Beidou precise point positioning method applied to flood environment

Abstract

The application discloses a Beidou precise point positioning method applied to a flood environment, and comprises the following steps: S1, collecting original observation values of Beidou satellites in the flood environment; S2, eliminating the influence of pseudo-range multipath error by a multi-path correction algorithm based on a time-space dual-domain strategy; S3, eliminating the influence of cycle slip by an adaptive threshold cycle slip detection and repair algorithm based on a signal-to-noise ratio constraint; S4, weighting the observation values by a random model based on a signal-to-noise ratio weighting, so as to improve positioning precision; S5, eliminating the influence of different Beidou satellite orbit types by a satellite type weighting strategy algorithm; S6, eliminating the influence of signal quality differences of different Beidou satellite frequencies by a frequency weighting strategy; and S7, solving a position by a Beidou precise point positioning algorithm, and saving and outputting a result. The method can effectively overcome problems such as large pseudo-range multipath error, high cycle slip mis-detection rate and decreased positioning precision caused by the flood, reduces the pseudo-range multipath error, and improves the pseudo-range observation value precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the global satellite navigation positioning technology field, and in particular to a Beidou precise point positioning method applied to a flood environment. BACKGROUND

[0002] At present, the conventional algorithm of precise point positioning based on the Beidou navigation satellite system is mainly applied to ordinary observation environments. In the ordinary observation environment, the observation conditions are good, the signal quality is good, and the corresponding positioning algorithm is mature. However, when in the complex environment of flood, the surrounding environment will change due to the flood, and the signal quality will decrease significantly, which will cause the existing related algorithm to be no longer applicable, and finally cause the positioning accuracy and positioning speed to decrease seriously. First, the reflectivity and refractivity of the flood surface are much higher than those of the dry land, so the flood environment will cause obvious multipath error, especially the pseudo-range multipath error will increase significantly, which will affect the accuracy of the pseudo-range observation value, and will cause the existing positioning algorithm to slow down the solving speed and the accuracy to decrease, etc. Secondly, the pseudo-range observation value accuracy caused by the multipath error decreases, which will further cause the false detection rate of cycle slip to increase, the small cycle slip to be difficult to repair, etc., which will cause the existing cycle slip detection and repair algorithm to fail, the carrier phase observation value error to increase, and the positioning accuracy and positioning speed to decrease. Therefore, it is necessary to study the corresponding cycle slip detection and repair algorithm to meet the cycle slip detection and repair in the flood environment. Finally, the correlation between the signal quality and the elevation angle in the flood environment is much lower than that between the signal-to-noise ratio. Therefore, the existing random model based on the elevation angle weighting is no longer applicable, and it is necessary to study the weighting model based on the signal-to-noise ratio to improve the positioning accuracy. In addition, the orbit and frequency characteristics of the Beidou satellite also need to be further refined to ensure the precision of the Beidou satellite precise point positioning. The flood experiment in Zhengzhou on July 20, 2021 shows that the flood will cause the Beidou navigation system satellite pseudo-range multipath error to increase by 30%, the cycle slip false detection rate to increase by about 13%, and the precise point positioning accuracy to decrease by about 25%. Therefore, it is one of the problems to be solved to study the Beidou high-precision precise point positioning algorithm in the flood environment, and to provide high-precision positioning results for the location service in the flood environment. SUMMARY

[0003] The present application is applied to the Beidou precise point positioning method under the flood environment, which can effectively overcome the problems of large pseudo-range multipath error, high false detection rate of cycle slip, and decline of positioning accuracy caused by flood. For the problem of increasing pseudo-range multipath error, the space-time dual-domain strategy based multipath correction algorithm is used to reduce the pseudo-range multipath error and improve the accuracy of pseudo-range observation value, laying a foundation for subsequent positioning. For the problems of high false detection rate of cycle slip and difficult repair of small cycle slip, the adaptive threshold code phase combination single frequency cycle slip detection and repair algorithm based on signal-to-noise ratio constraint is used to improve the cycle slip detection accuracy, reduce the false detection rate of cycle slip, improve the accuracy of the original carrier phase observation value, and ensure the accuracy of subsequent positioning. For the problem of decline of positioning accuracy, the improved signal-to-noise ratio weighted random model is used to eliminate the decline of positioning accuracy caused by low correlation of height angle random model and error under the flood environment. In addition, considering the differences of the three satellite orbit types of Beidou satellite navigation system and the different influences of flood environment, the satellite type weighting strategy is used to eliminate the influence of the differences of satellite characteristics, and ensure the accuracy and stability of Beidou high-precision positioning under the flood environment. At the same time, considering the differences of the three frequency bands of Beidou, the frequency weighting strategy is used to eliminate the influence of the differences between frequencies, and further ensure the accuracy of Beidou positioning under the flood environment.

[0004] In order to solve the above technical problems, the technical scheme of the present application is:

[0005] A Beidou precise point positioning method applied to flood environment, comprising the following steps:

[0006] S1, collecting the original observation value of Beidou satellite under the flood environment, the original observation value including original signal-to-noise ratio observation value, original pseudo-range observation value, original carrier phase observation value, and calculating the corresponding elevation angle and azimuth angle;

[0007] S2, eliminating the influence of pseudo-range multipath error based on the space-time dual-domain strategy multipath correction algorithm;

[0008] S2-1, original data preprocessing

[0009] The code phase observation value is generated by using the pseudo-range and carrier phase original observation value, and the expression is as follows:

[0010]

[0011] In the formula, CM represents the single frequency pseudo-range code phase combination observation value, P is the original pseudo-range observation value, is the original carrier phase observation value, the unit is cycle, and λ is the frequency wavelength;

[0012] The signal-to-noise ratio constraint strategy is used to smooth the single frequency code phase combination multipath observation value, and the fixed bias is eliminated, and the algorithm is as follows:

[0013]

[0014] CM o-c represents the single-frequency pseudo-range code-phase combined observation value after eliminating the fixed bias amount, which only contains single-frequency pseudo-range multipath error and noise, CM i represents the original single-frequency pseudo-range code-phase combined observation value, i represents the variable serial number, SNR represents the signal-to-noise ratio of the corresponding epoch, represents a smoothing method based on signal-to-noise ratio constraint;

[0015] A Gaussian filtering algorithm based on signal-to-noise ratio constraint is used to eliminate the influence of random noise and extract high-precision pseudo-range multipath error. A one-dimensional signal-to-noise ratio constraint Gaussian filtering function is used for denoising, which is specifically represented as:

[0016]

[0017] In the formula, G(x) is a Gaussian kernel function, the width of the Gaussian function is determined by τ, τ is calculated by the standard deviation of the signal-to-noise ratio, e and π are natural constants, and Std(˙) represents the calculation standard deviation.

[0018] The specific denoising process is as follows: assuming that the signal to be processed containing pseudo-range multipath error and noise is represented as CM o_c (x), the signal containing only pseudo-range multipath error after filtering is represented as CM m , and the calculation process is represented as follows:

[0019] CM m (x)=CM o_c (x)*G(x)

[0020] In the formula, x represents the serial number, G(x) is a Gaussian function, and the symbol * represents convolution operation.

[0021] S2-2, the pseudo-range multipath error CM m is calculated according to the Beidou satellite number, satellite frequency, elevation angle and azimuth angle information, and is stored in the local computer in the form of a database;

[0022] S2-3, a multipath correction algorithm based on a space-time dual-domain strategy is used to correct the pseudo-range multipath error of the observation day;

[0023] S3, an adaptive threshold cycle slip detection and repair algorithm based on signal-to-noise ratio constraint is used to eliminate the influence of cycle slip;

[0024] S4, a random model based on signal-to-noise ratio weighting is used to weight the observation value, thereby improving the positioning accuracy;

[0025] S5, using satellite type weighting strategy algorithm, eliminating the influence of different types of Beidou satellite orbits;

[0026] S6, using frequency weighting strategy to eliminate the influence of different frequency signal quality differences of Beidou satellite;

[0027] S7, Beidou precise point positioning algorithm is used to solve the position, and the result is saved and output.

[0028] As preferred, in step S2-3, the pseudo-range multipath error correction method is as follows:

[0029] S2-3-1, calculate the space information and time information required for pseudo-range multipath correction, which is convenient for subsequent pseudo-range multipath correction, the space information refers to the elevation angle and azimuth angle information of the satellite, which is calculated directly when receiving data, and the time information refers to the satellite orbit repetition period deviation, which is calculated by broadcast ephemeris and Kepler third law;

[0030] S2-3-2, using space strategy to correct the pseudo-range multipath of Beidou IGSO and MEO satellites, the space strategy is to use the elevation angle and azimuth angle information of the observed satellite to be corrected in the established pseudo-range multipath error database to search for the reference value of the pseudo-range multipath error in the database, and then subtract the value from the original pseudo-range observation value, which is expressed as:

[0031] P o-m =P o -CM m

[0032] In the formula, P o is the original pseudo-range observation value, CM m is the reference value of the pseudo-range multipath error extracted from the database, and P o-m is the accurate pseudo-range observation value after pseudo-range multipath error correction;

[0033] S2-3-3, using time domain strategy to correct the pseudo-range multipath of Beidou GEO satellite, the time domain strategy is to use the satellite orbit repetition period deviation to correct the pseudo-range multipath error of Beidou GEO satellite, and then use the satellite orbit repetition period deviation to search for the corresponding pseudo-range multipath error reference value in the database, which is expressed as:

[0034] X=T s / X T

[0035] In the formula, T s is the satellite orbit repetition period deviation, X T is the original observation value interval, and X is the corresponding epoch searched,

[0036] The value is used to extract the corresponding CM from the established database m The value is then subtracted from the original pseudo-range observation value, and after processing by the pseudo-range multipath correction method described above, an accurate pseudo-range observation value can be obtained.

[0037] As a preferred, in the step S2-3-1, the calculation method of the satellite orbit repetition period deviation is as follows:

[0038]

[0039] Wherein, n is the average angular velocity of the satellite at the observation time, is the earth gravity constant, G is the universal gravitational constant, M is the total mass of the earth. a is the long radius of the elliptical orbit, Δn is the perturbation parameter of the satellite, both of which are given in the broadcast ephemeris. T represents the satellite repetition period, once the satellite repetition period is determined, the time domain value of the search can be obtained by the following formula:

[0040] T s = 86400-T

[0041] Wherein, 86400 is a solar day in seconds, T s is the time domain satellite orbit repetition period deviation.

[0042] As a preferred, the step S3 includes the following sub-steps:

[0043] S3-1, the corrected pseudo-range observation value and the original carrier phase observation value are combined again to form a code phase observation value, which is specifically represented as:

[0044]

[0045] In the formula, CM o-m represents the single frequency pseudo-range code phase combination observation value obtained after pseudo-range multipath correction, P o-m represents the pseudo-range observation value after pseudo-range multipath correction, is the original carrier phase observation value, and λ is the frequency wavelength. Similarly, the code phase combination observation value after removing the multipath error is smoothed by step S2-1, the fixed bias component is eliminated, and the random noise effect is eliminated, and the high-precision code phase combination residual is extracted. The precise cycle slip detection quantity is obtained by taking the difference between epochs:

[0046] ΔCM d (x i ) = CM c (x i ) - CM c (x i-1 )

[0047] In the formula, CMc (x) is high-precision code phase combination residual, ΔCM d (x) represents the residual difference of two adjacent epochs, i.e. cycle slip detection measure, i represents the signal sequence;

[0048] S3-2, the cycle slip is detected by using an adaptive threshold strategy, and a cycle slip detection model of the adaptive threshold strategy is represented as:

[0049] |ΔCM d (x i )|>K

[0050] In the formula, K is a threshold weighting coefficient, ΔCM d (x) represents a cycle slip detection residual value;

[0051] S3-3, the cycle slip epoch detected is repaired, and the number of cycle slips generated is calculated, and the specific formula is represented as:

[0052]

[0053] In the formula, ΔCM d (x) represents a cycle slip detection residual value, and λ is a frequency wavelength. After the accurate number of cycle slips is determined, the repair is performed.

[0054] As preferred, the step S4 comprises the following sub-steps:

[0055] S4-1, a low-order polynomial fitting is adopted to eliminate the influence of a signal-to-noise ratio direct signal, and the low-order polynomial fitting algorithm is represented as:

[0056] S p (x i )=a0+a1x i +a2x i 2 +a3x i 3

[0057] In the formula, S p (x) represents a signal-to-noise ratio direct signal after third-order polynomial fitting, x represents a sequence of original signal-to-noise ratio observation values, i represents a data point, and a represents a third-order polynomial fitting coefficient. Since the third-order polynomial is adopted for fitting, i.e. the highest order of x is 3, the fitting coefficient is 4, which is a0, a1, a2, and a3. In order to minimize the error after fitting, the polynomial fitting coefficient is obtained by least square, which can be represented as:

[0058]

[0059] In the formula, P min represents that the root mean square of the overall error after fitting is minimized, and S p(x) represents the signal-to-noise ratio direct signal after third-order polynomial fitting, S o (x) represents the original signal-to-noise ratio observation value, i represents a data point, n represents the total length of data, and P min , the partial derivative of the above formula is obtained:

[0060]

[0061] In the formula, m represents the order of the polynomial, j = 0, 1,..., m, k represents the sequence number of m, and after sorting, the following formula is obtained:

[0062]

[0063] wherein, only a k is an unknown quantity, which can be solved by a linear equation to obtain a k coefficient,

[0064] After the polynomial fitting coefficient a k is obtained, the low-order polynomial fitting algorithm can be used to obtain the fitted signal-to-noise ratio direct signal, and then the signal-to-noise ratio direct signal is eliminated, which is represented as:

[0065] S o-p (x i ) = S o (x i ) - S p (x i )

[0066] The signal affected by the elimination of the signal-to-noise ratio direct signal is obtained, that is, S o-p (x);

[0067] S4-2, according to the algorithm in step S2-1, the signal-to-noise ratio constrained Gaussian filtering algorithm is used for denoising to eliminate the influence of noise, and the signal-to-noise ratio signal without the influence of noise is obtained, which is represented as: S r (x);

[0068] S4-3, using the signal-to-noise ratio without the influence of direct signal and noise, a weighted random model based on signal-to-noise ratio is established, and the specific weighted random model is represented as:

[0069]

[0070] In the formula, C represents a constant, S r (x i ) represents the signal-to-noise ratio without the influence of direct signal and noise at the i th epoch, and σ represents the accuracy at the epoch, which is used for weighted processing of the epoch.

[0071] As preferred, in step S5, the satellite type weighting strategy algorithm is realized by a least square algorithm:

[0072] Assuming that the least square algorithm is directly used to solve the position parameters, it is expressed as:

[0073] X x,y,z =G -1 b

[0074] In the formula, G is the Jacobian matrix of the linear equation set, -1 represents the matrix inversion, and b represents the residual formed by the equation set. Based on the weighting strategy of the satellite orbit type, the position parameters can be directly solved by using the above formula. Since the orbit types of Beidou satellites are different, it is necessary to perform weighting processing on the Beidou satellites according to the orbit types, that is, the solving formula is expressed as:

[0075] X x,y,z =(G T W T WG) -1 G T W T Wb

[0076] In the formula, T represents the matrix transposition operation, and W is the weighting matrix. Since the signal quality of the MEO satellite of the three orbit types of Beidou is the best, the IGSO satellite is the second, and the GEO satellite is the worst, the weighting matrix based on the orbit type of the Beidou satellite can be expressed as:

[0077]

[0078] In the formula, w geo ,w isgo ,w meo respectively represent the weighting coefficients of the Beidou GEO satellite, the IGSO satellite and the MEO satellite.

[0079] As a preferred, in the step S6, the algorithm of the frequency weighting strategy is the same as the algorithm of the satellite type weighting strategy in the step S5, and the difference lies in that the weighting matrix setting needs to be distinguished according to the frequency. Since among the three frequency types of Beidou, the signal-to-noise ratio of B3 is the highest under the same elevation angle, B1 is the second, and B2 is the worst, the weighting matrix based on the frequency type of the Beidou satellite can be expressed as:

[0080]

[0081] In the formula, w B2 ,w B1 ,w B3 respectively represent the weighting coefficients of the Beidou satellite B2, B1 and B3 frequencies.

[0082] As a preferred, in the step S7, the Beidou precise point positioning algorithm solves the position method as follows:

[0083] The pseudo-range observation value after multi-path correction and the carrier phase observation value after cycle slip detection and repair can be used for position solution of Beidou precise point positioning, assuming that the position of the receiver is: x r ,y r and z r , the basic equation for solution can be expressed as:

[0084]

[0085] In the formula, P o-m is the pseudo-range observation value after pseudo-range multi-path correction, is the carrier phase observation value after cycle slip detection and repair, P o-m and are known quantities, b represents the frequency sequence of Beidou satellite, s represents the satellite sequence of Beidou, x s ,y s and z s represent the position of the satellite, c represents the light speed in vacuum, represents the clock difference of the receiver and the satellite, I ion represents the ionospheric error, T ion represents the tropospheric error, lambda b is the frequency wavelength of the b frequency band, is the carrier phase integer ambiguity of the s satellite b frequency, epsilon p and represent the random noise of the pseudo-range and carrier phase observation values.

[0086] The present application has the following characteristics and beneficial effects:

[0087] With the technical scheme, the problem that the existing Beidou precise point positioning related algorithm is not applicable to the flood environment can be solved, compared with the existing method, the problem of increased multipath error, high false detection rate of cycle slip, and difficult repair of small cycle slip in the flood environment can be solved, and the problem of low positioning accuracy and slow positioning speed caused by weak correlation between elevation angle and error in the flood environment and the problem of low positioning accuracy and slow positioning speed caused by neglecting the differences in satellite orbit and frequency characteristics can also be solved. Considering that the flood environment can cause the multipath error of pseudorange to increase, a multipath correction algorithm based on a time-space dual domain strategy is used to correct the multipath error of the pseudorange observation value, and the accuracy of the pseudorange observation value is improved. The accuracy of cycle slip detection is improved, the false detection rate of cycle slip is reduced, and the accuracy of the carrier phase observation value is improved by using an adaptive threshold code phase combination single frequency cycle slip detection algorithm based on signal-to-noise ratio constraint. The problem of low positioning accuracy caused by low correlation between elevation angle and error in the flood environment is eliminated by using an improved signal-to-noise ratio weighted random model. The influence of the difference between the types of Beidou satellite orbits is eliminated by using a satellite orbit type weighting strategy algorithm, and the positioning accuracy and speed of Beidou are improved. The positioning accuracy and stability in the flood environment are ensured. The influence of the difference between the characteristics of the frequencies is eliminated by using a frequency weighting strategy, and the stability of Beidou positioning in the flood environment is further ensured. BRIEF DESCRIPTION OF DRAWINGS

[0088] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0089] Figure 1 A flow chart of a Beidou precise point positioning method applied to the flood environment according to an embodiment of the present application.

[0090] Figure 2 A flow chart of the method of step S2 in the embodiment of the present application.

[0091] Figure 3 A flow chart of the method of step S3 in the embodiment of the present application.

[0092] Figure 4 A flow chart of the method of step S3 in the embodiment of the present application. DETAILED DESCRIPTION

[0093] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0094] In the description of the present application, it needs to be understood that the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" and the like can be explicitly or implicitly included one or more. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0095] In the description of the present application, it needs to be understood that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood through specific circumstances.

[0096] The present application provides a Beidou precise point positioning method applied to flood environment, as shown in Figure 1 The method comprises the following steps:

[0097] S1, collecting the original observation value of Beidou satellite under flood environment, the original observation value includes original signal-to-noise ratio observation value, original pseudo-range observation value, original carrier phase observation value, and calculating the corresponding elevation angle and azimuth angle, it can be understood that the calculation process of auxiliary characteristic parameters such as elevation angle and azimuth angle is simple, which belongs to the conventional method in the field, which will not be described here.

[0098] S2, the multi-path correction algorithm based on space-time dual domain strategy eliminates the influence of pseudo-range multi-path error;

[0099] S3, the adaptive threshold cycle slip detection and repair algorithm based on signal-to-noise ratio constraint is used to eliminate the influence of cycle slip;

[0100] S4, the observation value is weighted based on signal-to-noise ratio weighted random model to improve the positioning accuracy;

[0101] S5, satellite type weighted strategy algorithm is used to eliminate the influence of different Beidou satellite orbit types;

[0102] S6, using a frequency weighting strategy to eliminate the influence of different frequency signal quality of Beidou satellite;

[0103] S7, Beidou precise point positioning algorithm to solve the position, the results are saved and output, it should be noted that the use of precise point positioning algorithm to solve the position, the position is saved and output. Precise point positioning algorithm in the field is usually solved by least squares or Kalman filter algorithm, which belongs to the common sense in the field, and the detailed process is not given here.

[0104] Specifically, as shown in Figure 2 In step S2, the multipath correction algorithm based on the space-time dual domain strategy corrects the multipath error of the pseudorange observation value, and filters out the influence of the multipath error, including the following substeps:

[0105] S2-1, original data preprocessing

[0106] The original observation value data of Beidou satellite on the reference day is collected, mainly including original pseudorange, carrier phase, signal-to-noise ratio and other observation values, and the corresponding elevation angle, azimuth angle and epoch time interval and other auxiliary characteristic information are calculated. The reference day is relative to the observation day, for example, the measurement on July 8, for Beidou IGSO and GEO satellites, the reference day refers to the day before the observation day, that is, July 7. Because the ground repetition period of Beidou IGSO and GEO satellites is 1 day. And for Beidou MEO satellites, the reference day refers to the day before the observation day, that is, July 1, because the ground repetition period of Beidou MEO satellites is 7 days. In addition, the calculation process of auxiliary characteristic parameters such as elevation angle and azimuth angle is relatively simple, which belongs to the conventional method in the field, and will not be described here. Further, the code phase combined observation value is generated by using the pseudorange and carrier phase original observation value, and the expression is as follows:

[0107]

[0108] In the formula, CM represents the single-frequency pseudorange code phase combined observation value, P is the original pseudorange observation value, is the original carrier phase observation value, and the unit is cycle. P and are known quantities, which are directly obtained from the original observation value collection. Lambda is the frequency wavelength, which belongs to the common sense in the field. In this embodiment, the wavelength of Beidou satellite B1 frequency is 19.0 cm.

[0109] Because the carrier phase observation value in formula (1) has integer ambiguity, it needs to be eliminated. Considering that the signal-to-noise ratio and the carrier phase error are positively correlated, the signal-to-noise ratio constraint strategy is used to smooth the code phase combined observation value to eliminate the fixed bias. The smoothing algorithm based on the signal-to-noise ratio constraint strategy is expressed as:

[0110]

[0111] CM o-c represents the single-frequency pseudorange code-phase combined observation value eliminating the fixed bias component, which only contains the single-frequency pseudorange multipath error and noise. i represents the original single-frequency pseudorange code-phase combined observation value, i.e. the value calculated in formula (1). i represents the variable serial number, and SNR represents the signal-to-noise ratio of the corresponding epoch signal, which is a known quantity. represents the smoothing method with signal-to-noise ratio constraint, which smoothes CM by dividing SNR into three categories: less than 35 dB-Hz, 35-50 dB-Hz, and greater than 50 dB-Hz, respectively. The correlation between signal-to-noise ratio and error is effectively taken into account, and the accuracy of the smoothed single-frequency pseudorange code-phase combined observation value is improved. After eliminating the fixed bias component, only the pseudorange multipath error and noise are obtained.

[0112] A Gaussian filtering algorithm based on signal-to-noise ratio constraint is used to eliminate the influence of random noise and extract high-precision pseudorange multipath error. Since the multipath signal is a one-dimensional signal, and the signal-to-noise ratio and noise have correlation, a one-dimensional signal-to-noise ratio constrained Gaussian filtering function is used for denoising, which is specifically represented as:

[0113]

[0114] In the formula, G(x) is the Gaussian kernel function, the width of the Gaussian function is determined by τ, τ is calculated by the standard deviation of the signal-to-noise ratio, e and π are natural constants, respectively, in the present embodiment, e and π are set to 2.72 and 3.14, respectively. Std(˙) represents the calculation of standard deviation, which is a mathematical field common sense, which will not be described here.

[0115] The specific denoising process is as follows: assuming that the signal containing pseudorange multipath error and noise to be processed is represented as CM o_c (x), the signal containing only pseudorange multipath error after filtering is represented as CM m , and the calculation process is represented as follows:

[0116] CM m (x) = CM o_c (x) * G(x) (4)

[0117] In the formula, x represents the serial number, G(x) is the Gaussian function, and the symbol * represents convolution operation, which is a basic knowledge in the field of signal processing, and will not be described in detail here. After the above processing, the accurate pseudorange multipath error is extracted, which provides a basis for subsequent multipath error modeling.

[0118] S2-2, the precise pseudo-range multipath error is stored according to the elevation angle and azimuth angle by using the satellite and frequency, which is convenient for subsequent search and correction. That is, the pseudo-range multipath error obtained in formula (4) is stored according to the Beidou satellite number, satellite frequency, elevation angle and azimuth angle information, and stored in the local computer in the form of a database.

[0119] S2-3, the multipath correction algorithm based on the space-time dual domain strategy is used to correct the pseudo-range multipath error of the observation day. That is, the pseudo-range multipath error correction is performed on the pseudo-range observation value in step 1. Since the Beidou satellite types are divided into IGSO, MEO and GEO three types of satellites, among which IGSO and MEO satellites belong to medium orbit satellites and are in motion state, the satellites will be affected by on-orbit maneuvering, so the space domain model needs to be used for correction. The GEO satellite cannot be corrected by using the space domain model because the elevation angle and azimuth angle are static, so the time domain model needs to be used for correction.

[0120] Further, the pseudo-range multipath error correction method is as follows:

[0121] S2-3-1, the space domain and time domain information required for pseudo-range multipath correction is calculated, which is convenient for subsequent pseudo-range multipath correction. The space domain information mainly refers to the satellite elevation angle and azimuth angle information, which is calculated directly when data is received, and belongs to the field of common knowledge. The time domain information mainly refers to the satellite orbit repetition period deviation, which is calculated by broadcast ephemeris and Kepler's third law, and the specific calculation formula is as follows:

[0122]

[0123] Wherein, n is the average angular velocity of the satellite at the observation time, G is the gravitational constant of the earth (1.996498×107 SI unit), G is the gravitational constant, and M is the total mass of the earth. a is the long radius of the elliptical orbit, and Δn is the perturbation parameter of the satellite, both of which are given in the broadcast ephemeris. T represents the satellite repetition period, once the satellite repetition period T is determined, the time domain value of the search is obtained by the following formula:

[0124] T s = 86400-T (6)

[0125] Wherein, 86400 is a solar day in seconds, and T is obtained by formula (5). T s is the time domain satellite orbit repetition period deviation.

[0126] S2-3-2, the space strategy is used to correct the pseudo-range multipath error of Beidou IGSO and MEO satellites, that is, the elevation angle and azimuth angle information of the satellite to be corrected on the observation day is used to search in the established pseudo-range multipath error database. In this embodiment, assuming that the current satellite is B07 satellite B1 frequency at a certain epoch, its elevation angle and azimuth angle are 30 degrees and 110 degrees respectively, the above information is used to search in the pseudo-range multipath error database established in S2-2. The satellite number and frequency are unique and easy to determine. Then the corresponding elevation angle and azimuth angle are searched, the closest one is the best search value, the reference value of the pseudo-range multipath error in the database is obtained, and then the value is subtracted from the original pseudo-range observation value, which is expressed as:

[0127] P o-m o -CM m (7)

[0128] In the formula, P o is the original pseudo-range observation value, CM m is the reference value of the pseudo-range multipath error extracted from the database, and P o-m is the accurate pseudo-range observation value after the pseudo-range multipath error correction.

[0129] S2-3-3, the time domain strategy is used to correct the pseudo-range multipath error of Beidou GEO satellites, that is, the satellite orbit repeat period deviation is used to correct the pseudo-range multipath error of Beidou GEO satellites. It can be understood that in this embodiment, the current satellite is B01 satellite B1 frequency, and the corresponding satellite orbit repeat period deviation is 200 seconds. Then find B01 B1 frequency from the data, since both of them are unique, it is easy to determine. Then the satellite orbit repeat period deviation is used to search the corresponding pseudo-range multipath error reference value in the database, which is expressed as:

[0130] X=T s / X T (8)

[0131] In the formula, T s is the satellite orbit repeat period deviation, which is obtained from formula (6). X T is the interval between original observation epochs, which is obtained in step 1, and the process is very simple, that is, the interval between two epochs can be obtained. X is the corresponding epoch searched, and the corresponding CM m value can be extracted from the established database by using the value, and then the value is subtracted from the original pseudo-range observation value. The specific process is the same as formula (7). After the above pseudo-range multipath correction method is processed, the accurate pseudo-range observation value is obtained, which lays a foundation for subsequent cycle slip detection and positioning solution.

[0132] ​Specifically, as shown in Figure 3 The step S3 detects and repairs the cycle slip by using an adaptive threshold code phase combination single-frequency cycle slip detection and repair algorithm based on signal-to-noise ratio constraint, improves the cycle slip detection accuracy, and improves the accuracy of the carrier phase observation value. The following sub-steps are included:

[0133] S3-1, the corrected pseudo-range observation value and the original carrier phase observation value are combined into a code phase observation value again, which is specifically represented as:

[0134]

[0135] In the formula, CM o-m represents the single-frequency pseudo-range code phase combination observation value obtained after pseudo-range multipath correction, P o-m represents the pseudo-range observation value after pseudo-range multipath correction, is the original carrier phase observation value, and λ is the frequency wavelength, which is common knowledge in the field. In this embodiment, the wavelength of the B1 frequency of the Beidou satellite is 19.0 cm. Similarly, the code phase combination observation value is smoothed based on the signal-to-noise ratio constraint strategy, and the specific process is represented as:

[0136]

[0137] In the formula, CM o-m-c represents the single-frequency pseudo-range code phase combination observation value after eliminating the fixed bias, which only contains single-frequency pseudo-range noise. CM o-m represents the single-frequency pseudo-range code phase combination observation value obtained after pseudo-range multipath correction, which is the value obtained in formula (9). SNR represents the signal-to-noise ratio of the corresponding epoch signal, which is a known quantity. represents the smoothing method of the signal-to-noise ratio constraint, which divides SNR into three categories: less than 35 dB-Hz, 35-50 dB-Hz, and greater than 50 dB-Hz. CM o-m is smoothed, effectively considering the correlation between signal-to-noise ratio and error, and improving the accuracy of the smoothed single-frequency pseudo-range code phase combination observation value.

[0138] A Gaussian filtering algorithm based on signal-to-noise ratio constraint is used to eliminate the influence of random noise and extract high-precision code phase combination residuals. The specific one-dimensional signal-to-noise ratio constraint Gaussian filtering function denoising process is as follows:

[0139]

[0140] In the formula, G(x) is a Gaussian kernel function, the width of the Gaussian function is determined by τ, τ is calculated by the standard deviation of the signal-to-noise ratio, e and π are natural constants, respectively, in the embodiment, e and π are set to 2.72 and 3.14. Std(·) represents the calculation of the standard deviation, which belongs to the mathematical field, and will not be described here.

[0141] The specific denoising process is represented as: assuming that the signal to be processed containing noise is represented as CM o-m-c (x) (i.e. obtained in formula 10), the signal after removing noise is represented as CM c (x), the calculation process is represented as follows:

[0142] CM c (x) = CM o-m-c (x) * G(x) (12)

[0143] In the formula, x represents the serial number, G(x) is a Gaussian function, and the * symbol represents convolution operation, which is basic knowledge in the field of signal processing and will not be described in detail here. After the above processing, the influence of random noise is effectively eliminated, and the code phase combination observation value precision is improved.

[0144] It should be noted that, at this time, the code phase combination is formed by removing the multipath error, therefore, it does not contain the multipath error, only the high-precision code phase combination residual is left. By taking the difference between epochs on the residual, the accurate cycle slip detection measure is obtained:

[0145] ΔCM d (x i ) = CM c (x i ) - CM c (x i-1 ) (13)

[0146] In the formula, CM c (x) is a high-precision code phase combination residual, ΔCM d (x) represents the residual difference value of adjacent two epochs, i.e. the cycle slip detection measure, i represents the signal sequence, and it should be noted that, since the difference algorithm is backward difference, i.e. the difference between the current epoch and the previous epoch, and for the first epoch, since there is no epoch value before for difference, the first epoch is not recorded, and the recording starts from the second epoch.

[0147] S3-2, the cycle slip is detected by using an adaptive threshold strategy, the existing threshold strategy can cause high false detection rate of cycle slip detection in flood environment, thereby causing the detection accuracy to decrease. Therefore, the cycle slip detection model based on the adaptive threshold strategy is represented as:

[0148] |ΔCMd (x i )|>K (14)

[0149] where K is a threshold weighting coefficient, and ΔCM d (x) is the cycle slip detection residual value,

[0150] It can be understood that in practical application, the weighting coefficient is selected according to the following rule

[0151] K = 0.5 * Std (SNR) (15)

[0152] where 0.5 is set as an initial threshold value, and an experienced value is given. SNR represents a signal-to-noise ratio, and Std( ) represents a calculation standard deviation, which is common knowledge in the mathematical field and will not be described here in detail. If the cycle slip detection value is greater than the detection threshold value, it is considered that there is a cycle slip, and marking is performed. If it is less than the value, it is considered that there is no cycle slip, and marking is not performed.

[0153] S3-3, repairing the cycle slip epochs detected, calculating the cycle slip number generated according to formula (16), and the specific formula is represented as:

[0154]

[0155] where ΔCM d (x) is the cycle slip detection residual value, and λ is a frequency wavelength, which is common knowledge in the field. In the embodiment, the wavelength of the B1 frequency of the Beidou satellite is 19.0 cm.

[0156] The accurate cycle slip number is determined by formula (16), and then repaired. In the embodiment, the total number of epochs of the B1 frequency of the B07 satellite is 1000, and if 2 cycle slips occur at the 50th epoch, 2 is added to the carrier phase observation value of the 50th epoch and all epochs thereafter. It is noted that 2 is added here because the unit of the carrier phase observation value is cycle.

[0157] After the above process is processed, the cycle slip detection success rate is effectively improved, the cycle slip is effectively repaired, the accuracy of the carrier phase observation value is improved, and a foundation is laid for subsequent positioning.

[0158] Further settings of the present application are as follows: Figure 4 As shown, the step S4 adopts a random model based on an improved signal-to-noise ratio to weight the positioning algorithm, improve the positioning calculation accuracy, and includes the following sub-steps:

[0159] S4-1, a low-order polynomial fitting algorithm is used to eliminate the influence of a signal-to-noise ratio direct signal, and the low-order polynomial fitting algorithm is represented as:

[0160] S p (x i ) = a0 + a1xi +a2x i 2 +a3x i 3 (17)

[0161] wherein S p (x) represents the signal-to-noise ratio direct signal after third-order polynomial fitting, x represents the sequence of original signal-to-noise ratio observation values, i represents the data sequence, and a represents the third-order polynomial fitting coefficient. Since the third-order polynomial is used for fitting, the highest order of x is 3, and thus the fitting coefficient is 4, i.e., a0, a1, a2, and a3. In order to minimize the error after fitting, the polynomial fitting coefficient is obtained by least square, and is represented as:

[0162]

[0163] wherein P min represents the minimum root mean square of the overall error after fitting, S p (x) represents the signal-to-noise ratio direct signal after third-order polynomial fitting, S o (x) represents the original signal-to-noise ratio observation value, i represents the data sequence, and n represents the total length of data. In order to calculate P min , the partial derivative of the above formula (18) is obtained as:

[0164]

[0165] wherein m represents the order of the polynomial, and in the embodiment, the third-order polynomial is used, and thus m is equal to 3. j = 0, 1,..., m, and k represents the sequence number of m. After the formula (19) is arranged, the following formula (20) is obtained:

[0166]

[0167] wherein all the symbols have the same meaning as formula (19). As can be seen, after the partial derivative is arranged, only a k coefficients in formula (20) are unknown, and thus the a k coefficients are obtained by solving the linear equation.

[0168] After the polynomial fitting coefficient a k is obtained, the signal-to-noise ratio direct signal after fitting can be obtained by using the low-order polynomial fitting algorithm, and then the signal-to-noise ratio direct signal is eliminated, and is represented as:

[0169] S o-p (x i ) = S o (x i ) - S p (x i ) (21)

[0170] The signal eliminating the influence of the signal-to-noise ratio direct signal, that is, S o-p (x);

[0171] S4-2, a Gaussian filtering algorithm based on signal-to-noise ratio constraint is used for denoising to eliminate the influence of noise. Since the signal-to-noise ratio is a one-dimensional signal, a one-dimensional signal-to-noise ratio constraint Gaussian filtering function denoising algorithm is used here for denoising, and the specific process is as follows:

[0172]

[0173] In the formula, G(x) is a Gaussian kernel function, the width of the Gaussian function is determined by τ, τ is calculated by the standard deviation of the signal-to-noise ratio, e and π are natural constants, respectively, in the embodiment, e and π are set to 2.72 and 3.14. Std(˙) represents the calculation of the standard deviation, which belongs to the common sense of the mathematical field, which will not be described here.

[0174] The specific denoising process is: assuming that the signal containing noise to be processed is represented as S o-p (x) (i.e. obtained in formula (21)), the signal after eliminating noise is represented as S r (x), the calculation process is represented as follows:

[0175] S r (x) = S o-p (x) * G(x) (23)

[0176] In the formula, x represents the serial number, G(x) is a Gaussian function, and the symbol represents convolution operation, which belongs to the basic knowledge of the signal processing field, and will not be described in detail here. After the above processing, the influence of random noise is effectively eliminated, and the signal-to-noise ratio signal without noise influence is obtained, represented as: S r (x);

[0177] S4-3, using the signal-to-noise ratio without the influence of direct signal and noise, a weighted random model based on signal-to-noise ratio is established to improve the positioning calculation accuracy. The specific weighted random model is represented as:

[0178]

[0179] In the formula, C represents a constant, which is given by an empirical value, and the specific Beidou satellite is: 0.0024. S r (x i ) represents the signal-to-noise ratio without the influence of direct signal and noise at the i-th epoch, and σ represents the accuracy at the epoch, which is used for weighted processing of the epoch, which belongs to the common sense of the field, which will not be described here.

[0180] Further, the step S5 adopts a satellite orbit type based weighting strategy algorithm to eliminate the influence of the differences between the orbit types of the Beidou satellites, and to improve the Beidou positioning accuracy and speed. In the field of Beidou navigation and positioning, in order to solve the positioning position, the least square algorithm or the Kalman filtering algorithm is usually used to solve the position parameters. In order to explain the satellite orbit type based weighting strategy, the least square algorithm is used to explain the weighting process. It should be noted that the weighting process can also be applied to the Kalman filtering algorithm. The satellite type weighting strategy algorithm is realized by the least square algorithm:

[0181] Suppose that the least square algorithm is directly used to solve the position parameters, which is represented as:

[0182] X x,y,z =G -1 b (25)

[0183] In the formula, G is the Jacobian matrix of the linear equation set, -1 represents the inverse of the matrix, b represents the residual formed by the equation set, and the above process belongs to the common sense in the positioning field, which will not be described here. The main purpose here is to explain the satellite orbit type based weighting strategy. The position parameters are directly solved by the formula (25). Since the orbit types of the Beidou satellites are different, the Beidou satellites need to be weighted according to the orbit types, that is, the formula for solving is represented as:

[0184] X x,y,z =(G T W T WG) -1 G T W T Wb (26)

[0185] In the formula, G and b are the same as formula (25). T represents the matrix transposition operation, which belongs to the common sense in the mathematical field. W is the weighting matrix. Since the signal quality of the MEO satellite of the three orbit types of the Beidou satellite is the best, the IGSO satellite is the second, and the GEO satellite is the worst, the weighting matrix based on the orbit type of the Beidou satellite can be represented as:

[0186]

[0187] In the formula, w geo ,w isgo ,w meo respectively represent the weighting coefficients of the Beidou GEO satellite, the IGSO satellite and the MEO satellite. In this embodiment, according to the signal quality of the three satellites, the weights are set as 1:2:3.

[0188] Through the above satellite orbit type based weighting strategy, the problem of different positioning solution accuracies caused by different orbit types of the Beidou satellites can be eliminated.

[0189] Further, in the step S6, the frequency weighting strategy is adopted to eliminate the influence of the difference between frequencies, further ensuring the stability of the Beidou positioning in the flood environment. The main process of the step algorithm is the same as that of step 5, and the main difference lies in that the weighting matrix setting needs to be distinguished according to the frequency. Among the three frequency types of Beidou, the signal-to-noise ratio of B3 is the highest, B1 is the second, and B2 is the worst at the same elevation angle. Therefore, the weighting matrix based on the frequency type of the Beidou satellite can be represented as:

[0190]

[0191] In the formula, w B2 ,w B1 ,w B3 respectively represent the weighting coefficients of the B2, B1 and B3 frequencies of the Beidou satellite. In the embodiment, w B2 ,w B1 ,w B3 are respectively set to 1:2:3.

[0192] Through the above-mentioned weighting strategy based on the frequency type of the Beidou satellite, the problem of different positioning solution accuracies caused by different frequency types of the Beidou satellite can be eliminated, and the Beidou positioning solution accuracy is further improved.

[0193] Specifically, in the step S7, the approximate process of the Beidou precise point positioning algorithm for solving the position is as follows. The pseudo-range observation value after the multipath correction and the carrier phase observation value after the cycle slip detection and repair can be used for the position solution of the Beidou precise point positioning. Assuming that the position of the receiver is: x r ,y r and z r , the basic equation for solving can be represented as:

[0194]

[0195] In the formula, P o-m is the pseudo-range observation value after the pseudo-range multipath correction, is the carrier phase observation value after the cycle slip detection and repair. Therefore, P o-m and are known quantities. b represents the frequency sequence of the Beidou satellite, and s represents the sequence of the Beidou satellite. x s ,y s and z s represent the positions of the satellites, which can be directly obtained by the Beidou precise ephemeris (the Beidou precise ephemeris can be downloaded from the official website) in the Beidou precise point positioning, and belong to the common knowledge in the field. c represents the speed of light in vacuum, which belongs to the common knowledge in the field and is also a known quantity. represents the clock difference between the receiver and the satellite, which is an unknown quantity and needs to be solved together with the position parameter of the receiver. I ionrepresents ionospheric error, which can be directly obtained through ionospheric grid file, T ion represents tropospheric error, which can be directly obtained through tropospheric grid file. Ionospheric and tropospheric grid files can be directly downloaded from the official website of International GNSS Organization, which belongs to common knowledge in the field. In addition, it needs to be pointed out that, I ion and T ion can also be solved by existing ionospheric parameters (directly given in broadcast ephemeris) or tropospheric parameters (obtained by Sa empirical model, which belongs to common knowledge in the field), but the accuracy of this method is low, and it is generally not used in the field. Of course, it can also be set as an unknown quantity for solving, but at this time more equations are needed to solve. The embodiment directly defaults to obtain ionospheric and tropospheric grid files. λ b is the frequency wavelength of b frequency band, which is a known quantity, such as the wavelength of B1 frequency band is 19.0 cm. is the carrier phase integer ambiguity of s satellite b frequency, which is an unknown quantity and needs to be solved. ε p and represent the random noise of pseudorange and carrier phase observation values, and the calculation process can be ignored.

[0196] Through the above analysis, it can be found that for formula (29), the unknown quantities include three position parameters (x r , y r and z r ), one clock bias , one ambiguity , and other parameters are known quantities. Assuming that the position calculation is performed at a certain time, since each satellite of Beidou has three frequencies, and each frequency includes pseudorange observation value and carrier phase observation value. Therefore, since the carrier phase ambiguity is different for each frequency, there is no linear correlation problem, and the unknown carrier phase ambiguity can be solved by using six equations of the three frequencies. Therefore, only three position parameters and one clock bias parameter need to be solved. Since the four parameters of each satellite have linear correlation, therefore it is impossible to solve by using six observation values of one satellite, and therefore at least four satellites are needed for solving. This is the reason why at least four satellites are needed for positioning in the navigation satellite system. Therefore, as long as there are four or more Beidou satellites, formula (29) can be solved by using least square or Kalman filtering algorithm to obtain the position parameters. It needs to be pointed out that when the position is solved by using least square or Kalman filtering, the signal-to-noise ratio constraint random model, satellite type weighting strategy and satellite frequency weighting strategy mentioned in the case are needed to be used to improve the positioning calculation accuracy.

[0197] From the above description, the positioning principle of the Beidou precise point positioning can be easily understood. Since the precise point positioning algorithm is common knowledge in the field, it will not be described in more details.

[0198] The embodiments of the present application are described in detail above with reference to the drawings, but the present application is not limited to the described embodiments. For those skilled in the art, various changes, modifications, replacements and variations of the embodiments including components can be made without departing from the principles and spirits of the present application, and still fall within the protection scope of the present application.

Claims

1. A Beidou precise point positioning method applied to a flood environment, characterized in that, Comprise the following steps: S1, collect the original observation value of Beidou satellite under flood environment, the original observation value includes original signal-to-noise ratio observation value, original pseudorange observation value, original carrier phase observation value, and calculate the corresponding elevation angle and azimuth angle; S2, the method for correcting the pseudorange multipath error based on the space-time dual-domain strategy is used to eliminate the influence of the pseudorange multipath error; S2-1, original data preprocessing The code phase combined observation value is generated by using the pseudorange and carrier phase original observation value, and the expression is as follows: In the formula, CM represents a single-frequency pseudorange code phase combined observation value, P is an original pseudorange observation value, is an original carrier phase observation value, and λ is a frequency wavelength. The single-frequency code phase combined observation value is smoothed by using the signal-to-noise ratio constraint strategy, and the fixed bias is eliminated, and the algorithm is expressed as follows: wherein CM o-c denotes the single-frequency pseudorange code-phase combined observation value after eliminating the fixed bias, which only contains single-frequency pseudorange multipath error and noise, CM i denotes the original single-frequency pseudorange code-phase combined observation value, i denotes the variable serial number, SNR denotes the signal-to-noise ratio of the corresponding epoch, denotes the smoothing method with signal-to-noise ratio constraint; The random noise is eliminated by using the Gauss filtering algorithm based on the signal-to-noise ratio constraint, and the high-precision pseudorange multipath error is extracted, and the one-dimensional signal-to-noise ratio constraint Gauss filtering function is used for denoising, and the specific expression is as follows: In the formula, G (x) is a Gaussian kernel function, the width of the Gaussian function is determined by tau, tau is calculated by the standard deviation of the signal-to-noise ratio, e and pi are natural constants, Std (.) represents the calculation of standard deviation, SNR represents the signal-to-noise ratio, The specific denoising process is represented as follows: assuming that the signal to be processed containing pseudorange multipath error and noise is represented as CM o_c (x), the signal after filtering only containing pseudorange multipath error is represented as CM m The calculation process is represented as follows: CM m (x) = CM o_c (x) * G(x) In the formula, x represents the serial number of the original signal-to-noise ratio observation value, G (x) is a Gaussian function, and the symbol * represents convolution operation; S2-2, the pseudo-range multipath error CM calculated in step S2-1 m According to the Beidou satellite number, satellite frequency, elevation angle and azimuth angle information is stored, through the form of database is stored in the local computer; S2-3, the pseudorange multipath error of the observation day is corrected by using the pseudorange multipath correction algorithm based on the space-time dual-domain strategy; S3, the adaptive threshold cycle slip detection and repair algorithm based on the signal-to-noise ratio constraint is used to eliminate the influence of cycle slip; S4, the observation value is weighted based on the random model of signal-to-noise ratio weighting, and the positioning accuracy is improved; S5, the satellite type weighting strategy algorithm is used to eliminate the influence of different types of Beidou satellite orbits; S6, the frequency weighting strategy is used to eliminate the influence of the quality difference of different frequency signals of Beidou satellite; S7, the Beidou precise point positioning algorithm is used to solve the position, and the result is saved and output.

2. The Beidou precise point positioning method applied to a flood environment according to claim 1, characterized in that, In the step S2-3, the method for correcting the pseudorange multipath error is as follows: S2-3-1, the spatial information and the time domain information required for pseudorange multipath correction are calculated, which is convenient for subsequent pseudorange multipath correction, the spatial information refers to the elevation angle and azimuth angle information of the satellite, which is directly calculated when the data is received, and the time domain information refers to the satellite orbit repeat period deviation, which is calculated by broadcast ephemeris and Kepler's third law; S2-3-2, the pseudorange multipath error of Beidou IGSO and MEO satellite is corrected by using the space strategy, that is, the elevation angle and azimuth angle information of the satellite to be corrected on the observation day is used to search in the established pseudorange multipath error database to obtain the reference value of the pseudorange multipath error in the database, and then the value is subtracted from the original pseudorange observation value, which is expressed as: P o-m = P o -CM m In the formula, P o is the original pseudo-range observation value, CM m is the reference value of the pseudo-range multipath error extracted from the database, P o-m is the accurate pseudo-range observation value after the pseudo-range multipath error correction; S2-3-3, the pseudorange multipath error of Beidou GEO satellite is corrected by using the time domain strategy, that is, the satellite orbit repeat period deviation is used to correct the pseudorange multipath error of Beidou GEO satellite, and then the satellite orbit repeat period deviation is used to search the corresponding pseudorange multipath error reference value in the database, which is expressed as: X = T s X T In the formula, T s is the satellite orbit repeat period bias, X T is the original observation value epoch interval, X is the corresponding epoch searched for The value is used to extract the corresponding CM from the established database m The value is then subtracted from the original pseudo-range observation value, and after the pseudo-range multipath correction method described above is processed, the accurate pseudo-range observation value can be obtained.

3. The Beidou precise point positioning method applied to a flood environment according to claim 2, characterized in that, In the step S2-3-1, the calculation method of the satellite orbit repeat period deviation is as follows: where n is the average angular velocity of the satellite at the observation time, is the Earth's gravitational constant, G is the universal gravitational constant, M is the total mass of the Earth, a is the semi-major axis of the elliptical orbit, Δn is the perturbation parameter of the satellite, and T represents the satellite repeat period. Once the satellite repeat period is determined, the search time domain value can be obtained by the following equation: T s =86400-T where 86400 is one solar day in seconds, T s is the time-domain satellite orbit repeat period bias.

4. The Beidou precise point positioning method applied to a flood environment according to claim 2, characterized in that, The step S3 comprises the following substeps: S3-1, the code phase observation value is combined again by the corrected pseudo-range observation value and the original carrier phase observation value, and is specifically expressed as: where CM o-m represents the single-frequency pseudo-range code-phase combined observation value after pseudo-range multipath correction, P o-m represents the pseudo-range observation value after pseudo-range multipath correction, is the original carrier phase observation value, and λ is the frequency wavelength. Similarly, the code-phase combined observation value after multipath error removal is smoothed by step S2-1, the fixed bias component is eliminated, the random noise influence is eliminated, the high-precision code-phase combined residual is extracted, the epoch-to-epoch difference of the residual is obtained, and the accurate cycle slip detection quantity is obtained. ACM d (x i ) = CM c (x i ) - CM c (x i-1 ) where CM c (x) is the high-precision code phase combination residual, ΔCM d (x) represents the residual difference of two adjacent epochs, i.e., the cycle slip detection measure, i represents the signal sequence; S3-2, the cycle slip is detected by using an adaptive threshold strategy, and a cycle slip detection model of the adaptive threshold strategy is expressed as: | ΔCM d (x i )|>K where K is a threshold weighting factor, ΔCM d (x) represents the cycle slip detection residual value; S3-3, the cycle slip epoch detected is repaired, and the number of cycle slips generated is calculated, and the specific formula is expressed as: where ΔCM d (x) represents the cycle slip detection residual value, λ is the frequency wavelength, and after the accurate cycle slip number is determined, the repair is performed.

5. The Beidou precise point positioning method applied to a flood environment according to claim 4, characterized in that, The step S4 includes the following sub-steps: S4-1, a low-order polynomial fitting is used to eliminate the influence of the signal-to-noise ratio direct signal, and the low-order polynomial fitting algorithm is expressed as: S p (x i )=a0+a1x i +a2x i 2 +a3x i 3 In the formula, S p (x) represents the signal-to-noise ratio of the direct signal after the third-order polynomial fitting, x represents the serial number of the original signal-to-noise ratio observation value, i represents a data point, a represents the third-order polynomial fitting coefficient, since the third-order polynomial is used for fitting, the highest order of x is 3, and therefore the fitting coefficient is 4, which are a0, a1, a2, and a3. In order to minimize the error after fitting, the polynomial fitting coefficient is obtained by least squares, which is represented as: where P min represents the root mean square of the overall error after fitting, S p (x) represents the signal-to-noise ratio of the direct signal after fitting a third-order polynomial, S o (x) represents the original signal-to-noise ratio observation value, i represents the data sequence, n represents the total length of the data, and P min , the partial derivative of the above formula is obtained: In the formula, m represents the polynomial order, j=0, 1,..., m, and k represents the sequence number of m, and after arrangement, the following can be obtained: where only a k coefficients are unknowns, which can be solved for a k coefficients, The polynomial fitting coefficient a is obtained k After that, the low-order polynomial fitting algorithm is used to obtain the fitting signal-to-noise ratio direct signal, and then the signal-to-noise ratio direct signal is eliminated, which is expressed as: S o-p (x i )=S o (x i )-S p (x i ) a signal free from the influence of the signal-to-noise ratio direct signal, i.e. S o-p (x); S4-2, according to the algorithm in step S2-1, denoising is performed by a signal-to-noise ratio constrained Gaussian filtering algorithm to eliminate the influence of noise, and a signal-to-noise ratio signal without the influence of noise is obtained, denoted as: S r (x); S4-3, a weighted random model based on the signal-to-noise ratio is established by using the signal-to-noise ratio without the influence of the direct signal and noise, and the specific weighted random model is expressed as: where C represents a constant, S r (x i ) represents the signal-to-noise ratio of the i-th epoch after removing the effects of direct signals and noise, and σ represents the accuracy at the time of the epoch.

6. The Beidou precise point positioning method applied to a flood environment according to claim 5, characterized in that, In the step S5, the satellite type weighted strategy algorithm is realized by using the least square algorithm: Assuming that the least square algorithm is directly used to solve the position parameter, which is expressed as: X x,y,z = G -1 b In the formula, G is the Jacobian matrix of the linear equation set, -1 represents the inverse of the matrix, b represents the residual error formed by the equation set, and based on the satellite orbit type weighted strategy, the position parameter can be directly solved by using the above formula. Since the orbit types of the Beidou satellites are different, the Beidou satellites need to be weighted according to the orbit type, that is, the solving formula is expressed as: X x,y,z = (G T W T WG) -1 G T W T Wb In the formula, T represents the matrix transposition operation, and W is the weighted matrix. Since the signal quality of the MEO satellite of the three orbit types of the Beidou satellite is the best, the IGSO is the second, and the GEO satellite is the worst, therefore, the weighted matrix based on the orbit type of the Beidou satellite can be expressed as: In the formula, w geo ,w isgo ,w meo respectively represent the weighting coefficients of the Beidou GEO satellite, the IGSO satellite and the MEO satellite.

7. The Beidou precise point positioning method applied to a flood environment according to claim 6, characterized in that, In the step S6, the algorithm of the frequency weighted strategy is the same as the satellite type weighted strategy algorithm in the step S5, and the difference lies in that the weighted matrix setting needs to be distinguished according to the frequency. Since among the three frequency types of the Beidou, the signal-to-noise ratio of B3 is the highest under the same elevation angle, B1 is the second, and B2 is the worst, therefore, the weighted matrix based on the frequency type of the Beidou satellite can be expressed as: where w B2 ,w B1 ,w B3 represent the weighting coefficients of the Beidou satellite B2, B1 and B3 frequencies, respectively.

8. The Beidou precise point positioning method applied to a flood environment according to claim 7, characterized in that, In the step S7, the Beidou precise point positioning algorithm is solved by the following position method: The pseudorange observation value after the multi-path correction and the carrier phase observation value after the cycle slip detection and repair can be used for the position solution of the Beidou precise point positioning. Assuming that the position of the receiver is x r ,y r and z r , the basic equation for solving can be expressed as: In the formula, P o-m is the pseudo-range observation value after pseudo-range multipath correction, is the carrier phase observation value after cycle slip detection and repair, P o-m and are known quantities, b represents the frequency sequence of the Beidou satellite, s represents the Beidou satellite sequence, x s , y s , and z s represent the position of the satellite, c represents the speed of light in a vacuum, represents the clock difference between the receiver and the satellite, I ion represents the ionospheric error, T ion represents the tropospheric error, λ b is the frequency wavelength of the b frequency band, is the carrier phase integer ambiguity of the s satellite b frequency, ε p and represent the random noise of the pseudo-range and carrier phase observation values.

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