A positioning method and a positioning device based on GNSS raw observation data

Abstract

The application relates to the positioning field, in particular to a positioning method and device based on global navigation satellite system (GNSS) original observation data. The method comprises the following steps: performing positioning calculation on first GNSS original observation data to obtain a first calculation result; performing chi-square test on the first GNSS original observation data and a state vector updated in time according to the positioning calculation to obtain a first chi-square test value of the first GNSS original observation data; and determining a target GNSS positioning result of a mobile station according to the first calculation result if the first calculation result is determined to be a valid result according to the first chi-square test value. According to the embodiment of the application, whether the positioning calculation result is valid can be reasonably determined, and the reliability and applicability of the positioning method based on the GNSS original observation data can be improved.

Description

Technical Field

[0001] This application relates to the field of positioning, and in particular to a positioning method and positioning device based on raw GNSS observation data. Background Technology

[0002] In recent years, with the continuous development of internet and manufacturing technologies, intelligent driving vehicles have gradually entered people's lives. For intelligent driving vehicles, high-precision point cloud maps are one of the crucial supporting technologies for achieving autonomous driving. In practical applications, high-precision point cloud maps are typically constructed by map acquisition equipment using offline post-processing. During the construction of high-precision point cloud maps by map acquisition equipment, the GNSS positioning result obtained by processing the raw observation data output from the Global Navigation Satellite System (GNSS) board (hereinafter referred to as GNSS raw observation data) is an important reference value. The accuracy of this GNSS positioning result directly affects the accuracy of the high-precision point cloud map. Therefore, how to accurately and efficiently process GNSS raw observation data to obtain GNSS positioning results has become a major research hotspot.

[0003] In existing technologies, forward and reverse positioning calculations are performed on the acquired raw GNSS observation data to obtain forward and reverse positioning results. If the positioning deviation between the forward and reverse results is within a set range, the result is considered valid, and the final GNSS positioning result can be determined based on these results. If the positioning deviation exceeds a preset range, the result is considered invalid. The currently used raw GNSS observation data is then discarded, and new raw GNSS observation data is acquired for further processing. This method of judging the validity of the result based on the magnitude of the positioning deviation is overly simplistic and crude, failing to adequately consider the causes of excessive positioning deviations. This results in poor reliability and applicability of existing positioning methods based on raw GNSS data. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a positioning method and device based on raw GNSS observation data, which can reasonably determine the validity of the positioning calculation results and improve the reliability and applicability of the positioning method.

[0005] It should be noted that the positioning method based on raw GNSS observation data provided in this application embodiment can be specifically executed by a positioning device. This positioning device can be any form of computing device capable of acquiring and processing raw GNSS observation data. Here, a computing device refers to a device that can be abstracted as a computer system. For example, the computing device can be the entire server (including local servers and / or cloud servers) used by high-precision map software, or some components within that server, such as the chip system or logic circuits (e.g., field-programmable gate arrays, FPGAs) within the server. Alternatively, the computing device can also be the entire device of a vehicle or its in-vehicle system (e.g., a telematics box, T-box), a personal computer, a mobile phone, or a system chip within these devices. For ease of understanding and description, the term "positioning device" will be used uniformly in the following description.

[0006] It should also be noted that the positioning method based on raw GNSS observation data provided in this application embodiment is applicable to the construction or generation process of high-precision point cloud maps.

[0007] Firstly, embodiments of this application provide a positioning method based on raw GNSS observation data. The positioning device first performs positioning calculations on the first raw GNSS observation data to obtain a first calculation result. Then, the positioning device performs a chi-square test on the first raw GNSS observation data and the time-updated state vector obtained from the positioning calculation to obtain a first chi-square test value for the first raw GNSS observation data. Then, if the positioning device determines that the first calculation result is a valid result based on the first chi-square test value, the target GNSS positioning result of the rover station is determined based on the first calculation result.

[0008] In the above implementation, after acquiring the solution results corresponding to the raw GNSS observation data, the positioning device determines the validity of the current solution results based on the chi-square test value corresponding to the raw GNSS observation data. Since the chi-square test value corresponding to the raw GNSS observation data can be used to characterize whether there is abnormal observation data in the raw GNSS observation data, using the chi-square test value corresponding to the raw GNSS observation data as a judgment condition makes the results obtained by the positioning device more reasonable and accurate, improving the reliability and applicability of the positioning method.

[0009] In conjunction with the first aspect, in the first optional implementation, if the positioning device determines that the first solution result is invalid based on the first chi-square test value, then abnormal observation data can be removed from the first GNSS raw observation data to obtain second GNSS raw observation data. Here, the second GNSS raw observation data is used to determine the target GNSS positioning result. In this implementation, when the positioning device determines that the first solution result is invalid, it will remove abnormal data from the first GNSS raw observation data to obtain second GNSS raw observation data, and continue to process the second GNSS raw observation data to obtain the mobile GNSS positioning result. This improves the data utilization efficiency of the GNSS raw observation data and avoids the waste of data resources and data processing resources.

[0010] In conjunction with the first optional implementation method described above, in the second optional implementation method, the positioning device can first determine the target satellite. Then, the positioning device can remove the GNSS raw observation data of the target satellite included in the first GNSS raw observation data to obtain the second GNSS raw observation data.

[0011] In conjunction with the second optional implementation described above, in the third optional implementation, the positioning device can obtain the first residual result of the first GNSS raw observation data. Here, the first residual result can be determined by the carrier double-difference observation value and pseudorange double-difference observation value corresponding to the first GNSS raw observation data obtained by the positioning solution, as well as the state vector after time update. The positioning device can determine the satellite corresponding to the maximum residual value in the first residual result as the target satellite. In this implementation, the method of determining the target satellite based on the residual result is simple and reliable, reduces the data processing load of the positioning device, and improves the positioning efficiency of the positioning method provided in this application.

[0012] Combining the first to third optional implementations described above, in the fourth optional implementation, the positioning device can determine the number of valid satellites corresponding to the second GNSS raw observation data. If the positioning device determines that the number of valid satellites is less than 4, then the second GNSS raw observation data is determined to be invalid data. In this implementation, using the number of valid satellites corresponding to the second GNSS raw observation data to determine whether the second GNSS raw observation data can meet the basic requirements of positioning calculation can avoid invalid processing of GNSS raw observation data that does not meet the requirements by the positioning device, thus saving the data processing capacity of the positioning device.

[0013] Combining the first to fourth optional implementations described above, in the fifth optional implementation, the positioning device can acquire S historical chi-square test values ​​obtained by performing chi-square tests at S time points prior to the first time point. Here, the first time point is the time when the first chi-square test value was determined, and S is a positive integer greater than or equal to 1. Then, the positioning device can determine whether the first solution result is a valid or invalid result based on the first chi-square test value and the S historical chi-square test values. Here, using the first chi-square test value, which can characterize whether there is abnormal observation data in the first GNSS raw observation data, and the chi-square test values ​​at the S historical time points as conditions for judging whether the current solution result is valid, fully considers the accuracy of the GNSS raw observation data. This makes the judgment result accurate and reliable, which is more reasonable than the existing method of judging the validity of the positioning solution result by deviation, thus improving the accuracy and reliability of the positioning method.

[0014] In conjunction with the fifth optional implementation method described above, in the sixth optional implementation method, the positioning device can determine the first standard deviation corresponding to the first chi-square test value and the S historical chi-square test values. If the positioning device determines that the first standard deviation is greater than a preset standard deviation, then the first solution result is determined to be invalid. If the positioning device determines that the first standard deviation is less than or equal to the preset standard deviation, then the first solution result is determined to be valid. Here, since the first chi-square test value and the standard deviation of the S historical chi-square test values ​​can more accurately characterize the presence of abnormal observation data in the raw GNSS observation data, using the first chi-square test value and the standard deviation of the chi-square test values ​​at the S historical moments as the condition for judging whether the current solution result is valid can make the judgment result more accurate and reliable.

[0015] Combining the third to sixth optional implementation methods described above, in the seventh optional implementation method, the first chi-square test value is based on the first residual result and S. k It is determined that the S k The measurement noise matrix, observation matrix, and time-updated covariance matrix obtained from the positioning solution are used to determine the location. In this implementation, the function matrix S obtained from the first positioning solution process is reused. k The first chi-square test value is directly calculated from the first residual result. The method is simple and easy to implement, which can reduce the amount of data processing of the positioning equipment.

[0016] In conjunction with the first aspect or the first to seventh optional implementations under the first aspect, in the eighth optional implementation, the positioning device can fix the integer ambiguity based on the first floating-point solution and the first covariance matrix to obtain an integer solution of the integer ambiguity. Then, the positioning device can correct the first floating-point solution based on the integer solution of the integer ambiguity to obtain the target GNSS positioning result of the mobile station.

[0017] Secondly, embodiments of this application provide an apparatus. The apparatus includes: a calculation unit, configured to perform positioning calculations on first raw GNSS observation data to obtain a first calculation result; a chi-square test unit, configured to perform a chi-square test on the first raw GNSS observation data and the time-updated state vector obtained from the positioning calculation to obtain a first chi-square test value for the first raw GNSS observation data; and a positioning result determination unit, configured to determine the target GNSS positioning result of the mobile station based on the first calculation result if the first calculation result is determined to be a valid result according to the first chi-square test value.

[0018] In conjunction with the second aspect described above, in a first optional implementation, the positioning result determination unit is configured to: if the first solution result is determined to be invalid based on the first chi-square test value, then remove abnormal observation data from the first GNSS raw observation data to obtain second GNSS raw observation data. Here, the second GNSS raw observation data is used to determine the target GNSS positioning result.

[0019] In conjunction with the first optional implementation described above, in the second optional implementation, the positioning result determination unit is used to: determine the target satellite; and remove the GNSS raw observation data of the target satellite included in the first GNSS raw observation data to obtain the second GNSS raw observation data.

[0020] In conjunction with the second optional implementation described above, in the third optional implementation, the positioning result determination unit is used to: obtain the first residual result of the first GNSS raw observation data. Here, the first residual result is determined by the carrier double-difference observation value and pseudorange double-difference observation value corresponding to the first GNSS raw observation data obtained from the first positioning solution, and the state vector after time update. The satellite corresponding to the largest residual value in the first residual result is determined as the target satellite.

[0021] In a fourth optional implementation, combining the first to third optional implementations, the positioning result determination unit is used to: determine the number of valid satellites corresponding to the second GNSS raw observation data. If the number of valid satellites is determined to be greater than or equal to 4, then the second GNSS raw observation data is determined to be valid data.

[0022] Combining the first to fourth optional implementations described above, in the fifth optional implementation, the positioning result determination unit is used to: obtain S historical chi-square test values ​​obtained by performing chi-square tests at S time points prior to the first time point. Here, the first time point is the time when the first chi-square test value is determined, and S is a positive integer greater than or equal to 1. Based on the first chi-square test value and the S historical chi-square test values, the first solution result is determined to be either a valid result or an invalid result.

[0023] In conjunction with the fifth optional implementation method described above, in the sixth optional implementation method, the positioning result determination unit is used to: determine the first standard deviation corresponding to the first chi-square test value and the S historical chi-square test values. If the first standard deviation is determined to be greater than a preset standard deviation, the first solution result is determined to be an invalid result. If the first standard deviation is determined to be less than or equal to the preset standard deviation, the first solution result is determined to be a valid result.

[0024] Combining the third to sixth optional implementation methods described above, in the seventh optional implementation method, the first chi-square test value is based on the first residual result and the function matrix S. k Determined, the function matrix S k The measurement noise matrix, observation matrix, and time-updated covariance matrix obtained from the positioning solution are determined.

[0025] In conjunction with the second aspect described above, or the first to seventh optional implementations of the second aspect, in the eighth optional implementation, the first solution result includes a first floating-point solution and a first covariance matrix. The positioning result determination unit is configured to: fix integer ambiguities based on the first floating-point solution and the first covariance matrix to obtain integer solutions of integer ambiguities; and correct the first floating-point solution based on the integer solutions of the integer ambiguities to obtain the target GNSS positioning result of the mobile station.

[0026] Thirdly, embodiments of this application provide a server. This server can be used to execute the positioning method based on raw GNSS observation data provided in any of the possible implementations of the first aspect above, and thus can also achieve the beneficial effects (or advantages) of the positioning method based on raw GNSS observation data provided in the first aspect.

[0027] Fourthly, embodiments of this application provide a vehicle. This vehicle can be used to perform the positioning method based on raw GNSS observation data provided in any of the possible implementations of the first aspect described above, and thus can also achieve the beneficial effects (or advantages) of the positioning method based on raw GNSS observation data provided in the first aspect.

[0028] Fifthly, embodiments of this application provide an apparatus. The apparatus includes at least one memory and a processor. The processor is used to invoke code stored in the memory to execute the positioning method based on raw GNSS observation data provided in any of the possible implementations of the first aspect, thus achieving the beneficial effects (or advantages) of the positioning method based on raw GNSS observation data provided in the first aspect.

[0029] Sixthly, embodiments of this application provide a chip. The chip includes at least one processor and an interface circuit. The interface circuit is used to receive code instructions and transmit them to the processor. The processor is used to execute the aforementioned code instructions to implement the positioning method based on raw GNSS observation data provided in any possible embodiment of the first aspect, and also to achieve the beneficial effects (or advantages) of the positioning method based on raw GNSS observation data provided in any possible embodiment of the first aspect.

[0030] Seventhly, this application provides a computer-readable storage medium storing instructions that can be executed by one or more processors on a processing circuit. When executed on a computer, these instructions cause the computer to perform the positioning method based on raw GNSS observation data provided in any of the possible embodiments of the first aspect, thereby achieving the beneficial effects of the positioning method based on raw GNSS observation data provided in the first aspect.

[0031] Eighthly, this application provides a computer program product containing instructions that, when run on a computer, causes the computer to execute the positioning method based on raw GNSS observation data provided in any possible implementation of the first aspect, and can also achieve the beneficial effects of the positioning method based on raw GNSS observation data provided in the first aspect.

[0032] Ninthly, this application provides a chip system including a processor for supporting an apparatus equipped with the chip system in implementing the positioning method based on raw GNSS observation data provided in the first aspect, such as generating or processing the data and / or information involved in the method. In one possible design, the chip system further includes a memory for storing program instructions and data necessary for a data transmission device. The chip system may be composed of chips or may include chips and other discrete devices.

[0033] In a tenth aspect, embodiments of this application provide a map construction method. This method employs the positioning method based on raw GNSS observation data provided in any of the possible implementations of the first aspect described above, and therefore can also achieve the beneficial effects (or advantages) of the positioning method based on raw GNSS observation data provided in the first aspect.

[0034] In the positioning method provided in this application embodiment, after obtaining the solution result corresponding to the raw GNSS observation data, the positioning device determines whether the current solution result is valid based on the chi-square test value corresponding to the raw GNSS observation data. Since the chi-square test value corresponding to the raw GNSS observation data can be used to characterize whether there is abnormal observation data in the raw GNSS observation data, using the chi-square test value corresponding to the raw GNSS observation data as a judgment condition makes the obtained result more reasonable and accurate, thereby improving the reliability and applicability of the positioning method based on raw GNSS observation data. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of a positioning scenario provided in an embodiment of this application;

[0036] Figure 2 This is a flowchart illustrating a positioning method based on raw GNSS observation data provided in an embodiment of this application.

[0037] Figure 3 This is another flowchart illustrating a positioning method based on raw GNSS observation data provided in this application embodiment;

[0038] Figure 4 This is a schematic diagram of a time-based sliding window provided in an embodiment of this application;

[0039] Figure 5 This is another flowchart illustrating a positioning method based on raw GNSS observation data provided in this application embodiment;

[0040] Figure 6 This is a schematic diagram of a device provided in an embodiment of this application;

[0041] Figure 7 This is another structural schematic diagram of a device provided in an embodiment of this application;

[0042] Figure 8 This is a schematic diagram of a chip structure provided in an embodiment of this application;

[0043] Figure 9 This is another structural schematic diagram of a device provided in an embodiment of this application. Detailed Implementation

[0044] To facilitate understanding and description in the following text, the applicant will first explain and clarify several concepts involved in this application.

[0045] 1. Base station and rover

[0046] A base station is a fixed ground-based detection station that continuously and over a long period of time detects satellite navigation signals and transmits the detected data to a data center in real time or at regular intervals via a communication device. In practical applications, a base station can be a physical station erected at a fixed location that meets the requirements for base station erection, or a virtual base station generated using virtual reference station (VRS) technology. A mobile station is a detection station established on a mobile device that operates within a certain range of the base station. For example, in the scenario of intelligent vehicle positioning, the base station can be fixedly installed on open ground or buildings around the road that meet the requirements for base station erection. The mobile station can be set up on the intelligent vehicle, or in other words, the mobile station is the intelligent vehicle itself. When the mobile station is set up on the intelligent vehicle, it can be installed on the top of the intelligent vehicle or in other locations on the intelligent vehicle. In practical applications, the base station and the mobile station can be collectively referred to as receivers. During actual operation, both the base station and the mobile station will detect certain satellites in GNSS (for ease of understanding, they will be referred to as detection satellites below) and obtain corresponding observation data.

[0047] 2. Pseudorange observations

[0048] First, pseudorange refers to the approximate distance between the receiver and the probe satellite. Taking the pseudorange of a base station as an example, assuming the clock of the base station and the clock of the probe satellite are strictly synchronized, the propagation time of the carrier signal can be obtained by calculating the time the probe satellite transmits the carrier signal and the time the base station receives the carrier signal. Multiplying this by the propagation speed gives the distance between the probe satellite and the base station. However, there is an inevitable clock difference between the base station's clock and the probe satellite's clock, and the carrier signal is also affected by factors such as atmospheric refraction during propagation. Therefore, the distance directly measured by this method is not equal to the true distance between the probe satellite and the base station, hence the term pseudorange. The pseudorange observation value is the pseudorange value actually measured by the receiver.

[0049] It should also be noted that in practical applications, the carrier signals transmitted by the probe satellite include three carrier frequency bands: L1, L2, and L5. Specifically, the frequency f1 of the L1 carrier band is 1575.42 MHz, the frequency f2 of the L2 carrier band is 1227.6 MHz, and the frequency f5 of the L5 carrier band is 1176.45 MHz.

[0050] 3. Carrier phase observations

[0051] First, the carrier phase refers to the phase difference between the carrier signal received by the receiver from the probe satellite and the receiver's local oscillator reference signal. In practical applications, because the wavelength of the carrier signal is relatively short, much shorter than the distance between the receiver and the probe satellite, the carrier signal may undergo phase changes over N cycles as it travels to the receiver. This N is called the integer ambiguity. Here, N is a positive integer. In actual measurements, only the instantaneous phase difference between the carrier signal transmitted by the probe satellite and the receiver's local oscillator reference signal within one cycle can be directly measured. The integer ambiguity needs to be estimated using appropriate algorithms, such as pseudorange methods or Doppler methods. The carrier phase observation value includes the instantaneous phase difference within one cycle actually measured by the receiver and the estimated integer ambiguity. In practical applications, the carrier phase observation value can also be simply referred to as the carrier observation value, and this application will also use the carrier observation value for consistent description.

[0052] 3. Raw GNSS observation data

[0053] The so-called raw GNSS observation data refers to the observation data output by the GNSS board, obtained by the receiver (including the base station and the rover station) from observations of certain detection satellites in the Global Positioning System (GPS). This raw GNSS observation data generally includes pseudorange observations, carrier observations, signal-to-noise ratio observations, and Doppler observations corresponding to the base station and the rover station.

[0054] 4. Single-difference observations and double-difference observations

[0055] The single-difference observations involved in this application include carrier single-difference observations and pseudorange single-difference observations. A carrier single-difference observation is the difference in carrier phase observations obtained by the base station and the rover station at the same time for each probe satellite. For example, please refer to... Figure 1 , Figure 1 This is a schematic diagram of a positioning scenario provided in an embodiment of this application. For example... Figure 1 As shown, the base station and rover can monitor multiple GNSS probe satellites ( Figure 1 The example diagram illustrates four satellites (satellite 1, satellite 2, satellite 3, and satellite 4) used for observation to obtain corresponding data. Assuming that at time t, the base station obtains carrier observation values ​​Z1, Z2, Z3, and Z4 for satellites 1, 2, 3, and 4, respectively, and the rover station obtains carrier observation values ​​Z5, Z6, Z7, and Z8 for the same satellites, then the single-difference carrier observation value at time t is (Z5-Z1, Z6-Z2, Z7-Z3, Z8-Z4). Similarly, the pseudorange single-difference observation value is the difference between the pseudorange observation values ​​obtained by the base station and the rover station at the same time for each satellite. The double-difference carrier observation value is the difference obtained by subtracting the single-difference carrier observation value of one of the satellites from the single-difference carrier observation values ​​of the remaining satellites, using one of the satellites as a reference satellite. Continuing with the previous example, when the carrier single-difference observation value at time t is (Z5-Z1, Z6-Z2, Z7-Z3, Z8-Z4), assuming probe satellite 1 is the reference satellite, then the carrier double-difference observation value at time t is [(Z5-Z1)-(Z6-Z2), (Z5-Z1)-(Z7-Z3), (Z5-Z1)-(Z8-Z4)]. Similarly, the so-called pseudorange double-difference observation value is the difference obtained by subtracting the pseudorange single-difference observation value of one of the multiple probe satellites from the pseudorange single-difference observation values ​​of the remaining probe satellites, using one of the probe satellites as the reference satellite.

[0056] 5. Solution results and GNSS positioning results

[0057] The so-called solution result refers to the result obtained by performing positioning calculations on raw GNSS observation data. This solution result typically includes a floating-point solution and a covariance matrix. The floating-point solution generally includes estimated values ​​for the position, velocity, and acceleration of the rover, as well as estimated values ​​for the single-difference carrier phase deviation corresponding to each probe satellite. The covariance matrix includes the covariance values ​​corresponding to each estimated value in the floating-point solution.

[0058] A GNSS positioning result is a relatively accurate positioning result obtained by correcting the solution result with integer solutions of integer ambiguities. Typically, there will still be some discrepancy between the accurate location of the rover included in the GNSS positioning result and the actual physical location of the rover.

[0059] The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the various concepts described above and other accompanying drawings provided in the embodiments of this application.

[0060] like Figure 1 As shown, the base station and rover station can obtain their respective raw observation data by detecting multiple GNSS satellites. The base station and rover station can transmit their respective raw observation data (hereinafter referred to as GNSS raw observation data) to the positioning device. In some specific application scenarios, the GNSS board can act as a rover station. The base station sends its raw observation data collected at different times to the GNSS board, while the GNSS board also detects GNSS satellites to collect corresponding raw observation data. The GNSS board then integrates the raw observation data transmitted by the base station and its own observed raw observation data to obtain the GNSS raw observation data for both the base station and the rover station. The positioning device can then obtain the GNSS raw observation data from the GNSS board. Of course, it is understood that the positioning device can also use other methods to obtain the GNSS raw observation data from the base station and the rover station; this application does not impose specific limitations on this. Afterwards, the positioning device can use offline post-processing to perform positioning calculations on the above-mentioned raw GNSS observation data and finally obtain high-precision GNSS positioning results that can be used to construct high-precision point cloud maps.

[0061] In the embodiments of this application, the aforementioned positioning device can specifically be a computing device of various forms capable of acquiring and processing raw GNSS observation data. Here, the so-called computing device refers to a device that can be abstracted as a computer system. For example, the computing device can be the entire server (including local servers and / or cloud servers) used by high-precision map software, or it can be some components in the server, such as the chip system or logic circuit (e.g., field-programmable gate array, FPGA) in the server. Another example is that the computing device can also be the entire device of a vehicle or its in-vehicle system (e.g., a telematics box, T-box), a personal computer, a mobile phone, or the system chip within these devices. For ease of understanding and description, the term "positioning device" will be used uniformly in the following description.

[0062] In actual implementation, the positioning calculation method involved in the embodiments of this application may specifically include carrier phase differential (real time kinematic, RTK) calculation, etc., and this application does not impose specific limitations on it.

[0063] In existing offline post-processing of raw GNSS observation data, the positioning device performs forward and reverse positioning calculations to obtain the forward and reverse positioning results. If the positioning device determines that the positioning deviation between the forward and reverse results is within a set range, the result is considered valid, and the final GNSS positioning result can be determined based on these results. If the positioning device determines that the positioning deviation exceeds a preset range, the result is considered invalid. The device then discards the currently used raw GNSS observation data, acquires new raw GNSS observation data, and performs the next positioning process. This method of judging the validity of the result based on the magnitude of the positioning deviation is too simplistic and crude, failing to adequately consider the causes of excessive positioning deviations, resulting in poor reliability and applicability of existing methods for processing raw GNSS data.

[0064] Therefore, the technical problem to be solved by this application is: how to reasonably determine whether the positioning solution is valid, so as to improve the reliability and applicability of the positioning method based on GNSS raw observation data.

[0065] Please see Figure 2 , Figure 2This is a flowchart illustrating a positioning method based on raw GNSS observation data provided in an embodiment of this application. This positioning method based on raw GNSS observation data is applicable to... Figure 1 The positioning device is shown. It should be noted that each time the positioning device acquires new raw GNSS observation data, it uses the positioning method provided in this application for positioning processing until a corresponding GNSS positioning result is obtained or the raw GNSS observation data is determined to be invalid. Since the positioning method based on raw GNSS observation data provided in this application is an iterative process, meaning the positioning method executed at the current moment is related to the result obtained from the positioning method executed at the previous moment, for ease of understanding, this application embodiment will describe the specific scenario of the positioning device executing the positioning method provided in this application on the acquired raw GNSS observation data at time k (that is, the positioning device executing the positioning method provided in this application for the kth time). It can be understood that the positioning device executing the positioning method provided in this application at time k-1 is the (k-1)th time the positioning device executes the positioning method provided in this application, and the positioning device executing the positioning method provided in this application at time k+1 is the (k+1)th time the positioning device executes the positioning method provided in this application. Here, time k-1 is the time before time k, time k+1 is the time after time k, and k is a positive integer. Figure 2 As shown, the positioning method includes the following steps:

[0066] S201, The positioning device performs positioning calculations on the first GNSS raw observation data to obtain the first calculation result.

[0067] In some feasible implementations, after the positioning device acquires the raw GNSS observation data at time k (for ease of distinction, it will be referred to as the first raw GNSS observation data below), it can perform positioning calculations on the first raw GNSS observation data (for ease of distinction, it will be referred to as the first positioning calculation below) and obtain the first calculation result. Here, the aforementioned first calculation result may specifically include the first floating-point solution (here assumed to be x). k，1 And the first covariance matrix corresponding to the first floating-point solution (here assumed to be P). k，1 It should be noted that the first floating-point solution mentioned above may specifically include the estimated values ​​of the position, velocity, and acceleration of the mobile station obtained by processing the first GNSS raw observation data. The first covariance matrix mentioned above includes the estimated values ​​of the covariance corresponding to the estimated values ​​of the position, velocity, and acceleration.

[0068] In specific implementation, the first localization solution mainly includes two processes: time update and measurement update. First, the localization device performs a time update, that is, it updates the state vector (here assumed to be x) obtained by applying the localization method provided in this application at time k-1.k-1 ) and the covariance matrix (here assumed to be P) k-1 The process is performed to obtain the state vector updated over time (let's assume it's x). k-1|k ) and covariance matrix (here assumed to be P) k-1|k Specifically, the positioning device can update the obtained state vector x using the following formula (1). k-1|k Formula (1) is shown below:

[0069]

[0070] in, This is the state transition matrix used from time k-1 to time k. It's important to note that the state transition matrix is ​​updated using a pre-defined state transition matrix update algorithm within the positioning device; a new state transition matrix is ​​obtained for each new time step. For example, at time k, the positioning device will update the state transition matrix used in the time update process from time k-1 based on the state transition matrix update algorithm. To update the state transition matrix required for the time update process at time k, we need to perform the update. .

[0071] It should also be noted here that the state vector x after time update described above... k-1|k And the state vector updated by the measurement (i.e., the first floating-point solution x) which will be described later. k，1 All of them use the same data structure. The following will provide a unified description of the data structures for the various state vectors involved in this application. Here, we assume that the state vector at any time t is x. t Then x t The following formula (2) should be satisfied:

[0072] x t =(r r T v r T a r T B1 T B2 T B5 T (2)

[0073] Where, at time t, r r This is the position vector of the mobile station in a preset coordinate system. Specifically, this preset coordinate system can be an earth-centered, earth-fixed (ECEF) coordinate system, etc. The above v... r This is the velocity vector of the mobile station in the preset coordinate system. (a) rThis represents the acceleration vector of the mobile station in the preset coordinate system. B1, B2, and B5 are the single-difference carrier phase deviation values ​​of the mobile station in carrier frequency bands L1, L2, and L5, respectively, as described above. It's important to understand that B1, B2, and B5 are estimated values ​​obtained through time updates, with the unit being weeks. Since B1, B2, and B5 have the same data structure, only their values ​​may differ under different carrier frequency bands, we will use B as an example below. j For example, j takes the value 1, 2, or 5, corresponding to carrier frequency bands L1, L2, and L5, respectively. In practical applications, B j It should satisfy the following formula (3).

[0074]

[0075] In the above formula, It is the single-difference carrier phase deviation value when the base station and the rover station have one common-view satellite. This is the single-difference carrier phase deviation value when the base station and the rover station have two co-viewing satellites. Similarly, This refers to the single-difference carrier phase deviation value when there are m satellites in common view between the base station and the rover station. It can be understood that m is the number of satellites in common view between the base station and the rover station, and m is a positive integer.

[0076] Furthermore, the positioning device can also obtain the time-updated covariance matrix P using the following formula (4). k-1|k Specifically, formula (4) is shown below:

[0077]

[0078] in, This represents the noise input matrix from time k-1 to time k. It's also important to note that the noise input matrix is ​​updated using a pre-defined noise input matrix update algorithm within the positioning device; a new noise input matrix is ​​generated for each new time step. For example, at time k, the positioning device will update the noise input matrix used in the time update process at time k-1 based on the noise input matrix update algorithm. The noise input matrix required for the time update process at time k is updated.

[0079] Furthermore, the positioning device obtains the time-updated state vector x through time updates. k-1|k and the time-updated covariance matrix P k-1|k Then, the state vector x can also be... k-1|k and covariance matrix P k-1|kPerform a measurement update to obtain the updated state vector (let's assume it's x). k ) and covariance matrix (here assumed to be P) k Here, the state vector x after measurement update. k This refers to the first floating-point solution mentioned earlier, and will be consistently used to describe it thereafter. The covariance matrix P after measurement and update... k This is the first covariance matrix mentioned above, and it will be used to describe the same matrix in the following text.

[0080] Specifically, the positioning device can first determine the function matrix S required for this measurement update. k The positioning device can calculate the above-mentioned functional matrix S using the following formula (5). k Formula (5) is shown below:

[0081] S k =H k T *P k-1|k *H k +R k (5)

[0082] In formula (5), R k Let R be the measurement noise matrix at time k. k The measurement noise matrix is ​​updated by a preset algorithm in the positioning device, and a new measurement noise matrix is ​​obtained at each new time. For example, at time k, the positioning device processes the first GNSS raw observation data based on the measurement noise matrix update algorithm to obtain the measurement noise matrix R at time k. k The above H k Let H be the observation matrix at time k. Assume the observation matrix at any time t is H. t Then H t The following formula (6) should be satisfied:

[0083]

[0084] It should be noted here that h(x) can be used to calculate the state at time x. t The carrier double-difference estimate and pseudorange double-difference estimate corresponding to each carrier frequency band at any time t are predicted. Specifically, h(x) may include the carrier double-difference observation vector and pseudorange double-difference observation vector for each carrier frequency band. Specifically, h(x) should satisfy the following formula (7):

[0085] h(x)=(h Φ，j T (x), h p，j T(x)) (7)

[0086] As shown in formula (7), h Φ，j T (x) represents the carrier double-difference observation vector corresponding to carrier frequency band j, and the state vector x at any time t. t Substitute into h Φ，j T (x) can then be used to obtain the carrier double-difference estimate corresponding to carrier frequency band j at any time t. Under the carrier frequency band values ​​described above, the aforementioned h... Φ，j T (x) may specifically include h Φ，1 T (x) (i.e., the carrier double-difference observation vector corresponding to carrier frequency band L1), h Φ，2 T (x) (i.e., the carrier double-difference observation vector corresponding to carrier frequency band L2) and h Φ，5 T (x) (i.e., the carrier double-difference observation vector corresponding to carrier frequency band L5). Using h Φ，1 T For example, when x = x t At that time, h Φ，1 T (x t This is the carrier double-difference estimate of carrier frequency band L1 at any time t. p，j T (x) represents the pseudorange double-difference observation vector corresponding to carrier frequency band j, and the state vector x at any time t. t Substitute into h p，j T (x) can then yield the pseudorange double-difference estimate of carrier frequency band j at any time t. Under the carrier frequency band values ​​described above, the aforementioned h... p，j T (x) may specifically include h p，1 T (x) (i.e., the pseudorange double-difference observation vector corresponding to carrier frequency band L1), h p，2 T (x) (i.e., the pseudorange double-difference observation vector corresponding to carrier frequency band L2) and h p，5 T (x) (i.e., the pseudorange double-difference observation vector corresponding to carrier frequency band L5). Using h p，1 T For example, when x = x t At that time, h p，1 T (x t This is the pseudorange double difference estimate of carrier frequency band L1 at any time t.

[0087] Combining the contents of formulas (6) and (7) above, the positioning device can first calculate the partial derivative function of h(x) with respect to x, and further calculate the partial derivative function at x = x k-1|k The partial derivative is taken at time k, and this partial derivative is used to determine the observation matrix H at time k. k Then, the positioning device can obtain the observation matrix H at time k. k Measurement noise matrix R at time k k Observation matrix H k The transpose matrix H k T And the covariance matrix P updated over time mentioned above k-1|k Substituting into formula (6) above, we can calculate the function matrix S at time k. k .

[0088] Furthermore, the positioning device can be based on the aforementioned functional matrix S k The observation matrix H at time k k and the time-updated covariance matrix P k-1|k The gain matrix K required for this measurement update has been determined. k (i.e., the gain matrix K at time k) k Specifically, the gain matrix K k It should satisfy the following formula (8).

[0089] K k =P k-1|k *H k *S k -1 (8)

[0090] Among them, S k -1 Functional matrix S k The inverse. The positioning device will use the function matrix S. k The observation matrix H at time k k and the time-updated covariance matrix P k-1|k Substituting these values ​​into formula (8) above, the gain matrix K at time k can be calculated. k .

[0091] Furthermore, the positioning device can calculate the carrier double-difference observation value and pseudorange double-difference observation value at time k based on the carrier observation value and pseudorange observation value in the first GNSS raw observation data mentioned above (for ease of understanding, y will be used as the reference in the following text). k，1 (This is used to represent the carrier double-difference observation and the pseudorange double-difference observation). Here, the positioning device calculates the pseudorange double-difference observation and the pseudorange double-difference observation y based on the carrier observation and pseudorange observation in the first GNSS raw observation data. k，1For the specific process, please refer to the explanation of carrier double-difference observations and pseudorange double-difference observations mentioned above, which will not be repeated here.

[0092] The positioning device can also update the state vector x over time. k-1|k Substituting into the above formula (6), the carrier double-difference estimate and pseudorange double-difference estimate h(x) at time k are then calculated. k-1|k Then, the positioning device can output both the carrier double-difference observations and the pseudorange double-difference observations y. k，1 The estimated values ​​of h(x) with carrier double difference and pseudorange double difference k-1|k The difference is determined as the residual result at time k (for ease of distinction, the first residual result will be used in the following text; here, it is assumed that the first residual result is C). k，1 C k，1 =y k，1 -h(x k-1|k ).

[0093] Determine the observation matrix H at time k. k Gain matrix K k and the first residual result C k，1 Then, the positioning device can use the observation matrix H k Gain matrix K k and the first residual result C k，1 The state vector x after the time update mentioned above k-1|k The covariance matrix P k-1|k Make corrections to obtain the first floating-point solution x mentioned above. k，1 and the first covariance matrix P k，1 Here, the first floating-point solution x mentioned above... k，1 Satisfy the following formula (9):

[0094] x k，1 =x k-1|k +K k *C k，1 (9)

[0095] It should be added here that, in conjunction with the previous description of the data structure of the state vector, this first floating-point solution x k，1 That is, equal to (r1) r T v1 r T a1 r T B11 T B12 T B15 T ), where r1 r v1 r a1 rB11, B12, and B15 are the position vector, velocity vector, and acceleration vector after measurement updates, respectively. B11, B12, and B15 are the estimated values ​​of the single-difference carrier phase deviation of the mobile station in carrier bands L1, L2, and L5 after measurement updates, respectively.

[0096] The first covariance matrix P mentioned above k，1 It satisfies the following formula (10):

[0097] P k，1 =(IK k *H k )*P k-1|k (10)

[0098] Where I is the identity matrix. The positioning device will update the state vector x over time. k-1|k Gain matrix K k and the first residual result C k，1 Substituting into formula (9) above, the first floating-point solution x can be calculated. k，1 The positioning device will update the covariance matrix P over time. k-1|k Gain matrix K k and the observation matrix H k Substituting into the above formula (10), we can obtain the first covariance matrix P. k，1 The positioning device determines that the first floating-point solution x is obtained above. k，1 and the first covariance matrix P k，1 Then, the time update process in the first positioning solution is completed, and the first floating-point solution x is obtained. k，1 and the first covariance matrix P k，1 This is the first solution result.

[0099] S202, the positioning device performs a chi-square test on the first GNSS raw observation data and the time-updated state vector obtained by the positioning solution to obtain the first chi-square test value of the first GNSS raw observation data.

[0100] In some feasible implementations, the positioning device obtains the time-updated state vector x obtained from the first positioning calculation. k-1|k Then, based on the first GNSS raw observation data and the time-updated state vector x, it is possible to... k-1|k Perform a chi-square test to obtain the first chi-square test value (here assumed to be λ) of the first raw GNSS observation data. k，1 This section further explains the chi-square test operation based on the principle of the chi-square test. In fact, the first chi-square test value λ mentioned above... k，1 This assumes the state vector x after time updates. k-1|kIt is accurate, while the current first GNSS raw observation data is inaccurate, and then the state vector x k-1 The first chi-square test value λ is the theoretical value, obtained by performing a chi-square test using the first raw GNSS observation data as the actual value. k，1 The magnitude of λ can be used to characterize whether the first raw GNSS observation data is accurate. Obviously, if the first raw GNSS observation data is accurate, then the first solution obtained after the first positioning calculation is generally accurate and valid. If the first raw GNSS observation data is inaccurate, then the first solution obtained after the first positioning calculation can be considered invalid. Therefore, subsequent positioning equipment can use the first chi-square test value λ... k，1 To determine whether the first solution result is valid or invalid.

[0101] Optionally, in the specific implementation, the positioning device can reuse the first GNSS raw observation data and the time-updated state vector x mentioned above. k-1|k The obtained function matrix S k and the first residual result C at time k k，1 And continue according to the function matrix S k and the first residual result C k，1 The first chi-square test value λ can be directly calculated. k，1 Here, the first chi-square test value λ k，1 With the function matrix S k and the first residual result C k，1 The relationship is shown in the following formula (11), that is, the positioning device will use the function matrix S k and the first residual result C at time k k，1 Substituting into the following formula (11), the first chi-square test value λ can be calculated. k，1 .

[0102] λ k，1 =C k，1 T *S k *C k，1 (11)

[0103] Here, the function matrix S obtained from the first positioning solution process is reused. k and the first residual result C at time k k The first chi-square test value λ can be directly calculated. k The method is simple and easy to implement, which can reduce the data processing load of positioning equipment and improve the first chi-square test value λ. k，1 The efficiency of acquisition.

[0104] S203, if the positioning device determines the first solution result as a valid result based on the first chi-square test value, then the target GNSS positioning result of the mobile station is determined based on the first solution result.

[0105] In some feasible implementations, the positioning device acquires the aforementioned first chi-square detection value λ k，1 Then, based on the first chi-square test value λ k，1 If the first solution result is determined to be valid, the GNSS positioning result of the mobile station can be determined based on the first solution result (for ease of distinction, the target GNSS positioning result will be used instead of the description below).

[0106] In specific implementation, based on the first chi-square test value λ k，1 If the first solution result is determined to be valid, the positioning device can use the first floating-point solution x in the first solution result. k，1 and the first covariance matrix P k，1 Integer ambiguity is fixed to obtain integer solutions for integer ambiguity (also known as fixed solutions, assumed to be N1 here). For example, the positioning device can extract the first floating-point solution x mentioned above. k，1 The algorithm estimates the integer ambiguity for each probe satellite and calculates the double-difference integer ambiguity estimate at time k (let's assume it's N2). The process of calculating the double-difference integer ambiguity estimate N2 from the integer ambiguity estimates for each probe satellite is similar to the process described earlier of calculating the carrier double-difference observation at time k from each carrier observation at time k, and will not be repeated here.

[0107] Furthermore, the positioning device can also transform the aforementioned first covariance matrix P using a preset transformation method. k，1 This is converted into a covariance matrix that can be used for integer ambiguity fixing (for clarity, it will be referred to as the second covariance matrix below). n，k Specifically, the positioning device can first obtain the transformation matrix (let's assume it's G) needed to convert from single-difference to double-difference. Then, the positioning device can use this transformation matrix G to solve the first floating-point problem x. k，1 Perform a conversion from single-difference to double-difference to obtain the floating-point solution under double-difference (here, let's assume it's x). k，1 Here, the floating-point solution x k，1 This can be used to subsequently determine the target's GNSS positioning results. (Floating-point solution x) k，1 ′ and the first floating-point solution x above k，1 And the transformation matrix G satisfies the following formula (12):

[0108] x k，1 ′=G*x k，1=(r1) r T v1 r T a1 r T N2 T (12)

[0109] In addition, the positioning device can also adjust the first covariance matrix P based on the transformation matrix G. k，1 Perform a transformation from single-difference to double-difference to obtain the covariance matrix under double-difference (here, we assume it to be P). k，1 Here, the first covariance matrix P k，1 Transformation matrix G and covariance matrix P′ k，1 It satisfies the following formula (13):

[0110]

[0111] Wherein, the above matrix Q r，k This refers to the position vector r1 updated after measurement. r Velocity vector v1 r and acceleration vector a1 r The corresponding covariance matrix, the above matrix Q nr and matrix Q rn These are diagonal matrices of the covariance matrix, and are mutually equivalent. Matrix Q nr and matrix Q rn It is the position vector r1 after measurement and update. r Velocity vector v1 r and acceleration vector a1 r The covariance matrix relative to the double-difference integer ambiguity estimate N2. Here, the positioning device obtains the above covariance matrix P. k，1 After that, we can obtain the covariance matrix P. k，1 The second covariance matrix Q is extracted from ′. n，k .

[0112] Then, the positioning device can use methods such as least squares to calculate the second covariance matrix Q. n，k The integer solution N1 of the integer ambiguity is obtained by processing the double-difference integer ambiguity estimate N2. Specifically, the positioning device can calculate the integer solution N1 of the integer ambiguity using the following formula (14).

[0113] N1 = arg min[(M-N2)] T *Q n，k -1 *(M-N2)] (14)

[0114] It should be noted that in this embodiment, the symbol "arg min[function expression]" represents the value of the independent variable that minimizes the function expression. For example, A1 = arg min[Y(a)], then A1 is the value of the independent variable a when Y(a) is minimized. Combining with the above formula (11), M is (M-N2). T *Q n，k -1 In the function expression *(M-N2), the independent variable is N1, which is the variable that makes (M-N2) true. T *Q n，k -1 The function *(M-N²) represents the value of M that minimizes this value. For example, suppose m1 can make (M-N²) such that... T *Q n，k -1 If the function *(M-N2) takes the minimum value, then N1 is equal to m1.

[0115] Furthermore, after determining the integer solution N1 of the aforementioned integer ambiguity, the positioning device can use the integer solution N1 of the integer ambiguity to determine the aforementioned first floating-point solution x. k，1 Make corrections, and then use the corrected first floating-point solution x k，1 The target GNSS positioning result is determined to be that of the mobile station. Specifically, the positioning terminal can first use the integer solution N1 of the above integer ambiguity to determine the first floating-point solution x. k，1 Includes the position vector r1 after measurement update. r Velocity vector v1 r and acceleration vector a1 r Make corrections to obtain the corrected position vector r2 r Velocity vector v2 r and acceleration vector a2 r Among them, the corrected position vector r2 r Velocity vector v2 r and acceleration vector a2 r With the position vector r1 updated by measurement r Velocity vector v1 r acceleration vector a1 r The relationship between the integer solutions N1 and the following formula (15) is satisfied:

[0116]

[0117] Then, the positioning device can use the corrected position vector r2 as described above. r Velocity vector v2 r acceleration vector a2 r and covariance matrix P k，1The target GNSS positioning result is identified as the mobile station.

[0118] Optional, please see also Figure 3 , Figure 3 This is another flowchart illustrating a positioning method based on raw GNSS observation data provided in this application. Figure 3 As shown, prior to step S203 above, the positioning method may further include the following steps:

[0119] S204, the positioning device determines whether the first solution result is a valid result or an invalid result based on the first chi-square test value.

[0120] In practical applications, the positioning device obtains the first chi-square test value λ mentioned above. k，1 Then, based on the first chi-square test value λ k，1 This is used to determine whether the first solution result is valid or invalid. Specifically, the positioning device can extract S historical chi-square test values ​​from its cached data obtained by performing chi-square tests on S time points prior to the first time point (for ease of distinction, these will be referred to as S historical time points below). The aforementioned first time point is used by the positioning device to determine the first chi-square detection value λ. k，1 At any given moment, within the allowable time precision, this first moment can be considered as the aforementioned moment k, and will be used as the first moment in the following description. Here, it is assumed that the aforementioned S historical moments specifically include historical moment k-1, historical moment k-2, ..., historical moment k-s+1 and historical moment ks, and the S historical chi-square test values ​​corresponding to these S historical moments are λ. k-1 , λ k-2 ......λ k-s+1 and λ k-s It should be noted that the positioning device executed the positioning method provided in this application at all S historical moments mentioned above, and obtained calculation results that were judged to be valid at each of these S historical moments. The historical chi-square test value λ corresponding to any historical moment S1 among these S historical moments... k1 This refers to the chi-square test value used when the positioning device obtains a solution result deemed valid at historical time S1. It can be understood that time k and the aforementioned S historical times constitute a sliding window in time. Please refer to [further details omitted]. Figure 4 , Figure 4 This is a schematic diagram of a time-based sliding window provided in an embodiment of this application. Figure 4As shown, time k and the aforementioned S historical times constitute a time window (for ease of distinction, it will be referred to as the first time window below). It can be understood that the length of the time window is fixed (i.e., S), but its position changes as the positioning device executes the positioning method provided in this application. For example, when the positioning device executes the positioning method provided in this application at time k+1 after time k, the time window will be updated from the first time window to the second time window. This second time window consists of time k+1, historical time k (in this case, time k also becomes a historical time), historical time k-1, historical time k-2, ..., historical time k-s+1. That is, the positioning device needs to perform a corresponding update operation on the time window each time it executes the positioning method provided in this application at a new time.

[0121] Furthermore, after extracting the aforementioned S historical chi-square test values, the positioning device can then use these S historical chi-square test values ​​and the first chi-square test value λ to... k，1 To determine whether the first solution result is valid or invalid.

[0122] In practical applications, the invalid positioning results are often determined by the inaccuracy of the raw GNSS observation data (i.e., the presence of anomalous observation data with significant errors), which is not directly related to the positioning process itself. Therefore, in the above implementation, the first chi-square test value, which characterizes the presence of anomalous observation data in the first raw GNSS observation data, and the chi-square test values ​​at S historical time points are used as conditions for judging the validity of the current solution. This fully considers the accuracy of the raw GNSS observation data, making the judgment accurate and reliable. Compared to existing methods that judge the validity of positioning results based on deviations, this approach is more reasonable, thus improving the accuracy and reliability of the positioning method.

[0123] Optionally, in a specific implementation, the positioning device can calculate the aforementioned S historical chi-square test values ​​and the first chi-square test value λ. k，1 The standard deviation of (for ease of distinction, it will be referred to as the first standard deviation below, and is assumed to be σ) k，1 Here, the positioning device can first calculate the above S historical chi-square test values ​​and the first chi-square test value λ. k，1 The average value (here assumed to be μ) λ，1 For example, the positioning device can use the above S historical chi-square test values ​​and the first chi-square test value λ. k，1 Substitute into the following formula (16) to calculate the above average value μ. λ，1 Here, λ eLet e ​​represent any historical chi-square test value, where the values ​​of e include ks, k-s+1, ..., k-1, k.

[0124]

[0125] Then, the positioning device can be based on the above average value μ λ，1 The above S historical chi-square test values ​​and the first chi-square test value λ k，1 The target standard deviation σ was calculated. λ，1 Specifically, the positioning device can calculate the above average value μ. λ，1 The above S historical chi-square test values ​​and the first chi-square test value λ k，1 Substitute into the following formula (17) to calculate the above target standard deviation σ. λ，1 .

[0126]

[0127] Then, the positioning device can measure the target standard deviation σ mentioned above. λ，1 The value is compared with a preset standard deviation σ. It should be noted that the preset standard deviation σ is an empirical value obtained from multiple experiments using the method provided in this application. If the positioning device determines the above target standard deviation σ... λ，1 If the result is less than or equal to the preset standard deviation σ, then the first solution result can be determined as a valid result. If the positioning device determines the target standard deviation σ... λ，1 If the result is greater than the preset standard deviation σ, then the first solution result can be determined to be invalid.

[0128] Here, the standard deviations of the first chi-square test value and the chi-square test values ​​over S historical time points are used as the criteria for determining the validity of the current solution. When this standard deviation is greater than the preset standard deviation, it indicates a significant abrupt change in the current raw GNSS observation data, thus confirming the presence of abnormal observation data and determining the inaccuracy of the current solution, rendering it invalid. Conversely, when this standard deviation is less than or equal to the preset standard deviation, it indicates that the current raw GNSS observation data is normal, thus confirming the validity of the current solution. Since the standard deviations of the first chi-square test value and the S historical chi-square test values ​​more accurately characterize the presence of abnormal observation data in the raw GNSS observation data, using these as the criteria for determining the validity of the current solution makes the obtained results more accurate and reliable.

[0129] For further details, please refer to the following: Figure 5 , Figure 5This is another flowchart illustrating a positioning method based on raw GNSS observation data provided in this application. Figure 5 As shown, the positioning method may further include the following steps:

[0130] S205, if the positioning device determines that the first solution result is invalid based on the first chi-square test value, then abnormal observation data is removed from the first GNSS raw observation data to obtain the second GNSS observation data.

[0131] In some feasible implementations, when the positioning device uses the first chi-square test value λ mentioned above... k，1 If the first solution result is determined to be invalid, it indicates that there is abnormal observation data in the first GNSS raw observation data. The positioning device can then remove the abnormal observation data from the first GNSS raw observation data to obtain the second GNSS raw observation data.

[0132] In specific implementation, the positioning device uses the aforementioned first chi-square test value λ k，1 After determining whether the first solution result is valid or invalid, the positioning device can identify the target satellite with abnormal observation data from multiple probe satellites observed by the rover and base station. Then, the positioning device can remove the original observation data corresponding to the target satellite from the first GNSS original observation data to obtain the second GNSS original observation data.

[0133] Optionally, the positioning device can be based on the aforementioned first residual result C. k，1 To determine the aforementioned target satellite. Specifically, the positioning equipment can determine the aforementioned first residual result C. k，1 The maximum residual value is included, and the satellite corresponding to this maximum residual value is identified as the target satellite. In practical applications, when the observation value corresponding to a certain satellite becomes abnormal, its corresponding residual value will also increase accordingly. Therefore, the first residual result C mentioned above can be used to determine the target satellite. k，1 The method involves identifying the target satellite and removing abnormal observation data. Here, the method of determining the target satellite based on residual results is simple and reliable, reduces the data processing load of the positioning equipment, and improves the positioning efficiency of the positioning method provided in this application.

[0134] Furthermore, after the positioning device obtains the aforementioned second GNSS raw observation data, it can perform a positioning calculation on the data (for clarity, this will be referred to as the second positioning calculation below) to obtain a second calculation result. Here, the second calculation result may include a second floating-point solution (here assumed to be x). k，2 ) and the second covariance matrix (here assumed to be P) k，2 The second floating-point solution x k，2 and the second covariance matrix Pk，2 Compared with the first floating-point solution x above k，1 and the first covariance matrix P k，1 The data structure is the same, so it will not be described again here. Since the second GNSS raw observation data is obtained by removing some abnormal observation data from the first GNSS raw observation data, in the second positioning calculation process, the positioning device only needs to recalculate the aforementioned second floating-point solution x based on the second GNSS raw observation data. k，2 and the second covariance matrix P k，1 In other words, after calculating the second floating-point solution x mentioned above... k，2 and the second covariance matrix P k，2 During the process, the positioning device will use the time-updated state vector x obtained from the first positioning solution process. k-1|k The covariance matrix P after time update k-1|k The function matrix S at time k k The carrier double-difference estimate and pseudorange double-difference estimate h(x) at time k k-1|k and the gain matrix K at time k k .

[0135] In practice, the positioning device can calculate a new carrier double-difference observation value and a pseudorange double-difference observation value based on the carrier observation value and pseudorange observation value in the second GNSS raw observation data mentioned above (for ease of understanding and distinction, it will be referred to as y in the following text). k，2 To represent this new carrier double-difference observation and pseudorange double-difference observation). Here, the positioning device calculates the carrier double-difference observation and the pseudorange double-difference observation y. k，2 The process is similar to the positioning equipment described above, which calculates the carrier double-difference observations and pseudorange double-difference observations y. k，1 The process is the same, so it will not be repeated here. Then, the positioning device can output the above carrier double-difference observations and pseudorange double-difference observations y. k，2 The carrier double difference and pseudorange double difference estimates h(x) at time k k-1|k The difference is determined as the new residual result at time k (for ease of distinction, it will be referred to as the second residual result in the following text; here, it is assumed that the second residual result is C). k，2 C k，2 =y k，2 -h(x k-1|k Then, the positioning device can use the observation matrix H at time k. k Gain matrix K k and the second residual result C k，2 The state vector x after the time update mentioned above k-1|k The covariance matrix P k-1|k Make corrections to obtain the second floating-point solution x mentioned above.k，2 Second covariance matrix P k，2 Here, the second floating-point solution x mentioned above... k It satisfies the following formula (18):

[0136] x k，2 =x k-1|k +K k *C k (18)

[0137] The second covariance matrix P mentioned above k，2 Equal to the first covariance matrix P mentioned above k，1 Then, the positioning device can determine the second residual result C based on the above. k，2 and the function matrix S k Calculate the new chi-square test value at time k (for clarity, the second chi-square test value will be used in the following text; here we assume it to be λ). k，2 Here, the positioning device calculates the second chi-square test value λ. k，2 The process can be found in the previous description of how the first chi-square test value λ was calculated. k，1 The process will not be repeated here. Afterwards, the positioning device can calculate the second chi-square test value λ mentioned above. k，2 The standard deviation of the above S historical chi-square test values ​​(for ease of distinction, the first standard deviation will be used in the following description; here it is assumed to be σ) λ，2 ), and through the first standard deviation σ λ，2 This is used to determine whether the second solution result is valid or invalid. Here, the positioning device calculates the first standard deviation σ. λ，2 and through the first standard deviation σ λ，2 For details on determining whether the second solution result is valid or invalid, please refer to the description of calculating the target standard deviation σ above. λ，1 And based on the target standard deviation σ λ，1 The process of determining whether the first solution result is valid or invalid will not be described here.

[0138] Furthermore, when the positioning device determines that the second solution result is valid, it can determine the target GNSS positioning result of the mobile station based on the second solution result. The process by which the positioning device determines the target GNSS positioning result of the mobile station based on the second solution result can be specifically referred to in the previously described process of the positioning device determining the target GNSS positioning result of the mobile station based on the first solution result, and will not be repeated here. When the positioning device determines that the second solution result is invalid, it can again determine the target GNSS positioning result of the mobile station based on the second residual result C. k，2Abnormal observation data is removed from the second GNSS raw observation data to obtain the third GNSS raw observation data. Then, the positioning device can continue to process the third GNSS raw observation data using a similar process to that described above for the second GNSS raw observation data, until the positioning device obtains the target GNSS positioning result of the rover station.

[0139] In the above implementation, when the positioning device determines that the first solution result is invalid, it will remove abnormal data from the first GNSS raw observation data to obtain the second GNSS raw observation data, and continue to process the second GNSS raw observation data to obtain the GNSS positioning result of the mobile device. This can improve the data utilization efficiency of the GNSS raw observation data and avoid the waste of data resources and data processing resources.

[0140] Optionally, after acquiring the second GNSS raw observation data, the positioning device can first determine the effective number of probe satellites corresponding to the second GNSS raw observation data. Specifically, the positioning device can determine the effective number by the difference between the number of probe satellites corresponding to the first GNSS raw observation data and the number of target satellites. Then, the positioning device can determine whether the effective number is greater than or equal to 4. If the positioning device determines that the effective number is greater than or equal to 4, then the second GNSS raw observation data is determined to be valid data, and the operation described above of performing a second positioning calculation on the second GNSS raw observation data to obtain a second calculation result can continue. If the positioning device determines that the effective number is less than 4, then the second GNSS raw observation data cannot meet the basic requirements of the positioning calculation, and the positioning device can determine that the second GNSS raw observation data is invalid data. Then, the positioning device can acquire new GNSS raw observation data again, and execute the positioning method provided in this application based on the new GNSS raw observation data at time k+1. Here, the number of detection satellites corresponding to the raw GNSS observation data after removing abnormal observation data is used to determine whether the raw GNSS observation data after removing abnormal observation data can meet the basic requirements of positioning calculation. This can avoid the positioning equipment from performing invalid processing on the raw GNSS observation data that does not meet the requirements, and can save the data processing capacity of the positioning equipment.

[0141] In the positioning method provided in this application, after acquiring the solution results corresponding to the raw GNSS observation data, the positioning device determines whether the current solution results are valid based on the chi-square test value corresponding to the raw GNSS observation data. Since the chi-square test value corresponding to the raw GNSS observation data can be used to characterize whether there is abnormal observation data in the raw GNSS observation data, using the chi-square test value corresponding to the raw GNSS observation data as a judgment condition makes the results obtained by the positioning device more reasonable and accurate, thus improving the reliability and applicability of the positioning method.

[0142] It should also be noted that, in practical applications, the positioning method provided in this application can be applied to the construction or generation process of high-precision point cloud maps. The specific implementation process of the positioning method provided in this application during the construction or generation of high-precision point cloud maps can be found above and will not be repeated here. Here, using the positioning method provided in this application during the construction or generation process of high-precision point cloud maps can improve the accuracy of the generated high-precision point cloud maps.

[0143] Please see Figure 6 , Figure 6 This is a schematic diagram of a device provided in an embodiment of this application. This device can be the positioning device described in the embodiment. Figure 6 As shown, the device includes:

[0144] The calculation unit 601 is used to perform positioning calculations on the first GNSS raw observation data to obtain the first calculation result.

[0145] Chi-square test unit 602 is used to perform a chi-square test on the first GNSS raw observation data and the time-updated state vector obtained by the positioning solution to obtain the first chi-square test value of the first GNSS raw observation data.

[0146] The positioning result determination unit 603 is used to determine the target GNSS positioning result of the mobile station based on the first solution result if the first solution result is determined to be a valid result based on the first chi-square test value.

[0147] In some possible implementations, the positioning result determination unit 603 is further configured to: if the first solution result is determined to be invalid based on the first chi-square test value, then remove abnormal observation data from the first GNSS raw observation data to obtain second GNSS raw observation data. Here, the second GNSS raw observation data is used to determine the target GNSS positioning result.

[0148] In some possible implementations, the positioning result determination unit 603 is further configured to: determine the target satellite, and remove the GNSS raw observation data of the target satellite contained in the first GNSS raw observation data to obtain the second GNSS raw observation data.

[0149] In some possible implementations, the positioning result determination unit 603 is further configured to obtain a first residual result of the first GNSS raw observation data. Here, the first residual result is determined by the carrier double-difference observation value and pseudorange double-difference observation value corresponding to the first GNSS raw observation data obtained from the first positioning solution, and the state vector after time update. The satellite corresponding to the largest residual value in the first residual result is determined as the target satellite.

[0150] In some possible implementations, the positioning result determination unit is further configured to determine the number of valid satellites corresponding to the second GNSS raw observation data. If the number of valid satellites is determined to be equal to or greater than 4, then the second GNSS raw observation data is determined to be valid data.

[0151] In some possible implementations, the positioning result determination unit 603 is further configured to obtain S historical chi-square test values ​​obtained by performing chi-square tests at S time points prior to the first time point. Here, the first time point is the time when the first chi-square test value is determined, and S is a positive integer greater than or equal to 1. Based on the first chi-square test value and the S historical chi-square test values, the first solution result is determined to be a valid result or an invalid result.

[0152] In some possible implementations, the positioning result determination unit is further configured to determine the first standard deviation corresponding to the first chi-square test value and the S historical chi-square test values. If the first standard deviation is determined to be greater than a preset standard deviation, the first solution result is determined to be invalid. If the first standard deviation is determined to be less than or equal to the preset standard deviation, the first solution result is determined to be valid.

[0153] In some possible implementations, the first chi-square test value is determined by the chi-square test unit 602 based on the first residual result and S. k It is determined that the S k The measurement noise matrix, observation matrix, and time-updated covariance matrix obtained from the positioning solution are determined.

[0154] In some possible implementations, the first solution result includes a first floating-point solution and a first covariance matrix. The positioning result determination unit 603 is further configured to perform integer ambiguity fixing based on the first floating-point solution and the first covariance matrix to obtain an integer solution of the integer ambiguity. The first floating-point solution is then corrected based on the integer solution of the integer ambiguity to obtain the target GNSS positioning result for the mobile station.

[0155] In some possible implementations, the apparatus may further include an acquisition unit 604, which can be used to acquire the aforementioned first GNSS raw observation data.

[0156] In specific implementation, the process by which the above-mentioned calculation unit 601, chi-square test unit 602, positioning result determination unit 603 and acquisition unit 604 implement the steps in the above-mentioned various possible implementation methods can be referred to the corresponding process performed by the positioning device in the above-mentioned embodiment 1, and will not be repeated here.

[0157] In this embodiment, after obtaining the solution result corresponding to the raw GNSS observation data, the positioning device determines whether the current solution result is valid based on the chi-square test value corresponding to the raw GNSS observation data. Since the chi-square test value corresponding to the raw GNSS observation data can be used to characterize whether there is abnormal observation data in the raw GNSS observation data, using the chi-square test value corresponding to the raw GNSS observation data as a judgment condition makes the result obtained by the positioning device more reasonable and accurate, improving the reliability and applicability of the positioning method.

[0158] This application also provides a computer-readable medium storing a computer program thereon, which, when executed by a computer, implements the method or steps performed by the positioning device in the first embodiment described above.

[0159] This application also provides a computer program product that, when executed by a computer, implements the method or steps performed by the positioning device in Embodiment 1 above.

[0160] This application also provides a processor for coupling with a memory that stores instructions. When the processor executes the instructions, it causes the processor to perform the method or function performed by the positioning device in the first embodiment above.

[0161] Please see Figure 7 , Figure 7This is another structural schematic diagram of a device provided in an embodiment of this application, and the positioning device can be implemented in the form of this device. The device mainly includes at least one processor 701 and at least one memory 702. The processor 701 and the memory 702 are connected and communicate with each other via a communication bus or communication interface. Here, the processor 701 and the memory 702 can be used to implement the above... Figure 6 The solution unit 601, chi-square test unit 602, and positioning result determination unit 603 shown in the diagram can realize various functions of the positioning device.

[0162] The memory 702 is used to store program code for executing the positioning method based on GNSS raw observation data implemented by the positioning device in Embodiment 1, and the processor 701 is used to execute the program code stored in the memory 702 to implement the steps of the positioning method based on GNSS raw observation data executed by the positioning device in Embodiment 1.

[0163] For example, processor 701 can be used to perform positioning calculations on the first raw GNSS observation data to obtain a first calculation result. Processor 701 can also be used to perform a chi-square test on the first raw GNSS observation data and the time-updated state vector obtained from the positioning calculation to obtain a first chi-square test value for the first raw GNSS observation data.

[0164] Optionally, in scenarios where the first raw GNSS observation data is acquired via a wired method, the device can acquire the first raw GNSS observation data via a communication bus or communication interface.

[0165] Optionally, in scenarios where the first raw GNSS observation data is acquired wirelessly, such as... Figure 7 As shown, the device may further include at least one wireless communication module 703, which can acquire the aforementioned first GNSS raw observation data. That is, the wireless communication module can be used to implement the positioning device function that the acquisition unit 604 can achieve. In practical applications, the wireless communication module 703 may be a communication chip including a radio frequency processing chip and a baseband processing chip, etc.

[0166] It can be understood here that the server involved in this application, which can be used to execute the positioning method based on raw GNSS observation data provided in this application, is... Figure 7 The architecture of the device shown is used to achieve this.

[0167] Please see Figure 8 , Figure 8This is a schematic diagram of a chip structure provided in an embodiment of this application. Positioning devices can also be implemented in the form of this chip. The chip mainly includes a processor 801 and one or more interface circuits 802 coupled to the processor 801. Here, the processor 801 and the one or more interface circuits 802 coupled to the processor 801 can be used to implement the above-mentioned... Figure 6 The solution unit 601, chi-square test unit 602, and positioning result determination unit 603 shown herein can realize various functions of the positioning device. The external wireless communication module connected to this chip can then be used to implement the aforementioned functions. Figure 6 The acquisition unit 604 shown can perform various functions of the positioning device.

[0168] For example, processor 801 can be used to read and execute computer-readable instructions. In a specific implementation, processor 801 may mainly include a controller, an arithmetic logic unit (ALU), and registers. For example, the controller is mainly responsible for instruction decoding and issuing control signals for the operations corresponding to the instructions. The ALU is mainly responsible for performing fixed-point or floating-point arithmetic operations, shift operations, and logical operations, and can also perform address operations and translations. Registers are mainly responsible for storing register operands and intermediate operation results temporarily stored during instruction execution. In a specific implementation, the hardware architecture of processor 801 can be an application-specific integrated circuit (ASIC) architecture, a microprocessor without interlocked piped stages architecture (MIPS) architecture, an advanced reduced instruction set machine (RISC) machine (ARM) architecture, or an NP architecture, etc. Processor 801 can be single-core or multi-core.

[0169] For example, interface circuit 802 can be used to input data to be processed to processor 801 and can output the processing results of processor 801. In a specific implementation, interface circuit 802 can be a general purpose input / output (GPIO) interface, which can be connected to multiple peripheral devices (such as wireless communication modules, sensing modules, etc.). Interface circuit 802 is connected to processor 801 through bus 803.

[0170] In specific implementation, processor 801 can be used to call the code of the positioning method based on raw GNSS observation data implemented by the positioning device in Embodiment 1 from the memory, so that the chip can implement each step of the positioning method based on raw GNSS observation data implemented by the positioning device in Embodiment 1. For example, in a scenario where the positioning device acquires the first raw GNSS observation data wirelessly, the wireless communication module can receive the first raw GNSS observation data from other devices and transmit the first raw GNSS observation data to processor 801 through interface circuit 802 and bus 803. Processor 801 can further process the first raw GNSS observation data to obtain the target GNSS positioning result of the mobile station. The specific implementation process of these functions can be found in the corresponding content described in Embodiment 1 above, and will not be repeated here.

[0171] It should be noted that the functions of the processor 801 and the interface circuit 802 can be implemented through hardware design, software design, or a combination of hardware and software; no restrictions are imposed here.

[0172] Please see Figure 9 , Figure 9 This is another structural schematic diagram of a device provided in an embodiment of this application. This device is for a smart car, and the positioning equipment can be implemented in the form of this device. Figure 9 It is understood that the device includes various systems, such as a travel system 902, a control system 903, one or more peripheral devices 904, and a computer system 901. Optionally, the device may include more or fewer systems, and each system may include multiple components. In addition, each system of the device can be interconnected via wired or wireless means.

[0173] The mobility system 902 may include components that provide powered motion to the device. In one embodiment, the mobility system 902 may include an engine, a transmission, and wheels / tires, etc.

[0174] The control system 903 can control the operation of the device and its components. The control system 903 may include various components, such as a steering system, throttle, braking unit, etc.

[0175] The device can also interact with other devices, other computer systems, or users via peripheral device 904. Peripheral device 904 may include wireless communication systems, microphones, and / or speakers, etc.

[0176] The computer control system includes a processor 9012 and a memory 9011. The processor 9012 can be any conventional processor, such as a commercially available CPU. Alternatively, the processor can also be a special-purpose device such as an ASIC or other hardware-based processor. Although... Figure 9 The processor, memory, and other components of a computer system in the same block are illustrated functionally, but those skilled in the art will understand that the processor, computer, or memory may actually include multiple processors, computers, or memories that may or may not be stored in the same physical housing.

[0177] In some embodiments, memory 9011 may contain instructions (e.g., program logic) that can be executed by processor 9012 to perform various functions of the device, including those described above. Memory 9011 may also contain additional instructions, including instructions for sending data to, receiving data from, interacting with, and / or controlling one or more of the propulsion system, sensor system, control system, and peripheral devices.

[0178] Optionally, the components described above are merely examples. In actual applications, components in the various systems described above may be added or removed as needed. Figure 9 This should not be construed as a limitation on the embodiments of the present invention.

[0179] It should be noted that the above Figure 6 The calculation unit 601, the chi-square test unit 602, and the positioning result determination unit 603 may be the computer system 901 in the device, and the acquisition unit 604 may be the wireless communication system in the device.

[0180] In specific implementation, the aforementioned systems cooperate with each other to ensure the device is in normal working condition. The memory 9011 can store the code corresponding to the positioning method based on raw GNSS observation data executed by the positioning device in Embodiment 1. The processor 9012 can execute this code to implement the various steps of the positioning method based on raw GNSS observation data executed by the positioning device in Embodiment 1. Here, the process by which the processor 9012 executes the code to implement the various steps of the positioning method based on raw GNSS observation data executed by the positioning device can refer to the process described in Embodiment 1 above, and will not be repeated here.

[0181] It can be understood here that the vehicle involved in this application, which can be used to perform the positioning method based on raw GNSS observation data provided in this application, is capable of... Figure 9 The architecture of the device shown is used to achieve this.

[0182] In the embodiments of this application, the processor may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the above scheme program.

[0183] The memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. The memory can exist independently and be connected to the processor via a bus. The memory can also be integrated with the processor.

[0184] A wireless communication module or wireless communication system can be a device or module that enables communication with other devices or communication networks, such as a radio frequency module.

[0185] In the above method embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The aforementioned computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The aforementioned computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The aforementioned computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the aforementioned computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The aforementioned computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The aforementioned available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0186] It should be understood that in the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0187] In the description of this application, the words "exemplary" or "for example" are used to indicate that something is an example, illustration, or illustration. Any embodiment or design that is described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0188] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0189] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus described above is merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0190] Furthermore, the functional units in the embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0191] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A positioning method based on global navigation satellite system (GNSS) raw observation data, characterized in that, The positioning method comprises: performing positioning calculation on first GNSS raw observation data to obtain a first calculation result; performing chi-square test on the first GNSS raw observation data and a state vector updated in time to obtain a first chi-square test value of the first GNSS raw observation data; obtaining S historical chi-square test values obtained by performing chi-square test at S time instants before a first time instant, wherein the first time instant is a time instant at which the first chi-square test value is determined, and S is a positive integer greater than or equal to 1; determining a first standard deviation corresponding to the first chi-square test value and the S historical chi-square test values; if it is determined that the first standard deviation is greater than a preset standard deviation, determining that the first calculation result is an invalid result, and performing outlier data rejection on the first GNSS raw observation data to obtain second GNSS raw observation data, wherein the second GNSS raw observation data is used to determine a target GNSS positioning result of a mobile station; if it is determined that the first standard deviation is less than or equal to the preset standard deviation, determining that the first calculation result is a valid result, and determining the target GNSS positioning result of the mobile station according to the first calculation result.

2. The positioning method according to claim 1, characterized in that, The performing outlier data rejection on the first GNSS raw observation data to obtain second GNSS raw observation data comprises: determining a target satellite; rejecting GNSS raw observation data of the target satellite contained in the first GNSS raw observation data to obtain second GNSS raw observation data.

3. The positioning method according to claim 2, characterized in that, The determining a target satellite comprises: obtaining a first residual result of the first GNSS raw observation data, wherein the first residual result is determined by carrier double-difference observation values and pseudo-range double-difference observation values corresponding to the first GNSS raw observation data obtained by the positioning calculation and the state vector updated in time; determining a satellite corresponding to a maximum residual value in the first residual result as the target satellite.

4. The positioning method according to claim 3, characterized in that, The positioning method further comprises: determining a valid number of satellites corresponding to the second GNSS raw observation data; if it is determined that the valid number is greater than or equal to 4, determining that the second GNSS raw observation data is valid data.

5. The positioning method of claim 4, wherein, The first chi-square test value is based on the first residual result and a function matrix It is determined that the function matrix A measurement noise matrix, an observation matrix, and a covariance matrix updated over time from the positioning solution are determined.

6. The positioning method of claim 4, wherein, The first calculation result comprises a first float solution and a first covariance matrix. The determining the target GNSS positioning result of the mobile station according to the first calculation result comprises: performing integer ambiguity fixing on the first float solution and the first covariance matrix to obtain an integer solution of integer ambiguity; correcting the first float solution according to the integer solution of integer ambiguity to obtain the target GNSS positioning result of the mobile station.

7. A positioning device, characterized in that The positioning device comprises: a calculation unit configured to perform positioning calculation on first GNSS raw observation data to obtain a first calculation result; a chi-square test unit configured to perform chi-square test on the first GNSS raw observation data and a state vector updated in time to obtain a first chi-square test value of the first GNSS raw observation data; The positioning result determination unit is configured to: obtain S historical chi-square test values obtained by performing chi-square test at S time instants before the first time instant, and determine a first standard deviation corresponding to the first chi-square test value and the S historical chi-square test values, wherein the first time instant is a time instant at which the first chi-square test value is determined, and S is a positive integer greater than or equal to 1; The positioning result determination unit is further configured to: if it is determined that the first standard deviation is greater than a preset standard deviation, determine that the first calculation result is an invalid result, and perform outlier rejection on the first GNSS raw observation data to obtain second GNSS raw observation data, wherein the second GNSS raw observation data is used to determine a target GNSS positioning result of the mobile station; The positioning result determination unit is further configured to: if it is determined that the first standard deviation is less than or equal to the preset standard deviation, determine that the first calculation result is a valid result, and determine the target GNSS positioning result of the mobile station according to the first calculation result.

8. The positioning device of claim 7, wherein, The positioning result determination unit is configured to: determine a target satellite; and reject GNSS raw observation data of the target satellite included in the first GNSS raw observation data to obtain second GNSS raw observation data.

9. The positioning device of claim 8, wherein, The positioning result determination unit is configured to: obtain a first residual result of the first GNSS raw observation data, wherein the first residual result is determined by a carrier double-difference observation value and a pseudo-range double-difference observation value corresponding to the first GNSS raw observation data obtained by the positioning calculation, and a state vector updated in time; and determine a target satellite corresponding to a maximum residual value in the first residual result as the target satellite.

10. The positioning device of claim 9, wherein, The positioning result determination unit is configured to: determine a valid number of satellites corresponding to the second GNSS raw observation data; and if it is determined that the valid number is less than 4, determine that the second GNSS raw observation data is invalid data.

11. The positioning device of claim 10, wherein, The first chi-square test value is based on the first residual result and a function matrix It is determined that the function matrix A measurement noise matrix, an observation matrix, and a covariance matrix updated over time from the position solution are determined.

12. The positioning device of claim 10, wherein, The first calculation result includes a first float solution and a first covariance matrix; The positioning result determination unit is configured to: perform integer ambiguity fixing according to the first float solution and the first covariance matrix to obtain an integer solution of integer ambiguity; and correct the first float solution according to the integer solution of integer ambiguity to obtain a target GNSS positioning result of the mobile station.

13. A positioning device, characterized by The positioning apparatus includes a processor and a memory; The memory is configured to store a computer program; The processor is configured to execute the computer program stored in the memory, so that the positioning apparatus performs the positioning method according to any one of claims 1-6.

14. A computer-readable storage medium configured to store instructions that, when executed, cause the positioning method according to any one of claims 1-6 to be implemented.

15. A computer program product comprising program instructions that, when executed on a computer, cause the computer to perform the positioning method according to any one of claims 1-6.

16. A server, characterized by The server is configured to perform the positioning method according to any one of claims 1-6.

17. A vehicle characterized by comprising: The vehicle is configured to perform a positioning method as claimed in any one of claims 1-6.

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