Single-point timing method, apparatus, receiver and its storage medium for receiver

Abstract

This application provides a single-point timing method, apparatus, receiver, and storage medium for a receiver, relating to the field of satellite positioning and timing technology. The method includes: acquiring historical parameter value vectors and historical covariance vectors of multiple undetermined parameters in the linearized observation equations of a non-differential, non-combined PPP model for multiple satellites and the receiver; acquiring satellite observations of multiple satellites at dual-frequency points in a standard single-point mode; obtaining the current parameter value vector and current covariance vector using a Kalman filter algorithm based on the satellite observations, historical parameter value vectors, and historical covariance vectors; determining and using the target satellite's satellite observations, the tropospheric delay of the target satellite in the current parameter value vector, and the receiver's current position to determine the target clock bias for timing, thereby achieving precise timing.

Description

Technical Field

[0001] This application relates to the field of satellite positioning and timing technology, specifically to a single-point timing method, apparatus, receiver, and storage medium for a receiver. Background Technology

[0002] In addition to providing positioning and navigation services, Global Navigation Satellite Systems (GNSS) also offer timing and time synchronization services, playing a vital role in national development and people's livelihoods. With the development of science and technology and the socio-economic landscape, next-generation mobile communications, deformation monitoring, indoor positioning, and distributed observation networks all demand nanosecond-level or even higher precision time synchronization. Currently, in traditional standard single-point positioning and timing scenarios, the timing is directly achieved using the single-point clock difference, resulting in low accuracy and an inability to provide high-precision time synchronization. Summary of the Invention

[0003] The purpose of this application is to provide a single-point timing method, apparatus, receiver, and storage medium for a receiver.

[0004] To achieve the above objectives, the first aspect of this application provides a single-point timing method for a receiver, applied to the receiver, comprising:

[0005] The historical parameter value vector and historical covariance vector of multiple undetermined parameters in the linearized observation equation of the non-differential non-combined PPP model of multiple satellites and receivers are obtained. Among them, the multiple undetermined parameters include: receiver clock bias of different types of satellites, tropospheric delay of each satellite, receiver azimuth and space signal ranging error.

[0006] Acquire satellite observations from multiple satellites at dual-frequency points in standard single-point mode;

[0007] Based on satellite observations, historical parameter value vectors, and historical covariance vectors, the current parameter value vector and current covariance vector are obtained through the Kalman filter algorithm.

[0008] If the satellite position covariance of the target satellite in the current covariance vector satisfies the convergence condition, the clock bias result of the target satellite is determined based on the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector, and the current position of the receiver, so as to determine the target clock bias used for time synchronization.

[0009] In this embodiment of the application, obtaining the current parameter value vector and the current covariance vector using the Kalman filter algorithm based on satellite observations, historical parameter value vectors, and historical covariance vectors includes: determining the parameter prediction value vector of the current parameter value vector based on the historical parameter value vector and the state prediction equation, and determining the covariance prediction value vector of the current covariance vector based on the historical covariance vector and the covariance prediction equation; determining the current parameter value vector based on the satellite observations, the parameter prediction value vector, and the parameter value vector update equation, and determining the current covariance vector based on the satellite observations, the parameter prediction value vector, the covariance prediction value vector, and the covariance vector update equation.

[0010] In this embodiment of the application, determining the current parameter value vector based on satellite observations, parameter prediction vectors, and parameter value vector update equations includes: determining the observation update amount based on satellite observations, parameter prediction vectors, and linearized observation equations; and determining the current parameter value vector based on the observation update amount, parameter prediction vectors, and parameter value vector update equations.

[0011] In this embodiment, satellite observations include: pseudorange observations, carrier phase observations, satellite azimuth observations, and satellite clock bias observations for each satellite; the parameter prediction vector includes receiver azimuth predictions; the observation update includes: pseudorange update and carrier phase update for each satellite; determining the observation update based on satellite observations, parameter prediction vectors, and linearized observation equations includes: for any satellite, determining the pseudorange update based on pseudorange observations, satellite azimuth observations, clock bias observations, and receiver azimuth predictions using linearized observation equations; and for any satellite, determining the carrier phase update based on carrier phase observations, satellite azimuth observations, clock bias observations, and receiver azimuth predictions using linearized observation equations.

[0012] In this embodiment, the linearized observation equation includes: a first linearized observation equation (1) and a second linearized observation equation (2):

[0013] (1)

[0014] (2)

[0015] in, Indicates satellite With receiver In the pseudorange update amount at frequency points Indicates satellite With receiver In the Pseudorange observations at frequency points Indicates satellite satellite azimuth, This represents the receiver's predicted azimuth value. Represents the speed of light. Indicates satellite Satellite clock bias observations Indicates satellite With receiver In the Carrier phase update amount at frequency point Indicates satellite With receiver In the Carrier phase observations at frequency points.

[0016] In this embodiment, the parameter value vector update equation (3) is:

[0017] (3)

[0018] in, Represents the vector of current parameter values. Represents a vector of predicted parameter values. This represents the observation vector, which includes the observation updates from different satellites. Indicates the current time The Kalman filter gain matrix, Indicates the current time The observation matrix.

[0019] In this embodiment, the state prediction equation (4) is, and the covariance prediction equation (5) is.

[0020] (4)

[0021] (5)

[0022] in, Represents a vector of predicted parameter values. Represents a vector of historical parameter values. Indicates the current time The state transition matrix, Indicates the current time The control vector, Represents the vector of covariance predicted values. Represents the historical covariance vector. Indicates the current time The control noise vector.

[0023] In this embodiment, determining the current covariance vector based on satellite observations, the predicted covariance vector, and the covariance vector update equation includes: determining the observation update amount based on satellite observations, the predicted parameter vector, and the linearized observation equation; determining the observation noise vector based on the observation update amount, the predicted parameter vector, and the Kalman observation equation; and determining the current covariance vector based on the predicted covariance vector, the observation noise vector, and the covariance vector update equation.

[0024] In this embodiment, the covariance vector update equation (6) is:

[0025] (6)

[0026] in, , Indicates the current time The current covariance vector, Represents the identity matrix. Indicates the current time The Kalman filter gain matrix, Indicates the current time The observation matrix Represents the vector of covariance predicted values. This represents the observation noise vector.

[0027] In this embodiment, the satellite observations of the target satellite include: the first pseudorange observation and the second pseudorange observation of the target satellite at the dual-frequency point, the satellite clock bias observation and the satellite azimuth observation of the target satellite; determining the clock bias result of the target satellite based on the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector and the current position of the receiver, and determining the target clock bias for time synchronization based on the clock bias result includes: determining the ionospheric descaling pseudorange of the target satellite based on the first pseudorange observation and the second pseudorange observation of the target satellite at the dual-frequency point; determining the clock bias result based on the combined pseudorange, the satellite clock bias observation and the satellite azimuth observation of the target satellite, the tropospheric delay of the target satellite and the current position of the receiver, and determining the target clock bias for time synchronization based on the clock bias result.

[0028] In this embodiment of the application, the clock bias result is determined based on formula (7):

[0029] (7)

[0030] in, Indicates target satellite relative receiver The clock difference results Indicates the combined pseudorange. Indicates satellite azimuth observations. This indicates the receiver's current position. Indicates satellite clock bias observations, This indicates the tropospheric time delay.

[0031] In this embodiment of the application, determining the target clock difference for time synchronization based on the clock difference results includes: when there are multiple target satellites, determining the target clock difference based on the median of the clock difference results of the multiple target satellites.

[0032] A second aspect of this application provides a single-point timing device for a receiver, comprising: a processor configured to execute a single-point timing method for a receiver according to a first aspect of this application.

[0033] A third aspect of this application provides a receiver, comprising: a single-point timing device for a receiver as provided in the second aspect of this application; and a receiving antenna for receiving satellite observations.

[0034] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned single-point timing method for a receiver.

[0035] Through the above technical solution, the single-point timing method for receivers provided in this application embodiment enables the receiver to combine satellite observations from multiple satellites in standard single-point mode. Based on the historical parameter value vector and historical covariance vector of multiple undetermined parameters in the linearized observation equation of the non-differential non-combined precise single-point positioning model, the current parameter value vector and current covariance vector of multiple undetermined parameters of multiple satellites can be determined by the Kalman filtering algorithm. Based on the convergence condition and the satellite position covariance, the target satellite is selected. The clock bias result of the target satellite is determined according to the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector, and the current position of the receiver. This clock bias is then used as the target clock bias for receiver timing. Since spatial signal ranging error is also introduced in the linearized observation equation of the non-differential non-combined PPP model, the problem of the absence of satellite orbit correction, satellite clock bias correction, and differential code deviation correction is compensated for, thereby achieving high-precision timing in standard single-point mode.

[0036] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0038] Figure 1The illustration shows a flowchart of a single-point timing method for a receiver according to an embodiment of this application;

[0039] Figure 2 The schematic diagram illustrates a flow chart of another single-point timing method for a receiver according to an embodiment of this application;

[0040] Figure 3 The schematic diagram illustrates a flow chart of another single-point timing method for a receiver according to an embodiment of this application;

[0041] Figure 4 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0043] It should be noted that if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0044] The acquisition, transmission, storage, use, and processing of data in this application comply with relevant laws and regulations. Furthermore, it should be noted that existing industry solutions such as software, components, and models may be mentioned in the embodiments of this application. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the solution has been or necessarily been used.

[0045] In current traditional standard point positioning and timing scenarios, the timing is directly based on the single-point clock difference results, which is easily affected by ionospheric activity and interference. Furthermore, there is no information on satellite orbit correction, satellite clock difference correction, and differential code deviation correction. As a result, the timing accuracy of traditional standard point positioning and timing is not high, and it cannot provide high-precision time synchronization.

[0046] Based on the above analysis, in order to solve the problem of low accuracy of traditional standard single-point positioning and timing, this application provides a single-point timing method for a receiver. The receiver can be, for example, a smartphone, a personal computer, a vehicle positioning device, or other device that needs to perform timing based on satellite communication. Figure 1 A schematic flowchart illustrating a single-point timing method for a receiver according to an embodiment of this application is shown. Figure 1 As shown, the single-point timing method for a receiver provided in this application includes:

[0047] S102. Obtain the historical parameter value vector and historical covariance vector of multiple undetermined parameters in the linearized observation equation of the non-differential non-combined PPP (Precise Point Positioning) model of multiple satellites and receivers. Among them, the multiple undetermined parameters include: receiver clock bias of different types of satellites, tropospheric delay of each satellite, receiver azimuth and space signal ranging error.

[0048] Specifically, multiple undetermined parameters can form a parameter value vector, each of which has its own covariance, and the covariances of these parameters can form a covariance vector. The historical parameter value vector and historical covariance vector can be the current parameter value vector and current covariance vector obtained from the previous round of the Kalman filter algorithm. In the initial iteration of the Kalman filter algorithm, the historical parameter value vector and historical covariance vector can also be pre-set preset parameter value vectors and preset covariance vectors.

[0049] S104. Acquire satellite observations of multiple satellites at dual-frequency points in standard single-point mode.

[0050] Understandably, in standard single-point mode, the receiver does not receive precise ephemeris data.

[0051] S106. Based on satellite observations, historical parameter value vectors, and historical covariance vectors, obtain the current parameter value vector and current covariance vector using the Kalman filter algorithm.

[0052] S108. If the satellite position covariance of the target satellite in the current covariance vector satisfies the convergence condition, determine the clock bias result of the target satellite based on the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector, and the current position of the receiver, so as to determine the target clock bias used for time synchronization based on the clock bias result.

[0053] Specifically, convergence conditions may include satellite position covariance being less than or equal to a preset covariance threshold. As an example, the preset covariance threshold may be set to 1.

[0054] The single-point timing method for a receiver provided in this application allows the receiver to combine satellite observations from multiple satellites in standard single-point mode. Based on the historical parameter value vector and historical covariance vector of multiple undetermined parameters in the linearized observation equation of the non-differential non-combined PPP model, the current parameter value vector and current covariance vector of multiple undetermined parameters of multiple satellites can be determined by the Kalman filtering algorithm. Based on the convergence condition and the satellite position covariance, the target satellite is selected. The clock bias result of the target satellite is determined according to the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector, and the current position of the receiver. This clock bias is then used as the target clock bias for receiver timing. Since space signal ranging error is also introduced in the linearized observation equation of the non-differential non-combined PPP model, the problems of lacking satellite orbit correction, satellite clock bias correction, and differential code deviation correction are compensated for, thereby achieving high-precision timing in standard single-point mode.

[0055] In some embodiments of this application, steps S102 to S108 can be repeatedly executed. In the initial iteration of the Kalman filter algorithm, the historical parameter value vector and the historical covariance vector are respectively the preset parameter value vector and the preset covariance vector. In the case of multiple iterations of the Kalman filter algorithm, the historical parameter value vector and the historical covariance vector are the current parameter value vector and the current covariance vector obtained from the previous iteration of the Kalman filter algorithm. The satellite observations corresponding to each round of Kalman filter iteration are the satellite observations currently acquired by the receiver.

[0056] See Figure 2 In some embodiments of this application, step S106 may include:

[0057] S202. Determine the parameter prediction value vector of the current parameter value vector based on the historical parameter value vector and the state prediction equation, and determine the covariance prediction value vector of the current covariance vector based on the historical covariance vector and the covariance prediction equation.

[0058] S204. Determine the current parameter value vector based on the satellite observations, the parameter prediction vector, and the parameter value vector update equation, and determine the current covariance vector based on the satellite observations, the parameter prediction vector, the covariance prediction vector, and the covariance vector update equation.

[0059] Through the above steps, multiple undetermined parameters can be predicted based on historical parameter value vectors to obtain a parameter prediction value vector. Then, the covariance of multiple undetermined parameters is predicted based on historical covariance vectors to obtain a covariance prediction value vector. Next, the parameter prediction value vector is updated according to the acquired satellite observations and parameter value vector update equations to obtain the current parameter value vector; and the covariance prediction value vector is updated according to the acquired satellite observations and covariance vector update equations to obtain the current covariance vector. This completes one iteration of the Kalman filter, and step S108 is executed. If the satellite position covariance of the target satellite satisfies the convergence condition, the target clock error used for timing is obtained.

[0060] In some embodiments of this application, determining the current parameter value vector based on satellite observations, parameter prediction vectors, and parameter value vector update equations may include: determining the observation update amount based on satellite observations, parameter prediction vectors, and linearized observation equations; and determining the current parameter value vector based on the observation update amount, parameter prediction vectors, and parameter value vector update equations.

[0061] In the above steps, the satellite observations and parameter prediction vectors can be processed by linearizing the observation equations using a non-difference, non-combined PPP model to obtain the observation update quantity. Based on this observation update quantity, the parameter prediction vector is updated using the parameter value vector update equation to obtain the current parameter value vector.

[0062] In some embodiments of this application, satellite observations include: pseudorange observations and carrier phase observations, satellite azimuth observations, and satellite clock bias observations for each satellite; the parameter prediction vector includes receiver azimuth predictions; and the observation update quantities include: pseudorange update quantities and carrier phase update quantities for each satellite.

[0063] Understandably, pseudorange observations, carrier phase observations, satellite azimuth observations, and satellite clock bias observations can all be obtained based on satellite signals acquired by the receiver in standard single-point mode. The satellite observations for each satellite correspond to the satellite signals acquired by the receiver.

[0064] The determination of observation updates based on satellite observations, parameter prediction vectors, and linearized observation equations includes: for any satellite, determining the pseudorange update using linearized observation equations based on pseudorange observations, satellite azimuth observations, clock error observations, and receiver azimuth predictions; and for any satellite, determining the carrier phase update using linearized observation equations based on carrier phase observations, satellite azimuth observations, clock error observations, and receiver azimuth predictions.

[0065] Based on the above steps, the pseudorange update and carrier phase update of any satellite can be obtained, and thus the pseudorange update and carrier phase update of each satellite can be obtained. The observation update is equivalent to the observation update vector representing the pseudorange update and carrier phase update of multiple satellites.

[0066] Specifically, the linearized observation equations of the non-difference, non-combined PPP model may include: a first linearized observation equation (1) and a second linearized observation equation (2):

[0067] (1)

[0068] (2)

[0069] in, Indicates satellite With receiver In the pseudorange update amount at frequency points Indicates satellite With receiver In the Pseudorange observations at frequency points Indicates satellite satellite azimuth, This represents the receiver's predicted azimuth value. Represents the speed of light. Indicates satellite Satellite clock bias observations Indicates satellite With receiver In the Carrier phase update amount at frequency point Indicates satellite With receiver In the Carrier phase observations at frequency points.

[0070] Specifically, the linearized observation equations of the non-difference, non-combined PPP model may also include:

[0071] (8)

[0072] (9)

[0073] in, Indicates satellite With receiver In the pseudorange update amount at frequency points Indicates satellite With receiver In the Carrier phase update amount at frequency point Indicates receiver For satellites Satellite observation vectors, This represents the position increment used to determine the receiver's azimuth. Represents the speed of light. Indicates the first Type of receiver Receiver clock bias, Indicates the first The ionospheric delay amplification factor at the frequency point, Indicates the tilted ionospheric delay at the first frequency point. This represents the tropospheric wet delay projection function. Indicates the wet tropospheric delay at the zenith of the station. Indicates the ranging error of spatial signals. This indicates pseudorange observation noise. Indicates the carrier wavelength. Indicates integer phase ambiguity. Carrier phase observation noise.

[0074] In some embodiments of this application, multiple satellites may include: BeiDou-2 type satellites and BeiDou-3 type satellites. The different receiver clock biases caused by BeiDou-2 type satellites and BeiDou-3 type satellites are reflected in the linearized observation equation of the non-difference non-combination PPP model provided in the embodiments of this application, which can achieve more accurate receiver timing.

[0075] The current parameter value vector obtained by any round of the Kalman filter algorithm is equivalent to determining multiple undetermined parameters in formulas (8) and (9). Based on formulas (8) and (9), the state vector of the Kalman filter can be used. express:

[0076]

[0077] in, It can represent the receiver clock bias for Beidou-2 type satellites and Beidou-3 type satellites, respectively.

[0078] In some embodiments of this application, the state prediction equation (4) is, and the covariance prediction equation (5) is.

[0079] (4)

[0080] (5)

[0081] in, Represents a vector of predicted parameter values. Represents a vector of historical parameter values. Indicates the current time The state transition matrix, Indicates the current time The control vector, Represents the vector of covariance predicted values. Represents the historical covariance vector. Indicates the current time The control noise vector.

[0082] Specifically, the receiver timing mode is static. The receiver azimuth, tropospheric wet delay projection function, tilted ionospheric delay at the first frequency, integer phase ambiguity, and spatial ranging error are all represented by historical parameter value vectors. The receiver clock bias for BeiDou-2 and BeiDou-3 satellites uses the current single-point positioning result, based on the current time. The control vector representation; the covariance of receiver azimuth, tropospheric wet delay projection function, tilted ionospheric delay at the first frequency, integer phase ambiguity, and spatial ranging error is added to the process noise Q. The receiver clock bias covariance for BeiDou-2 and BeiDou-3 satellites uses the initial receiver clock bias covariance value. Different initial values ​​are given for different state variables. The receiver azimuth covariance can be initially set to 3600. The initial value of the receiver clock error covariance for BeiDou-2 and BeiDou-3 satellites can be set to 8100. The initial covariance of the tropospheric wet delay projection function can be given according to the tropospheric calculation model. The initial covariance of the tilted ionospheric delay at the first frequency point can be set to 8100. The initial covariance of the integer phase ambiguity can be set to 3600. The initial covariance of the spatial ranging error can be set to 3600.

[0083] As an example, the state transition matrix may not change over time. Hengwei , For example:

[0084] ;

[0085] Current moment control vector and the current moment The control noise vector can be, for example, as follows:

[0086] ;

[0087] ;

[0088] in, and They are respectively The receiver clock difference between BeiDou-2 and BeiDou-3 satellites at all times. and These represent the receiver clock covariance values ​​for BeiDou-2 and BeiDou-3 satellites, respectively. This indicates that other parameters are pending. The covariance at time t.

[0089] In some embodiments of this application, step S204, which involves determining the current covariance vector based on satellite observations, the predicted covariance value vector, and the covariance vector update equation, may include:

[0090] The observation update quantity is determined based on satellite observations, parameter prediction vectors, and linearized observation equations;

[0091] The observation noise vector is determined based on the observation update quantity, the parameter prediction vector, and the Kalman observation equation.

[0092] The current covariance vector is determined based on the covariance prediction vector, the observation noise vector, and the covariance vector update equation.

[0093] In the above steps, the satellite observations and parameter prediction vectors can be processed by linearizing the observation equations using a non-difference, non-combined PPP model to obtain the observation update quantity. Based on this observation update quantity, the parameter prediction vector, and the Kalman observation equation, the observation noise vector is determined. Then, based on this observation noise vector, the covariance prediction value is updated using the covariance vector update equation to obtain the current covariance vector.

[0094] Specifically, the Kalman observation equation (10) can be expressed as follows:

[0095] (10)

[0096] in:

[0097]

[0098]

[0099]

[0100] in, This represents the observation vector, which includes pseudorange observations and carrier phase observations from different satellites at different frequencies. Indicates receiver The first observation The pseudorange observations of each satellite at the second frequency point Indicates receiver The first observation The carrier phase observation values ​​of each satellite at the second frequency point; This represents the observation noise vector, which includes pseudorange observation noise and carrier phase observation noise from different satellites at different frequencies. Indicates the first The pseudorange observation noise corresponding to the pseudorange observation value of the second frequency point of each satellite This represents the carrier phase observation noise corresponding to the carrier phase observation value at the second frequency point. The observation matrix can be determined based on formulas (8) and (9) in the linearized observation equation of the non-difference, non-combined PPP model. Indicates receiver For satellite number Satellite observation vectors of each satellite, This represents the ratio of the square of the first frequency point to the square of the second frequency point. Indicates the first Tropospheric wet delay projection function for each satellite. This represents the vector of predicted parameter values.

[0101] In some embodiments of this application, after obtaining the parameter prediction value vector, the current parameter value vector can be determined according to the parameter vector update equation (3). The parameter vector update equation (3) can be, for example, as follows:

[0102] (3)

[0103] in, Represents the vector of current parameter values. Represents a vector of predicted parameter values. This represents the observation vector, which includes the observation updates from different satellites. Indicates the current time The Kalman filter gain matrix, Indicates the current time The observation matrix.

[0104] In some embodiments of this application, after obtaining the covariance prediction vector and the observation noise vector, the current covariance vector can be determined according to the covariance vector update equation (6). The covariance vector update equation (6) can be, for example, as follows:

[0105] (6)

[0106] in, , Indicates the current time The current covariance vector, Represents the identity matrix. Indicates the current time The Kalman filter gain matrix, Indicates the current time The observation matrix Represents the vector of covariance predicted values. This represents the observation noise vector.

[0107] After obtaining the current parameter value vector and the current covariance vector based on the above steps, the target satellite whose position covariance meets the convergence condition can be determined according to the convergence condition and the current covariance vector. The clock error result of the target satellite can be determined according to the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector and the current position of the receiver.

[0108] In some embodiments of this application, the satellite observations of the target satellite include: first pseudorange observations and second pseudorange observations of the target satellite at dual-frequency points, satellite clock bias observations of the target satellite, and satellite azimuth observations. For example... Figure 3 As shown, step S108 may include:

[0109] S302. Determine the deionization combined pseudorange of the target satellite based on the first pseudorange observation and the second pseudorange observation at the dual-frequency point;

[0110] S304. Based on the combined pseudorange, the satellite clock bias observation and satellite azimuth observation of the target satellite, the tropospheric delay of the target satellite, and the current position of the receiver, determine the clock bias result, and determine the target clock bias for time synchronization based on the clock bias result.

[0111] In some embodiments of this application, the deionization pseudorange of the target satellite can be determined based on formula (11):

[0112] (11)

[0113] in, Indicates the pseudo-distance of the deionization layer. This represents the signal frequency corresponding to the first frequency point of the first pseudorange observation. This indicates the signal frequency corresponding to the second pseudorange observation point. This represents the first pseudorange observation. This represents the second pseudorange observation.

[0114] In some embodiments of this application, the clock bias result is determined based on formula (7):

[0115] (7)

[0116] in, Indicates target satellite relative receiver The clock difference results Indicates the combined pseudorange. Indicates satellite azimuth observations. This indicates the receiver's current position. Indicates satellite clock bias observations, This indicates the tropospheric time delay.

[0117] In determining the target clock difference for timing based on the single-point timing method for a receiver provided in the embodiments of this application, since the current covariance vector output by any round of the Kalman filter algorithm may include multiple satellite position covariances that can satisfy the convergence condition, multiple clock difference results can be used for timing. Therefore, in some embodiments of this application, determining the target clock difference for timing based on the clock difference results includes: when there are multiple target satellites, determining the target clock difference based on the median of the clock difference results of the multiple target satellites.

[0118] Based on the above-described embodiments of the single-point timing method for a receiver provided in this application, the single-point timing method for a receiver provided in this application can enable the receiver to achieve high-precision timing in standard single-point mode without the availability of precise ephemeris data. It is also unaffected by the activity level of the ionosphere, resulting in higher timing accuracy and stability, and extremely strong anti-interference performance.

[0119] This application also provides a single-point timing device for a receiver, including: a processor configured to execute a single-point timing method for a receiver according to the embodiments of this application.

[0120] This application also provides a receiver, including: a single-point timing device for the receiver as described above, and a receiving antenna. The receiving antenna is used to receive satellite observations.

[0121] This application also provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform a single-point timing method for a receiver according to an embodiment of this application.

[0122] It should be understood that although the steps in the flowcharts provided in this application are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0123] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a single-point time synchronization method for a receiver. The display screen A04 can be a liquid crystal display (LCD) or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0124] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0125] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0128] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0129] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0130] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0131] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0132] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

[0133] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A single-point timing method for a receiver, characterized in that, The single-point timing method, applied to a receiver, includes: The historical parameter value vector and historical covariance vector of multiple undetermined parameters in the linearized observation equation of the non-differential non-combined PPP model of multiple satellites and the receiver are obtained. The multiple undetermined parameters include: receiver clock bias of different types of satellites, tropospheric delay of each satellite, receiver azimuth and space signal ranging error. Acquire satellite observations from multiple satellites at dual-frequency points in standard single-point mode; Based on the satellite observations, the historical parameter value vector, and the historical covariance vector, the current parameter value vector and the current covariance vector are obtained through the Kalman filter algorithm. If the satellite position covariance of the target satellite in the current covariance vector satisfies the convergence condition, the clock bias result of the target satellite is determined based on the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector, and the current position of the receiver, so as to determine the target clock bias for time synchronization based on the clock bias result; The satellite observations of the target satellite include: the first pseudorange observation and the second pseudorange observation of the target satellite at the dual-frequency point, the satellite clock error observation and the satellite azimuth observation of the target satellite; The step of determining the clock bias result of the target satellite based on the satellite observations of the target satellite, the tropospheric delay of the target satellite in the current parameter value vector, and the current position of the receiver, and determining the target clock bias for time synchronization based on the clock bias result includes: The deionization combined pseudorange of the target satellite is determined based on the first pseudorange observation and the second pseudorange observation at the dual-frequency point. The clock bias result is determined based on the combined pseudorange, the satellite clock bias observation and satellite azimuth observation of the target satellite, the tropospheric delay of the target satellite, and the current position of the receiver, so as to determine the target clock bias for time synchronization based on the clock bias result.

2. The single-point time synchronization method according to claim 1, characterized in that, The step of obtaining the current parameter value vector and the current covariance vector using a Kalman filter algorithm based on the satellite observations, the historical parameter value vector, and the historical covariance vector includes: The parameter prediction vector of the current parameter value vector is determined based on the historical parameter value vector and the state prediction equation, and the covariance prediction vector of the current covariance vector is determined based on the historical covariance vector and the covariance prediction equation. The current parameter value vector is determined based on the satellite observations, the parameter prediction vector, and the parameter value vector update equation. The current covariance vector is determined based on the satellite observations, the parameter prediction vector, the covariance prediction vector, and the covariance vector update equation.

3. The single-point time synchronization method according to claim 2, characterized in that, The step of determining the current parameter value vector based on the satellite observations, the predicted parameter value vector, and the parameter value vector update equation includes: The observation update quantity is determined based on the satellite observations, the parameter prediction vector, and the linearized observation equation. The current parameter value vector is determined based on the observation update amount, the parameter prediction value vector, and the parameter value vector update equation.

4. The single-point time synchronization method according to claim 3, characterized in that, The satellite observations include: pseudorange observations and carrier phase observations, satellite azimuth observations, and satellite clock bias observations for each satellite; the parameter prediction vector includes receiver azimuth predictions; the observation updates include: pseudorange updates and carrier phase updates for each satellite. The step of determining the observation update based on the satellite observations, the parameter prediction vector, and the linearized observation equation includes: For any satellite, the pseudorange update is determined by the linearized observation equation based on the pseudorange observation, the satellite azimuth observation, the clock error observation, and the receiver azimuth prediction. For any given satellite, the carrier phase update is determined by the linearized observation equation based on the carrier phase observation, the satellite azimuth observation, the clock error observation, and the receiver azimuth prediction.

5. The single-point time synchronization method according to claim 4, characterized in that, The linearized observation equations include: a first linearized observation equation (1) and a second linearized observation equation (2): ；（1） ；（2） in, Indicates satellite With receiver In the pseudorange update amount at frequency points Indicates satellite With receiver In the Pseudorange observations at frequency points Indicates satellite Satellite position, This represents the receiver's predicted azimuth value. Represents the speed of light. Indicates satellite Satellite clock bias observations Indicates satellite With receiver In the Carrier phase update amount at frequency point Indicates satellite With receiver In the Carrier phase observations at frequency points.

6. The single-point time synchronization method according to claim 3, characterized in that, The parameter value vector update equation (3) is: ；（3） in, This represents the vector of current parameter values. This represents the vector of predicted parameter values. This represents the observation vector, which includes observation updates from different satellites. Indicates the current time The Kalman filter gain matrix, Indicates the current time The observation matrix.

7. The single-point time synchronization method according to claim 2, characterized in that, The state prediction equation (4) is, and the covariance prediction equation (5) is. ；（4） ； （5） in, This represents the vector of predicted parameter values. This represents the vector of historical parameter values. Indicates the current time The state transition matrix, Indicates the current time The control vector, This represents the covariance predicted value vector. Represents the historical covariance vector. Indicates the current time The control noise vector.

8. The single-point time synchronization method according to claim 2, characterized in that, The step of determining the current covariance vector based on the satellite observations, the predicted covariance vector, and the covariance vector update equation includes: The observation update quantity is determined based on the satellite observations, the parameter prediction vector, and the linearized observation equation. The observation noise vector is determined based on the observation update amount, the parameter prediction vector, and the Kalman observation equation. The current covariance vector is determined based on the predicted covariance value vector, the observed noise vector, and the covariance vector update equation.

9. The single-point time synchronization method according to claim 8, characterized in that, The covariance vector update equation (6) is as follows: ；（6） in, , Indicates the current time The current covariance vector, Represents the identity matrix. Indicates the current time The Kalman filter gain matrix, Indicates the current time The observation matrix This represents the covariance predicted value vector. This represents the observed noise vector.

10. The single-point time synchronization method according to claim 1, characterized in that, The clock difference result is determined based on formula (7): ； （7） in, Indicates target satellite relative receiver The clock difference results This represents the combined pseudorange. This indicates the satellite's azimuth observations. This indicates the current position of the receiver. This indicates the satellite clock bias observation. This indicates the tropospheric delay.

11. The single-point time synchronization method according to claim 1, characterized in that, The step of determining the target clock difference for time synchronization based on the clock difference result includes: When there are multiple target satellites, the target clock bias is determined based on the median of the clock bias results of the multiple target satellites.

12. A single-point timing device for a receiver, characterized in that, include: The processor is configured to execute the single-point timing method for a receiver according to any one of claims 1 to 11.

13. A receiver, characterized in that, include: The single-point timing device for a receiver as described in claim 12; A receiving antenna is used to receive the satellite observations.

14. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions that, when executed by a processor, cause the processor to perform the single-point timing method for a receiver according to any one of claims 1 to 11.

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