A fusion filtering method for BDS-3 / INS tight integration system

Abstract

The present invention relates to a fusion filtering method for a BDS-3 / INS tightly combined system, and belongs to the field of satellite navigation technology. The present invention separates an ambiguity resolution module from a Kalman filter, constructs a combined observation quantity through the carrier phase of each frequency point of BDS-3, determines the ionospheric delay error, carrier observation noise and ambiguity rounding influence coefficient based on the combined observation quantity, determines the combination coefficient in the combined observation quantity with the goal of the ionospheric delay error, carrier observation noise and ambiguity rounding influence coefficient reaching the corresponding range, thereby obtaining a high-quality combined observation quantity, performs ambiguity resolution based on the obtained high-quality combined observation quantity, and brings the ambiguity resolution result into the measurement equation, so that the parameter to be estimated in the measurement equation no longer contains ambiguity, so that the estimation of the ionospheric delay error can be effectively carried out, and the final positioning accuracy is improved.

Description

Technical Field

[0001] The invention relates to a fusion filtering method for a BDS-3 / INS tight combination system, belonging to the technical field of satellite navigation. Background Art

[0002] The satellite / inertial tightly integrated precise positioning and attitude determination system (GNSS / INS) can provide position, velocity, and attitude information, and is widely used in mobile measurement, smart city mapping and other fields. Applications in these fields all require GNSS / INS to provide centimeter-level position information. In order to obtain centimeter-level positioning accuracy and high-precision velocity and attitude measurement accuracy, it is necessary to first determine the integer ambiguity parameters of the carrier phase in the satellite observation. Among them, the key factor restricting the resolution of ambiguity is whether the various errors such as satellite clock error, receiver clock error, and atmospheric delay error are effectively weakened. Usually, by setting up a fixed and stationary receiver (also known as a base station) in an open area and installing a receiver (also known as a mobile station) on the carrier of the mobile measurement system, the double difference method between stations and satellites can be used. By utilizing the spatiotemporal correlation of the errors between the base station and the mobile station, the influence of the common error is effectively eliminated, thereby successfully obtaining the integer solution of the ambiguity, allowing the mobile station to obtain a centimeter-level high-precision position, and obtain high-precision velocity and attitude measurement results.

[0003] However, as urban areas gradually expand, the distance between the mobile station and the base station can easily exceed 10km when conducting mobile surveying in large cities. Taking Zhengzhou, a typical Central Plains ring city, as an example, the main city diameter can reach 35km. When the baseline length exceeds 10km, the double difference method can no longer eliminate the influence of ionospheric delay error. The residual ionospheric delay error significantly reduces the success rate of calculating the integer solution of ambiguity. Therefore, the application scenarios of medium and short baselines in cities (baseline length <100km) urgently need more practical precise positioning methods. The effective solution of ionospheric delay is the key to achieving fast and instantaneous resolution of ambiguity. Precision positioning under medium and short baseline conditions puts forward requirements and challenges for mathematical models, data processing strategies, and ambiguity resolution methods.

[0004] There are two main methods for ionospheric delay error processing strategies suitable for dynamic real-time measurement. The first is to use an ionospheric elimination combination. This method uses a multi-frequency point combination, and each satellite can only provide one observation. Therefore, the number of observation equations decreases, which is not conducive to parameter estimation. The second method is based on the idea of ​​parameter estimation, which introduces ionospheric delay error parameters for each satellite pair to estimate and eliminate the impact. The above two methods either increase the unknown parameters to be estimated or reduce the number of observation equations, which are not conducive to the convergence of Kalman filtering and the acquisition of high-precision ambiguity floating-point solutions, further affecting the fixation of ambiguity integer solutions, and seriously restricting the tight combination of GNSS / INS to obtain high-precision position, velocity, and attitude results. Summary of the invention

[0005] The purpose of the present invention is to provide a fusion filtering method for a BDS-3 / INS tightly combined system, so as to solve the problem that the current ambiguity solution is implemented in a Kalman filter, resulting in inaccurate ambiguity, which in turn affects the final positioning result.

[0006] In order to solve the above technical problems, the present invention provides a fusion filtering method for a BDS / INS tightly combined system, and the filtering method comprises the following steps:

[0007] 1) Obtain the carrier phase data and pseudorange data of each frequency point of BDS-3 in the BDS-3 / INS tight integration system;

[0008] 2) The carrier phase of each frequency point of BDS-3 is used to construct the combined observation quantity, including the combined pseudorange observation quantity and the combined carrier phase observation quantity. The combined frequency, combined wavelength and combined integer ambiguity of the combined observation quantity are all expressed by the combination coefficient of each frequency point;

[0009] 3) constructing a mathematical model of a double-difference ambiguity floating point solution, a double-difference ambiguity integer solution and a standard deviation of the double-difference ambiguity floating point solution based on the constructed combined observations;

[0010] 4) According to the constructed mathematical model, select the high-quality combined observation quantity that minimizes the fuzzy rounding influence coefficient to obtain the corresponding combination coefficient;

[0011] 5) Determine the ambiguity based on the obtained combination coefficients, and substitute the obtained ambiguity integer solution into the measurement equation of the BDS-3 / INS tight combination system to achieve fusion filtering.

[0012] The present invention separates the ambiguity resolution module from the Kalman filter, constructs a combined observation quantity through the carrier phase of each frequency point of BDS-3, determines the ionospheric delay error, carrier observation noise and ambiguity rounding influence coefficient based on the combined observation quantity, and determines the combination coefficient in the combined observation quantity with the goal of minimizing the ambiguity rounding influence coefficient, thereby obtaining a high-quality combined observation quantity, performs ambiguity resolution based on the obtained high-quality combined observation quantity, and brings the ambiguity resolution result into the measurement equation, so that the parameter to be estimated in the measurement equation no longer contains ambiguity, so that the estimation of the ionospheric delay error can be effectively carried out, and the final positioning accuracy is improved.

[0013] Furthermore, in step 4), when determining the high-quality combined observation quantity, the ambiguity rounding influence coefficient is firstly ensured to be minimal, and then the combined wavelength value is ensured to be large.

[0014] According to the difference of each frequency coefficient, the number of combination coefficients is huge. The present invention reduces the number of combination coefficients that meet the conditions by gradually setting strict optimization conditions, that is, first minimize the coefficient affecting ambiguity rounding, and then further screen the candidate combination coefficients using the combination wavelength.

[0015] Furthermore, the sum of the combination coefficients of the high-quality combination observations determined in step 4) is zero, and the coefficients satisfy group nonlinearity.

[0016] The present invention further improves the quality of the combination coefficients by limiting the sum of the combination coefficients and the nonlinearity between the coefficients.

[0017] Furthermore, the high-quality combined observation quantity in step 4) is also related to the values ​​of the ionospheric delay scale factor and the carrier noise scale factor.

[0018] The present invention gradually sets the optimal conditions, reduces the number of combination coefficients that meet the conditions according to the value ranges of the ionospheric delay scale factor and the carrier noise scale factor, and further improves the quality of the combination coefficients.

[0019] Furthermore, the mathematical model constructed in step 3) is:

[0020]

[0021]

[0022]

[0023] in represents the double difference ambiguity float solution; represents the rounding solution of the double difference ambiguity; Round{} represents the rounding function; represents the carrier phase observation noise error; δ ρINS represents the satellite-to-ground distance error predicted by inertial navigation; δ I and δ T represents the ionospheric delay error and the tropospheric delay error; δ Orb Indicates the satellite ephemeris orbit error; represents the carrier phase observation value; a represents the combination coefficient of the frequency point; is a double difference operator, indicating that the variable following it is a double difference.

[0024] Furthermore, in step 2), the combined pseudorange observation quantity P (i,1～n) , combined carrier phase observation Combination frequency f (i,1～n) , combined wavelength λ (i,1～n) and the combined integer ambiguity N (i,1～n) The expression is divided into:

[0025]

[0026]

[0027]

[0028] λ (i,1～n) =c / f (i,1～5)

[0029]

[0030] Where c represents the speed of light; the subscript i indicates the frequency point corresponding to the quantity, the subscript brackets indicate the frequency points used by the combined quantity, represents the number of frequency points, and a represents the combination coefficient of the frequency points. The subscript number is consistent with the frequency point serial number.

[0031] Furthermore, the measurement equation in step 5) is:

[0032]

[0033] λ i The wavelength representing the carrier phase of the i-th frequency point; N represents the geometric distance between the satellite and the receiver antenna calculated by INS; i represents the integer ambiguity of the carrier phase of the i-th frequency point; P represents the pseudorange observation value; represents the carrier phase observation value; D represents the Doppler observation value; represents the relative speed between the satellite and the receiver calculated by INS; E represents the direction cosine matrix from the receiver to the satellite; γ i represents the ionospheric delay error coefficient of the i-th frequency point; M wb 、M wr They represent the tropospheric wet delay coefficient matrices of the reference station and the mobile station respectively; I1 represents the ionospheric delay error of each satellite pair at the f1 frequency point; T wb , T wb They represent the residual errors of tropospheric wet delay in the zenith direction of the reference station and the mobile station, respectively, both of which are scalars; ε P , ε D Represent the noise of carrier phase, pseudorange, and Doppler measurements respectively; is a double difference operator, indicating that the variable following it is a double difference. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the principle of the fusion filtering method of the BDS-3 / INS tight combination system of the present invention. DETAILED DESCRIPTION

[0035] The specific implementation of the present invention will be further described below in conjunction with the accompanying drawings.

[0036] The present invention separates the ambiguity resolution module from the Kalman filter, constructs a combined observation quantity through the carrier phase of each frequency point of BDS-3, determines the ionospheric delay error, carrier observation noise and ambiguity rounding influence coefficient based on the combined observation quantity, determines the combination coefficient in the combined observation quantity with the goal of ensuring that the ionospheric delay error, carrier observation noise and ambiguity rounding influence coefficient reach the corresponding range, thereby obtaining a high-quality combined observation quantity, performs ambiguity resolution based on the obtained high-quality combined observation quantity, brings the ambiguity resolution result into the measurement equation, and realizes the fusion filtering of the BDS-3 / INS tight combination system through Kalman filtering.

[0037] The structure of the BDS-3 / INS tight integration system is as follows Figure 1 As shown in the figure, the main process of data processing includes three parts: 1) state update based on inertial navigation system (INS); 2) measurement update based on BDS-3 observations; 3) ambiguity resolution. Among them, the fusion filter algorithm is used to integrate the INS state update and the BD-3S measurement update. Generally, the extended Kalman filter algorithm is used. The ambiguity resolution is to obtain the integer solution of the ambiguity, which is a key step to obtain centimeter-level high-precision position results.

[0038] The state update equation and measurement update equation have the following form:

[0039]

[0040] L=Hδx+e (2)

[0041] In the formula, F represents the state transfer matrix; G represents the noise projection matrix; w represents the noise vector of the system; L represents the observation vector; H represents the observation matrix; and e represents the observation noise vector. x represents the state parameter to be estimated, which includes two types of parameters: one is the position r, velocity v, and attitude ψ that are directly useful to the user; the other is other parameters that make the Kalman filter converge correctly, which generally include the accelerometer zero bias b a 、Gyroscope bias b g , ambiguity floating point N, etc. Since both the state update equation and the measurement update equation are nonlinearly processed, the extended Kalman filter is actually used. So the error δx of x is used here, and the relationship between the two is:

[0042] x=x0+δx (3)

[0043] Among them, x0 represents the initial value before updating, and the complete expression of the state parameter error δx is:

[0044]

[0045] Among them, the ionospheric delay error parameter The dimension is determined by the number of observed satellites, and the tropospheric delay error parameter is a scalar, and the remaining state parameters are three-dimensional vectors. Not all state parameters will participate in the state update and measurement update process at the same time. For the sake of simplicity, the δx that appears in the state equation and measurement equation respectively below only contains the state parameters that participate in the process. It should be pointed out that the state parameters that do not appear in the state equation and measurement equation will also be connected through the state parameter covariance matrix.

[0046] 1) State update equation of BDS-3 / INS

[0047] The state update equation is constructed based on the dynamic model of the inertial navigation system. The detailed expression of the state update equation (1) in the Earth-centered Earth-fixed coordinate system is as follows:

[0048]

[0049] In the formula, Represents the direction cosine matrix from the carrier coordinate system (b system) to the Earth-centered Earth-fixed coordinate system (e system); Indicates the accelerometer specific force output; The Earth's rotation angular velocity The constructed skew-symmetric matrix; ε a and ε g represents the system noise vector composed of accelerometer and gyroscope noise. The symbol [(A)×] represents a skew-symmetric matrix constructed based on matrix A. I represents the identity matrix and 0 represents the zero matrix, and the dimensions of the two are determined by the expressions accordingly.

[0050] State parameter estimation directly affects the accuracy and reliability of the BDS-3 / INS tight combination. However, the estimation of state parameters requires a period of filtering and convergence of the Kalman filter. The more state parameters there are and the lower the observability of the state parameters, the more difficult it is to estimate them correctly.

[0051] 2) Measurement update equations for BDS-3 / INS

[0052] The measurement update equation is constructed based on the BDS-3 double-difference observation equation. Assume that the observable satellite pair is m (the observable satellite data is m+1), and the BDS-3 BeiDou-3 system broadcasts a total of 5 frequency points (note that the CEO satellite can only broadcast four frequency points). For the i-th frequency point, the observation equation can be constructed using the observation value of this frequency point as follows:

[0053] L i =H i δx+e i(6)

[0054] The detailed expression is as follows:

[0055]

[0056] λ i The wavelength representing the phase of the carrier wave; represents the geometric distance between the satellite and the receiver antenna calculated by INS; N represents the integer ambiguity of the carrier phase; P represents the pseudorange observation value; represents the carrier phase observation value; D represents the Doppler observation value; represents the relative speed between the satellite and the receiver calculated by INS; E represents the direction cosine matrix from the receiver to the satellite; γ i Represents the ionospheric delay error coefficient of the i-th frequency point, and the specific expression is γ i =f1 2 / f i 2 ;M wb 、M wr Represents the tropospheric wet delay coefficient matrix of the base station and the mobile station respectively. I1 represents the ionospheric delay error of each satellite pair at the f1 frequency point (unit: m). T wb , T wb They represent the residual errors of tropospheric wet delay in the zenith direction of the reference station and the mobile station respectively (unit: m), both of which are scalars.

[0057] The tropospheric delay error is first corrected using the tropospheric error correction model, and the remaining component is mainly the wet component, so it needs to be estimated in the measurement update equation. i Represents the wavelength of the carrier phase at the i-th frequency point; is a double difference operator, indicating that the variable following it is a double difference; ε P , ε D They represent the noise of carrier phase, pseudorange and Doppler measurement respectively; I represents the unit matrix and 0 represents the zero matrix. The dimensions of both are determined by the corresponding expressions. Most of the symbols in equation (7) are expressed in vector form, indicating that they are the corresponding observations of multiple satellites.

[0058] From formula (7), we can see that the BDS-3 / INS observation equation contains many parameters to be estimated, totaling (8+m)·n f, and too many estimated parameters will lead to the inability to estimate parameters, the inability to converge filtering, and the inability to fix the integer solution of ambiguity in actual data processing, thus affecting the entire positioning process. Based on this, the present invention proposes a new filtering method, which separates the ambiguity resolution process from the Kalman filter, first resolves the ambiguity, and then substitutes the resolution result into formula (7), so that the parameters to be estimated in formula (7) are reduced by m·n compared with the traditional model. f .

[0059] The present invention uses the multi-frequency observations of BDS-3 to assist in solving the integer solution of ambiguity. This method essentially uses inertial assistance to round the floating-point solution of ambiguity to obtain the integer solution of ambiguity. The following will first introduce the INS-assisted rounding ambiguity solution method (referred to as I-CAR), and then derive the I-MACR method of this patent on this basis. The solution process of the I-CAR method is as follows.

[0060] According to the carrier phase observation at any frequency point, the basic mathematical model of I-CAR is constructed:

[0061]

[0062]

[0063] in represents the double difference ambiguity float solution; Indicates the integer solution of the double difference ambiguity; Round{} indicates the integer function (rounding). The key to I-CAR is to minimize the floating point solution. The error contained in it, thus improving the rounding solution The probability of the ambiguity being equal to the true integer solution is theoretically reduced. The standard deviation of .

[0064] From formula (8), we know that The standard deviation is mainly affected by and Therefore, The standard deviation of can be expressed as:

[0065]

[0066] In the formula, represents the carrier phase observation noise error; δ ρINS represents the satellite-to-ground distance error predicted by inertial navigation; δ I and δ T represents the ionospheric delay error and the tropospheric delay error; δ Orb Indicates the satellite ephemeris orbit error; and Represents the clock difference between the receiver and the satellite. (The unit of the above error term is: week).

[0067] For short and medium baselines, the influence of satellite clock error and receiver clock error can be ignored, so:

[0068]

[0069] The above is the mathematical model of I-CAR. This model is simple, but due to the short wavelength of the carrier phase, its ability to resist various error sources is weak. The following is a theoretical analysis.

[0070] The wavelength range of the BeiDou-3 system is [20cm, 24cm], and the middle value λ is taken as 0.22m. In order to observe the influence of the error term on the ambiguity resolution, the ambiguity resolution influence coefficient is defined as:

[0071] dδ=δ / λ (12)

[0072] The above formula indicates that the error term δ has an influence coefficient of dδ (unit: week) on the ambiguity resolution of the carrier phase ambiguity with a wavelength of λ. So:

[0073]

[0074] Under the medium and short baseline, the typical values ​​of the errors in formula (11) are (taking into account first and second order ionospheric delays), The value of is strictly affected by the vector angle between the reference star and the selected star. However, in BDS / INS, after the INS measurement error is estimated and corrected by the Kalman filter state, the INS position error between two epochs is very small. Here, Substituting the typical value of the error into Therefore, direct rounding is prone to errors. It can be seen that the I-CAR method has limitations. On the other hand, in the case of medium and short baselines, it is less practical due to the large ionospheric delay error. In addition, since the carrier wavelength of the BeiDou-3 system is only about 20cm, its resistance to non-modeling errors is also weak.

[0075] Based on this, the present invention proposes an INS-aided Multi-frequency Ambiguity Resolution Method (I-MAR) for BDS-3, which combines the advantages of INS measurements and Beidou multi-frequency observations. The specific solution process is as follows.

[0076] 1. Construct the combined observation quantity based on the carrier phase of each frequency point of BDS-3.

[0077] The BeiDou-3 system has a maximum of five frequency points of carrier phase. According to the carrier phase of each frequency point, a combined observation can be constructed, including a combined pseudorange observation P (i,1～5) and combined carrier phase observations The combined frequency of the combined observations is f (i,1～5) , combined wavelength λ (i,j,k) , combined integer ambiguity N (i,j,k) The expression is as follows:

[0078]

[0079]

[0080]

[0081] λ (i,1～5) =c / f (i,1～5) (17)

[0082]

[0083] Where c represents the speed of light; the subscript number i indicates the frequency point corresponding to the quantity, and the subscript brackets indicate the frequency point used by the combined quantity. a represents the combination coefficient of the frequency point, and its subscript number is consistent with the frequency point sequence number. The subscripts 1 to 5 of the coefficients correspond to the combination coefficients of the B1C, B1I, B2a, B2b, and B3I frequency points, respectively. For the BeiDou-3 system, the number of available frequency point signals is five, that is, the maximum value of n in the above multiple formulas is 5.

[0084] 2. Determine the mathematical model of I-MAR based on the constructed combined observations.

[0085] The magnification of the combined observation quantity to the ionospheric delay error and carrier observation noise at the first frequency point refers to the ionospheric delay scale factor (ISF) and the carrier noise scale factor (PNF), and their corresponding relationships are as follows:

[0086]

[0087]

[0088] Since the wavelengths of the various frequencies are relatively close, it can be assumed that the observation noises of the various frequencies are equal. For the combined observation quantity with the combination coefficient a=[a1, a2, a3, a4, a5], the mathematical model established by the present invention is:

[0089]

[0090]

[0091]

[0092] Therefore, the biggest influences on ambiguity rounding are ionospheric delay error, carrier observation noise and ambiguity rounding influence coefficient, which can be expressed as:

[0093]

[0094]

[0095]

[0096] 3. According to the constructed I-MAR mathematical model, select high-quality combined observations with the least impact on ambiguity rounding.

[0097] The key to the I-MAR method lies in the selection of combined observations, that is, the determination of the combination coefficient [a]. According to the document of the China Satellite Navigation System Management Office, the BeiDou-3 triple-frequency recommends the use of B1C, B2a, and B3I; the quad-frequency adds B1I on the basis of the triple-frequency; the penta-frequency adds B2b on the basis of the quad-frequency combination. Depending on the number of frequencies used, I-MAR is called I-TAR (Triple-frequency), I-QAR (Quad-frequency), and I-FAR (Five-frequency) respectively. Therefore, the present invention gives the recommended combination coefficients under the conditions of three, four, and five frequency points, respectively.

[0098] When determining the combination coefficient, a linear inverse transformation is required to restore the original single frequency ambiguity from the combined ambiguity. Therefore, the number of combinations that need to be optimized under different frequency number conditions is equal to the total number of frequencies used. The formula for restoring the original frequency ambiguity from the combined ambiguity is as follows:

[0099]

[0100] By enumerating all the combination coefficients, it can be found that the coefficient sum of the combination coefficients with better quality is generally zero, so the selection of combination coefficients can only be preferentially selected from the combination coefficients with a coefficient sum of zero. On the other hand, it is required that the combination coefficients a1~a5 cannot be linearly related, so there must be at least one combination quantity, and its combination coefficient sum (the sum of the coefficients corresponding to each frequency point) cannot be zero. For this special combination quantity, the present invention selects a single-frequency combination quantity, only the coefficient of this single frequency is 1, the coefficients of the other frequency points are zero, and the combination coefficient sum is 1, and the single frequency B2a with the largest wavelength is used, as shown in Table 1.

[0101] Table 1

[0102]

[0103] 1) Determination of the combination coefficient of three frequency points

[0104] The preferred conditions for setting three frequency points include ionospheric delay error, carrier observation noise and ambiguity rounding influence coefficients. At the same time, they can also be limited according to the range of ionospheric delay scale factor, carrier noise scale factor and carrier phase wavelength. In addition, it is necessary to ensure that the sum of the combination coefficients is 0, and the combination coefficients are nonlinear. Depending on the coefficients of each frequency point, the number of combination coefficients is huge. By gradually setting strict preferred conditions, the number of combination coefficients that meet the conditions is reduced. Further screening is then carried out among these candidate combination coefficients. The first consideration is The smaller the better, consider λ next (i,j,k) As large as possible. Then consider other conditions and select the optimal combination coefficient. [a]∈[-10,10] means that the value of the combination coefficient is between -10 and 10.

[0105] Based on the above definition, the combination coefficients of the three frequency points obtained by the present invention are shown in Table 2.

[0106] Table 2

[0107]

[0108] 2) Determination of the combination coefficient of the four frequency points

[0109] The optimal conditions for the four-frequency points are similar to those for the three-frequency points, and also include ionospheric delay error, carrier observation noise, ambiguity rounding influence coefficient, ionospheric delay scale factor, carrier noise scale factor and carrier phase wavelength. Based on the above limitations, the combination coefficients of the four-frequency points obtained by the present invention are shown in Table 3.

[0110] Table 3

[0111]

[0112]

[0113] 3) Determination of the combination coefficient of five frequency points

[0114] Similarly, the optimal conditions for the five-frequency point are similar to those for the three-frequency point, including ionospheric delay error, carrier observation noise, ambiguity rounding effect coefficient, ionospheric delay scale factor, carrier noise scale factor, and carrier phase wavelength. The results are shown in Table 4.

[0115] Table 4

[0116]

[0117] There are only three linearly independent combinations in the above table (marked with *). In order to obtain the fourth linearly independent combination, the screening conditions are relaxed and the linearly related combinations in the above table are removed. There are two combinations that meet the conditions, as shown in Table 5.

[0118] Table 5

[0119]

[0120] Therefore, the final five-frequency optimal combinations of nonlinear conditions are [0,-1,0,0,1], [0,0,1,0,-1], [1,-1,0,-1,1], [0,1,-4,1,2].

[0121] Through the above process, the optimal combination quantity under different numbers of frequency points, that is, the corresponding combination coefficient, can be obtained. The obtained optimal combination coefficient can be used to determine the high-quality combination observation quantity.

[0122] 4. Resolve ambiguity based on the determined combined observations.

[0123] Substitute the determined combination coefficient a into equation (21), and use equation (22) to obtain the combined ambiguity of the combined carrier phase observation value. Then use equation (27) to restore the combined ambiguity to the original frequency point ambiguity integer solution.

[0124] Substituting the determined integer solution of the ambiguity into the measurement update equation, see formula (7), the ambiguity rounding of the measurement update equation is no longer a parameter to be estimated, but a known quantity, which greatly reduces the parameter data to be estimated, accelerates the convergence process of the Kalman filter, improves the reliability of the filtering result, and thus ensures the final positioning accuracy.

Claims

1. A fusion filtering method for a BDS-3 / INS tight combination system, characterized in that: The filtering method comprises the following steps: 1) Obtain the carrier phase data and pseudorange data of each frequency point of BDS-3 in the BDS-3 / INS tight integration system; 2) The carrier phase of each frequency point of BDS-3 is used to construct the combined observation quantity, including the combined pseudorange observation quantity and the combined carrier phase observation quantity. The combined frequency, combined wavelength and combined integer ambiguity of the combined observation quantity are all expressed by the combination coefficient of each frequency point; 3) constructing a mathematical model of a double-difference ambiguity floating point solution, a double-difference ambiguity integer solution and a standard deviation of the double-difference ambiguity floating point solution based on the constructed combined observations; 4) According to the constructed mathematical model, select the high-quality combined observation quantity that minimizes the fuzzy rounding influence coefficient to obtain the corresponding combination coefficient; 5) Determine the ambiguity according to the obtained combination coefficient, and substitute the obtained ambiguity integer solution into the measurement equation of the BDS-3 / INS tight combination system to realize fusion filtering; The mathematical model constructed in step 3) is: in represents the double difference ambiguity float solution; represents the rounding solution of the double difference ambiguity; Round{} represents the rounding function; represents the carrier phase observation noise error; δ ρINS represents the satellite-to-ground distance error predicted by inertial navigation; δ I and δ T represents the ionospheric delay error and the tropospheric delay error; δ Orb Indicates the satellite ephemeris orbit error; represents the carrier phase observation value; a represents the combination coefficient of the frequency point; is a double difference operator, indicating that the variable following it is a double difference; The measurement equation in step 5) is: λ i The wavelength representing the carrier phase of the i-th frequency point; N represents the geometric distance between the satellite and the receiver antenna calculated by INS; i represents the integer ambiguity of the carrier phase of the i-th frequency point; P represents the pseudorange observation value; represents the carrier phase observation value; D represents the Doppler observation value; represents the relative speed between the satellite and the receiver calculated by INS; E represents the direction cosine matrix from the receiver to the satellite; γ i represents the ionospheric delay error coefficient of the i-th frequency point; M wb 、M wr They represent the tropospheric wet delay coefficient matrices of the reference station and the mobile station respectively; I1 represents the ionospheric delay error of each satellite pair at the f1 frequency point; T wb , T wb They represent the residual errors of tropospheric wet delay in the zenith direction of the reference station and the mobile station, respectively, both of which are scalars; ε P , ε D Represent the noise of carrier phase, pseudorange, and Doppler measurements respectively; is a double difference operator, indicating that the variable following it is a double difference.

2. The fusion filtering method of the BDS-3 / INS tight combination system according to claim 1 is characterized in that: In the step 4), when determining the high-quality combined observation quantity, the ambiguity rounding influence coefficient is firstly ensured to be minimal, and then the combined wavelength value is ensured to be large.

3. The fusion filtering method of the BDS-3 / INS tight combination system according to claim 2 is characterized in that: The sum of the combination coefficients of the high-quality combination observations determined in step 4) is zero, and the coefficients satisfy group nonlinearity.

4. The fusion filtering method of the BDS-3 / INS tight combination system according to claim 2 is characterized in that: The high-quality combined observation quantity in step 4) is also related to the values ​​of the ionospheric delay scale factor and the carrier noise scale factor.

5. The fusion filtering method of the BDS-3 / INS tight combination system according to claim 3 is characterized in that: In step 2), the pseudo-range observation quantity P is combined (i,1～n) , combined carrier phase observation Combination frequency f (i,1～n) , combined wavelength λ (i,1～n) and the combined integer ambiguity N (i,1～n) The expression is divided into: l (i,1～n) =c / f (i,1～5) Where c represents the speed of light; the subscript i indicates the frequency point corresponding to the quantity, the subscript brackets indicate the frequency points used by the combined quantity, represents the number of frequency points, and a represents the combination coefficient of the frequency points. The subscript number is consistent with the frequency point serial number.

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