High-precision regional positioning system signal processing method and device

Abstract

The present application proposes a signal processing method and device for a high-precision regional positioning system. The method includes: establishing a state equation and a measurement equation that integrates a high-precision regional positioning system and an inertial navigation system; using an extended Kalman filter to implement online carrier phase ambiguity resolution, and obtaining an ambiguity floating-point solution estimate and an ambiguity estimation covariance matrix; using a LAMBDA algorithm to search for a carrier measurement ambiguity fixed solution based on the ambiguity floating-point solution and the ambiguity estimation covariance matrix. The present invention retains the advantages of dynamic resolution and real-time operation of real-time online carrier phase ambiguity resolution algorithms, and compared with existing real-time online carrier phase ambiguity resolution algorithms, the proposed algorithm can still resolve the carrier measurement ambiguity of a high-precision regional positioning system when the initial solution error is large. The present invention can be applied in strong multipath environments such as indoors to improve the reliability and availability of ambiguity resolution of high-precision regional positioning system receivers.

Description

Technical Field

[0001] The present invention relates to the field of navigation and positioning technology, and in particular to a method and device for processing signals of a high-precision regional positioning system. Background Art

[0002] The high-precision regional positioning system is a ground-based navigation system that provides positioning services to users within the coverage area of ​​the system by broadcasting radio ranging signals. The radio ranging signals broadcast by the system adopt a spread spectrum signal system, including three parts: spread spectrum code, navigation message and carrier. If you want to achieve high-precision positioning with decimeter to centimeter accuracy based on the high-precision regional positioning system, you must use the carrier phase observations in its signal that are more accurate and less susceptible to multipath for positioning solution. However, the measurement of the carrier phase contains unknown integer ambiguities, so carrier ambiguity solution has become an indispensable step before the high-precision regional positioning system receiver can achieve high-precision positioning. Although the carrier ambiguity solution problem has been studied for a long time in the field of satellite navigation, due to the characteristics of the high-precision regional positioning system itself, the existing carrier ambiguity solution technology in the GNSS field is not suitable for copying to the high-precision regional positioning system.

[0003] The existing high-precision regional positioning system receiver carrier phase measurement ambiguity resolution methods are divided into three categories:

[0004] The first type of method relies on the ambiguity resolution algorithm of the initial calibration point. This type of algorithm requires the receiver of the high-precision regional positioning system to be stationary at a point with known coordinates at the initial startup, and then use the coordinate information to infer the carrier measurement ambiguity. Or it requires that there are multiple points with known coordinates in advance in the site where the high-precision regional positioning system receiver works. The high-precision regional positioning system receiver needs to move through these calibration points in turn and stay for a period of time. Then the high-precision regional positioning system receiver uses the coordinates of these calibration points and the carrier phase measurement values ​​measured at the above calibration points to jointly calculate and estimate the carrier measurement ambiguity. There are two unavoidable defects in this type of method. First, these methods all need to use other technologies to measure the coordinates of some known calibration points; second, they all require the receiver to move to these features before they can start working. These constraints limit the promotion and application of this type of method in practice.

[0005] The second type of method is mainly based on the characteristic that the carrier phase measurement ambiguity does not change with the observation epoch when there is no cycle slip in the receiver tracking loop. The carrier phase measurement values ​​of multiple epochs are collected during the movement of the high-precision regional positioning system receiver, and these measurements are combined through a large optimization algorithm to complete the carrier ambiguity resolution. However, hundreds of unknown position states will be generated during the movement of the high-precision regional positioning system receiver, and these position states also need to be resolved together with the carrier phase measurement ambiguity in the optimization algorithm. This makes the above-mentioned optimization algorithm often contain hundreds of observations and parameters to be estimated, resulting in a huge amount of calculation during the solution. Therefore, such algorithms are often difficult to run in real time, which limits the promotion and application of such methods in practice.

[0006] The third type of algorithm first uses the code pseudo-range observation with poor accuracy to estimate the initial solution of the receiver position, clock error and carrier phase measurement ambiguity, and then uses the Taylor series expansion to approximate a linear measurement equation near the initial solution, and uses the Kalman filter algorithm to estimate the receiver position, clock error and ambiguity. As the receiver moves, the filter's estimation of the carrier phase measurement ambiguity and other states will gradually converge, and finally complete the carrier phase ambiguity resolution. On the one hand, this type of algorithm does not need to restrict the motion trajectory of the receiver, and on the other hand, the Kalman filter algorithm has good real-time performance in resolving ambiguities, so it is more practical. However, due to the particularity of the high-precision regional positioning system, compared with the ambiguity resolution in the RTK algorithm of satellite positioning, the carrier phase ambiguity resolution of the high-precision regional positioning system receiver faces more difficulties. Because the high-precision regional positioning system is usually used in complex environments where the satellite positioning performance is reduced. Due to the limitations of terrain or occlusion, the number of high-precision regional positioning system base stations that can meet the line-of-sight conditions with the high-precision regional positioning system receiver at the same time is small, that is, the high-precision regional positioning system receiver can only obtain a small number of observations at the same time. Moreover, as the high-precision regional positioning system receiver moves, the signal reception of some base stations may be intermittent.

[0007] The inertial navigation system can provide inertial measurement observations, thereby constraining the change of the position and attitude of the high-precision regional positioning system receiver and accelerating the convergence speed of the carrier phase ambiguity resolution. However, the algorithm for real-time online carrier phase ambiguity resolution of the high-precision regional positioning system receiver assisted by the inertial navigation system also needs to construct a linearized measurement equation near the initial solution, and use the extended Kalman filter to complete the estimation of parameters such as the inertial navigation system error and the carrier measurement ambiguity. In the existing method, the measurement equation is obtained by performing a Taylor series expansion on the observation equation, then ignoring the high-order error terms and retaining only the first order. However, in the high-precision regional positioning system, since the distance between the high-precision regional positioning system base station and the high-precision regional positioning system receiver is relatively close, the above linearization approximation process may produce serious truncation errors when the initial solution error is large. When the truncation error is too large, even with the assistance of the inertial navigation system, the existing real-time online carrier phase ambiguity resolution algorithm is still difficult to converge. It can be seen that the existing inertial navigation system-assisted high-precision regional positioning system ambiguity resolution algorithm still requires a more accurate initial solution. However, the initial solution of the real-time online carrier phase ambiguity resolution process is often based on the spread spectrum code pseudorange measurement estimation in the signal broadcast by the high-precision regional positioning system base station, and the code pseudorange measurement result is very susceptible to errors such as multipath. Indoors or in some more complex scenarios, multipath may cause meter-level deviations in the code pseudorange measurement results. Therefore, it is still difficult to obtain correct ambiguity estimates using traditional real-time online carrier phase ambiguity resolution or real-time online carrier phase ambiguity resolution algorithms assisted by inertial navigation systems. Summary of the invention

[0008] The present invention aims to provide a high-precision regional positioning system signal processing method and device to overcome the defect of low accuracy in the prior art.

[0009] The high-precision regional positioning system signal processing method proposed in the first aspect of the present invention includes: step A: establishing state equations and measurement equations that integrate the high-precision regional positioning system and the inertial navigation system; step B: using extended Kalman filtering to implement online carrier phase ambiguity resolution to obtain ambiguity floating-point solution estimation and ambiguity estimation covariance matrix; step C: using the LAMBDA algorithm to search for carrier measurement ambiguity fixed solutions based on the ambiguity floating-point solution and the ambiguity estimation covariance matrix.

[0010] The high-precision regional positioning system signal processing device proposed in the second aspect of the present invention includes: an equation construction unit, which is used to establish state equations and measurement equations that integrate the high-precision regional positioning system and the inertial navigation system; a floating-point solution calculation unit, which is used to use an extended Kalman filter to implement online carrier phase ambiguity resolution, and obtain an ambiguity floating-point solution estimate and an ambiguity estimation covariance matrix; a fixed solution calculation unit, which is used to search for a carrier measurement ambiguity fixed solution using a LAMBDA algorithm based on the ambiguity floating-point solution and the ambiguity estimation covariance matrix.

[0011] In summary, the present invention proposes an improved real-time online carrier phase ambiguity resolution algorithm for an inertial navigation system-assisted high-precision regional positioning system receiver. The technical solution of the present invention retains the advantages of dynamic resolution and real-time operation of the real-time online carrier phase ambiguity resolution algorithm, and compared with the existing real-time online carrier phase ambiguity resolution algorithm, the proposed algorithm can still resolve the carrier measurement ambiguity of the high-precision regional positioning system when the initial solution error is large. The technical solution of the present invention can be applied in strong multipath environments such as indoors to improve the reliability and availability of ambiguity resolution of high-precision regional positioning system receivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings illustrate exemplary embodiments of the present application and together with the description are used to explain the principles of the present application. These drawings are included to provide a further understanding of the present application, and the drawings are included in and constitute a part of this specification.

[0013] Figure 1 It is a flow chart of the signal processing method of the high-precision regional positioning system of the present invention;

[0014] Figure 2 It is a structural block diagram of a high-precision regional positioning system signal processing device of the present invention;

[0015] Figure 3 The statistics of the mean absolute error of the single-difference carrier ambiguity between base stations obtained by the prior art method;

[0016] Figure 4 The figure is the statistics of the mean absolute error of the single-difference carrier ambiguity between base stations obtained by the method proposed in the present invention. DETAILED DESCRIPTION

[0017] The present invention aims to propose a high-precision regional positioning system signal processing method in view of the characteristics of the high-precision regional positioning system. From the principle of the invention, this method is understood to be a method of using an inertial navigation system to assist in solving the carrier ambiguity of the high-precision regional positioning system. The present application is further described in detail below in conjunction with the accompanying drawings and implementation methods. It is to be understood that the specific implementation methods described herein are only used to explain the relevant content, rather than to limit the present application. For the sake of simplicity, in the description of each implementation method of the present application, the same or similar figure marks are used for the same or similar device / method steps. It should be noted that, in the absence of conflict, the implementation methods in the present application and the features in the implementation methods can be combined with each other.

[0018] The present invention proposes a high-precision regional positioning system signal processing method. Figure 1 As shown, the high-precision regional positioning system signal processing method of the embodiment of the present invention mainly includes: step A: establishing the state equation and measurement equation of the integrated high-precision regional positioning system and the inertial navigation system; step B: using the extended Kalman filter to realize the online carrier phase ambiguity resolution, and obtain the ambiguity floating point solution estimation and the ambiguity estimation covariance matrix; step C: using the LAMBDA algorithm to search for the carrier measurement ambiguity fixed solution according to the ambiguity floating point solution and the ambiguity estimation covariance matrix.

[0019] The main feature of this method is that it uses basic carrier phase observations and pseudorange observations and a difference power nonlinear combination observation that is not affected by truncation errors. At the initial moment, the receiver position estimate is not accurate enough. At this time, the difference power nonlinear combination observation that is not affected by truncation errors can guide the real-time online carrier phase ambiguity resolution process in the right direction; when the real-time online carrier phase ambiguity resolution converges to a certain extent, the basic observations with small measurement noise will help the real-time online carrier phase ambiguity resolution to obtain more accurate ambiguity estimates. This method requires insensitivity to the initial state error, so it is more suitable for use when the initial state cannot be accurately estimated using code pseudorange measurements.

[0020] When using an inertial navigation system to assist in real-time online carrier phase ambiguity resolution, the state quantity that needs to be estimated in the kth epoch is

[0021]

[0022] in is the attitude angle error of the inertial navigation system, δv n is the velocity error of the inertial navigation system, δp is the position error of the inertial navigation system, ε b is the gyro bias of the inertial navigation system, is the accelerometer bias of the inertial navigation system, is the receiver clock bias, is the receiver clock deviation change rate, L k ; δλ k ; δh k is the position error of the receiver at the reference epoch. δN=[δN 1 ,δN 2 ,…,δN I ] T is the ambiguity estimation error. To ensure the uniqueness of the ambiguity resolution, we set the receiver clock bias to zero in the initial observation epoch.

[0023] The state equation of the system is expressed as:

[0024] X k =F k X k-1 +w k (2)

[0025] where w k ~N(0,Q k ) represents the system noise vector. The state transfer matrix F k Defined as

[0026]

[0027] in It can be obtained by discretizing the inertial navigation system error equation as follows:

[0028]

[0029] Where T is the update interval of inertial measurement, N is the discretization order,

[0030]

[0031] where F ie Describes the accumulation of attitude angle error, velocity error, and position error during the attitude update process of the inertial navigation system. sg The effect of gyroscope bias error and accelerometer bias error on the attitude update of the inertial navigation system can be written as

[0032]

[0033] in is the direction cosine matrix from the carrier coordinate system to the navigation coordinate system. Defined as

[0034]

[0035] In the real-time online carrier phase ambiguity resolution algorithm for receiving the high-precision regional positioning system assisted by the inertial navigation system proposed in the present invention, the measurement vector consists of two parts. The first part is the difference between the pseudorange observation predicted by the inertial navigation system and the carrier pseudorange observation (see the upper part on the right side of the equal sign in the formula (7) below). The second part is the error between the difference power nonlinear combination observation predicted by the inertial navigation system and the observation obtained by measurement (see the lower part on the right side of the equal sign in the formula (7) below).

[0036]

[0037] in

[0038] ρ i,k =r i,k +τ u -λN i +ε i,k (8)

[0039] The superscript i in the above formula indicates that the modified parameter is related to the i-th base station; the superscript k indicates that the parameter corresponds to the k-th observation epoch. According to the above rules, ρ i,k represents the carrier pseudorange observation value obtained by the receiver at the observation epoch k for the base station i; τ u represents the receiver clock error at the kth observation epoch; Ni represents the ambiguity of the carrier measurement of base station i, which is independent of the observation epoch; εi,k represents the measurement error, which can be assumed to obey the Gaussian distribution N(0,σ2); ri,k represents the geometric distance between the receiver and base station i in the observation epoch k.

[0040] The difference power nonlinear combination observation is shown as follows:

[0041]

[0042] It is the square of the carrier pseudorange observation measured by the receiver at observation epoch k for base station i minus the square of the carrier pseudorange observation measured by the receiver at observation epoch k for base station j, minus the square of the carrier pseudorange observation measured by the receiver at observation epoch m for base station i, minus the square of the carrier pseudorange observation measured by the receiver at observation epoch m for base station j. Since the operation involves the subtraction of the sum of squares of carrier pseudorange observations of different base stations and different observation epochs, we call this observation a difference power nonlinear combination observation.

[0043] The measurement equation of the system and its corresponding Jacobian matrix can be defined as

[0044]

[0045] in The elements of

[0046]

[0047] in

[0048]

[0049]

[0050] Where R N Indicates the main curvature radius of the meridian circle (perpendicular to the meridian circle) where the position is located. The calculation method is

[0051]

[0052] Where R e is the semi-major axis of the Earth model, e is the Earth's eccentricity, and L is the local latitude. The elements include

[0053]

[0054] in

[0055]

[0056]

[0057]

[0058] The covariance matrix of the measurement noise vector is defined as:

[0059]

[0060] Where α is a proportional factor used to adjust the confidence level of the difference power nonlinear combination observations. In the above formula, It can be approximated as

[0061]

[0062] in

[0063]

[0064] in is the estimated geometric distance from the receiver to base station i at the current moment, σ 2 is the variance of the carrier phase measurement.

[0065] Based on the state equation and measurement equation established above, the extended Kalman filter can be used to realize online carrier phase ambiguity resolution and obtain the floating point solution estimation of the ambiguity, and the LAMBDA algorithm is used to search for the fixed solution of the ambiguity using the obtained floating point solution of the ambiguity and the ambiguity estimation covariance matrix. After the ambiguity fixed solution is obtained, the carrier pseudorange measurement value and the resolved carrier measurement ambiguity can be used to realize precise single point positioning, or high-precision regional positioning system / inertial navigation system tight combination positioning.

[0066] The high-precision regional positioning system signal processing device of the embodiment of the present invention is as follows: Figure 2 As shown, it mainly includes: an equation construction unit 100, a floating-point solution calculation unit 200 and a fixed solution calculation unit 300. The equation construction unit 100 is used to establish the state equation and measurement equation of the fusion high-precision regional positioning system and the inertial navigation system; the floating-point solution calculation unit 200 is used to use the extended Kalman filter to realize the online carrier phase ambiguity resolution, and obtain the ambiguity floating-point solution estimate and the ambiguity estimation covariance matrix; the fixed solution calculation unit 300 is used to search for the carrier measurement ambiguity fixed solution using the LAMBDA algorithm according to the ambiguity floating-point solution and the ambiguity estimation covariance matrix. In some other embodiments, the high-precision regional positioning system signal processing device may also include a positioning unit, which is used to realize precise single-point positioning or high-precision regional positioning system / inertial navigation system tight combination positioning using the carrier pseudorange measurement value and the carrier measurement ambiguity fixed solution.

[0067] In order to demonstrate the advantages of the algorithm proposed in this invention, we selected the traditional inertial navigation system-assisted satellite navigation receiver ambiguity resolution algorithm for comparative experiments.

[0068] Traditional algorithms all directly adopt linearized measurement models based on Taylor series expansion.

[0069] The simulation experiment assumes that six high-precision regional positioning system base stations are deployed in a rectangular area of ​​25 meters × 10 meters × 6 meters. These high-precision regional positioning system base stations broadcast ranging signals at a frequency of 2465.43MHz. We select a reference point in the area. The positions of the six high-precision regional positioning system base stations in the area are described in the navigation coordinate system with the reference point as the origin. Assuming that there is a high-precision regional positioning system receiver in the area moving near the reference point, the motion trajectory of the high-precision regional positioning system receiver and the layout of the high-precision regional positioning system base stations are shown in the figure.

[0070] During the movement of the receiver, the carrier phase of each base station is measured at a frequency of 10Hz. It is assumed that the carrier phase measurement results include integer ambiguity, true geometric distance, receiver clock error and noise error. Among them, the receiver clock error obeys a Gaussian distribution with a mean of 5m / s and a standard deviation of 1m / s; the noise error obeys a Gaussian distribution with a zero mean and a standard deviation of 1cm. The receiver is equipped with an inertial navigation system. We set the zero bias of the gyroscope to 10° / h, the random measurement noise of the gyroscope to 1° / √h, the zero bias of the accelerometer to 50μg, and the random measurement noise of the accelerometer to 50μg / √Hz. The update rate of inertial measurement is set to 200Hz.

[0071] The comparison method and the method proposed in the present invention are both initialized based on the code pseudo-range measurement results. We assume that the code pseudo-range measurement results of the receiver for the six base stations in the simulation obey a Gaussian distribution with a mean of 0 and a standard deviation of ∑. Figure 3 The figure shows the statistics of the mean absolute error of the single-difference carrier ambiguity between base stations obtained by the traditional method after 100 simulation experiments. The X-axis represents the value of ∑, that is, the uncertainty of the initial solution; the Y-axis represents the error of the ambiguity estimation. Figure 3 It can be seen that when the initial solution error is small, the traditional method can obtain high-precision ambiguity estimation. In particular, when ∑≤3.5m, the ambiguity estimation error can be less than one carrier cycle. However, as the uncertainty of the initial state increases, the performance of the traditional method algorithm drops sharply. When ∑=5m, the ambiguity estimation error can even reach dozens of carrier cycles, which means that the accuracy of the ambiguity calculated by the traditional method is not significantly improved compared with the initial solution. It can be seen that the traditional method can complete the carrier ambiguity resolution when the initial solution error is small, but once the initial solution error increases, the traditional method will easily fail.

[0072] Figure 4 It shows the statistics of the mean absolute error of the single-difference carrier ambiguity between base stations obtained by the method proposed in the present invention after 100 simulation experiments. The X-axis represents the value of ∑; the Y-axis represents the error of the ambiguity estimation. It can be seen that with the increase of the uncertainty of the initial solution, the accuracy of the ambiguity resolution of the method proposed in the present invention will still decrease. However, compared with the traditional method, the speed of performance degradation of the algorithm proposed in the present invention is significantly reduced. Even under the worst conditions in the simulation, that is, when ∑=5m, the mean absolute error estimated by the method proposed in the present invention still does not exceed 3 carrier cycles.

[0073] It can be seen that the floating-point solution of the ambiguity resolution method proposed in the present invention can achieve decimeter-level positioning accuracy, which can meet the needs of most users. If the user has a higher demand for positioning accuracy, based on the floating-point solution with an error within 3 carrier cycles, we can also easily obtain a more accurate carrier ambiguity fixed solution through an integer ambiguity search method represented by the LAMBDA algorithm, thereby achieving higher positioning accuracy.

[0074] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0075] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0076] Those skilled in the art should understand that the above embodiments are only for the purpose of clearly illustrating the present application, and are not intended to limit the scope of the present application. For those skilled in the art, other changes or modifications may be made based on the above disclosure, and these changes or modifications are still within the scope of the present application.

Claims

1. A high-precision regional positioning system signal processing method, characterized in that: include: Step A: Establish the state equation and measurement equation integrating the high-precision regional positioning system and the inertial navigation system; Step B: Use extended Kalman filtering to implement online carrier phase ambiguity resolution, and obtain ambiguity floating point solution estimation and ambiguity estimation covariance matrix; Step C: searching for a carrier measurement ambiguity fixed solution using a LAMBDA algorithm according to the ambiguity floating point solution and the ambiguity estimation covariance matrix, Wherein, the step A comprises: The state quantity to be estimated in the kth epoch is expressed as Among them, φ k Represents the attitude angle error of the inertial navigation system, δv k represents the velocity error of the inertial navigation system, δp represents the position error of the inertial navigation system, and ε b represents the gyro bias of the inertial navigation system, represents the accelerometer bias of the inertial navigation system, represents the receiver clock deviation, represents the receiver clock deviation change rate, δL 0 ,δλ 0 ,δh 0 represents the position error of the receiver at the reference epoch, δN = [δN 1 ,δN 2 ,…,δN I ] T represents the ambiguity estimation error, The state equation is expressed as: X k =F k X k-1 +w k , in, w k ~N(0,Q k ) represents the system noise vector, F k represents the state transition matrix, and in, T represents the update interval of inertial measurement, N represents the discretization order, Among them, F ie It represents the accumulation of attitude angle error, velocity error, and position error during the attitude update process of the inertial navigation system. sg It represents the influence of gyroscope bias error and accelerometer bias error on the attitude update of the inertial navigation system. in, represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system, The measurement vector is expressed as two parts. The first part is the difference between the pseudorange observation predicted by the inertial navigation system and the carrier pseudorange observation. The second part is the error between the difference power nonlinear combination observation predicted by the inertial navigation system and the observation obtained by measurement. Among them, ρ i,k =r i,k +τ u -λN i +ε i,k , ρ i,k represents the carrier pseudorange observation value obtained by the receiver at the observation epoch k for base station i, τ u represents the receiver clock error at the kth observation epoch, N i represents the ambiguity of the measurement of the carrier of base station i, ε i,k Represents the measurement error, which follows the Gaussian distribution N(0,σ 2 ), r i,k represents the geometric distance between the receiver and base station i in observation epoch k, The difference power nonlinear combination observation is expressed as: The measurement equation of the system and its corresponding Jacobian matrix are expressed as, in, in, Among them, R N Indicates the main curvature radius of the circle where the position is located. Among them, R e represents the semi-major axis of the earth model, e represents the eccentricity of the earth, L represents the local latitude, in, The covariance matrix of the measurement noise vector is expressed as: Among them, α represents the proportional factor used to adjust the confidence of the difference power nonlinear combination observations, in, Among them, r i,k represents the estimated geometric distance from the receiver to base station i at the current moment, σ 2 Represents the variance of the carrier phase measurement.

2. The method according to claim 1, characterized in that After step C, the following steps are also included: Step D: Use the carrier pseudorange measurement value and the carrier measurement ambiguity fixed solution to achieve precise single-point positioning, or high-precision regional positioning system / inertial navigation system tightly combined positioning.

3. The method according to claim 1, characterized in that The clock bias within the initial observation epoch is set to zero.

4. A high-precision regional positioning system signal processing device, characterized in that: include: Equation building unit, used to build state equations and measurement equations that integrate high-precision regional positioning system and inertial navigation system; A floating point solution calculation unit, used for implementing online carrier phase ambiguity resolution by using an extended Kalman filter, and obtaining an ambiguity floating point solution estimate and an ambiguity estimate covariance matrix; a fixed solution calculation unit, configured to search for a carrier measurement ambiguity fixed solution using a LAMBDA algorithm according to the ambiguity floating point solution and the ambiguity estimation covariance matrix, Wherein, the equation building unit is also used for: The state quantity to be estimated in the kth epoch is expressed as Among them, φ k Represents the attitude angle error of the inertial navigation system, δv k represents the velocity error of the inertial navigation system, δp represents the position error of the inertial navigation system, and ε b represents the gyro bias of the inertial navigation system, represents the accelerometer bias of the inertial navigation system, represents the receiver clock deviation, represents the receiver clock deviation change rate, δL 0 ,δλ 0 ,δh 0 represents the position error of the receiver at the reference epoch, δN = [δN 1 ,δN 2 ,…,δN I ] T represents the ambiguity estimation error, The state equation is expressed as: X k =F k X k-1 +w k , in, w k ~N(0,Q k ) represents the system noise vector, F k represents the state transition matrix, and in, T represents the update interval of inertial measurement, N represents the discretization order, Among them, F ie It represents the accumulation of attitude angle error, velocity error, and position error during the attitude update process of the inertial navigation system. sg It represents the influence of gyroscope bias error and accelerometer bias error on the attitude update of the inertial navigation system. in, represents the direction cosine matrix from the carrier coordinate system to the navigation coordinate system, The measurement vector is expressed as two parts. The first part is the difference between the pseudorange observation predicted by the inertial navigation system and the carrier pseudorange observation. The second part is the error between the difference power nonlinear combination observation predicted by the inertial navigation system and the observation obtained by measurement. Among them, ρ i,k =r i,k +τ u -λN i +ε i,k , ρ i,k represents the carrier pseudorange observation value obtained by the receiver at the observation epoch k for base station i, τ u represents the receiver clock error at the kth observation epoch, N i represents the ambiguity of the measurement of the carrier of base station i, ε i,k Represents the measurement error, which follows the Gaussian distribution N(0,σ 2 ), r i,k represents the geometric distance between the receiver and base station i in observation epoch k, The difference power nonlinear combination observation is expressed as: The measurement equation of the system and its corresponding Jacobian matrix are expressed as, in, in, Among them, R N Indicates the main curvature radius of the circle where the position is located. Among them, R e represents the semi-major axis of the earth model, e represents the eccentricity of the earth, L represents the local latitude, in, The covariance matrix of the measurement noise vector is expressed as: Among them, α represents the proportional factor used to adjust the confidence of the difference power nonlinear combination observations, in, Among them, r i,k represents the estimated geometric distance from the receiver to base station i at the current moment, σ 2 Represents the variance of the carrier phase measurement.

5. The device according to claim 4, characterized in that Also includes: The positioning unit is used to realize precise single-point positioning or high-precision regional positioning system / inertial navigation system tightly combined positioning by using carrier pseudorange measurement values ​​and the carrier measurement ambiguity fixed solution.

6. The device according to claim 4, characterized in that The clock bias within the initial observation epoch is set to zero.

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