Pseudorange determination method considering time correlation, positioning method and related device

Abstract

The application discloses a pseudo-range determination method considering time correlation, a positioning method and related equipment, and the method comprises the following steps: determining code-minus-phase combined (CMC) data based on pseudo-range observation data and carrier phase observation data; performing sliding average on the CMC data by using an adaptive window to obtain a pseudo-range error correction result; and determining target pseudo-range data based on the pseudo-range error correction result and the pseudo-range observation data; wherein the size of the adaptive window is calculated based on the CMC data by using an autocorrelation function. By correcting the pseudo-range observation data by using the pseudo-range error correction result, the target pseudo-range data obtained to some extent relieves errors related to time caused by multipath effects, atmospheric delay and the like, so that subsequent positioning is more accurate.

Description

Technical Field

[0001] This application relates to the field of satellite navigation and positioning technology, and more specifically, to a pseudorange determination method, positioning method and related equipment that takes into account time correlation. Background Technology

[0002] Satellite navigation and positioning technology is widely used in ubiquitous navigation and positioning terminals and is closely related to people's daily lives, such as in personal location services like shared bicycles and map navigation. Currently, most smartphones on the market can provide raw Global Navigation Satellite System (GNSS) observation data. Typically, smartphone positioning accuracy can reach within 10 meters; however, in scenarios severely affected by multipath signals and non-line-of-sight signals, positioning accuracy may deteriorate to tens of meters. Time-related errors caused by multipath effects and atmospheric delays will also directly affect the weight matrix estimation problem in positioning algorithms, thus impacting the performance of the positioning algorithm. Summary of the Invention

[0003] In view of this, this application provides a pseudorange determination method, positioning method and related equipment that considers time correlation, so as to alleviate time-related errors caused by multipath effects, atmospheric delay and other factors, and achieve more accurate positioning.

[0004] To achieve the above objectives, the first aspect of this application provides a pseudorange determination method that considers time correlation, comprising:

[0005] Based on pseudorange observation data and carrier phase observation data, determine the code-subtracted phase combination (CMC) data;

[0006] The pseudorange error correction result is obtained by performing a moving average on the CMC data using an adaptive window.

[0007] Based on the pseudorange error correction results and the pseudorange observation data, the target pseudorange data is determined.

[0008] The size of the adaptive window is calculated based on the CMC data using an autocorrelation function.

[0009] Preferably, the process of calculating the size of the adaptive window using the autocorrelation function based on the CMC data includes:

[0010] Based on the CMC data, the correlation time of the CMC data is obtained by performing exponential fitting using the autocorrelation function.

[0011] The size of the adaptive window is determined based on the CMC data and the relevant time.

[0012] Preferably, the process of obtaining the correlation time of the CMC data by performing exponential fitting using the autocorrelation function based on the CMC data includes:

[0013] Determine whether there is a data jump in the CMC data;

[0014] If so, based on the breakpoints in the CMC data, the CMC data is divided into multiple CMC subsequences, wherein the length of each CMC subsequence is greater than a preset length value;

[0015] The autocorrelation function is used to perform exponential fitting on each CMC subsequence to obtain the correlation time of each CMC subsequence;

[0016] If not, the CMC data is subjected to exponential fitting using the autocorrelation function to obtain the correlation time of the CMC data.

[0017] Preferably, the process of using the autocorrelation function to perform exponential fitting on the CMC data to obtain the correlation time of the CMC data includes:

[0018] The autocorrelation results of the CMC data were calculated using the following equation. :

[0019]

[0020]

[0021] Among them, Y (n) ={Y1, Y2,…,Y n} represents the first n data points in the CMC data;

[0022] Y (n-k) ={Y1, Y2,…,Y n-k} represents the first (nk) data points in the CMC dataset; Cov() is the covariance function;

[0023] The autocorrelation results are used in the following equations. Perform exponential fitting:

[0024]

[0025] in, This refers to the relevant time of the CMC data.

[0026] Preferably, the process of determining the size of the adaptive window based on the CMC data and the relevant time includes:

[0027] Determine whether the CMC data has drifted;

[0028] If so, determine whether the relevant time is greater than a preset time value;

[0029] If so, set the size of the adaptive window to a preset window value;

[0030] If not, set the size of the adaptive window to the value of the relevant time.

[0031] If not, set the size of the adaptive window to the value of the relevant time.

[0032] Preferably, the process of using an adaptive window to perform a moving average on the CMC data to obtain the pseudorange error correction result includes:

[0033] The moving average of the CMC data is calculated using the following equation. :

[0034]

[0035] The pseudorange error correction result is calculated using the following equation. :

[0036]

[0037] Where Y is the CMC data, Y i Let M be the i-th element of Y, M be the total length of Y, and WS be the size of the adaptive window.

[0038] Preferably, before determining the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data, the method further includes:

[0039] Outlier removal is performed on the pseudorange observation data and the carrier phase observation data using the 3σ principle.

[0040] A second aspect of this application provides a positioning method that considers time correlation, including:

[0041] The target pseudorange data is determined using the steps of the pseudorange determination method that takes into account time correlation, as described above.

[0042] The positioning result is determined by combining the target pseudorange data, navigation message data, and carrier-to-noise ratio observation data.

[0043] A third aspect of this application provides a pseudorange determination system that considers time correlation, comprising:

[0044] The data acquisition unit is used to acquire pseudorange observation data, carrier phase observation data, navigation message data, and carrier-to-noise ratio observation data.

[0045] The data correction unit is used to determine the target pseudorange data based on the pseudorange observation data and the carrier phase observation data, using the steps of the pseudorange determination method considering time correlation as described above.

[0046] The positioning calculation unit is used to determine the positioning result by combining the target pseudorange data, navigation message data, and carrier-to-noise ratio observation data.

[0047] A fourth aspect of this application provides a pseudorange determination apparatus that considers time correlation, comprising:

[0048] The data acquisition unit is used to determine the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data.

[0049] An error determination unit is used to perform a moving average on the CMC data using an adaptive window to obtain a pseudorange error correction result.

[0050] A data correction unit is used to determine target pseudorange data based on the pseudorange error correction result and the pseudorange observation data;

[0051] The size of the adaptive window is calculated based on the CMC data using an autocorrelation function.

[0052] A fifth aspect of this application provides a pseudorange determination device that takes into account time correlation, comprising: a memory and a processor;

[0053] The memory is used to store programs;

[0054] The processor is used to execute the program to implement the various steps of the pseudorange determination method that considers time correlation described above.

[0055] A sixth aspect of this application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the pseudorange determination method considering time correlation as described above.

[0056] As described in the above technical solution, this application first determines the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data. Next, an adaptive window is used to perform a moving average on the CMC data to obtain the pseudorange error correction result. The size of the adaptive window is calculated based on the CMC data using an autocorrelation function. By adaptively determining the size of the adaptive window according to the characteristics of different satellite signals and different time series, and utilizing the correlation of the data, pseudorange errors can be better captured. Finally, based on the pseudorange error correction result and the pseudorange observation data, the target pseudorange data is determined. By using the pseudorange error correction result to correct the pseudorange observation data, the obtained target pseudorange data alleviates, to some extent, time-related errors caused by multipath effects, atmospheric delay, etc., thereby making subsequent positioning more accurate. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of a positioning system that considers time correlation, as disclosed in an embodiment of this application.

[0059] Figure 2 This is a schematic diagram of a positioning method that considers time correlation as disclosed in an embodiment of this application;

[0060] Figure 3 This is a schematic diagram of the adaptive window size setting process disclosed in an embodiment of this application;

[0061] Figure 4 This is a schematic diagram of a positioning device that considers time correlation, as disclosed in an embodiment of this application.

[0062] Figure 5 This is a schematic diagram of a positioning device that takes into account time correlation, as disclosed in an embodiment of this application. Detailed Implementation

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] The following describes a positioning system that considers time correlation, provided by an embodiment of this application. Please refer to... Figure 1 The positioning system considering time correlation provided in this application embodiment may include a data acquisition unit 10, a data correction unit 20, and a positioning calculation unit 30.

[0065] The data acquisition unit 10 is used to acquire pseudorange observation data, carrier phase observation data, navigation message data, and carrier-to-noise ratio observation data.

[0066] For example, a smartphone can be used as the data acquisition unit. Specifically, the smartphone is fixed to a rotating circular platform using a fixing device. The smartphone can rotate with the platform to capture GNSS observation data (i.e., pseudorange observation data, carrier phase observation data, navigation message data, and carrier-to-noise ratio observation data) from different azimuths. The rotation diameter can be set according to the length of the smartphone, and the rotation angular frequency is 2πrad / 10s. The smartphone's antenna is placed facing upwards, and the rotation moves the antenna, thereby reducing the correlation of the received data.

[0067] The data correction unit 20 is used to determine the target pseudorange data based on pseudorange observation data and carrier phase observation data, using the pseudorange determination method considering time correlation provided in this application.

[0068] The positioning calculation unit 30 is used to combine target pseudorange data, navigation message data and carrier-to-noise ratio observation data to determine the positioning result.

[0069] Specifically, firstly, by parsing the navigation message data, information such as satellite clock bias and broadcast ephemeris can be obtained; then, a stochastic model based on the weighted least squares algorithm is established using carrier-to-noise ratio observation data; finally, the positioning result is calculated by combining target pseudorange data, satellite clock bias, satellite coordinates, and the stochastic model.

[0070] The pseudorange determination method considering time correlation provided in the embodiments of this application is described below. Please refer to... Figure 2 The pseudorange determination method considering time correlation provided in the embodiments of this application may include the following steps:

[0071] Step S101: Determine CMC data based on pseudorange observation data and carrier phase observation data.

[0072] Among them, pseudorange observation data It can be represented as:

[0073] (1)

[0074] Carrier phase observation data can be represented as:

[0075] (2)

[0076] Where R represents the geometric range between the receiver's location and the satellite's location; c is the speed of light in a vacuum; τ r and τ s These represent the receiver and satellite clock offsets, respectively; I and T represent the ionospheric delay and tropospheric delay, respectively; m p and m Φ ε represents the multipath error present in the pseudorange and carrier phase observations, respectively; p and ε Φ λ represents the noise present in the pseudorange and carrier phase observations, respectively; λ is the carrier wavelength; and N represents the integer ambiguity.

[0077] The error terms present in the pseudorange and carrier phase observations can be eliminated by differential equations. Subtracting equation (2) from equation (1) yields the CMC (Code-Minus-Carrier Combination) data Y:

[0078] (3)

[0079] Where I is a slowly varying term with a frequency below 0.1 mHz. If there is no cycle slip, N is considered a constant. Since carrier phase observation accuracy is sub-centimeter level and pseudorange observation accuracy is meter level, m Φ and ε Φ Compared to m p and ε p At least an order of magnitude lower. Therefore, m Φ and ε Φ It can be ignored.

[0080] Step S102: Use an adaptive window to perform a moving average on the CMC data to obtain the pseudorange error correction result.

[0081] The size of the adaptive window is calculated based on the CMC data Y using the autocorrelation function. By adaptively determining the size of the adaptive window according to the characteristics of different satellite signals and different time series, and utilizing the correlation of the data, pseudorange errors can be better captured.

[0082] Understandably, since the moving average of CMC data that is too short is not very meaningful, the length of the CMC data has reached the preset length threshold.

[0083] Step S103: Based on the pseudorange error correction results and pseudorange observation data, determine the target pseudorange data.

[0084] In particular, the pseudorange data of this target alleviates, to some extent, the time-related errors caused by multipath effects and atmospheric delay.

[0085] For example, assume the pseudorange error correction result is The target pseudorange data P can then be calculated using the following equation. c :

[0086] (4)

[0087] This application first determines the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data. Next, an adaptive window is used to perform a moving average on the CMC data to obtain a pseudorange error correction result. The size of the adaptive window is calculated using the autocorrelation function based on the CMC data. By adaptively determining the size of the adaptive window according to the characteristics of different satellite signals and different time series, and utilizing the correlation of the data, pseudorange errors can be better captured. Finally, based on the pseudorange error correction result and the pseudorange observation data, target pseudorange data is determined. By correcting the pseudorange observation data using the pseudorange error correction result, the obtained target pseudorange data mitigates time-related errors caused by multipath effects, atmospheric delay, etc., to a certain extent, thus making subsequent positioning more accurate.

[0088] In some embodiments of this application, before determining the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data in step S101, the following may be included:

[0089] Outlier removal is performed on pseudorange and carrier phase observation data using the 3σ principle.

[0090] The process of removing outliers from a target sequence using the 3σ principle can include:

[0091] S1, the standard deviation σ of the target sequence is calculated using the following equation:

[0092] (5)

[0093] S2, for each element x in the target sequence i Determine whether the following equation is satisfied:

[0094] (6)

[0095] If so, element x i Remove from the target sequence.

[0096] In some embodiments of this application, the process of using an adaptive window to perform a moving average on the CMC data to obtain the pseudorange error correction result may include:

[0097] S1, the moving average result of the CMC data is calculated using the following equation. :

[0098] (7)

[0099] S2, the pseudorange error correction result is calculated using the following equation. :

[0100] (8)

[0101] Where Y is CMC data, Yi is the i-th element of Y, M is the total length of Y, and WS is the size of the adaptive window.

[0102] In some embodiments of this application, step S102, which calculates the size of the adaptive window based on CMC data using an autocorrelation function, may include:

[0103] S1, based on CMC data, uses the autocorrelation function to perform exponential fitting to obtain the correlation time of CMC data.

[0104] S2 determines the size of the adaptive window based on CMC data and relevant time.

[0105] In some embodiments of this application, the process of obtaining the correlation time of the CMC data by performing exponential fitting using the autocorrelation function based on the CMC data in S1 may include:

[0106] S11, determine if there is a data jump in the CMC data; if yes, execute S12; if no, execute S14.

[0107] In this context, a data jump in a time series is defined as a significant change in the average value of the time series. Specifically, if the difference between two adjacent elements in a time series exceeds a preset difference threshold, a data jump is considered to exist between these two elements. For example, this difference threshold can be set to 50; therefore, if... Therefore, time t can be considered the interruption point.

[0108] S12, based on the breakpoints of the CMC data, divides the CMC data into multiple CMC subsequences.

[0109] In this system, the length of each CMC subsequence is greater than a preset length value. This preset length value can be an integer greater than or equal to 10.

[0110] It is understandable that if the length of one of the CMC subsequences is less than the preset length value, then there is no need to perform a moving average on the CMC subsequence, that is, there is no need to calculate the relevant time of the CMC subsequence.

[0111] S13, use the autocorrelation function to perform exponential fitting on each CMC subsequence to obtain the correlation time of each CMC subsequence.

[0112] S14, use the autocorrelation function to perform exponential fitting on the CMC data to obtain the correlation time of the CMC data.

[0113] In some embodiments of this application, the process of S14 above, which uses the autocorrelation function to exponentially fit the CMC data to obtain the correlation time of the CMC data, may include:

[0114] S141, the autocorrelation results of the CMC data are calculated using the following equation. :

[0115] (9)

[0116] (10)

[0117] Among them, Y (n) ={Y1, Y2,…,Y n} represents the first n data points in the CMC data;

[0118] Y (n-k) ={Y1, Y2,…,Y n-k} represents the first (nk) data points in the CMC data;

[0119] Cov() is the covariance function.

[0120] S142, using the autocorrelation results in the following equation. Perform exponential fitting:

[0121] (11)

[0122] Where τ is the correlation time of the CMC data.

[0123] In some embodiments of this application, please refer to Figure 3 The process described above, where S2 determines the size of the adaptive window based on CMC data and relevant time, may include:

[0124] S21, determine if there is any drift in the CMC data; if yes, proceed to S22; if no, proceed to S24.

[0125] The CMC data exhibits drift, characterized by significant variations in the moving average of the time series, rather than fluctuating slightly around a fixed value. Specifically, a sliding window of a preset size is used to traverse the entire time series. For each subsequence within the traversed sliding window, its average is calculated. The averages of the subsequences within each sliding window are compared. If the averages of some subsequences differ significantly from the averages of other subsequences (exceeding a preset threshold), data drift is considered to exist.

[0126] S22, determine whether the relevant time is greater than the preset time value; if yes, execute S23; if no, execute S24.

[0127] The preset time value is an integer greater than or equal to 1000. Preferably, the preset time value can be set to 1000.

[0128] S23, set the size of the adaptive window to the preset window value.

[0129] The preset window value is 300.

[0130] S24, set the size of the adaptive window to the value of the relevant time.

[0131] The pseudorange determination device considering time correlation provided in the embodiments of this application will be described below. The pseudorange determination device considering time correlation described below can be referred to in correspondence with the pseudorange determination method considering time correlation described above.

[0132] Please see Figure 4 The pseudorange determination device considering time correlation provided in the embodiments of this application may include:

[0133] Data acquisition unit 21 is used to determine code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data;

[0134] Error determination unit 22 is used to perform a moving average on the CMC data using an adaptive window to obtain pseudorange error correction results;

[0135] Data correction unit 23 is used to determine target pseudorange data based on the pseudorange error correction result and the pseudorange observation data;

[0136] The size of the adaptive window is calculated based on the CMC data using an autocorrelation function.

[0137] In some embodiments of this application, before the data acquisition unit 21 determines the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data, it may further include:

[0138] Outlier removal is performed on the pseudorange observation data and the carrier phase observation data using the 3σ principle.

[0139] In some embodiments of this application, the process by which the error determination unit 22 calculates the size of the adaptive window based on the CMC data using an autocorrelation function may include:

[0140] Based on the CMC data, the correlation time of the CMC data is obtained by performing exponential fitting using the autocorrelation function.

[0141] The size of the adaptive window is determined based on the CMC data and the relevant time.

[0142] In some embodiments of this application, the process by which the error determination unit 22 obtains the correlation time of the CMC data by performing exponential fitting using an autocorrelation function based on the CMC data may include:

[0143] Determine whether there is a data jump in the CMC data;

[0144] If so, based on the breakpoints in the CMC data, the CMC data is divided into multiple CMC subsequences, wherein the length of each CMC subsequence is greater than a preset length value;

[0145] The autocorrelation function is used to perform exponential fitting on each CMC subsequence to obtain the correlation time of each CMC subsequence;

[0146] If not, the CMC data is subjected to exponential fitting using the autocorrelation function to obtain the correlation time of the CMC data.

[0147] In some embodiments of this application, the process by which the error determination unit 22 performs exponential fitting on the CMC data using an autocorrelation function to obtain the correlation time of the CMC data may include:

[0148] The autocorrelation results of the CMC data were calculated using the following equation. :

[0149]

[0150]

[0151] Among them, Y (n) ={Y1, Y2,…,Y n} represents the first n data points in the CMC data;

[0152] Y (n-k) ={Y1, Y2,…,Y n-k} represents the first (nk) data points in the CMC dataset; Cov() is the covariance function;

[0153] The autocorrelation results are used in the following equations. Perform exponential fitting:

[0154]

[0155] in, This refers to the relevant time of the CMC data.

[0156] In some embodiments of this application, the process by which the error determination unit 22 determines the size of the adaptive window based on the CMC data and the relevant time may include:

[0157] Determine whether the CMC data has drifted;

[0158] If so, determine whether the relevant time is greater than a preset time value;

[0159] If so, set the size of the adaptive window to a preset window value;

[0160] If not, set the size of the adaptive window to the value of the relevant time.

[0161] If not, set the size of the adaptive window to the value of the relevant time.

[0162] In some embodiments of this application, the process by which the error determination unit 22 uses an adaptive window to perform a moving average on the CMC data to obtain the pseudorange error correction result may include:

[0163] The moving average of the CMC data is calculated using the following equation. :

[0164]

[0165] The pseudorange error correction result is calculated using the following equation. :

[0166]

[0167] Where Y is the CMC data, Y i Let M be the i-th element of Y, M be the total length of Y, and WS be the size of the adaptive window.

[0168] The pseudorange determination device considering time correlation provided in this application embodiment can be applied to pseudorange determination devices considering time correlation, such as computers. Optionally, Figure 5A hardware block diagram of a pseudorange determination device considering time correlation is shown, with reference to... Figure 5 The hardware structure of a pseudorange determination device that considers time correlation may include: at least one processor 31, at least one communication interface 32, at least one memory 33, and at least one communication bus 34.

[0169] In this embodiment, the number of processor 31, communication interface 32, memory 33 and communication bus 34 is at least one, and processor 31, communication interface 32 and memory 33 communicate with each other through communication bus 34;

[0170] The processor 31 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0171] The memory 33 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0172] The memory 33 stores a program, and the processor 31 can call the program stored in the memory 33. The program is used for:

[0173] Based on pseudorange observation data and carrier phase observation data, determine the code-subtracted phase combination (CMC) data;

[0174] The pseudorange error correction result is obtained by performing a moving average on the CMC data using an adaptive window.

[0175] Based on the pseudorange error correction results and the pseudorange observation data, the target pseudorange data is determined.

[0176] The size of the adaptive window is calculated based on the CMC data using an autocorrelation function.

[0177] Optionally, the refined and extended functions of the program can be found in the description above.

[0178] This application embodiment also provides a storage medium that can store a program suitable for execution by a processor, the program being used for:

[0179] Based on pseudorange observation data and carrier phase observation data, determine the code-subtracted phase combination (CMC) data;

[0180] The pseudorange error correction result is obtained by performing a moving average on the CMC data using an adaptive window.

[0181] Based on the pseudorange error correction results and the pseudorange observation data, the target pseudorange data is determined.

[0182] The size of the adaptive window is calculated based on the CMC data using an autocorrelation function.

[0183] Optionally, the refined and extended functions of the program can be found in the description above.

[0184] In summary:

[0185] This application first determines the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data. Next, an adaptive window is used to perform a moving average on the CMC data to obtain a pseudorange error correction result. The size of the adaptive window is calculated using the autocorrelation function based on the CMC data. By adaptively determining the size of the adaptive window according to the characteristics of different satellite signals and different time series, and utilizing the correlation of the data, pseudorange errors can be better captured. Finally, based on the pseudorange error correction result and the pseudorange observation data, target pseudorange data is determined. By correcting the pseudorange observation data using the pseudorange error correction result, the obtained target pseudorange data mitigates time-related errors caused by multipath effects, atmospheric delay, etc., to a certain extent, thus making subsequent positioning more accurate.

[0186] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0187] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0188] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A pseudorange determination method considering time correlation, characterized in that, include: Based on pseudorange observation data and carrier phase observation data, determine the code-subtracted phase combination (CMC) data; The pseudorange error correction result is obtained by performing a moving average on the CMC data using an adaptive window. Based on the pseudorange error correction results and the pseudorange observation data, the target pseudorange data is determined. The size of the adaptive window is calculated based on the CMC data using an autocorrelation function. Specific steps include: performing exponential fitting on the CMC data using an autocorrelation function to obtain the correlation time of the CMC data; determining whether there are data jumps in the CMC data; if so, dividing the CMC data into multiple CMC subsequences based on the breakpoints in the CMC data, wherein the length of each CMC subsequence is greater than a preset length value; performing exponential fitting on each CMC subsequence using an autocorrelation function to obtain the correlation time of each CMC subsequence; if not, performing exponential fitting on the CMC data using an autocorrelation function to obtain the correlation time of the CMC data. The autocorrelation result of the CMC data is calculated using the following equation. : ； ； in, (n) ={Y1, Y2,…,Y n } represents the first n data points in the CMC data; Y (n-k) ={Y1, Y2,…,Y n-k } represents the first (nk) data points in the CMC dataset; Cov() is the covariance function; The autocorrelation results are used in the following equations. Perform exponential fitting: ； in, This refers to the relevant time of the CMC data.

2. The method according to claim 1, characterized in that, The process of determining the size of the adaptive window based on the CMC data and the relevant time includes: Determine whether the CMC data has drifted; If so, determine whether the relevant time is greater than a preset time value; If so, set the size of the adaptive window to a preset window value; If not, set the size of the adaptive window to the value of the relevant time. If not, set the size of the adaptive window to the value of the relevant time.

3. The method according to claim 1, characterized in that, The process of obtaining pseudorange error correction results by performing a moving average on the CMC data using an adaptive window includes: The moving average of the CMC data is calculated using the following equation. : ； The pseudorange error correction result is calculated using the following equation. : ； Where Y is the CMC data, Y i Let M be the i-th element of Y, M be the total length of Y, and WS be the size of the adaptive window.

4. The method according to claim 1, characterized in that, Before determining the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data, the method further includes: Outlier removal is performed on the pseudorange observation data and the carrier phase observation data using the 3σ principle.

5. A positioning method considering time correlation, characterized in that, include: The target pseudorange data is determined using each step of the pseudorange determination method considering time correlation as described in any one of claims 1 to 4; The positioning result is determined by combining the target pseudorange data, navigation message data, and carrier-to-noise ratio observation data.

6. A positioning system that considers time correlation, characterized in that, include: The data acquisition unit is used to acquire pseudorange observation data, carrier phase observation data, navigation message data, and carrier-to-noise ratio observation data. A data correction unit is used to determine target pseudorange data based on the pseudorange observation data and the carrier phase observation data, using the steps of the pseudorange determination method considering time correlation as described in any one of claims 1 to 4. The positioning calculation unit is used to determine the positioning result by combining the target pseudorange data, navigation message data, and carrier-to-noise ratio observation data.

7. A pseudorange determination device considering time correlation, characterized in that, include: The data acquisition unit is used to determine the code-subtracted phase combination (CMC) data based on pseudorange observation data and carrier phase observation data. An error determination unit is used to perform a moving average on the CMC data using an adaptive window to obtain a pseudorange error correction result. A data correction unit is used to determine target pseudorange data based on the pseudorange error correction result and the pseudorange observation data; The adaptive window size is calculated based on the CMC data using an autocorrelation function. Specific steps include: performing exponential fitting on the CMC data using an autocorrelation function to obtain the correlation time of the CMC data; determining whether there are data jumps in the CMC data; if so, dividing the CMC data into multiple CMC subsequences based on the breakpoints in the CMC data, wherein the length of each CMC subsequence is greater than a preset length value; performing exponential fitting on each CMC subsequence using an autocorrelation function to obtain the correlation time of each CMC subsequence; if not, performing exponential fitting on the CMC data using an autocorrelation function to obtain the correlation time of the CMC data. The autocorrelation result of the CMC data is calculated using the following equation. : ； ； in, (n) ={Y1, Y2,…,Y n } represents the first n data points in the CMC data; Y (n-k) ={Y1, Y2,…,Y n-k } represents the first (nk) data points in the CMC dataset; Cov() is the covariance function; The autocorrelation results are used in the following equations. Perform exponential fitting: ； in, This refers to the relevant time of the CMC data.

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