GNSS Positioning Method, Device and Electronic Equipment Based on Random Sampling

Abstract

The present invention relates to the technical field of positioning or presence detection using reflection or re-radiation of radio waves, and particularly relates to a GNSS positioning method, device and electronic device based on random sampling. The method includes: performing sky partitioning based on the currently receivable satellite list and its azimuth information to determine the sky region to which each satellite belongs; performing distance-optimized random sampling of satellites within each sky region to generate a sample group; respectively performing least squares calculation on the pseudorange observations of each satellite within the sample group to obtain the user position and road side separation information corresponding to the sample group; and determining the expected road side based on the user position and road side separation information, so as to obtain the positioning information of the mobile terminal based on the expected road side. Thereby, the technical problem in the related art that the GNSS positioning accuracy and road side separation accuracy rate are low in complex scenarios such as urban canyons, thus affecting the actual user experience, is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning or presence detection using reflection or reradiation of radio waves, and particularly relates to a GNSS positioning method, device, and electronic device based on random sampling. Background Art

[0002] GNSS (Global Navigation Satellite System) can provide all-weather positioning, navigation, and timing (PNT) services globally, and thus is widely used in many industries. Among them, intelligent mobile terminals represented by smart phones have gradually become popular, and a large number of intelligent scenario services relying on location services have provided new development prospects for the industry and users. The refined and scenario-based development trend of intelligent mobile terminals continuously puts forward higher requirements for location services, and also makes the high-precision GNSS positioning based on Android smart phones a current research hotspot.

[0003] Road side determination refers to determining the side of the road where a user is located based on the user's position and road network information in a navigation and positioning system. For navigation users, in the criss-crossing urban traffic environment, the paths connecting both sides of adjacent roads are likely to be complex. An accurate road side determination system can help users determine the correct traveling direction and path, avoid wasting time and costs caused by incorrect side determination, and improve the user experience and satisfaction. For example, for online car-hailing users, affected by actual road conditions and traffic regulations, an accurate road side determination system can assist in judging the operating vehicle that best matches the location of the passenger, shortening the time costs for both the driver and the passenger, and improving the operating efficiency and user experience.

[0004] The main application scenarios of intelligent mobile terminals, such as smart phones, are in urban areas. However, in the urban environment with many high-rise buildings and elevated roads, it is easy to form an "urban canyon" environment, resulting in signal loss and multipath propagation of GNSS signals. In addition, since smart phones mostly use consumer-grade GNSS modules, their built-in patch-type linearly polarized antennas and highly integrated GNSS chips have a low cost, which easily brings problems such as large noise interference, serious cycle slips, and poor multipath suppression. Moreover, the specific model is related to the mobile phone model, motion state, environment, etc., further reducing the GNSS signal quality of smart phones. As a result, the positioning accuracy of smart phones in urban canyon areas is poor, thereby affecting the accuracy of road side calculation.

[0005] Regarding the problems existing in positioning, related technologies focus on optimizing the positioning random model. For example, using the carrier-to-noise ratio weighting model, machine learning to identify non-line-of-sight signals, etc. Although the positioning accuracy of smartphones can be improved, the GNSS positioning accuracy and road-side splitting accuracy of smartphones in complex scenarios such as urban canyons are still low and need to be improved. Summary of the Invention

[0006] The present invention provides a GNSS positioning method, device and electronic device based on random sampling to solve the technical problem in related technologies that the GNSS positioning accuracy and road-side splitting accuracy are low in complex scenarios such as urban canyons, thus affecting the actual user experience.

[0007] The first aspect embodiment of the present invention provides a GNSS positioning method based on random sampling, including the following steps: performing sky partitioning based on the current receivable satellite list and its azimuth information to determine the sky area to which each satellite belongs; performing distance-optimized random sampling of satellites within each sky area to generate a sample group; respectively performing least squares calculation on the pseudorange observations of each satellite in the sample group to obtain the user position and road-side splitting information corresponding to the sample group; determining the expected road side based on the user position and road-side splitting information, so as to obtain the positioning information of the mobile terminal based on the expected road side.

[0008] Optionally, in an embodiment of the present invention, the performing sky partitioning based on the current receivable satellite list and its azimuth information and determining the sky area to which each satellite belongs includes: calling the interface function of the mobile terminal to obtain the current receivable satellite list and obtain the azimuth information of each satellite in the satellite list; dividing the sky into multiple regions and determining the sky area to which each satellite belongs in the multiple regions according to the azimuth information.

[0009] Optionally, in an embodiment of the present invention, the performing distance-optimized random sampling of satellites within each sky area to generate a sample group includes: initializing a sample set, randomly selecting one satellite from each sky area to generate an initial sample set; calculating the distance matrix between the selected samples and calculating the standard deviation of the distances between each satellite in the selected samples based on the distance matrix to evaluate the geometric distribution of all satellites in the selected samples using the standard deviation; based on the standard deviation, newly selecting satellites that meet the preset distance condition from the satellite list and adding the newly selected satellites to the sample set until the total number of samples in the sample set reaches the preset sampling quantity to form the sample group.

[0010] Optionally, in an embodiment of the present invention, the step of respectively performing least squares calculation on the pseudorange observations of each satellite in the sample group to obtain the user position and road side information corresponding to the sample group includes: respectively calculating the pseudorange observations of each satellite in the sample group; constructing a pseudorange error equation based on the pseudorange observations; calculating the pseudorange positioning result of the sample group using the least squares method; and comparing the pseudorange positioning result with the road network data in a preset database to obtain the user position and road side information.

[0011] Optionally, in an embodiment of the present invention, the expression of the pseudorange error equation is:

[0012] ,

[0013] where is the pseudorange observation, is the satellite orbit product, is the coordinate of the position to be estimated, is the receiver clock error, is the satellite clock error, is the ionospheric delay term, is the tropospheric delay term, is the pseudorange hardware delay.

[0014] Optionally, in an embodiment of the present invention, the step of determining the expected road side based on the user position and road side information to obtain the positioning information of the mobile terminal based on the expected road side includes: statistically analyzing all the road side information to obtain a statistical result; and determining the expected road side where the user is located based on the statistical result to obtain the positioning information based on the expected road side.

[0015] An embodiment of the second aspect of the present invention provides a GNSS positioning device based on random sampling, including: a partitioning module for partitioning the sky based on the currently receivable satellite list and its azimuth information to determine the sky region to which each satellite belongs; a sampling module for performing distance-optimized random sampling of satellites in each sky region to generate a sample group; a calculation module for respectively performing least squares calculation on the pseudorange observations of each satellite in the sample group to obtain the user position and road side information corresponding to the sample group; and a positioning module for determining the expected road side based on the user position and road side information to obtain the positioning information of the mobile terminal based on the expected road side.

[0016] Optionally, in an embodiment of the present invention, the zoning module includes: an acquisition unit, configured to call an interface function of the mobile terminal to obtain the currently receivable satellite list and obtain the azimuth information of each satellite in the satellite list; a zoning unit, configured to divide the sky into multiple regions and determine the sky region to which each satellite belongs in the multiple regions according to the azimuth information.

[0017] Optionally, in an embodiment of the present invention, the sampling module includes: an initialization unit, configured to initialize a sample set and randomly select a satellite from each sky region to generate an initial sample set; a first calculation unit, configured to calculate a distance matrix between the selected samples and calculate a standard deviation of the distances between each pair of satellites among the selected samples based on the distance matrix, so as to evaluate the geometric distribution of all satellites among the selected samples; a sampling unit, configured to newly select satellites that meet a preset distance condition from the satellite list based on the standard deviation and add the newly selected satellites to the sample set until the total number of samples in the sample set reaches a preset sampling quantity, thereby forming the sample group.

[0018] Optionally, in an embodiment of the present invention, the calculation module includes: a second calculation unit, configured to calculate the pseudorange observation value of each satellite in the sample group respectively; a construction unit, configured to construct a pseudorange error equation based on the pseudorange observation value; a third calculation unit, configured to calculate the pseudorange positioning result of the sample group by using the least squares method; a comparison unit, configured to compare the pseudorange positioning result with the road network data in a preset database to obtain the user location and road side information.

[0019] Optionally, in an embodiment of the present invention, the expression of the pseudorange error equation is:

[0020] ,

[0021] wherein, is the pseudorange observation value, is the satellite orbit product, is the coordinate of the position to be estimated, is the receiver clock error, is the satellite clock error, is the ionospheric delay term, is the tropospheric delay term, is the pseudorange hardware delay.

[0022] Optionally, in an embodiment of the present invention, the positioning module includes: a statistics unit, configured to count all the road side information to obtain a statistical result; a positioning unit, configured to determine the expected road side where the user is located based on the statistical result, so as to obtain the positioning information based on the expected road side.

[0023] In a third aspect embodiment of the present invention, an electronic device is provided, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the GNSS positioning method based on random sampling as described in the above embodiments.

[0024] In a fourth aspect embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions for causing the computer to execute the GNSS positioning method based on random sampling as described in the above embodiments.

[0025] In a fifth aspect embodiment of the present invention, a computer program product is provided, including a computer program that, when executed, is used to implement the above-mentioned GNSS positioning method based on random sampling.

[0026] Embodiments of the present invention can partition the sky according to the currently receivable satellite list and its azimuth information of the mobile terminal, determine the sky region to which each satellite belongs, and perform distance-optimized random sampling of satellites within each sky region to generate a sample group, so as to perform least squares calculation on the pseudorange observation values of each satellite in the sample group respectively to obtain the user position and road side information corresponding to the sample group, and determine the expected road side according to the user position and road side information, so as to obtain the positioning information of the mobile terminal based on the expected road side. The road side information of the mobile terminal is determined by random sampling to weaken the influence of multipath and non-line-of-sight signals on the road side of the mobile terminal. By using the hierarchical random sampling and the sampling scheme based on distance optimization, the geometric distribution of the sampled satellites can be optimized while satisfying the characteristics of random sampling, and a more accurate positioning result can be obtained. Thus, the technical problem in the related art that the GNSS positioning accuracy and road side accuracy rate are relatively low in complex scenarios such as urban canyons, thereby affecting the actual use experience of users, is solved.

[0027] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0029] Figure 1 is a flowchart of a GNSS positioning method based on random sampling according to an embodiment of the present invention;

[0030] Figure 2 is a schematic diagram of sky partitioning according to an embodiment of the present invention;

[0031] Figure 3 Schematic diagram of a satellite sample group provided according to an embodiment of the present invention;

[0032] Figure 4 Schematic diagram of road network matching provided according to an embodiment of the present invention;

[0033] Figure 5 Schematic diagram of repeated positioning statistics provided according to an embodiment of the present invention;

[0034] Figure 6 Schematic diagram of the principle of a GNSS positioning method based on random sampling provided according to an embodiment of the present invention;

[0035] Figure 7 Schematic diagram of the structure of a GNSS positioning device based on random sampling provided according to an embodiment of the present invention;

[0036] Figure 8 Schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed implementation manners

[0037] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0038] The GNSS positioning method, device, and electronic device based on random sampling according to the embodiments of the present invention will be described below with reference to the accompanying drawings. In view of the technical problem in the related art mentioned in the above background art that the GNSS positioning accuracy and road side division accuracy are relatively low in complex scenarios such as urban canyons, thus affecting the actual user experience, the present invention provides a GNSS positioning method based on random sampling. In this method, sky partitioning can be performed according to the currently receivable satellite list of the mobile terminal and its azimuth information, the sky region to which each satellite belongs can be determined, and distance-optimized random sampling of satellites can be performed within each sky region to generate a sample group. Then, least squares calculation can be performed on the pseudorange observation values of each satellite in the sample group to obtain the user position and road side division information corresponding to the sample group, and the expected road side can be determined based on the user position and road side division information, so as to obtain the positioning information of the mobile terminal based on the expected road side. The road side division information of the mobile terminal is determined by random sampling to weaken the influence of multipath and non-line-of-sight signals on the road side division of the mobile terminal. By using the hierarchical random sampling and the sampling scheme based on distance optimization, the geometric distribution of the sampled satellites can be optimized under the condition of meeting the characteristics of random sampling, and a more accurate positioning result can be obtained. Thus, the technical problem in the related art that the GNSS positioning accuracy and road side division accuracy are relatively low in complex scenarios such as urban canyons, thus affecting the actual user experience, is solved.

[0039] Specifically, Figure 1 FIG. is a schematic flowchart of a GNSS positioning method based on random sampling provided by an embodiment of the present invention.

[0040] As Figure 1 shown, the GNSS positioning method based on random sampling includes the following steps:

[0041] In step S101, sky partitioning is performed based on the currently receivable satellite list and its azimuth information, and the sky region to which each satellite belongs is determined.

[0042] In the actual execution process, the embodiments of the present invention can perform sky partitioning based on the currently receivable satellite list and its azimuth information, divide the sky into multiple regions, such as four regions, and determine the sky region to which each satellite belongs.

[0043] Optionally, in an embodiment of the present invention, performing sky partitioning based on the currently receivable satellite list and its azimuth information, and determining the sky region to which each satellite belongs, includes: calling the interface function of the mobile terminal to obtain the currently receivable satellite list, and obtaining the azimuth information of each satellite in the satellite list; dividing the sky into multiple regions, and determining the sky region to which each satellite belongs in the multiple regions according to the azimuth information.

[0044] As Figure 2As shown in the figure, embodiments of the present invention can obtain the azimuth information of each satellite based on the interface function (getAzimuthDegrees) of the Android system of the mobile terminal.

[0045] According to the azimuth of each satellite The sky is divided into four partitions, where: the first quadrant , the second quadrant , the third quadrant , the fourth quadrant .

[0046] Based on the observed satellite azimuth information, embodiments of the present invention can distribute the satellites in the sky partitions of the four quadrants and count the number of visible satellites in each partition. .

[0047] In step S102, distance-optimized random sampling of satellites is performed within each sky region to generate a sample group.

[0048] Furthermore, embodiments of the present invention can perform distance-optimized random sampling of satellites based on the sky partition to obtain a sample group.

[0049] Optionally, in an embodiment of the present invention, performing distance-optimized random sampling of satellites within each sky region to generate a sample group includes: initializing a sample set, randomly selecting one satellite from each sky region to generate an initial sample set; calculating the distance matrix between the selected samples, and calculating the standard deviation of the distances between each pair of satellites in the selected samples based on the distance matrix to evaluate the geometric distribution of all satellites in the selected samples; based on the standard deviation, newly select satellites that meet the preset distance condition from the satellite list and add the newly selected satellites to the sample set until the total number of samples in the sample set reaches the preset sampling quantity to form a sample group.

[0050] As a possible implementation manner, embodiments of the present invention can initialize the sample set and use the random sampling method to randomly select one satellite from each region to form an initial sample set :

[0051] (1)

[0052] Wherein, represents the sampled satellite, is the satellite number in the sample.

[0053] For the sampled samples , embodiments of the present invention can calculate the distance matrix of this sample group :

[0054] (2)

[0055] Among them, is the satellite and is the distance between. According to the distance matrix , the embodiments of the present invention can calculate the standard deviation of the distances between current sample points , which is used as an index of the geometric distribution characteristics of the satellites:

[0056] (3)

[0057] Among them, is the total number of sample pairs; is the non-repeated elements of the sample pairs in the distance matrix; is the average distance, .

[0058] The embodiments of the present invention can set a threshold . If the standard deviation of the sample , it means that the current satellite samples are relatively concentrated, and the satellite farthest from the existing samples is selected to be added to the sample set; if the standard deviation of the sample , it means that the current sample distribution is relatively wide, and a satellite in the partition with the largest number of unsampled satellites is randomly selected:

[0059] (4)

[0060] Among them, is the selected satellite sample; is the set of unsampled satellite samples; is the satellite and is the distance between, is the satellite in the unsampled group, is the satellite in the sample group; is the partition with the largest number of unsampled satellites. If there are multiple such partitions, one of them is randomly selected as ; is selected randomly from as a satellite sample. Update the sample set :

[0061] (5)

[0062] The embodiments of the present invention can set the random sampling quantity :

[0063] (6)

[0064] Among them, is the sampling ratio. When the number of visible satellites is sufficient, it can be taken as ; When the number of visible satellites is small, it should be ensured that the minimum number of observed satellites is satisfied.

[0065] Repeat the above process. As Figure 3 shown, until the number of satellite samples in the sample group reaches the required number of satellites for the preset minimum positioning. requirements.

[0066] In step S103, least-squares calculations are respectively performed on the pseudorange observation values of each satellite in the sample group to obtain the corresponding user position and road side information of the sample group.

[0067] Furthermore, embodiments of the present invention can perform least-squares calculations based on the pseudorange observation values of each satellite to obtain the user position and road side information of the sample group.

[0068] Optionally, in an embodiment of the present invention, least-squares calculations are respectively performed on the pseudorange observation values of each satellite in the sample group to obtain the corresponding user position and road side information of the sample group, including: respectively calculating the pseudorange observation values of each satellite in the sample group; constructing a pseudorange error equation based on the pseudorange observation values; calculating the pseudorange positioning result of the sample group using the least-squares method; comparing the pseudorange positioning result with the road network data in the preset database to obtain the user position and road side information. Among them, the expression of the pseudorange error equation is:

[0069] ,

[0070] Among them, is the pseudorange observation value, is the satellite orbit product, is the position coordinate to be estimated, is the receiver clock error, is the satellite clock error, is the ionospheric delay term, is the tropospheric delay term, is the pseudorange hardware delay.

[0071] In some embodiments, embodiments of the present invention can, for the sample group , according to the satellite pseudorange observation values, satellite ephemeris, approximate position of the mobile terminal, etc., use a non-differential and non-combination model to construct a pseudorange error equation:

[0072] (7)

[0073] Embodiments of the present invention can use the least-squares method to solve the point position determined by the satellites in this sample group .

[0074] Such as Figure 4As shown, embodiments of the present invention can convert the coordinates of a point in the Earth-Centered Earth-Fixed coordinate system into longitude and latitude coordinates , and then match them with the road network coordinates in the high-precision map to determine the road side where the point is located.

[0075] In step S104, based on the user's location and the road side information, determine the expected road side, so as to obtain the positioning information of the mobile terminal based on the expected road side.

[0076] Repeating the above steps, embodiments of the present invention can determine the expected road side according to multiple positioning results, thereby realizing the positioning of the mobile terminal.

[0077] Optionally, in an embodiment of the present invention, based on the user's location and the road side information, determine the expected road side, so as to obtain the positioning information of the mobile terminal based on the expected road side, including: statistically analyzing all the road side information to obtain a statistical result; determining the expected road side where the user is located based on the statistical result, so as to obtain the positioning information based on the expected road side where the user is located.

[0078] As Figure 5 shown, embodiments of the present invention can repeatedly perform the random sampling and positioning side determination processes of steps S102 and S103 multiple times, count the frequency of the road side information, and identify the side with a higher frequency as the expected road side where the receiver is located.

[0079] Combined with Figures 2 to 6 shown, an embodiment is used to elaborate in detail on the working principle of the GNSS positioning method based on random sampling according to embodiments of the present invention.

[0080] As Figure 6 shown, it is a schematic diagram of the principle according to embodiments of the present invention. Embodiments of the present invention can address the problem that GNSS data of a mobile terminal, such as a smart phone, is affected by multipath and non-line-of-sight signals. By using a sample group selected by the random sampling method after sky partitioning, the approximate positions of several users are calculated, and finally, combined with the road network data, the road side information of the users is determined to solve the problem of user road side determination in complex scenarios such as urban canyons.

[0081] In the actual execution process, embodiments of the present invention may include the following steps:

[0082] Step S1: Sky partitioning. Embodiments of the present invention can perform sky partitioning based on the currently receivable satellite list and its azimuth information, divide the sky into four regions, and determine the sky region to which each satellite belongs.

[0083] As Figure 2As shown, embodiments of the present invention can call the interface function of the Android system to obtain the azimuth information of each satellite; divide the sky into four regions, and determine the sky region to which each satellite belongs based on the azimuth information.

[0084] Step S2: Random sampling. Based on the sky partition, perform distance-optimized random sampling on the satellites to obtain a sample group. Specifically, it can include the following steps:

[0085] Step S2.1: Embodiments of the present invention can initialize the sample set and randomly select one satellite from each region to form an initial sample set.

[0086] Step S2.2: For the selected samples, embodiments of the present invention can calculate the distance matrix between them and calculate the standard deviation of the distances between the current sample points to evaluate the geometric distribution of the currently sampled satellites.

[0087] Step S2.3 According to the current satellite distance standard deviation, embodiments of the present invention can select satellites at corresponding distances and add the newly selected satellite samples to the sample set. If the current samples are relatively concentrated, the satellite farthest from the existing samples needs to be selected. If the current samples are widely distributed, continue to sample other satellites close to the selected samples;

[0088] Step S2.4 Repeat the above steps S2.2 and S2.3 several times, as Figure 3 shown, until the total number of samples reaches the preset sampling quantity.

[0089] Step S3: Positioning and road side separation. Based on the pseudorange observations of each satellite, perform least squares calculation to obtain the user position and road side separation information of the sample group.

[0090] For the satellites included in the sample group, embodiments of the present invention can calculate their pseudorange observations, construct a pseudorange error equation based on the pseudorange observations, and use the least squares method to solve for the three-dimensional coordinates as the pseudorange positioning result of this sample group; as Figure 4 shown, embodiments of the present invention can compare the pseudorange positioning result with the road network data in the database to determine the road side information where this point is located.

[0091] Step S4: Output the result. As Figure 5 shown, embodiments of the present invention can repeat steps S2 and S3 several times, count the road side information of each positioning result, and consider the side with more statistical frequencies as the expected road side where the receiver is located.

[0092] The GNSS positioning method based on random sampling proposed according to an embodiment of the present invention can partition the sky according to the currently receivable satellite list of the mobile terminal and its azimuth information, determine the sky area to which each satellite belongs, and perform distance-optimized random sampling of satellites within each sky area to generate a sample group, so as to perform least squares calculation on the pseudorange observation values of each satellite in the sample group respectively to obtain the user position and road side information corresponding to the sample group, and determine the expected road side based on the user position and road side information, so as to obtain the positioning information of the mobile terminal based on the expected road side. By determining the road side information of the mobile terminal through random sampling, the influence of multipath and non-line-of-sight signals on the road side of the mobile terminal can be weakened. By using the hierarchical random sampling and the sampling scheme based on distance optimization, the geometric distribution of the sampled satellites can be optimized under the condition of meeting the characteristics of random sampling, and a more accurate positioning result can be obtained. Thereby, the technical problem in the related art that the GNSS positioning accuracy and road side accuracy rate are relatively low in complex scenarios such as urban canyons, thus affecting the actual user experience, is solved.

[0093] Next, a GNSS positioning device based on random sampling proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0094] Figure 7 It is a block diagram of a GNSS positioning device based on random sampling according to an embodiment of the present invention.

[0095] As Figure 7 shown, the GNSS positioning device 10 based on random sampling includes: a partitioning module 100, a sampling module 200, a calculation module 300, and a positioning module 400.

[0096] Specifically, the partitioning module 100 is configured to perform sky partitioning based on the currently receivable satellite list and its azimuth information, and determine the sky area to which each satellite belongs.

[0097] The sampling module 200 is configured to perform distance-optimized random sampling of satellites within each sky area to generate a sample group.

[0098] The calculation module 300 is configured to perform least squares calculation on the pseudorange observation values of each satellite in the sample group respectively to obtain the user position and road side information corresponding to the sample group.

[0099] The positioning module 400 is configured to determine the expected road side based on the user position and road side information, so as to obtain the positioning information of the mobile terminal based on the expected road side.

[0100] Optionally, in an embodiment of the present invention, the partitioning module 100 includes: an acquisition unit and a partitioning unit.

[0101] Among them, an acquisition unit is configured to call an interface function of a mobile terminal to acquire a currently receivable satellite list and acquire the azimuth information of each satellite in the satellite list.

[0102] A partitioning unit is configured to divide the sky into multiple regions and determine the sky region to which each satellite belongs in the multiple regions according to the azimuth information.

[0103] Optionally, in an embodiment of the present invention, the sampling module 200 includes: an initialization unit, a first calculation unit, and a sampling unit.

[0104] Among them, the initialization unit is configured to initialize a sample set and randomly select a satellite from each sky region to generate an initial sample set.

[0105] The first calculation unit is configured to calculate a distance matrix between the selected samples and calculate the standard deviation of the distances between each pair of satellites among the selected samples based on the distance matrix, so as to evaluate the geometric distribution of all satellites among the selected samples by using the standard deviation.

[0106] The sampling unit is configured to, based on the standard deviation, newly select satellites that meet a preset distance condition from the satellite list and add the newly selected satellites to the sample set until the total number of samples in the sample set reaches a preset sampling quantity, forming a sample group.

[0107] Optionally, in an embodiment of the present invention, the calculation module 300 includes: a second calculation unit, a construction unit, a third calculation unit, and a comparison unit.

[0108] Among them, the second calculation unit is configured to calculate the pseudorange observation value of each satellite within the sample group respectively.

[0109] The construction unit is configured to construct a pseudorange error equation based on the pseudorange observation value.

[0110] The third calculation unit is configured to calculate the pseudorange positioning result of the sample group by using the least squares method.

[0111] The comparison unit is configured to compare the pseudorange positioning result with the road network data in a preset database to obtain the user location and road side information.

[0112] Optionally, in an embodiment of the present invention, the expression of the pseudorange error equation is:

[0113] ,

[0114] Among them, is the pseudorange observation value, is the satellite orbit product, is the coordinate of the position to be estimated, is the receiver clock error, is the satellite clock error, is the ionospheric delay term, is the tropospheric delay term, is the pseudorange hardware delay.

[0115] Optionally, in an embodiment of the present invention, the positioning module 400 includes: a statistical unit and a positioning unit.

[0116] The statistical unit is configured to count all road side information to obtain a statistical result.

[0117] The positioning unit is configured to determine the expected road side where the user is located based on the statistical result, so as to obtain positioning information based on the expected road side.

[0118] It should be noted that the foregoing explanation of the embodiment of the GNSS positioning method based on random sampling is also applicable to the GNSS positioning device based on random sampling in this embodiment, and will not be elaborated here.

[0119] The GNSS positioning device based on random sampling proposed according to the embodiment of the present invention can perform sky partitioning according to the current list of receivable satellites of the mobile terminal and their azimuth information, determine the sky region to which each satellite belongs, and perform distance-optimized random sampling of satellites within each sky region to generate a sample group. Then, the least squares calculation is respectively performed on the pseudorange observation values of each satellite in the sample group to obtain the user position and road side information corresponding to the sample group, and based on the user position and road side information, the expected road side is determined, so as to obtain the positioning information of the mobile terminal based on the expected road side. The road side information of the mobile terminal is determined by random sampling to weaken the influence of multipath and non-line-of-sight signals on the road side of the mobile terminal. By using the hierarchical random sampling and the sampling scheme based on distance optimization, the geometric distribution of the sampled satellites can be optimized under the condition of satisfying the characteristics of random sampling, and a more accurate positioning result can be obtained. Thus, the technical problem in the related art that the GNSS positioning accuracy and road side accuracy rate are relatively low in complex scenarios such as urban canyons, thereby affecting the actual use experience of users, is solved.

[0120] Figure 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0121] A memory 801, a processor 802, and a computer program stored on the memory 801 and executable on the processor 802.

[0122] When the processor 802 executes the program, it implements the GNSS positioning method based on random sampling provided in the foregoing embodiment.

[0123] Furthermore, the electronic device further includes:

[0124] A communication interface 803 for communication between the memory 801 and the processor 802.

[0125] A memory 801 for storing a computer program that can run on the processor 802.

[0126] The memory 801 may include a high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0127] If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 can be interconnected via a bus to complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 8 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0128] Optionally, in a specific implementation, if the memory 801, the processor 802, and the communication interface 803 are integrated on a single chip, the memory 801, the processor 802, and the communication interface 803 can complete communication with each other through an internal interface.

[0129] The processor 802 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0130] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the GNSS positioning method based on random sampling as described above is implemented.

[0131] The embodiments of the present invention also provide a computer program product, including a computer program. When the computer program is executed by a processor, the GNSS positioning method based on random sampling provided by the embodiments of the present invention is implemented.

[0132] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0133] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0134] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0136] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0138] In addition, each functional unit in various embodiments of the present invention may be integrated into one processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0139] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A GNSS positioning method based on random sampling, characterized in that including the following methods: Performing sky partitioning based on the currently receivable satellite list and its azimuth information to determine the sky region to which each satellite belongs; Performing distance-optimized random sampling of satellites within each of the sky regions to generate a sample group; Performing least-squares calculations on the pseudorange observations of each satellite within the sample group to obtain the user position and road-side information corresponding to the sample group; Based on the user position and road-side information, determining the desired road side to obtain the positioning information of the mobile terminal based on the desired road side; Among them, the performing distance-optimized random sampling of satellites within each of the sky regions to generate a sample group includes: initializing a sample set, randomly selecting one satellite from each sky region to generate an initial sample set; calculating the distance matrix between the selected samples, and calculating the standard deviation of the distances between each pair of satellites among the selected samples based on the distance matrix to evaluate the geometric distribution of all satellites among the selected samples; based on the standard deviation, newly selecting satellites that meet the preset distance condition from the satellite list and adding the newly selected satellites to the sample set until the total number of samples in the sample set reaches the preset sampling quantity to form the sample group.

2. The method according to claim 1, wherein The performing sky partitioning based on the currently receivable satellite list and its azimuth information and determining the sky region to which each satellite belongs includes: Invoking the interface function of the mobile terminal to obtain the currently receivable satellite list and obtain the azimuth information of each satellite in the satellite list; Dividing the sky into multiple regions and determining the sky region to which each satellite belongs among the multiple regions based on the azimuth information.

3. The method according to claim 1, wherein The performing least-squares calculations on the pseudorange observations of each satellite within the sample group to obtain the user position and road-side information corresponding to the sample group includes: Calculating the pseudorange observations of each satellite within the sample group respectively; Constructing a pseudorange error equation based on the pseudorange observations; Calculating the pseudorange positioning result of the sample group using the least-squares method; Comparing the pseudorange positioning result with the road network data in the preset database to obtain the user position and road-side information.

4. The method according to claim 3, characterized in that, The expression of the pseudorange error equation is: ， Wherein, is the pseudorange observation value, is the satellite orbit product, is the position coordinate to be estimated, is the receiver clock error, is the satellite clock error, is the ionospheric delay term, is the tropospheric delay term, is the pseudorange hardware delay.

5. The method according to claim 1, wherein The determining the desired road side based on the user position and road-side information to obtain the positioning information of the mobile terminal based on the desired road side includes: Counting all the road-side information to obtain a statistical result; Based on the statistical result, determining the desired road side where the user is located to obtain the positioning information based on the desired road side where the user is located.

6. A GNSS positioning device based on random sampling, characterized in that, including: A partitioning module for performing sky partitioning based on the currently receivable satellite list and its azimuth information to determine the sky region to which each satellite belongs; A sampling module for performing distance-optimized random sampling of satellites within each of the sky regions to generate a sample group; A calculation module for performing least-squares calculations on the pseudorange observations of each satellite within the sample group to obtain the user position and road-side information corresponding to the sample group; A positioning module, configured to determine an expected road side based on the user location and road side information, so as to obtain the positioning information of the mobile terminal based on the expected road side; Wherein, the sampling module includes: an initialization unit, configured to initialize a sample set, and randomly select a satellite from each sky region to generate an initial sample set; a first calculation unit, configured to calculate a distance matrix between the selected samples, and calculate a standard deviation of distances between each pair of satellites among the selected samples based on the distance matrix, so as to evaluate the geometric distribution of all satellites among the selected samples; a sampling unit, configured to, based on the standard deviation, newly select satellites that meet a preset distance condition from the satellite list, and add the newly selected satellites to the sample set until the total number of samples in the sample set reaches a preset sampling quantity, thereby forming the sample group.

7. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the GNSS positioning method based on random sampling according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the GNSS positioning method based on random sampling according to any one of claims 1-5.

9. A computer program product comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the GNSS positioning method based on random sampling according to any one of claims 1-5.

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