Method and device for improving GNSS walking positioning smoothness, memory and storage medium

Abstract

The invention relates to the technical field of satellite positioning, and discloses a method for improving GNSS walking positioning smoothness. Comprising the following steps: grading environment quality, and judging the environment level of the current environment; defining the motion mode, and judging the current motion mode; judging the accuracy of the current positioning position precision; according to whether the accuracy of the current positioning position precision, the environment level of the current environment and the current motion mode meet preset conditions or not, entering a track constraint mode if the preset conditions are met, and not entering the track constraint mode if the preset conditions are not met; entering a track constraint mode, and judging whether the track needs to be modified or not; the method has the advantage of improving the precision and smoothness of the positioning track.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a method, device, memory and storage medium for improving the smoothness of GNSS walking positioning. Background Art

[0002] For GNSS, the accuracy and smoothness of the positioning trajectory are two of the most important indicators. Especially for devices with small antennas such as watches and handheld devices, the satellite signal power is generally low, and the usage scenarios are usually in environments with severe multipath scenarios such as unilateral occlusion and bilateral occlusion. Therefore, it is very difficult to obtain good positioning trajectory accuracy and smoothness.

[0003] Therefore, a method, device, memory and storage medium for improving the smoothness of GNSS walking positioning are provided to solve the above problems. Summary of the invention

[0004] The main purpose of the present invention is to solve the problem in the prior art that it is very difficult to obtain good positioning trajectory accuracy and smoothness for devices with small antenna size such as watches and handheld devices, generally low satellite signal power, and the usage scenarios are usually environments with severe multipath scenarios such as single-sided occlusion, double-sided occlusion, etc.

[0005] A first aspect of the present invention provides an analysis method for improving the smoothness of GNSS walking positioning, the method for improving the smoothness of GNSS walking positioning comprising:

[0006] Classify the environmental quality and determine the current environmental level according to the classification standards;

[0007] Define the motion mode, and determine the current motion mode according to the definition of the motion mode;

[0008] Determine the accuracy of the current positioning position;

[0009] According to the accuracy of the current positioning position, the environmental level of the current environment and whether the current motion mode meets the preset conditions, if the preset conditions are met, the track constraint mode is entered; if the preset conditions are not met, the track constraint mode is not entered;

[0010] Enter the track constraint mode, and for each pseudo-range observation, use the sequential Kalman filter method to calculate the state correction deltaX of the i-th observation at time k k_i , calculate the corrected track of the i-th observation. If the first preset condition is met, the i-th observation is downgraded and the state vector correction is recalculated; if the first preset condition is not met, the process ends directly.

[0011] Optionally, the grading of environmental quality includes:

[0012] The environmental qualities are marked from good to bad as open ENV1, slightly blocked ENV2, moderately blocked ENV3, severely blocked ENV4 and severely blocked ENV5.

[0013] Optionally, the specific method of determining the environmental level of the current environment according to the classification standard includes:

[0014] Count the number of satellites with CN0 greater than 30dBHz (up30Cnt), the number of satellites with CN0 greater than 25dBHz (up25Cnt), the number of satellites with CN0 greater than 20dBHz (up20Cnt), and the number of satellites with CN0 greater than 15dBHz (up15Cnt);

[0015] Calculate the satellite geometric distribution level value hdop30 of the satellite with CN0 greater than 30dBHz, the satellite geometric distribution level value hdop25 of the satellite with CN0 greater than 25dBHz, the satellite geometric distribution level value hdop20 of the satellite with CN0 greater than 20dBHz, and the satellite geometric distribution level value hdop15 of the satellite with CN0 greater than 15dBHz respectively;

[0016] When up30Cnt>TH1, up25Cnt>TH2, and hdop30>TH3, the current value is ENV1;

[0017] When up25Cnt>TH4, up20Cnt>TH5, and hdop25>TH6, the current value is ENV2;

[0018] When up20Cnt>TH7, up15Cnt>TH8, and hdop15>TH9, the current value is ENV3;

[0019] When up20Cnt>TH10, up15Cnt>TH11, and hdop15>TH12, the current value is ENV4;

[0020] If the current environment does not meet ENV1-4, it is determined to be ENV5.

[0021] Optionally, the defining of the motion mode and determining the current motion mode according to the definition of the motion mode specifically includes:

[0022] The movement modes are defined as stationary mode, straight-line travel mode, turning mode, and other modes;

[0023] A certain window period length n is set. Within the window period length n, when the heading remains within the preset error range, it is considered to be a straight-line travel mode; when the heading continues to deviate in one direction, it is considered to be a turning mode; when the speed is continuously less than the threshold THStatic, it is considered to be a static mode. If the above conditions are not met, it is considered to be other modes.

[0024] Optionally, the determining the accuracy of the current positioning position precision specifically includes:

[0025] Calculate the accuracy of the last positioning result posAcc. The specific calculation formula is:

[0026] posAcc = averRes * dop;

[0027] Where averRes is the square root of the mean of the sum of squares of the posterior residuals of the pseudorange observations after the last positioning, and dop is the satellite geometric distribution;

[0028] Calculate the variance psrStd of the prior residual of this pseudorange observation.

[0029] Determine whether posAcc<THPosAcc and psrStd<THPsrSTD are simultaneously established; if so, the position is determined to be accurate; if not, the position is determined to be inaccurate.

[0030] Optionally, the accuracy of the current positioning position, the environmental level of the current environment, and whether the current motion mode meets the preset conditions specifically include:

[0031] Determine whether the current environment level is one of slight occlusion ENV2, medium occlusion ENV3 and severe occlusion ENV4, the current motion mode is linear motion mode and the accuracy of the current positioning position is accurate. If the above three conditions are met at the same time, enter the track constraint mode, if not, do not enter the track constraint mode.

[0032] Optionally, the track constraint mode is entered, and for each pseudo-range observation, a sequential Kalman filter method is used to calculate the state correction deltaX of the i-th observation at time k k_i , calculate the corrected track of the i-th observation, if it meets the first preset condition, then reduce the weight of the i-th observation and recalculate the state vector correction; if it does not meet the first preset condition, then directly end including:

[0033] Enter the track constraint mode, and for each pseudo-range observation, use the sequential Kalman filter method to calculate the state vector correction deltaX of the i-th observation at time k k_i , the specific calculation formula includes:

[0034]

[0035] deltaX k_i =K k_i res i ;

[0036] Among them, K k_i is the gain of the i-th observation at time k, is the prior state error matrix of the state vector at the Kth moment, H i is the linearization matrix of the i-th observation, R i is the observation error of the i-th observation, that is, the solution weight, res i is the prior residual of the i-th observation;

[0037] Assume that the first three dimensions of the state vector are the positions of the positioning device in the ECEF coordinate system, namely posX, posY, posZ; then deltaX k_i The first three dimensions are the position corrections in the ECEF coordinate system, namely deltaX, deltaY, deltaZ; convert deltaX, deltaY, deltaZ to deltaE, deltaN, deltaU in the ENU coordinate system;

[0038] Calculate the corrected track of the i-th observation. The specific calculation formula includes:

[0039] head i = atan2(deltaE,deltaN);

[0040] if(head i <0)head i =head i +360;

[0041] diffi=ABS(headi-headRCV);

[0042] if(diffi>180)diffi=360–diffi;

[0043] Among them, headRCV is the heading of the positioning device;

[0044] Determine whether diffi is between TH13 and TH14. If so, downgrade the weight of the i-th observation and repeat "For each pseudo-range observation, use the sequential Kalman filter method to calculate the state vector correction deltaX of the i-th observation at time k k_i "The calculation steps are used to recalculate the correction of this observation.

[0045] A second aspect of the present invention provides a device for improving the smoothness of GNSS walking positioning, comprising:

[0046] The environmental quality classification judgment module is used to classify the environmental quality and judge the environmental level of the current environment according to the classification standards;

[0047] A sports mode classification judgment module is used to define the sports mode and judge the current sports mode according to the definition of the sports mode;

[0048] Positioning progress judgment module, used to judge the accuracy of the current positioning precision;

[0049] The track constraint judgment module is used to determine whether the current positioning accuracy, the current environmental level and the current motion mode meet the preset conditions. If the preset conditions are met, the track constraint mode is entered; if the preset conditions are not met, the track constraint mode is not entered;

[0050] The track constraint mode calculation module is used to enter the track constraint mode and calculate the state correction deltaX of the i-th observation at time k for each pseudo-range observation using the sequential Kalman filter method. k_i , calculate the corrected track of the i-th observation. If the first preset condition is met, the i-th observation is downgraded and the state vector correction is recalculated; if the first preset condition is not met, the process ends directly.

[0051] A third aspect of the present invention provides an electronic device, the electronic device comprising a memory and at least one processor, the memory storing instructions;

[0052] The at least one processor calls the instructions in the memory to enable the electronic device to execute the various steps of the method for improving the smoothness of GNSS walking positioning as described above.

[0053] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, and when the instructions are executed by a processor, the various steps of the method for improving the smoothness of GNSS walking positioning as described above are implemented.

[0054] In the technical solution of the present invention, by judging the signal environment, straight-line turning behavior, and position accuracy, under certain conditions, the pseudorange observation quantity is weighted according to the track and the pseudorange correction angle of each satellite, thereby effectively improving the accuracy and smoothness of the positioning trajectory in open and complex scenes, greatly improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1A flow chart of a method for improving GNSS walking positioning smoothness provided by an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of the structure of a device for improving the smoothness of GNSS walking positioning provided by an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The embodiment of the present invention provides a method for improving the smoothness of GNSS walking positioning, including grading the environmental quality, judging the environmental level of the current environment according to the grading standard; defining the motion mode, judging the current motion mode according to the definition of the motion mode; judging the accuracy of the current positioning position precision; according to whether the accuracy of the current positioning position precision, the environmental level of the current environment and the current motion mode meet the preset conditions, if the preset conditions are met, entering the track constraint mode, if the preset conditions are not met, not entering the track constraint mode; entering the track constraint mode, for each pseudo-range observation, using the sequential Kalman filtering method to calculate the state correction deltaX of the i-th observation at the kth time k_i , calculate the corrected track of the i-th observation. If the first preset condition is met, the i-th observation is downgraded and the state vector correction is recalculated; if the first preset condition is not met, the procedure ends directly. The present invention solves the problem in the prior art that the pseudo-range observation is weighted according to the pseudo-range signal strength indicator carrier-to-noise ratio (CN0), elevation angle, prior residual and other information, the trajectory cannot be very smooth, and it is more likely to deviate when entering a complex section from an open section.

[0059] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , a first embodiment of the method for improving the smoothness of GNSS walking positioning in an embodiment of the present invention includes:

[0061] Classify the environmental quality and determine the current environmental level according to the classification standards;

[0062] Specifically include:

[0063] The environmental quality is marked from good to bad as open ENV1, slightly blocked ENV2, moderately blocked ENV3, severely blocked ENV4 and severely blocked ENV5;

[0064] Count the number of satellites with CN0 greater than 30dBHz (up30Cnt), the number of satellites with CN0 greater than 25dBHz (up25Cnt), the number of satellites with CN0 greater than 20dBHz (up20Cnt), and the number of satellites with CN0 greater than 15dBHz (up15Cnt);

[0065] Calculate the satellite geometric distribution level value hdop30 of the satellite with CN0 greater than 30dBHz, the satellite geometric distribution level value hdop25 of the satellite with CN0 greater than 25dBHz, the satellite geometric distribution level value hdop20 of the satellite with CN0 greater than 20dBHz, and the satellite geometric distribution level value hdop15 of the satellite with CN0 greater than 15dBHz respectively;

[0066] When up30Cnt>TH1, up25Cnt>TH2, and hdop30>TH3, the current value is ENV1;

[0067] When up25Cnt>TH4, up20Cnt>TH5, and hdop25>TH6, the current value is ENV2;

[0068] When up20Cnt>TH7, up15Cnt>TH8, and hdop15>TH9, the current value is ENV3;

[0069] When up20Cnt>TH10, up15Cnt>TH11, and hdop15>TH12, the current value is ENV4;

[0070] If the current environment does not meet ENV1-4, it is determined to be ENV5.

[0071] Define the motion mode, and determine the current motion mode according to the definition of the motion mode;

[0072] Specifically include:

[0073] The movement modes are defined as stationary mode, straight-line travel mode, turning mode, and other modes;

[0074] A certain window period length n is set. Within the window period length n, when the heading remains within the preset error range, it is considered to be a straight-line travel mode; when the heading continues to deviate in one direction, it is considered to be a turning mode; when the speed is continuously less than the threshold THStatic, it is considered to be a static mode. If the above conditions are not met, it is considered to be other modes.

[0075] Determine the accuracy of the current positioning position;

[0076] Specifically, they include:

[0077] Calculate the accuracy of the last positioning result posAcc. The specific calculation formula is:

[0078] posAcc = averRes * dop;

[0079] Where averRes is the square root of the mean of the sum of squares of the posterior residuals of the pseudorange observations after the last positioning, and dop is the satellite geometric distribution;

[0080] Calculate the variance psrStd of the prior residual of this pseudorange observation.

[0081] Determine whether posAcc<THPosAcc and psrStd<THPsrSTD are simultaneously established; if so, the position is determined to be accurate; if not, the position is determined to be inaccurate.

[0082] According to the accuracy of the current positioning position, the environmental level of the current environment and whether the current motion mode meets the preset conditions, if the preset conditions are met, the track constraint mode is entered; if the preset conditions are not met, the track constraint mode is not entered;

[0083] Specifically: determine whether the current environment level is one of slight occlusion ENV2, medium occlusion ENV3 and severe occlusion ENV4, the current motion mode is a linear motion mode and the accuracy of the current positioning position is determined to be accurate. If the above three conditions are met at the same time, if so, enter the track constraint mode, if not, do not enter the track constraint mode.

[0084] Enter the track constraint mode, and for each pseudo-range observation, use the sequential Kalman filter method to calculate the state correction deltaX of the i-th observation at time k k_i , calculate the corrected track of the i-th observation. If the first preset condition is met, the i-th observation is downgraded and the state vector correction is recalculated; if the first preset condition is not met, the process ends directly;

[0085] Specifically, they include:

[0086] Enter the track constraint mode, and for each pseudo-range observation, use the sequential Kalman filter method to calculate the state vector correction deltaX of the i-th observation at time k k_i , the specific calculation formula includes:

[0087]

[0088] deltaX k_i =K k_i res i ;

[0089] Among them, K k_i is the gain of the i-th observation at time k, is the prior state error matrix of the state vector at the Kth moment, H i is the linearization matrix of the i-th observation, R i is the observation error of the i-th observation, that is, the solution weight, res i is the prior residual of the i-th observation;

[0090] Assume that the first three dimensions of the state vector are the positions of the positioning device in the ECEF coordinate system, namely posX, posY, posZ; then deltaX k_i The first three dimensions are the position corrections in the ECEF coordinate system, namely deltaX, deltaY, deltaZ; convert deltaX, deltaY, deltaZ to deltaE, deltaN, deltaU in the ENU coordinate system;

[0091] Calculate the corrected track of the i-th observation. The specific calculation formula includes:

[0092] head i = atan2(deltaE,deltaN);

[0093] if(head i <0)head i =head i +360;

[0094] diffi=ABS(headi-headRCV);

[0095] if(diffi>180)diffi=360–diffi;

[0096] Among them, headRCV is the heading of the positioning device;

[0097] Determine whether diffi is between TH13 and TH14. If so, downgrade the weight of the i-th observation and repeat "For each pseudo-range observation, use the sequential Kalman filter method to calculate the state vector correction deltaX of the i-th observation at time k k_i "The calculation steps are used to recalculate the correction of this observation.

[0098] The above describes the method for improving the smoothness of GNSS walking positioning in an embodiment of the present invention. The following describes the device for improving the smoothness of GNSS walking positioning in an embodiment of the present invention. Figure 2 The device for improving the smoothness of GNSS walking positioning in an embodiment of the present invention includes:

[0099] The environmental quality classification judgment module 201 is used to classify the environmental quality and judge the environmental level of the current environment according to the classification standard;

[0100] The motion mode classification determination module 202 is used to define the motion mode and determine the current motion mode according to the definition of the motion mode;

[0101] The positioning progress determination module 203 is used to determine the accuracy of the current positioning precision;

[0102] The track constraint judgment module 204 is used to determine whether the current positioning position accuracy, the current environment level and the current motion mode meet the preset conditions. If the preset conditions are met, the track constraint mode is entered; if the preset conditions are not met, the track constraint mode is not entered;

[0103] The track constraint mode calculation module 205 is used to enter the track constraint mode and calculate the state correction deltaX of the i-th observation at time k for each pseudo-range observation using the sequential Kalman filter method. k_i , calculate the corrected track of the i-th observation. If the first preset condition is met, the i-th observation is downgraded and the state vector correction is recalculated; if the first preset condition is not met, the process ends directly.

[0104] above Figure 2 The apparatus for improving the smoothness of GNSS walking positioning in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0105] Figure 37 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 can be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0106] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0107] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of a method for improving the smoothness of GNSS walking positioning.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0110] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. 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 invention.

Claims

1. A method for improving the smoothness of GNSS walking positioning, characterized in that: The method for improving the smoothness of GNSS walking positioning includes: Classify the environmental quality and determine the current environmental level according to the classification standards; Define the motion mode, and determine the current motion mode according to the definition of the motion mode; Determine the accuracy of the current positioning position; According to the accuracy of the current positioning position, the environmental level of the current environment and whether the current motion mode meets the preset conditions, if the preset conditions are met, the track constraint mode is entered; if the preset conditions are not met, the track constraint mode is not entered; Enter the track constraint mode, and for each pseudo-range observation, use the sequential Kalman filter method to calculate the state correction deltaX of the i-th observation at time k k_i , calculate the corrected track of the i-th observation. If the first preset condition is met, the i-th observation is downgraded and the state vector correction is recalculated; if the first preset condition is not met, the process ends directly.

2. The method for improving GNSS walking positioning smoothness according to claim 1, characterized in that: The environmental quality classification includes: The environmental qualities are marked from good to bad as open ENV1, slightly blocked ENV2, moderately blocked ENV3, severely blocked ENV4 and severely blocked ENV5.

3. The method for improving GNSS walking positioning smoothness according to claim 1, characterized in that: The specific method of determining the environmental level of the current environment according to the classification standard includes: Count the number of satellites with CN0 greater than 30dBHz (up30Cnt), the number of satellites with CN0 greater than 25dBHz (up25Cnt), the number of satellites with CN0 greater than 20dBHz (up20Cnt), and the number of satellites with CN0 greater than 15dBHz (up15Cnt); Calculate the satellite geometric distribution level value hdop30 of the satellite with CN0 greater than 30dBHz, the satellite geometric distribution level value hdop25 of the satellite with CN0 greater than 25dBHz, the satellite geometric distribution level value hdop20 of the satellite with CN0 greater than 20dBHz, and the satellite geometric distribution level value hdop15 of the satellite with CN0 greater than 15dBHz respectively; When up30Cnt>TH1, up25Cnt>TH2, and hdop30>TH3, the current value is ENV1; When up25Cnt>TH4, up20Cnt>TH5, and hdop25>TH6, the current value is ENV2; When up20Cnt>TH7, up15Cnt>TH8, and hdop15>TH9, the current value is ENV3; When up20Cnt>TH10, up15Cnt>TH11, and hdop15>TH12, the current value is ENV4; If the current environment does not meet ENV1-4, it is determined to be ENV5.

4. The method for improving GNSS walking positioning smoothness according to claim 3, characterized in that: Defining the motion mode and determining the current motion mode according to the definition of the motion mode specifically includes: The movement modes are defined as stationary mode, straight-line travel mode, turning mode, and other modes; A certain window period length n is set. Within the window period length n, when the heading remains within the preset error range, it is considered to be a straight-line travel mode; when the heading continues to deviate in one direction, it is considered to be a turning mode; when the speed is continuously less than the threshold THStatic, it is considered to be a static mode. If the above conditions are not met, it is considered to be other modes.

5. The method for improving GNSS walking positioning smoothness according to claim 4, characterized in that: The accuracy of determining the current positioning position precision specifically includes: Calculate the accuracy of the last positioning result posAcc. The specific calculation formula is: posAcc = averRes * dop; Where averRes is the square root of the mean of the sum of squares of the posterior residuals of the pseudorange observations after the last positioning, and dop is the satellite geometric distribution; Calculate the variance psrStd of the prior residual of this pseudorange observation. Determine whether posAcc<THPosAcc and psrStd<THPsrSTD are simultaneously established; if so, the position is determined to be accurate; if not, the position is determined to be inaccurate.

6. The method for improving GNSS walking positioning smoothness according to claim 5, characterized in that: The accuracy of the current positioning position, the environmental level of the current environment, and whether the current motion mode meets the preset conditions specifically include: Determine whether the current environment level is one of slight occlusion ENV2, medium occlusion ENV3 and severe occlusion ENV4, the current motion mode is linear motion mode and the accuracy of the current positioning position is accurate. If the above three conditions are met at the same time, enter the track constraint mode, if not, do not enter the track constraint mode.

7. The method for improving GNSS walking positioning smoothness according to claim 6, characterized in that: Entering the track constraint mode, for each pseudo-range observation, the state correction deltaX of the i-th observation at time k is calculated using the sequential Kalman filter method. k_i , calculate the corrected track of the i-th observation, and if the first preset condition is met, then downgrade the i-th observation and recalculate the state vector correction; If the first preset condition is not met, the process ends directly including: Enter the track constraint mode, and for each pseudo-range observation, use the sequential Kalman filter method to calculate the state vector correction deltaX of the i-th observation at time k k_i , the specific calculation formula includes: deltaX k_i =K k_i res i ; Among them, K k_i is the gain of the i-th observation at time k, is the prior state error matrix of the state vector at the Kth moment, H i is the linearization matrix of the i-th observation, R i is the observation error of the i-th observation, that is, the solution weight, res i is the prior residual of the i-th observation; Assume that the first three dimensions of the state vector are the positions of the positioning device in the ECEF coordinate system, namely posX, posY, posZ; then deltaX k_i The first three dimensions are the position corrections in the ECEF coordinate system, namely deltaX, deltaY, deltaZ; convert deltaX, deltaY, deltaZ to deltaE, deltaN, deltaU in the ENU coordinate system; Calculate the corrected track of the i-th observation. The specific calculation formula includes: head i =atan2(deltaE,deltaN); if(head i <0)head i =head i +360; diffi = ABS (headi - headRCV); if(diffi>180)diffi=360–diffi; Among them, headRCV is the heading of the positioning device; Determine whether diffi is between TH13 and TH14. If so, downgrade the weight of the i-th observation and repeat "For each pseudo-range observation, use the sequential Kalman filter method to calculate the state vector correction deltaX of the i-th observation at time k k_i "The calculation steps are used to recalculate the correction of this observation.

8. A device for improving the smoothness of GNSS walking positioning, characterized in that: include: The environmental quality classification judgment module is used to classify the environmental quality and judge the environmental level of the current environment according to the classification standards; A sports mode classification judgment module is used to define the sports mode and judge the current sports mode according to the definition of the sports mode; Positioning progress judgment module, used to judge the accuracy of the current positioning precision; The track constraint judgment module is used to determine whether the current positioning accuracy, the current environmental level and the current motion mode meet the preset conditions. If the preset conditions are met, the track constraint mode is entered; if the preset conditions are not met, the track constraint mode is not entered; The track constraint mode calculation module is used to enter the track constraint mode and calculate the state correction deltaX of the i-th observation at time k for each pseudo-range observation using the sequential Kalman filter method. k_i , calculate the corrected track of the i-th observation. If the first preset condition is met, the i-th observation is downgraded and the state vector correction is recalculated; if the first preset condition is not met, the process ends directly.

9. An electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the electronic device to execute each step of the method for improving the smoothness of GNSS walking positioning as described in any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the method for improving the smoothness of GNSS walking positioning as claimed in any one of claims 1 to 7 are implemented.

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