A non-differential, non-combination, precise single-point positioning method that takes into account signal distortion deviation

Abstract

This invention relates to a non-differential, non-combinational precise point positioning method that takes into account signal distortion deviation, belonging to the field of satellite navigation. The method acquires and preprocesses signal distortion deviation estimation data; it then constructs a non-differential, non-combinational precise point positioning function model that takes into account signal distortion deviation using the preprocessed data. Within the framework of non-differential, non-combinational precise point positioning, the function model introduces a signal distortion deviation parameter related to the satellite, receiver, and frequency into the pseudorange observation equation to absorb the residual effects of unresolved signal distortion; it employs a constraint-based elimination function model with zero reference satellite signal distortion deviation to eliminate rank deficiency; and it performs point positioning calculations based on the function model, estimating the optimal state parameters, including signal distortion deviation, at the current moment, thus achieving a fusion of positioning calculation and signal distortion deviation compensation. This invention provides a low-cost, high-efficiency, one-step solution for real-time high-precision positioning applications.

Description

Technical Field

[0001] This invention relates to the fields of satellite navigation, aerospace measurement and control, and in particular to a non-differential, non-combination precision single-point positioning method that takes into account signal distortion deviations. Background Technology

[0002] Precise point positioning services provide users with dynamic decimeter-level and static centimeter-level location and time information, making them highly valuable for applications. Compared to traditional ionospheric-free combined models, the non-differential, non-combined precise point positioning model retains all original observations and uses the ionosphere as a parameter estimate. It possesses logical rigor, including complete information, a rigorous model, model uniformity, and inherent compatibility, achieving faster convergence speed, higher positioning accuracy, and better reliability. Non-differential, non-combined precise point positioning services that integrate multiple frequencies and constellations can accelerate users' convergence time to the minute level and are widely used in high-precision positioning, ionospheric research, and other rapid convergence applications.

[0003] Signal distortion bias, a pseudorange measurement constant deviation exhibited by user terminals due to satellite navigation signal distortion, is a significant error source discovered in recent years that impacts the service performance and business processing performance of satellite navigation systems. Signal distortion bias reduces the convergence time of precise point positioning (PPS) services, affecting the real-time performance of PPS processing. Therefore, it is necessary to optimize and upgrade the function model of non-differential, non-combined PPS services to overcome the impact of signal distortion bias on the convergence performance and real-time processing performance of non-differential, non-combined PPS services.

[0004] The existing technology has the following limitations:

[0005] In the BDS-2 / 3 tightly coupled scheme, signal distortion bias is eliminated by re-estimating satellite clock bias and system bias. However, the system-side calculation is complex and it is not adapted to the non-differential non-combined model architecture.

[0006] In the common-clock zero-baseline correction scheme, the zero baseline is used to calibrate the signal distortion deviation at the receiver end, but a user positioning solution model that takes into account this deviation is not constructed, so the positioning performance is still affected.

[0007] In the hardware-delay time-varying precision single-point positioning-AR scheme, the time-varying differential code deviation and phase fractional deviation are estimated to improve positioning performance, but the direct impact of signal distortion deviation on the non-differential non-combination model is not considered.

[0008] In the ambiguity weighted constraint scheme, a full-rank, non-difference, non-combination full-parameter estimation model is constructed to uniformly estimate various delay parameters, but signal distortion deviation is not included in the parameter system to be estimated.

[0009] Therefore, existing technologies do not estimate and compensate for signal distortion deviation as an independent parameter in real time during the non-differential and non-combined precise single-point positioning solution at the user end, making it difficult to eliminate its impact on convergence performance from the source. Summary of the Invention

[0010] Based on the above analysis, this invention aims to disclose a non-differential, non-combination precise single-point positioning method that takes into account signal distortion deviation; it establishes a non-differential, non-combination precise single-point positioning function model that takes into account signal distortion deviation, and estimates the deviation in real time through the user terminal to avoid its adverse effects on convergence speed and real-time performance, thereby achieving deep integration of positioning solution and deviation compensation.

[0011] This invention discloses a non-differential, non-combination precise single-point positioning method that takes into account signal distortion deviation, comprising:

[0012] Acquire station observation data, broadcast ephemeris, precise ephemeris and differential code deviation products for signal distortion bias estimation, and perform preprocessing including cycle slip detection and gross error removal;

[0013] A non-differential, non-combined, precise single-point positioning function model that takes into account signal distortion bias is constructed using preprocessed data. Under the framework of non-differential, non-combined, precise single-point positioning, the function model introduces a signal distortion bias parameter that is related to the satellite, receiver, and frequency into the pseudorange observation equation to absorb the residual effects of signal distortion that have not been eliminated.

[0014] The constraint elimination function model with zero distortion deviation of the reference satellite signal is used to eliminate rank deficiency;

[0015] Based on the function model, single-point positioning is calculated, and the optimal estimate of state parameters, including signal distortion deviation, is calculated at the current moment, realizing the fusion processing of positioning calculation and signal distortion deviation compensation.

[0016] Furthermore, in the non-differential, non-combined, precise single-point positioning function model that takes into account signal distortion bias, the first... The original pseudorange and carrier phase observation equations for the frequency are expressed as:

[0017] ;

[0018] In the formula, Indicates frequency, Indicates satellite, Indicates receiver, and These represent the pseudorange and phase observations, respectively. Indicates the geometric distance between the satellite and the receiver. ,in and These represent the three-dimensional coordinates of the receiver and the satellite in the geocentric-ground-fixed coordinate system, respectively. and These represent the frequency-independent physical clock biases at the receiver and satellite ends, respectively. The first-order tilted ionospheric delay is the first frequency. For the first The ionospheric delay factor of frequency, , For the first carrier frequency of frequency For tilted tropospheric delay, and The receiver end and the satellite end are respectively the first The pseudorange hardware delay of the frequency, For the first Frequency signal distortion deviation and The receiver end and the satellite end are respectively the first frequency phase hardware delay, For the first Phase ambiguity at frequency, Indicates the first Frequency pseudorange measurement noise, multipath effects, and other unmodeled errors. Indicates the first Frequency pseudorange and carrier phase measurement noise, multipath effects, and other unmodeled errors.

[0019] Furthermore, the satellite clock bias product of the precise ephemeris is estimated based on a dual-frequency deionization combination, and its absorbed bias component is expressed as follows:

[0020] ;

[0021] in, For actual precision satellite clock bias, and carrier frequency and The combination coefficients, and For the pseudorange hardware delay at the satellite end of the two carrier frequencies, The signal distortion deviation absorbed by the satellite clock bias.

[0022] Furthermore, the linearized dual-frequency pseudorange and carrier phase observation equations are expressed as follows:

[0023] ;

[0024] in, and Carrier frequency Pseudorange and carrier phase observations minus calculated values; The unit vector representing the line-of-sight direction from the receiver to the satellite; Represents the receiver's three-dimensional coordinates; This indicates that the pseudorange hardware delay at the receiver end has been absorbed. This represents the first frequency tilted path ionospheric delay estimate, which incorporates the receiver pseudorange hardware delay. Indicates the wet delay of the zenith troposphere. Represents the wet delay projection function. They represent the carrier frequencies respectively. It incorporates pseudorange and phase hardware delays from both the receiver and satellite ends, as well as signal distortion bias and phase ambiguity estimations from precision satellite clock products. Indicates carrier frequency Signal distortion bias estimation =1,2.

[0025] Furthermore, , , , , , Specifically, it is expressed as follows:

[0026] ;

[0027] in, carrier frequency at the receiver end pseudorange hardware delay; , The carrier frequencies for the receiver and satellite are respectively. Phase hardware delay.

[0028] Furthermore, the satellite with the best observation geometry was selected as the reference satellite. its first frequency The signal distortion deviation parameter is forcibly constrained to zero; that is...

[0029] ;

[0030] To eliminate the rank deficiency problem between signal distortion deviation parameters.

[0031] Furthermore, the selection of the reference satellite is based on the following criteria: among all observation satellites in the current epoch, the satellite with the highest elevation angle and the best pseudorange observation quality is selected as the reference satellite.

[0032] Furthermore, the Kalman filtering algorithm is used for single-point localization; the state equation and observation equation are as follows:

[0033] ;

[0034] In the formula, , The first Liyuanhedi All state parameters of the epoch, For the first to The state transition matrix of an epoch. For the first A vector consisting of all observations in an epoch. To design the matrix, and The mean is zero and the variance-covariance matrix is ​​respectively and Dynamic noise and observation noise.

[0035] Furthermore, the state parameters estimated by the Kalman filter include: receiver three-dimensional coordinates, receiver clock error, zenith tropospheric wet delay, ionospheric delay in the line-of-sight direction of each satellite, phase ambiguity of each frequency of each satellite, and signal distortion deviation parameters of each frequency of each satellite.

[0036] Furthermore, the Kalman filter uses the following recursive formula to calculate the optimal estimate of the current state parameters:

[0037] ;

[0038] in, For the reason epochal prediction The predicted state vector of an epoch. For the first The state estimation vector after epoch filtering;

[0039] The covariance matrix of the predicted state, For the first The covariance matrix of the estimated state at each epoch; Let Variance be the variance-covariance matrix of the dynamic noise;

[0040] Here is the gain matrix. The variance-covariance matrix of the observation noise; superscript " " is the matrix transpose symbol;

[0041] For the first State estimation after epoch filtering;

[0042] The covariance matrix of the filtered state; It is an identity matrix.

[0043] This invention can achieve one of the following beneficial effects:

[0044] The present invention discloses a non-differential non-combination precise single-point positioning method that takes into account signal distortion deviation. The signal distortion deviation is treated as an independent and estimable parameter to be estimated. There is no need to calibrate the receiver for signal distortion deviation in advance. By absorbing the signal distortion deviation at the model level, it is estimated in real time along with other parameters during the positioning solution process. This eliminates the impact of the deviation on the convergence speed and accuracy from the source of the user positioning solution.

[0045] In this method, signal distortion deviation is embedded as an independent parameter strongly correlated with the satellite-receiver-frequency three-dimensional model into the non-differential, non-combined precise single-point positioning function model. It is estimated and compensated synchronously in real time during user-end positioning calculation, without any prior calibration or system-end preprocessing. This completely eliminates the pollution of pseudorange observation by the deviation from the source, shortens the convergence time, and improves positioning accuracy. The method uses zero constraints of the reference satellite to solve the parameter rank deficiency problem and relies on Kalman filtering to achieve epoch-by-epoch dynamic decoupling. This significantly reduces the dependence on external precision products and hardware configuration costs, and significantly enhances the autonomy and robustness in complex urban environments and high-dynamic scenarios. It provides a low-cost, high-efficiency, and one-step innovative solution for real-time high-precision positioning applications. Attached Figure Description

[0046] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0047] Figure 1 This is a flowchart of the non-differential non-combination precise single-point positioning method that takes into account signal distortion deviation in an embodiment of the present invention. Detailed Implementation

[0048] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and, together with the embodiments of the present invention, serve to illustrate the principles of the present invention.

[0049] One embodiment of the present invention discloses a non-differential, non-combination precise single-point positioning method that takes into account signal distortion deviations, such as... Figure 1 As shown, it includes:

[0050] S1. Acquire station observation data, broadcast ephemeris, precise ephemeris and differential code deviation products for signal distortion deviation estimation, and perform preprocessing including cycle slip detection and gross error removal;

[0051] S2. Construct a non-differential, non-combined, precise single-point positioning function model that takes into account signal distortion deviation using preprocessed data; Under the framework of non-differential, non-combined, precise single-point positioning, the function model introduces a signal distortion deviation parameter that is related to the satellite, receiver, and frequency into the pseudorange observation equation to absorb the residual effects of signal distortion that have not been eliminated.

[0052] S3. The constraint elimination function model with zero distortion deviation of the reference star signal is adopted to eliminate rank deficiency;

[0053] S4. Based on the function model, perform single-point positioning calculation, calculate the optimal estimate of state parameters including signal distortion deviation at the current moment, and realize the fusion processing of positioning calculation and signal distortion deviation compensation.

[0054] Specifically, S1, acquire station observation data, broadcast ephemeris, precise ephemeris and differential code deviation products for signal distortion bias estimation, and perform preprocessing including cycle slip detection and gross error removal;

[0055] In the preprocessing, TurboEdit-type algorithms can be used in conjunction with geometrically independent combinations and Melbourne-Wübbena combinations to detect cycle slips and mark them for reset. The 3σ criterion is used to remove gross errors. Based on the satellite elevation angle and signal-to-noise ratio, dynamic weighting is used to filter out low elevation angle and low-quality observations.

[0056] Specifically, in S2, in the non-differential, non-combined, precise single-point positioning function model that takes into account signal distortion deviation, the first... The original pseudorange and carrier phase observation equations for the frequency are expressed as:

[0057] ;

[0058] In the formula, Indicates frequency, Indicates satellite, Indicates receiver, and These represent the pseudorange and phase observations, respectively. Indicates the geometric distance between the satellite and the receiver. ,in and These represent the three-dimensional coordinates of the receiver and the satellite in the geocentric-ground-fixed coordinate system, respectively. and These represent the frequency-independent physical clock biases at the receiver and satellite ends, respectively. The first-order tilted ionospheric delay is the first frequency. For the first The ionospheric delay factor of frequency, , For the first carrier frequency of frequency For tilted tropospheric delay, and The receiver end and the satellite end are respectively the first The pseudorange hardware delay of the frequency, For the first Frequency signal distortion deviation and The receiver end and the satellite end are respectively the first frequency phase hardware delay, For the first Phase ambiguity at frequency, Indicates the first Frequency pseudorange measurement noise, multipath effects, and other unmodeled errors. Indicates the first Frequency pseudorange and carrier phase measurement noise, multipath effects, and other unmodeled errors.

[0059] Precision satellite clock products use two carrier frequencies based on a ground tracking network. and The ionosphere-free (IF) combination is estimated, therefore the precision satellite clock bias will not only absorb the pseudorange hardware delay at the satellite end of the two carrier frequencies, but will also be affected by signal distortion deviation.

[0060] The absorption bias component of the satellite clock error product obtained based on the dual-frequency de-ionization combination estimation is expressed as follows:

[0061] ;

[0062] in, For actual precision satellite clock bias, and carrier frequency and The combination coefficients, and For the pseudorange hardware delay at the satellite end of the two carrier frequencies, The signal distortion deviation absorbed by the satellite clock bias.

[0063] When using the precision satellite clock error product after differential code deviation correction for precision single-point positioning calculation, the pseudorange hardware delay contained in the satellite end will cancel each other out with the pseudorange hardware delay of the pseudorange observation value itself. However, due to the existence of signal distortion deviation, there will be parts in the pseudorange observation equation that cannot be canceled out.

[0064] The signal distortion bias parameter introduced in the pseudorange observation equation is specifically the residual signal distortion bias parameter, and its expression is:

[0065] ;

[0066] parameter Related to satellites, receivers, and frequencies, it is used to absorb the residual effects of signal distortion that has not been eliminated in precision satellite clock products.

[0067] This part of the phase observation equation can be absorbed by the ambiguity parameter, thus affecting the accuracy of the pseudorange observation equation and prolonging the convergence time. Therefore, a new signal distortion bias parameter is introduced into the pseudorange observation equation to absorb this bias.

[0068] The linearized dual-frequency pseudorange and carrier phase observation equations are expressed as follows:

[0069] ;

[0070] in, and Carrier frequency Pseudorange and carrier phase observations minus calculated values; The unit vector representing the line-of-sight direction from the receiver to the satellite; Represents the receiver's three-dimensional coordinates; This indicates that the pseudorange hardware delay at the receiver end has been absorbed. This represents the first frequency tilted path ionospheric delay estimate, which incorporates the receiver pseudorange hardware delay. Indicates the wet delay of the zenith troposphere. Represents the wet delay projection function. They represent the carrier frequencies respectively. It incorporates pseudorange and phase hardware delays from both the receiver and satellite ends, as well as signal distortion bias and phase ambiguity estimations from precision satellite clock products. Indicates carrier frequency Signal distortion bias estimation =1,2.

[0071] Furthermore, , , , , , Specifically, it is expressed as follows:

[0072] ;

[0073] in, carrier frequency at the receiver end pseudorange hardware delay; , The carrier frequencies for the receiver and satellite are respectively. Phase hardware delay.

[0074] By establishing linearized dual-frequency pseudorange and carrier phase observation equations, a standard form for synchronously estimating bias and positioning parameters is provided for parameter estimation, thereby eliminating the impact of residual bias on convergence speed and accuracy from the source.

[0075] In S3, the satellite with the best observation geometry is selected as the reference satellite. The signal distortion deviation parameter of its first frequency is forcibly constrained to zero.

[0076] ;

[0077] To eliminate the rank deficiency problem between signal distortion deviation parameters.

[0078] In the preferred embodiment, the reference satellite is selected based on the following criteria: among all observation satellites in the current epoch, the satellite with the highest elevation angle and the best pseudorange observation quality is selected as the reference satellite.

[0079] In S4, based on the established linearized dual-frequency pseudorange and carrier phase observation equations, the Kalman filtering algorithm is used to perform single-point positioning calculation. The Kalman filtering algorithm is based on the minimum variance criterion. It predicts the state at the current time based on the previous time estimate of the parameter to be estimated, and corrects the predicted value using the current time observation value, thereby deriving the optimal estimate of the state parameter at the current time.

[0080] The state equations and observation equations for single-point localization are as follows:

[0081] ;

[0082] In the formula, , The first Liyuanhedi All state parameters of the epoch, For the first to The state transition matrix of an epoch. For the first A vector consisting of all observations in an epoch. To design the matrix, and The mean is zero and the variance-covariance matrix is ​​respectively and Dynamic noise and observation noise.

[0083] Preferably, the state parameters estimated by the Kalman filter include: receiver three-dimensional coordinates, receiver clock error, zenith tropospheric wet delay, ionospheric delay in the line-of-sight direction of each satellite, phase ambiguity of each frequency of each satellite, and signal distortion deviation parameters of each frequency of each satellite.

[0084] ;

[0085] in, 3D receiver coordinates; For receiver clock bias; Let be the ionospheric delay vector of n visible satellites. For the zenith tropospheric wet delay, It is a 2n-dimensional ambiguity vector; It is a 2n-dimensional signal distortion deviation vector.

[0086] State transition matrix :

[0087] ;

[0088] The first-order Gaussian Markov attenuation factor for ionospheric delay. The sampling interval is... is the ionospheric delay-related time constant.

[0089] The observation vector estimated by the Kalman filter includes all the observed satellites s arranged in sequence. The residual value.

[0090] The Kalman filter uses the following recursive formula to calculate the optimal estimate of the current state parameters:

[0091] ;

[0092] in, For the reason epochal prediction The predicted state vector of an epoch. For the first The state estimation vector after epoch filtering;

[0093] The covariance matrix of the predicted state, For the first The covariance matrix of the estimated state at each epoch; Let Variance be the variance-covariance matrix of the dynamic noise;

[0094] Here is the gain matrix. The variance-covariance matrix of the observation noise; superscript " " is the matrix transpose symbol;

[0095] For the first State estimation after epoch filtering;

[0096] The covariance matrix of the filtered state; It is a unit moment.

[0097] Thus, the signal distortion deviation estimate can be obtained epoch by epoch. The accuracy of this estimate will gradually improve as filtering is performed, thereby realizing the fusion processing of positioning solution and signal distortion deviation compensation.

[0098] In summary, the non-differential non-combination precise single-point positioning method that takes into account signal distortion deviation disclosed in this embodiment of the invention treats signal distortion deviation as an independent and estimable parameter to be estimated. It eliminates the need for prior calibration of the receiver to assess signal distortion deviation. By absorbing the signal distortion deviation at the model level, it estimates it in real time along with other parameters during the positioning calculation process, thereby eliminating the impact of this deviation on convergence speed and accuracy from the source of the user's positioning calculation.

[0099] In this method, signal distortion deviation is embedded as an independent parameter strongly correlated with the satellite-receiver-frequency three-dimensional model into the non-differential, non-combined precise single-point positioning function model. It is estimated and compensated synchronously in real time during user-end positioning calculation, without any prior calibration or system-end preprocessing. This completely eliminates the pollution of pseudorange observation by the deviation from the source, shortens the convergence time, and improves positioning accuracy. The method uses zero constraints of the reference satellite to solve the parameter rank deficiency problem and relies on Kalman filtering to achieve epoch-by-epoch dynamic decoupling. This significantly reduces the dependence on external precision products and hardware configuration costs, and significantly enhances the autonomy and robustness in complex urban environments and high-dynamic scenarios. It provides a low-cost, high-efficiency, and one-step innovative solution for real-time high-precision positioning applications.

[0100] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A non-differential, non-combination precision single-point positioning method that takes into account signal distortion deviation, characterized in that, include: Acquire station observation data, broadcast ephemeris, precise ephemeris and differential code deviation products for signal distortion bias estimation, and perform preprocessing including cycle slip detection and gross error removal; A non-differential, non-combined, precise single-point positioning function model that takes into account signal distortion bias is constructed using preprocessed data. Under the framework of non-differential, non-combined, precise single-point positioning, the function model introduces a signal distortion bias parameter that is related to the satellite, receiver, and frequency into the pseudorange observation equation to absorb the residual effects of signal distortion that has not been eliminated. The constraint elimination function model with zero distortion deviation of the reference satellite signal is used to eliminate rank deficiency; Based on the function model, single-point positioning is calculated, and the optimal estimate of state parameters, including signal distortion deviation, is calculated at the current moment, realizing the fusion processing of positioning calculation and signal distortion deviation compensation. In the non-differential, non-combined, precise single-point positioning function model that takes into account signal distortion bias, the first... The original pseudorange and carrier phase observation equations for the frequency are expressed as follows: ； In the formula, Indicates frequency, Indicates satellite, Indicates receiver, and These represent the pseudorange and phase observations, respectively. Indicates the geometric distance between the satellite and the receiver. ,in and These represent the three-dimensional coordinates of the receiver and the satellite in the geocentric coordinate system, respectively. and These represent the frequency-independent physical clock biases at the receiver and satellite ends, respectively. The first-order tilted ionospheric delay is the first frequency. For the first The ionospheric delay factor of frequency, , For the first carrier frequency of frequency For tilted tropospheric delay, and The receiver end and the satellite end are respectively the first The pseudorange hardware delay of the frequency, For the first Frequency signal distortion deviation and The receiver end and the satellite end are respectively the first frequency phase hardware delay, For the first Phase ambiguity at frequency, Indicates the first Frequency pseudorange measurement noise, multipath effects, and other unmodeled errors. Indicates the first Frequency pseudorange and carrier phase measurement noise, multipath effects, and other unmodeled errors.

2. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation as described in claim 1, characterized in that, The satellite clock bias product of the precise ephemeris is estimated based on a dual-frequency deionization combination, and its absorbed bias component is expressed as follows: ； in, For actual precision satellite clock bias, and carrier frequency and The combination coefficients, and For the pseudorange hardware delay at the satellite end of the two carrier frequencies, The signal distortion deviation absorbed by the satellite clock bias.

3. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation according to claim 2, characterized in that, The linearized dual-frequency pseudorange and carrier phase observation equations are expressed as follows: ； in, and Carrier frequency Pseudorange and carrier phase observations minus calculated values; The unit vector representing the line-of-sight direction from the receiver to the satellite; Represents the receiver's three-dimensional coordinates; This indicates that the pseudorange hardware delay at the receiver end has been absorbed. This represents the first frequency tilted path ionospheric delay estimate, which incorporates the receiver pseudorange hardware delay. Indicates the wet delay of the zenith troposphere. Represents the wet delay projection function. They represent the carrier frequencies respectively. It incorporates pseudorange and phase hardware delays from both the receiver and satellite ends, as well as signal distortion bias and phase ambiguity estimations from precision satellite clock products. Indicates carrier frequency Signal distortion bias estimation =1,2.

4. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation according to claim 3, characterized in that, , , , , , Specifically, it is expressed as follows: ； in, carrier frequency at the receiver end pseudorange hardware delay; , The carrier frequencies for the receiver and satellite are respectively. Phase hardware delay.

5. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation according to claim 4, characterized in that, Select the satellite with the best observation geometry as the reference satellite. its first frequency The signal distortion deviation parameter is forcibly constrained to zero; that is... ； To eliminate the rank deficiency problem between signal distortion deviation parameters.

6. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation according to claim 5, characterized in that, The selection criteria for the reference satellite are as follows: among all observation satellites in the current epoch, the satellite with the highest elevation angle and the best pseudorange observation quality is selected as the reference satellite.

7. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation according to claim 3, characterized in that, The Kalman filtering algorithm is used for single-point localization; the state equation and observation equation are as follows: ； In the formula, , The first Liyuanhedi All state parameters of the epoch, For the first to The state transition matrix of an epoch. For the first A vector consisting of all observations in an epoch. To design the matrix, and The mean is zero and the variance-covariance matrix is ​​respectively and Dynamic noise and observation noise.

8. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation according to claim 7, characterized in that, The state parameters estimated by the Kalman filter include: receiver three-dimensional coordinates, receiver clock error, zenith tropospheric wet delay, ionospheric delay in the line-of-sight direction of each satellite, phase ambiguity of each frequency of each satellite, and signal distortion deviation parameters of each frequency of each satellite.

9. The non-differential, non-combination, precise single-point positioning method considering signal distortion deviation according to claim 8, characterized in that, The Kalman filter uses the following recursive formula to calculate the optimal estimate of the current state parameters: ； in, For the reason epochal prediction The predicted state vector of an epoch. For the first The state estimation vector after epoch filtering; The covariance matrix of the predicted state, For the first The covariance matrix of the estimated state at each epoch; Let Variance be the variance-covariance matrix of the dynamic noise; Here is the gain matrix. The variance-covariance matrix of the observation noise; superscript " " is the matrix transpose symbol; For the first State estimation after epoch filtering; The covariance matrix of the filtered state; It is an identity matrix.

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