CN116879936B - INS-assisted Beidou three-frequency ambiguity initialization method and system between dynamic targets - Google Patents

Abstract

In order to solve the problem that in a complex time-varying occlusion environment, the Beidou observation signal is interrupted, the three-frequency ambiguity cannot be quickly initialized, and the relative positioning reliability and continuity between targets cannot be guaranteed, the present invention discloses an INS-assisted dynamic target. Inter-Beidou three-frequency ambiguity initialization method and system. The method of the present invention first uses the inter-carrier phase epoch difference and INS fusion to solve the real-time high-frequency dynamic reference position; on the basis of the dynamic reference position difference and Beidou RTK positioning, the baseline is solved Vector deviation; use baseline vector deviation to correct the dynamic reference position difference, construct and weight virtual baseline vector observations; combine pseudo-range and carrier phase observations to assist in three-frequency ambiguity resolution and fixation; use double-difference phase observations Perform baseline calculation using the value to obtain relative position results. The proposed Beidou three-frequency ambiguity rapid initialization method has great advantages in accuracy, timeliness and applicability.

Description

INS-assisted Beidou three-frequency ambiguity initialization method and system between dynamic targets

Technical field

The invention belongs to the field of global satellite navigation systems, and particularly relates to the problem of rapid re-initialization of Beidou three-frequency ambiguities in complex time-varying environments where Beidou signals are interfered and blocked, relative positioning between targets is interrupted and jumped, and ambiguities are quickly re-initialized. technology.

Background technique

The Beidou satellite navigation system has the advantages of global, all-weather, and high accuracy, and has natural advantages for high-precision dynamic positioning between targets. Dongdong relative positioning technology selects a specific dynamic target as the base station. Once the ambiguity is correctly fixed, centimeter-level positioning results relative to the base stations are obtained. Therefore, Dongdong relative positioning technology is more suitable for positioning scenarios between dynamic targets. Real-time and accurate relative position relationships are the prerequisite for safe and effective collaborative operations.

Three-frequency ambiguity fixation is the key to obtaining stable centimeter-level Beidou motion relative positioning accuracy. When the observation environment is good, motion relative positioning can complete ambiguity fixation within a few seconds. However, for complex time-varying observation environments, such as urban canyons, viaducts, and tunnels, Beidou satellite signals are easily blocked, causing observation interruptions. The three-frequency ambiguity needs to be re-initialized, and the reliability and continuity of relative positioning between targets cannot be guaranteed. Therefore, the rapid initialization of Beidou three-frequency ambiguity is of great significance to improving relative positioning performance. The high-precision position information recursively generated by INS in a short period of time is not affected by signal obstruction and interruption, and can be used to assist in ambiguity fixation and achieve continuous high-precision positioning as much as possible.

Grejner-Brzezinska et al. used the position predicted by INS to improve the accuracy of candidate ambiguities, shorten the ambiguity search time, and can still be instantly fixed after the GPS satellite signal is interrupted for 50 seconds. Han Houzeng proposed an inertial-assisted partial ambiguity fixing method. For data with a 19-second interruption in the GPS/BDS dual system, the fixing success rate exceeded 90% and the re-fixing time was less than 5 seconds. The high-precision position information recursively generated by INS in a short time is used to replace pseudo-range single-point positioning to improve the floating-point ambiguity resolution accuracy, thereby reducing the ambiguity search space and accelerating ambiguity fixation to improve subsequent history. Meta-blur fixed effect. At present, there are many studies and analyzes on INS-assisted RTK ambiguity fixation, which is basically mature. However, it is difficult to achieve rapid initialization of Beidou three-frequency ambiguity between dynamic targets assisted by INS, especially in complex time-varying occlusion environments. Facing greater challenges is an urgent problem that needs to be solved.

Contents of the invention

In order to solve the problem that in a complex time-varying occlusion environment, Beidou observation signals are interrupted, the three-frequency ambiguity cannot be quickly initialized, and the relative positioning reliability and continuity between targets cannot be guaranteed, the present invention proposes an INS-assisted dynamic target Beidou The three-frequency ambiguity initialization method includes the following steps:

Step 1: Use Beidou/INS fusion to calculate real-time high-frequency dynamic reference positions, including dynamic reference positions of dynamic reference targets and dynamic flow targets;

Step 2: Determine the baseline vector deviation and construct a virtual baseline vector observation value based on the dynamic reference position result in step 1;

Step 3: Use the Beidou pseudorange and carrier phase double-difference observations, combined with the virtual baseline vector observations and their variances in step 2, to obtain the Beidou three-frequency original ambiguity floating-point solution through least squares estimation, and search and fix it;

Step 4: Use the ambiguity fixed result of Step 3 to perform baseline calculation to determine the real-time relative position results between dynamic targets.

Further, the specific implementation method of step 1 is as follows:

Step 1.1, the dynamic reference target and the mobile target respectively use the carrier phase observation value to perform inter-epoch differences, obtain the position change from the current moment to the next moment through the least squares solution, and recursively deduce the next moment itself based on the current moment position. dynamic position;

Step 1.2, the dynamic reference target and the flowing target respectively loosely combine the dynamic position at the next moment and the predicted position result of the inertial navigation in step 1.1, and obtain the dynamic benchmark target A and the dynamic flowing target B at the next moment through the extended Kalman filter. own dynamic reference position result.

Further, in step 1.1, the dynamic position of the dynamic reference target at the next moment is recursed, as follows:

Among them, A represents the dynamic benchmark target, Express/> The dynamic position of the dynamic reference target at all times,/> Express/> The dynamic position of the dynamic reference target recursion at time, Express/> Time's up/> The amount of position change between times,/> Time represents the current moment,/> Time indication/> The next moment of,/> Represents the Beidou data observation interval;

In step 1.1, the dynamic position of the dynamic flow target at the next moment is recursed, as follows:

Among them, B represents the dynamic flow target, Express/> The dynamic position of the dynamically flowing target at all times,/> Express/> The dynamic position of the dynamic flow target recursion at all times, Express/> Time's up/> The amount of position change between moments.

Further, the specific implementation method of step 2 is as follows:

Step 2.1. At the next moment, the relative position results between the dynamic reference target A and the dynamic flowing target B are obtained according to Beidou's real-time dynamic positioning solution. Combined with the dynamic reference position results of targets A and B in step 1, the baseline vector deviation between the targets is determined as :

in, Express/> The relative position result between target A and B at the moment, Express/> The dynamic reference position of the dynamic reference target A at time,/> Express/> The dynamic reference position of the dynamic flow target B at all times,/> Express/> Time baseline vector deviation,/> Time indication/> the next moment;

According to the baseline vector deviation, the dynamic reference position difference between the targets is corrected to construct the virtual baseline vector result at the next moment:

in, Express/> time virtual baseline vector, Express/> The dynamic reference position of the dynamic reference target at all times,/> Express/> The dynamic reference position of the dynamic flow target at all times,/> Time indication/> the next moment;

Step 2.2, based on the covariance of the dynamic reference position results of targets A and B in step 1 and the relative position variance in Beidou real-time dynamic positioning, determine the variance of the virtual baseline vector at the next moment:

in, Express/> The covariance value of the dynamic reference position result of reference target A at time, Express/> The covariance value of the dynamic reference position result of the mobile target B at any time,/> Express/> Relative position variance value at time,/> Express/> The variance value of the virtual baseline vector at time.

Further, the specific implementation of step 3 is as follows;

Step 3.1, use the virtual baseline vector observation value constructed in step 2 as an additional constraint. The expression of the observation equation is:

in, Express/> Baseline vector at time,/> A moment represents the next moment;

Combining the Beidou pseudorange and carrier phase double-difference observation equations, the three-frequency dynamic ambiguity solution equation is obtained as follows. Through least squares solution, the three-frequency original ambiguity floating point solution is obtained:

in, and/> Represents the double-differenced pseudo-range observation value and the double-differenced carrier phase observation value,/> Indicates double difference between guard and ground distance,/> , I and/> Represents the coefficient matrix, identity matrix and zero matrix respectively,/> and/> Represents the wavelength and double-difference original ambiguity of the original observation value respectively,/> and/> represent the double-difference pseudorange and carrier observation noise and error respectively, Express/> Baseline vector at time;

Step 3.2: Search and fix the floating point solution of the Beidou three-frequency original ambiguity in step 3.1: first determine the fixed solution of ultra-wide lane ambiguity, then combine the observation value and the wide lane observation value to calculate the wide lane ambiguity, and finally use The wide lane observation value with correct fixed ambiguity is combined with the original observation value to solve the original ambiguity.

Further, the specific implementation of step 3.2 is as follows;

Step 3.2.1: Select reference satellites in the Beidou 2 and Beidou 3 systems to determine the double-difference ultra-wide lane ambiguity of each system. For the Beidou 2 system, use B3I and B2I to determine the double-difference ultra-wide lane ambiguity; for Beidou 3 The system uses B1C and B1I and B3I and B2a to determine the double-difference ultra-wide lane ambiguity, and determines the ultra-wide lane ambiguity through direct rounding. The calculation expression is:

In the formula, Represents ultra-wide lane ambiguity,/> and/> Represents the frequency (Hz) corresponding to frequency band m and frequency band n respectively,/> and/> Represents the double-difference pseudorange observation value (m) corresponding to frequency band m and frequency band n ,/> and/> Represents the double-difference carrier phase observation values (m) corresponding to frequency band m and frequency band n respectively,/> and/> Respectively represent the wavelength (m) of the linear combination observation value and the ambiguity (week) of the carrier phase double difference observation value,/> Represents the observation noise (m) of linear combination observations caused by environmental factors;

Step 3.2.2, combine the ultra-wide lane ambiguity integer value in step 3.2.1 and the B1I-B3I wide lane carrier phase observation equation, use least squares to estimate the floating point solution of the wide lane ambiguity, and use the LAMBDA method to calculate the floating point ambiguity The degree is searched and fixed, and a fixed solution of wide lane ambiguity is obtained. The error equation is:

In the formula, and/> represent the residual vectors of the ultra-wide lane ambiguity fixed value and the wide lane carrier phase observation value respectively,/> and I represent the coefficient matrix and identity matrix respectively,/> Indicates the correction number of the flowing target coordinates,/> Correction number vector representing the fixed value of ultra-wide lane ambiguity,/> Represents the correction vector of wide-lane carrier phase observations,/> and/> represent the wavelength and ambiguity of the wide-lane carrier phase observations respectively.

Step 3.2.3, combine the phase observation equation of the wide lane ambiguity in step 3.2.2 and the double-difference original ambiguity floating point solution solved in step 3.1, and search and fix it through the LAMBDA method. The error equation is:

In the formula, and/> represent the residual vectors of the exact wide-lane and original double-differenced carrier phase observations, respectively, Represents the correction vector of wide-lane carrier phase observations,/> Represents the original double difference carrier phase observation value/> correction number vector,/> and/> Represents the original double-difference carrier phase observation values/> wavelength and fuzziness.

when After the fixed value of ambiguity is determined,/> and/> The integer ambiguity of is based on the known fixed values of ultra-wide lane and wide lane ambiguity and/> The linear relationship of ambiguity is determined, and the specific calculation formula is as follows:

in, ,/> and/> Represents the original observation value of the double-difference carrier phase/> ,/> and/> The corresponding raw integer ambiguity;

Finally, the linear relationship and Ratio test between N ₁ , N ₂ and N ₃ ambiguities are used to verify the reliability of the ambiguity fixation:

In the formula , δ represents the tolerance threshold, ranging from 0.1 to 0.5, k and b are constants, and M represents the Ratio test threshold.

Further, the specific implementation of step 4 is as follows;

Use the ambiguity fixed in step 3.2.3 The double-difference carrier phase observation value is used for baseline calculation to obtain the baseline vector between targets, and the position of the flowing target relative to the reference target is determined based on the baseline vector result;

in, Express/> The baseline vector result between the dynamic baseline target A and the dynamic flow target B at any time,/> Express/> The dynamic reference position of the dynamic reference target at all times, Express/> The relative position of dynamically flowing targets at all times.

The invention also provides an INS-assisted Beidou three-frequency ambiguity rapid initialization system between dynamic targets, including the following modules:

The reference position calculation module uses Beidou/INS fusion to calculate real-time high-frequency dynamic reference positions, including dynamic reference positions of dynamic reference targets and dynamic flow targets;

The baseline vector determination module determines the baseline vector deviation and constructs a virtual baseline vector observation based on the dynamic reference position results;

The ambiguity acquisition module uses the Beidou pseudo-range and carrier phase double-difference observations, combined with the virtual baseline vector observations and their variances, to obtain the Beidou three-frequency original ambiguity floating-point solution through least squares estimation, and performs search and fixation;

The relative position determination module uses the ambiguity fixed results to perform baseline calculations and determines the real-time relative position results between dynamic targets.

Further, the specific implementation method of the baseline vector determination module is as follows:

Step 2.1. At the next moment, the relative position results between the dynamic reference target A and the dynamic flowing target B are obtained according to Beidou's real-time dynamic positioning solution. Combined with the dynamic reference position results of targets A and B, the baseline vector deviation between the targets is determined as:

in, Express/> The relative position result between target A and B at the moment, Express/> The dynamic reference position of the dynamic reference target A at time,/> Express/> The dynamic reference position of the dynamic flow target B at all times,/> Express/> Time baseline vector deviation,/> Time indication/> the next moment;

According to the baseline vector deviation, the dynamic reference position difference between the targets is corrected to construct the virtual baseline vector result at the next moment:

in, Express/> time virtual baseline vector, Express/> The dynamic reference position of the dynamic reference target at all times,/> Express/> The dynamic reference position of the dynamic flow target at all times,/> Time indication/> the next moment;

Step 2.2, determine the variance of the virtual baseline vector at the next moment based on the covariance of the dynamic reference position results of targets A and B and the relative position variance in Beidou real-time dynamic positioning:

in, Express/> The covariance value of the dynamic reference position result of reference target A at time, Express/> The covariance value of the dynamic reference position result of the mobile target B at any time,/> Express/> Relative position variance value at time,/> Express/> The variance value of the virtual baseline vector at time.

Further, the specific implementation of the fuzziness acquisition module is as follows;

Step 3.1, use the constructed virtual baseline vector observation value as an additional constraint, and the expression of the observation equation is:

in, Express/> Baseline vector at time,/> A moment represents the next moment;

Combining the Beidou pseudorange and carrier phase double-difference observation equations, the three-frequency dynamic ambiguity solution equation is obtained as follows. Through least squares solution, the three-frequency original ambiguity floating point solution is obtained:

in, and/> Represents the double-differenced pseudo-range observation value and the double-differenced carrier phase observation value,/> Indicates double difference between guard and ground distance,/> , I and/> Represents the coefficient matrix, identity matrix and zero matrix respectively,/> and/> Represents the wavelength and double-difference original ambiguity of the original observation value respectively,/> and/> represent the double-difference pseudorange and carrier observation noise and error respectively, Express/> Baseline vector at time;

Step 3.2: Search and fix the floating point solution of the Beidou three-frequency original ambiguity in step 3.1: first determine the fixed solution of ultra-wide lane ambiguity, then combine the observation value and the wide lane observation value to calculate the wide lane ambiguity, and finally use The wide lane observation value with correct fixed ambiguity is combined with the original observation value to solve the original ambiguity.

The beneficial effects produced by the present invention are:

1. The present invention proposes a Beidou three-frequency ambiguity initialization method and system between INS-assisted dynamic targets, which realizes complementary advantages through the fusion of Beidou and INS data, and is suitable for complex time-varying occlusion environments.

2. The present invention constructs a virtual baseline observation value through INS to improve the floating-point solution accuracy of three-frequency ambiguity, thereby reducing the search space of ambiguity and accelerating the fixation of three-frequency ambiguity.

Description of the drawings

Figure 1 is a method flow chart of an embodiment of the present invention.

Figure 2 is a flow chart of the Beidou three-frequency ambiguity fixing method according to the embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

The present invention provides a Beidou three-frequency ambiguity initialization method and system between INS-assisted dynamic targets, using Beidou/INS fusion to solve the real-time high-frequency dynamic reference position (step 1); determine the baseline vector deviation and construct a virtual baseline vector observation value (step 1) Step 2); estimate the Beidou three-frequency ambiguity floating-point solution and search for fixation (step 3); finally solve the baseline through least squares and determine the real-time relative position results between dynamic targets (step 4). Referring to Figures 1 and 2, an INS-assisted inter-dynamic target Beidou three-frequency ambiguity initialization method and system provided in the embodiment of the present invention specifically includes the following steps:

Step 1. Dynamic reference targets and mobile targets are respectively calculated through Beidou/INS fusion to calculate the dynamic reference position, and obtain their own real-time and high-frequency dynamic reference position information, including:·

Step 1.1. The dynamic reference target and the flowing target respectively use the carrier phase observation value to perform inter-epoch differences, obtain the position change from the current moment to the next moment through the least square solution, and recursively deduce the next moment itself based on the current moment position. dynamic position;

As described in step 1.1, recurse the dynamic position of the dynamic reference target at the next moment, as follows:

Among them, A represents the dynamic benchmark target, Express/> The dynamic position of the dynamic reference target at all times,/> Express/> The dynamic position of the dynamic reference target recursion at time, Express/> Time's up/> The amount of position change between times,/> Time represents the current moment,/> Time represents the next moment,/> Indicates the Beidou data observation interval.

As described in step 1.1, recurse the dynamic position of the dynamic flow target at the next moment, as follows:

Among them, B represents the dynamic flow target, Express/> The dynamic position of the dynamically flowing target at all times,/> Express/> The dynamic position of the dynamic flow target recursion at all times, Express/> Time's up/> The amount of position change between moments.

Step 1.2. Dynamic reference targets and flowing targets respectively loosely combine the dynamic position at the next moment in step 1.1 with the predicted position results of inertial navigation, and obtain the dynamic benchmarks of target A and target B at the next moment through extended Kalman filtering. Location results.

The dynamic reference position of the dynamic reference target at the next moment as described in step 1.2 is as follows:

Among them, A represents the dynamic benchmark target, Express/> The dynamic reference position of the dynamic reference target at all times,/> Moment represents the next moment.

The dynamic reference position of the dynamic flow target at the next moment as described in step 1 is as follows:

Among them, B represents the dynamic flow target, Express/> The dynamic reference position of the dynamic flow target at all times,/> Moment represents the next moment.

Step 2: Calculate the baseline vector deviation and construct virtual baseline vector observations, including:

Step 2.1. At the next moment, obtain the relative position results between targets A and B based on Beidou's real-time dynamic positioning solution. Combined with the dynamic reference position results of targets A and B in step 1, determine the baseline vector deviation between the targets as:

in, Express/> The relative position result between target A and B at the moment, Express/> Time baseline vector deviation,/> Moment represents the next moment.

According to the baseline vector deviation, the dynamic reference position difference between the targets is corrected to construct the virtual baseline vector result at the next moment:

in, Express/> time virtual baseline vector, Express/> The dynamic reference position of the dynamic reference target at all times,/> Express/> The dynamic reference position of the dynamic flow target at all times,/> Moment represents the next moment.

Step 2.2. Based on the covariance of the dynamic reference position results of targets A and B in step 1 and the relative position variance in Beidou real-time dynamic positioning, determine the variance of the virtual baseline vector at the next moment:

in, Express/> The covariance value of the dynamic reference position result of reference target A at time, Express/> The covariance value of the dynamic reference position result of the mobile target B at any time,/> Express/> Relative position variance value at time,/> Express/> The variance value of the virtual baseline vector at time,/> Moment represents the next moment.

Step 3. Use the Beidou pseudorange and carrier phase double-difference observations, combined with the virtual baseline vector observations and their variances in step 2, to obtain the Beidou three-frequency original ambiguity floating point solution through least squares estimation, and search and fix it. Specifically include:

Step 3.1. Use the virtual baseline vector observation value constructed in step 2 as an additional constraint. The expression of the observation equation is:

in, Express/> Baseline vector at time,/> Moment represents the next moment.

Combining the Beidou pseudorange and carrier phase double-difference observation equations, the three-frequency dynamic ambiguity solution equation is obtained as follows. Through least squares solution, the three-frequency original ambiguity floating point solution is obtained:

in, and/> Represents the double-difference pseudo-range observation value (m) and the double-difference carrier phase observation value (m),/> Represents the double-difference guard-to-ground distance (m),/> , I and/> Represents the coefficient matrix, identity matrix and zero matrix respectively,/> and/> Represents the wavelength (m) and double-difference original ambiguity (week) of the original observation value respectively,/> and/> Represents the double-difference pseudorange and carrier observation noise and error respectively,/> Express/> baseline vector at time.

Step 3.2: Search and fix the Beidou three-frequency original ambiguity floating point solution of Step 3.1. Referring to Figure 2, first determine the fixed solution of ultra-wide lane ambiguity, then combine the observation value with the wide lane observation value to calculate the wide lane ambiguity, and finally use the wide lane observation value with the ambiguity correctly fixed and the original observation value to solve the original ambiguity, including the following sub-steps,

Step 3.2.1. Select reference satellites in each Beidou 2 and Beidou 3 system to determine the double-difference ultra-wide lane ambiguity of each system. For the Beidou 2 system, B3I and B2I are used to determine the double-difference ultra-wide lane ambiguity; for the Beidou 3 system, B1C and B1I and B3I and B2a are used to determine the double-difference ultra-wide lane ambiguity, and the ultra-wide lane ambiguity is determined by direct rounding. degree, the calculation expression is:

In the formula, and/> Represents the ultra-wide lane ambiguity (week) and its wavelength (m) respectively,/> and/> Represents the frequency (Hz) corresponding to frequency band m and frequency band n respectively,/> and/> Represents the double-difference pseudorange observation value (m) corresponding to frequency band m and frequency band n ,/> and/> Represents the double-difference carrier phase observation values (m) corresponding to frequency band m and frequency band n respectively,/> Represents the observation noise (m) of the linear combination of observations caused by environmental factors.

Step 3.2.2, combine the ultra-wide lane ambiguity integer value of step 3.2.1 and the B1I-B3I wide lane carrier phase observation equation, use least squares to estimate the floating point solution of wide lane ambiguity, and use the LAMBDA method to calculate the floating point ambiguity The degree is searched and fixed, and a fixed solution of wide lane ambiguity is obtained. The error equation is:

In the formula, and/> represent the residual vectors of the ultra-wide lane ambiguity fixed value and the wide lane carrier phase observation value respectively,/> and I represent the coefficient matrix and identity matrix respectively,/> Indicates the correction number of the flowing target coordinates,/> Correction number vector representing the fixed value of ultra-wide lane ambiguity,/> Represents the correction vector of wide-lane carrier phase observations,/> and/> represent the wavelength and ambiguity of the wide-lane carrier phase observations respectively.

Step 3.2.3, combine the phase observation equation of the wide lane ambiguity in step 3.2.2 and the double-difference original ambiguity floating point solution solved in step 3.1, and search and fix it through the LAMBDA method. The error equation is:

In the formula, and/> represent the residual vectors of the exact wide-lane and original double-differenced carrier phase observations, respectively, Represents the correction vector of wide-lane carrier phase observations,/> Represents the original double difference carrier phase observation value/> correction number vector,/> and/> Represents the original double-difference carrier phase observation values/> wavelength (m) and ambiguity (cycle).

when After the fixed value of ambiguity is determined,/> and/> The integer ambiguity of is based on the known fixed values of ultra-wide lane and wide lane ambiguity and/> The linear relationship of ambiguity is determined, and the specific calculation formula is as follows:

in, ,/> and/> Represents the original observation value of the double-difference carrier phase/> ,/> and/> The corresponding raw integer ambiguity.

Finally, the linear relationship and Ratio test between N ₁ , N ₂ and N ₃ ambiguities are used to verify the reliability of the ambiguity fixation:

In the formula , δ represents the tolerance threshold, ranging from 0.1 to 0.5, k and b are constants, and M represents the Ratio test threshold.

Step 4. Use the ambiguity fixed results of Step 3 to perform baseline calculation and determine the relative positions between dynamic targets, including:

Use the ambiguity fixed in step 3.2.3 The double-difference carrier phase observation value is used for baseline calculation to obtain the baseline vector between targets. The position of the flowing target relative to the reference target is determined based on the baseline vector result:

in, Express/> The baseline vector result between A and B calculated using Beidou real-time dynamic positioning of the target at the moment,/> Express/> The relative position of dynamically flowing targets at all times.

During specific implementation, the above process can use computer software technology to realize the automatic operation process, and the system device for running the method process of the present invention should also be within the protection scope of the present invention.

The invention provides an INS-assisted Beidou three-frequency ambiguity rapid initialization system between dynamic targets, including the following modules:

The reference position calculation module uses Beidou/INS fusion to calculate real-time high-frequency dynamic reference positions, including dynamic reference positions of dynamic reference targets and dynamic flow targets;

The baseline vector determination module determines the baseline vector deviation and constructs a virtual baseline vector observation based on the dynamic reference position results;

The ambiguity acquisition module uses the Beidou pseudo-range and carrier phase double-difference observations, combined with the virtual baseline vector observations and their variances, to obtain the Beidou three-frequency original ambiguity floating-point solution through least squares estimation, and performs search and fixation;

The relative position determination module uses the ambiguity fixed results to perform baseline calculations and determines the real-time relative position results between dynamic targets.

The specific implementation manner of each module is the same as each step, and will not be described in the present invention.

According to the technical solution of the present invention, the statistical results of the INS-assisted Beidou three-frequency ambiguity test in Table 1 are obtained, where MAX represents the maximum value of the baseline vector error in the E/N/U direction. The results in Table 1 show that INS-assisted Beidou three-frequency ambiguity has higher baseline vector accuracy and ambiguity fixation rate, and smaller baseline vector error outliers.

Table 1 Statistical results of INS-assisted Beidou three-frequency ambiguity test

The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which the present invention belongs can make various modifications or additions to the described specific embodiments or substitute them in similar ways, but this will not deviate from the spirit of the present invention or exceed the definition of the appended claims. range.

Claims (8)

1. An INS-assisted Beidou three-frequency ambiguity initialization method between dynamic targets, which is characterized by including the following steps: Step 1: Use Beidou/INS fusion to calculate real-time high-frequency dynamic reference positions, including dynamic reference positions of dynamic reference targets and dynamic flow targets; Step 2: Determine the baseline vector deviation and construct a virtual baseline vector observation value based on the dynamic reference position result in step 1; The specific implementation method of step 2 is as follows: Step 2.1. At the next moment, the relative position results between the dynamic reference target A and the dynamic flowing target B are obtained according to Beidou's real-time dynamic positioning solution. Combined with the dynamic reference position results of targets A and B in step 1, the baseline vector deviation between the targets is determined as : in, Express/> The relative position result between target A and B at the moment, Express/> The dynamic reference position of the dynamic reference target A at time,/> Express/> The dynamic reference position of the dynamic flow target B at all times,/> Express/> Time baseline vector deviation,/> Time indication/> the next moment; According to the baseline vector deviation, the dynamic reference position difference between the targets is corrected to construct the virtual baseline vector result at the next moment: in, Express/> Time virtual baseline vector, /> Express/> The dynamic reference position of the dynamic reference target at all times,/> Express/> The dynamic reference position of the dynamic flow target at all times,/> Time indication/> the next moment; Step 2.2, based on the covariance of the dynamic reference position results of targets A and B in step 1 and the relative position variance in Beidou real-time dynamic positioning, determine the variance of the virtual baseline vector at the next moment: in, Express/> The covariance value of the dynamic reference position result of reference target A at time, Express/> The covariance value of the dynamic reference position result of the mobile target B at any time,/> Express/> Relative position variance value at time,/> Express/> The variance value of the virtual baseline vector at time; Step 3: Use the Beidou pseudorange and carrier phase double-difference observations, combined with the virtual baseline vector observations and their variances in step 2, to obtain the Beidou three-frequency original ambiguity floating-point solution through least squares estimation, and search and fix it; Step 4: Use the ambiguity fixed result of Step 3 to perform baseline calculation to determine the real-time relative position results between dynamic targets. 2. A Beidou three-frequency ambiguity initialization method between INS-assisted dynamic targets according to claim 1, characterized in that: the specific implementation method of step 1 is as follows: Step 1.1, the dynamic reference target and the mobile target respectively use the carrier phase observation value to perform inter-epoch differences, obtain the position change from the current moment to the next moment through the least squares solution, and recursively deduce the next moment itself based on the current moment position. dynamic position; Step 1.2, the dynamic reference target and the flowing target respectively loosely combine the dynamic position at the next moment and the predicted position result of the inertial navigation in step 1.1, and obtain the dynamic benchmark target A and the dynamic flowing target B at the next moment through the extended Kalman filter. own dynamic reference position result. 3. A Beidou three-frequency ambiguity initialization method between INS-assisted dynamic targets according to claim 2, characterized in that: in step 1.1, the dynamic position of the dynamic reference target at the next moment is recursed, specifically as follows: Among them, A represents the dynamic benchmark target, Express/> The dynamic position of the dynamic reference target at all times,/> Express/> The dynamic position of the dynamic reference target recursion at time, Express/> Time's up/> The amount of position change between times,/> Time represents the current moment,/> Time indication/> The next moment of,/> Represents the Beidou data observation interval; In step 1.1, the dynamic position of the dynamic flow target at the next moment is recursed, as follows: Among them, B represents the dynamic flow target, Express/> The dynamic position of the dynamically flowing target at all times,/> Express/> The dynamic position of the dynamic flow target recursion at all times, Express/> Time's up/> The amount of position change between moments. 4. A Beidou three-frequency ambiguity initialization method between INS-assisted dynamic targets according to claim 1, characterized in that: the specific implementation of step 3 is as follows; Step 3.1, use the virtual baseline vector observation value constructed in step 2 as an additional constraint. The expression of the observation equation is: in, Express/> Baseline vector at time,/> A moment represents the next moment; Combining the Beidou pseudorange and carrier phase double-difference observation equations, the three-frequency dynamic ambiguity solution equation is obtained as follows. Through least squares solution, the three-frequency original ambiguity floating point solution is obtained: in, and/> Represents the double-differenced pseudo-range observation value and the double-differenced carrier phase observation value,/> Represents the double-difference guard-to-ground distance, , I and/> Represents the coefficient matrix, identity matrix and zero matrix respectively,/> and/> Represents the wavelength and double-difference original ambiguity of the original observation value respectively,/> and/> represent the double-difference pseudorange and carrier observation noise and error respectively, Express/> Baseline vector at time; Step 3.2: Search and fix the floating point solution of the Beidou three-frequency original ambiguity in step 3.1: first determine the fixed solution of ultra-wide lane ambiguity, then combine the observation value and the wide lane observation value to calculate the wide lane ambiguity, and finally use The wide lane observation value with correct fixed ambiguity is combined with the original observation value to solve the original ambiguity. 5. A Beidou three-frequency ambiguity initialization method between INS-assisted dynamic targets according to claim 4, characterized in that: the specific implementation of step 3.2 is as follows; Step 3.2.1: Select reference satellites in the Beidou 2 and Beidou 3 systems to determine the double-difference ultra-wide lane ambiguity of each system. For the Beidou 2 system, use B3I and B2I to determine the double-difference ultra-wide lane ambiguity; for Beidou 3 The system uses B1C and B1I and B3I and B2a to determine the double-difference ultra-wide lane ambiguity, and determines the ultra-wide lane ambiguity through direct rounding. The calculation expression is: In the formula, Represents ultra-wide lane ambiguity,/> and/> Represents the frequencies corresponding to frequency band m and frequency band n respectively,/> and Represents the double-difference pseudorange observation values corresponding to frequency band m and frequency band n ,/> and/> Represents the double difference carrier phase observation values corresponding to frequency band m and frequency band n respectively,/> Represents the wavelength of the linear combination of observations,/> Represents the observation noise of linear combination observations caused by environmental factors; Step 3.2.2, combine the ultra-wide lane ambiguity integer value in step 3.2.1 and the B1I-B3I wide lane carrier phase observation equation, use least squares to estimate the floating point solution of the wide lane ambiguity, and use the LAMBDA method to calculate the floating point ambiguity The degree is searched and fixed, and a fixed solution of wide lane ambiguity is obtained. The error equation is: In the formula, and/> represent the residual vectors of the ultra-wide lane ambiguity fixed value and the wide lane carrier phase observation value respectively, and I represent the coefficient matrix and identity matrix respectively,/> Indicates the correction number of the flowing target coordinates,/> Correction number vector representing the fixed value of ultra-wide lane ambiguity,/> Represents the correction vector of wide-lane carrier phase observations,/> and/> Represent the wavelength and ambiguity of the wide-lane carrier phase observation value respectively; Step 3.2.3, combine the phase observation equation of the wide lane ambiguity in step 3.2.2 and the double-difference original ambiguity floating point solution solved in step 3.1, and search and fix it through the LAMBDA method. The error equation is: In the formula, and/> represent the residual vectors of the exact wide-lane and original double-differenced carrier phase observations respectively, /> Represents the correction vector of wide-lane carrier phase observations,/> Represents the original double difference carrier phase observation value/> correction number vector,/> and/> Represents the original double-difference carrier phase observation values/> wavelength and blur; when After the fixed value of ambiguity is determined,/> and/> The integer ambiguity of is based on the known fixed values of ultra-wide lane and wide lane ambiguity and/> The linear relationship of ambiguity is determined, and the specific calculation formula is as follows: in, ,/> and/> Represents the original observation value of the double-difference carrier phase/> ,/> and/> The corresponding raw integer ambiguity; Finally, the linear relationship and Ratio test between N ₁ , N ₂ and N ₃ ambiguities are used to verify the reliability of the ambiguity fixation: In the formula , δ represents the tolerance threshold, ranging from 0.1 to 0.5, k and b are constants, and M represents the Ratio test threshold. 6. A Beidou three-frequency ambiguity initialization method between INS-assisted dynamic targets according to claim 5, characterized in that: the specific implementation of step 4 is as follows; Use the ambiguity fixed in step 3.2.3 The double-difference carrier phase observation value is used for baseline calculation to obtain the baseline vector between targets, and the position of the flowing target relative to the reference target is determined based on the baseline vector result; in, Express/> The baseline vector result between the dynamic baseline target A and the dynamic flow target B at any time,/> Express/> The dynamic reference position of the dynamic reference target at all times, Express/> The relative position of dynamically flowing targets at all times. 7. An INS-assisted Beidou three-frequency ambiguity rapid initialization system between dynamic targets, which is characterized by including the following modules: The reference position calculation module uses Beidou/INS fusion to calculate real-time high-frequency dynamic reference positions, including dynamic reference positions of dynamic reference targets and dynamic flow targets; The baseline vector determination module determines the baseline vector deviation and constructs a virtual baseline vector observation based on the dynamic reference position results; The specific implementation method of the baseline vector determination module is as follows: Step 2.1. At the next moment, the relative position results between the dynamic reference target A and the dynamic flowing target B are obtained according to Beidou's real-time dynamic positioning solution. Combined with the dynamic reference position results of targets A and B, the baseline vector deviation between the targets is determined as: in, Express/> The relative position result between target A and B at the moment, Express/> The dynamic reference position of the dynamic reference target A at time,/> Express/> The dynamic reference position of the dynamic flow target B at all times,/> Express/> Time baseline vector deviation,/> Time indication/> the next moment; According to the baseline vector deviation, the dynamic reference position difference between the targets is corrected to construct the virtual baseline vector result at the next moment: in, Express/> Time virtual baseline vector, /> Express/> The dynamic reference position of the dynamic reference target at all times,/> Express/> The dynamic reference position of the dynamic flow target at all times,/> Time indication/> the next moment; Step 2.2, determine the variance of the virtual baseline vector at the next moment based on the covariance of the dynamic reference position results of targets A and B and the relative position variance of Beidou real-time dynamic positioning: in, Express/> The covariance value of the dynamic reference position result of reference target A at time, Express/> The covariance value of the dynamic reference position result of the mobile target B at any time,/> Express/> Relative position variance value at time,/> Express/> The variance value of the virtual baseline vector at time; The ambiguity acquisition module uses the Beidou pseudo-range and carrier phase double-difference observations, combined with the virtual baseline vector observations and their variances, to obtain the Beidou three-frequency original ambiguity floating-point solution through least squares estimation, and performs search and fixation; The relative position determination module uses the ambiguity fixed results to perform baseline calculations and determines the real-time relative position results between dynamic targets. 8. An INS-assisted Beidou three-frequency ambiguity rapid initialization system between dynamic targets according to claim 7, characterized in that: the specific implementation of the ambiguity acquisition module is as follows; Step 3.1, use the constructed virtual baseline vector observation value as an additional constraint, and the expression of the observation equation is: in, Express/> Baseline vector at time,/> A moment represents the next moment; Combining the Beidou pseudorange and carrier phase double-difference observation equations, the three-frequency dynamic ambiguity solution equation is obtained as follows. Through least squares solution, the three-frequency original ambiguity floating point solution is obtained: in, and/> Represents the double-differenced pseudo-range observation value and the double-differenced carrier phase observation value,/> Represents the double-difference guard-to-ground distance, , I and/> Represents the coefficient matrix, identity matrix and zero matrix respectively,/> and/> Represents the wavelength and double-difference original ambiguity of the original observation value respectively,/> and/> represent the double-difference pseudorange and carrier observation noise and error respectively, Express/> Baseline vector at time; Step 3.2: Search and fix the floating point solution of the Beidou three-frequency original ambiguity in step 3.1: first determine the fixed solution of ultra-wide lane ambiguity, then combine the observation value and the wide lane observation value to calculate the wide lane ambiguity, and finally use The wide lane observation value with correct fixed ambiguity is combined with the original observation value to solve the original ambiguity.