each epoch, the method further includes: If the constraint parameter of the current epoch is less than or equal to the preset constraint condition threshold, the estimated state vector of the current epoch is input into the extended Kalman filter, the predicted state vector of the signal receiving end in the current epoch is obtained through the extended Kalman filter, and the positioning result of the signal receiving end in the current epoch is obtained based on the predicted state vector.

5. A GNSS dynamic positioning method according to claim 1, characterized in that: The process of determining the estimated state vector of the signal receiving end at the current epoch and determining the estimated state vector of the signal receiving end at the previous epoch adjacent to the current epoch includes: Establishing multiple types of error models to construct a pseudorange observation equation of the signal receiving end; wherein the multiple types of error models include a multipath error model, a non-line-of-sight error model, and a tropospheric delay error model; A positioning equation is constructed based on the pseudorange observation equation, and an estimated state vector of a corresponding epoch is calculated based on Kalman filtering and the positioning equation.

6. A GNSS dynamic positioning device, characterized in that: include: A data processing unit, configured to determine an estimated state vector of the signal receiving end at a previous epoch adjacent to the current epoch based on GNSS observation data acquired at each preset time interval; The data processing unit is further used to determine the estimated state vector of the signal receiving end at the current epoch according to the Kalman filter algorithm and the estimated state vector of the previous epoch; A constraint unit, configured to multiply the estimated state vector of the current epoch by a preset constraint matrix to obtain a constraint parameter, and compare the constraint parameter of the current epoch with a preset constraint condition threshold; If the constraint parameter of the current epoch obtained by the constraint unit through comparison is greater than a preset constraint condition threshold, and the constraint parameters of at most n consecutive epochs up to the current epoch are greater than the preset constraint condition threshold, then the constraint unit constructs a virtual observation equation containing a virtual observation error based on the estimated state vector of the current epoch, the preset constraint matrix and the preset constraint condition threshold; wherein n is an integer greater than 1; A positioning unit is used to calculate the predicted state vector of the signal receiving end at the current epoch according to the Kalman filter algorithm and the virtual observation equation, and obtain the positioning result of the signal receiving end at the current epoch based on the predicted state vector.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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