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

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

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CN116879936B
CN116879936B CN202311150130.9A CN202311150130A CN116879936B CN 116879936 B CN116879936 B CN 116879936B CN 202311150130 A CN202311150130 A CN 202311150130A CN 116879936 B CN116879936 B CN 116879936B
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beidou
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CN116879936A (en
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李洋洋
唐卫明
邓辰龙
邹璇
张永峰
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Wuhan University WHU
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled

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  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

In order to solve the problems that in a complex time-varying shielding environment, the Beidou observation signal is interrupted, three-frequency ambiguity cannot be initialized quickly and the reliability and continuity of relative positioning between targets cannot be guaranteed, the invention discloses a method and a system for initializing the Beidou three-frequency ambiguity between INS-assisted dynamic targets, and the method firstly utilizes the difference between carrier phase epochs and INS fusion to calculate a real-time high-frequency dynamic reference position; based on the dynamic reference position difference and Beidou RTK positioning, calculating a baseline vector deviation; correcting the dynamic reference position difference by utilizing the baseline vector deviation, and constructing and weighting a virtual baseline vector observation value; combining the pseudo range and the carrier phase observation value, and assisting in three-frequency ambiguity resolution and fixation; and (5) carrying out baseline calculation by using the double-difference phase observation value to obtain a relative position result. The 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 a technology for realizing rapid initialization of Beidou three-frequency ambiguity, which is related to the problem that the Beidou signals are interfered and blocked, the relative positioning among targets is interrupted and jumped, and the ambiguity is rapidly initialized again under a complex time-varying environment.
Background
The Beidou satellite navigation system has the advantages of being global, all-weather, high in precision and the like, and has natural advantages for high-precision dynamic positioning and resolving among targets. The dynamic relative positioning technology selects a specific dynamic target as a base station, and once the ambiguity is fixed correctly, a centimeter-level positioning result relative to the base station is obtained. Therefore, the dynamic relative positioning technology is more suitable for positioning scenes among dynamic targets, and the real-time accurate relative position relationship is a precondition of safe and effective collaborative operation.
The three-frequency ambiguity fixing is a key for obtaining stable centimeter-level Beidou moving relative positioning accuracy, and the moving relative positioning can finish the ambiguity fixing within a few seconds under the condition of good observation environment. However, for complex time-varying observation environments, such as urban canyons, overhead overpasses, tunnels and the like, beidou satellite signals are easy to block, observation interruption occurs, three-frequency ambiguity needs to be reinitialized, and reliability and continuity of relative positioning between targets cannot be guaranteed. Therefore, the rapid initialization of the Beidou three-frequency ambiguity has important significance for improving the relative positioning performance. The high-precision position information recursively provided by the INS in a short time is not influenced by signal shielding and interruption, and can be used for assisting in ambiguity fixation and realizing continuous high-precision positioning as far as possible.
Grejner-Brzinska et al, using INS predicted position, improves candidate ambiguity accuracy, shortens ambiguity search time, and remains instantaneous stationary after a GPS satellite signal interruption of 50 seconds. Han Houzeng proposes an inertial assisted partial ambiguity fixing method with a fix success rate of over 90% and a fix time of less than 5s for data of the GPS/BDS dual system interrupt 19 s. The INS recursively high-precision position information in a short time is utilized to replace pseudo-range single-point positioning, and the resolving precision of floating ambiguity is improved, so that the searching space of the ambiguity is reduced, the ambiguity fixing is quickened, and the effect of the subsequent epoch ambiguity fixing is improved. At present, research and analysis of INS-assisted RTK ambiguity fixing are more and basically mature, however, quick initialization of the INS-assisted Beidou three-frequency ambiguity between dynamic targets is to be realized, the overall difficulty is high, and the INS-assisted RTK ambiguity fixing is more challenging especially in a complex time-varying shielding environment, so that the INS-assisted RTK ambiguity fixing is a problem to be solved urgently.
Disclosure of Invention
Aiming at the problems that in a complex time-varying shielding environment, the Beidou observation signal is interrupted, the three-frequency ambiguity cannot be initialized quickly and the reliability and the continuity of relative positioning between targets cannot be ensured, the invention provides an INS-assisted Beidou three-frequency ambiguity initialization method between dynamic targets, which comprises the following steps:
step 1, resolving a real-time high-frequency dynamic reference position by Beidou/INS fusion, wherein the real-time high-frequency dynamic reference position comprises a dynamic reference target and a dynamic reference position of a dynamic flowing target;
step 2, determining baseline vector deviation and constructing a virtual baseline vector observation value according to the dynamic reference position result in the step 1;
step 3, obtaining a Beidou three-frequency original ambiguity floating solution by least square estimation by utilizing the Beidou pseudo-range and carrier phase double-difference observed value and combining the virtual baseline vector observed value and the variance of the virtual baseline vector observed value in the step 2, and searching and fixing;
and step 4, carrying out baseline calculation by using the ambiguity fixing result in the step 3, and determining a real-time relative position result between the dynamic targets.
Further, the specific implementation method of the step 1 is as follows:
step 1.1, a dynamic reference target and a mobile target respectively utilize carrier phase observation values to conduct inter-epoch difference, the position change quantity from the current moment to the next moment is obtained through least square solution, and the dynamic position of the next moment is recursively calculated according to the position of the current moment;
and step 1.2, respectively performing loose combination calculation on the dynamic position and the inertial navigation predicted position result at the next moment in the step 1.1 by the dynamic reference target and the flow target, and respectively obtaining the dynamic reference position results of the dynamic reference target A and the dynamic flow target B at the next moment by the extended Kalman filtering.
Further, in step 1.1, the dynamic position of the dynamic reference target at the next moment is recursively calculated, specifically as follows:
wherein A represents a dynamic reference target,representation->Dynamic position of time dynamic reference object, +.>Representation->The dynamic position of the time of day dynamic reference target recursion,representation->Time to->The amount of change in position between moments>The moment indicates the current moment,/->Time of day indication->Is the next moment of->Representing the Beidou data observation interval;
in step 1.1, the dynamic position of the dynamic flow target at the next moment is recursively calculated, which is as follows:
wherein B represents a dynamic flow target,representation->Dynamic position of the dynamic flow object at the moment, +.>Representation->Dynamic position of the dynamic flow target recursion at the moment,representation->Time to->Amount of positional change between time instants.
Further, the specific implementation method of the step 2 is as follows:
step 2.1, at the next moment, according to the Beidou real-time dynamic positioning solution, obtaining a relative position result between the dynamic reference target A and the dynamic flow target B, and combining the dynamic reference position results of the targets A and B in the step 1, determining that the baseline vector deviation between the targets is as follows:
wherein,representation->The relative position between time targets a and B results,representation->Dynamic reference position of time dynamic reference target a, +.>Representation->Dynamic reference position of time dynamic flow object B, +.>Representation->Time base line vector bias,/->Time of day indication->Is the next time of (a);
correcting the dynamic reference position difference value between targets according to the baseline vector deviation, and constructing a virtual baseline vector result at the next moment:
wherein,representation->The moment of time virtual baseline vector is calculated,representation->Time dynamic reference meshTarget dynamic reference position ∈>Representation->Dynamic reference position of the time-of-day dynamic flow object, +.>Time of day indication->Is the next time of (a);
step 2.2, determining the variance of the virtual baseline vector at the next moment according to the covariance of the dynamic reference position results of the targets A and B in the step 1 and the relative position variance in the Beidou real-time dynamic positioning:
wherein,representation->The covariance value of the dynamic reference position result of time reference target a,representation->A value of the covariance of the dynamic reference position result of the time-of-day flow object B,/->Representation->Time relative position variance value, +.>Representation->Variance value of the virtual baseline vector at the moment.
Further, the specific implementation manner of the step 3 is as follows;
step 3.1, using the virtual baseline vector observation constructed in step 2 as an additional constraint, the observation equation expression is:
wherein,representation->Baseline vector of time of day>The time represents the next time;
the three-frequency original ambiguity floating solution is obtained by combining the Beidou pseudo-range and carrier phase double-difference observation equation, wherein the three-frequency dynamic ambiguity solving equation is as follows:
wherein,and->Representing double-difference pseudo-range observations and double-difference carrier-phase observations, +.>Representing double difference distance between the ground and the earth->、IAnd->Respectively representing coefficient matrix, identity matrix and zero matrix, < ->And->Wavelength and double difference original ambiguity representing original observations, respectively, +.>And->Representing double-difference pseudoranges and carrier observed noise and errors respectively,representation->A baseline vector of time;
step 3.2, searching and fixing the Beidou three-frequency original ambiguity floating solution in step 3.1: firstly, determining an ultra-wide lane ambiguity fixing solution, then, combining the observation value with a wide lane observation value to calculate the wide lane ambiguity, and finally, combining the wide lane observation value with the correctly fixed ambiguity with an original observation value to calculate the original ambiguity.
Further, the specific implementation manner of the step 3.2 is as follows;
step 3.2.1, respectively selecting reference satellites in the Beidou 2 and the Beidou 3 systems, determining double-difference ultra-wide lane ambiguity of each system, and determining double-difference ultra-wide lane ambiguity of the Beidou 2 system by using B3I and B2I; for the Beidou 3 system, the B1C and B1I and the B3I and B2a are respectively utilized to determine the double-difference ultra-wide lane ambiguity, and the ultra-wide lane ambiguity is determined by directly rounding, so that the calculation expression is as follows:
in the method, in the process of the invention,indicating ultra-wide lane ambiguity, < >>And->Respectively represent frequency bandsmSum frequency bandnCorresponding frequency (Hz), ->And->Representing frequency bandsmSum frequency bandnCorresponding double difference pseudo-range observations (m),>and->Respectively represent frequency bandsmSum frequency bandnCorresponding double difference carrier phase observations (m),>and->Ambiguity (week) of the carrier phase double difference observations and wavelength (m) of the linear combination observations, respectively, +.>Observation noise (m) representing the linear combined observations due to environmental factors;
and 3.2.2, combining the ultra-wide lane ambiguity integer value and the B1I-B3I wide lane carrier phase observation equation of the step 3.2.1, estimating a wide lane ambiguity floating solution by using least square, and searching and fixing the floating ambiguity by using an LAMBDA method to obtain a wide lane ambiguity fixed solution, wherein the error equation is as follows:
in the method, in the process of the invention,and->Residual vectors representing ultra-wide lane ambiguity fixed value and wide lane carrier-phase observation, respectively, ++>AndIrespectively represent coefficient matrix and identity matrix, < >>Correction representing coordinates of the flow object, +.>Correction vector representing ultra-wide lane ambiguity fixed value, < ->Correction vector representing wide-lane carrier-phase observations,/->And->The wavelength and ambiguity of the wide-lane carrier-phase observations are represented, respectively.
Step 3.2.3, combining the wide-lane ambiguity phase observation equation of step 3.2.2 and the double-difference original ambiguity floating solution calculated in step 3.1, searching and fixing by using an LAMBDA method, wherein the error equation is as follows:
in the method, in the process of the invention,and->The residual vectors representing the exact wide-lane and original double-difference carrier-phase observations respectively,correction vector representing wide-lane carrier-phase observations,/->Representing the original double difference carrier phase observations +.>Correction vector of->And->Respectively representing the original double-difference carrier phase observation value +.>Wavelength and ambiguity of (a).
When (when)After the ambiguity fixed value of +.>And->According to the known ultra-wide lane, the fixed value of the widelane ambiguity and +.>The linear relation of the ambiguity is determined, and a specific calculation formula is as follows:
wherein,、/>and->Respectively representing the original observation value of double-difference carrier phase +.>、/>And->Corresponding original integer ambiguity;
finally utilizeN 1 、N 2 AndN 3 and (3) verifying the reliability of the ambiguity fixing by linear relation and Ratio test between the ambiguities:
in the middle ofδRepresents a threshold value of the difference limit, the range is 0.1 to 0.5,kandbis a constant value, and is used for the treatment of the skin,Mrepresenting the Ratio check threshold.
Further, the specific implementation manner of the step 4 is as follows;
after fixing with step 3.2.3 ambiguityThe double-difference carrier phase observation value is subjected to baseline solution to obtain a baseline vector between targets, and the position of the flowing target relative to the reference target is determined according to a baseline vector result;
wherein,representation->Baseline vector results between time dynamic reference target a and dynamic flow target B, +.>Representation->The dynamic reference position of the time of day dynamic reference target,representation->The relative position of the targets is dynamically flowed at the moment.
The invention also provides an INS auxiliary dynamic inter-target Beidou three-frequency ambiguity rapid initialization system, which comprises the following modules:
the reference position resolving module utilizes Beidou/INS fusion to resolve real-time high-frequency dynamic reference positions, and the real-time high-frequency dynamic reference positions comprise dynamic reference positions of dynamic reference targets and dynamic flowing targets;
the baseline vector determining module is used for determining baseline vector deviation and constructing virtual baseline vector observation according to the dynamic reference position result;
the ambiguity acquisition module is used for obtaining a Beidou three-frequency original ambiguity floating solution through least square estimation by combining a Beidou pseudo-range and carrier phase double-difference observation value with a virtual baseline vector observation value and a variance thereof, and searching and fixing the Beidou three-frequency original ambiguity floating solution;
and the relative position determining module is used for carrying out baseline calculation by using the ambiguity fixing result and determining a real-time relative position result between the dynamic targets.
Further, the specific implementation method of the baseline vector determination module is as follows:
step 2.1, at the next moment, according to the Beidou real-time dynamic positioning solution, obtaining a relative position result between a dynamic reference target A and a dynamic flow target B, and combining the dynamic reference position results of the targets A and B, determining the baseline vector deviation between the targets as follows:
wherein,representation->The relative position between time targets a and B results,representation->Dynamic reference position of time dynamic reference target a, +.>Representation->Dynamic reference position of time dynamic flow object B, +.>Representation->Time base line vector bias,/->Time of day indication->Is the next time of (a);
correcting the dynamic reference position difference value between targets according to the baseline vector deviation, and constructing a virtual baseline vector result at the next moment:
wherein,representation->The moment of time virtual baseline vector is calculated,representation->Dynamic reference position of the time-of-day dynamic reference object, +.>Representation->Dynamic reference position of the time-of-day dynamic flow object, +.>Time of day indication->Is the next time of (a);
step 2.2, determining the variance of the virtual baseline vector at the next moment according to the covariance of the dynamic reference position results of the targets A and B and the relative position variance in the Beidou real-time dynamic positioning:
wherein,representation->The covariance value of the dynamic reference position result of time reference target a,representation->Dynamic reference position junction of time-of-day flow target BFruit covariance value,/->Representation->Time relative position variance value, +.>Representation->Variance value of the virtual baseline vector at the moment.
Further, the specific implementation manner of the ambiguity acquisition module is as follows;
step 3.1, using the constructed virtual baseline vector observation as an additional constraint, the observation equation expression is:
wherein,representation->Baseline vector of time of day>The time represents the next time;
the three-frequency original ambiguity floating solution is obtained by combining the Beidou pseudo-range and carrier phase double-difference observation equation, wherein the three-frequency dynamic ambiguity solving equation is as follows:
wherein,and->Representing double-difference pseudo-range observations and double-difference carrier-phase observations, +.>Representing double difference distance between the ground and the earth->、IAnd->Respectively representing coefficient matrix, identity matrix and zero matrix, < ->And->Wavelength and double difference original ambiguity representing original observations, respectively, +.>And->Representing double-difference pseudoranges and carrier observed noise and errors respectively,representation->A baseline vector of time;
step 3.2, searching and fixing the Beidou three-frequency original ambiguity floating solution in step 3.1: firstly, determining an ultra-wide lane ambiguity fixing solution, then, combining the observation value with a wide lane observation value to calculate the wide lane ambiguity, and finally, combining the wide lane observation value with the correctly fixed ambiguity with an original observation value to calculate the original ambiguity.
The invention has the beneficial effects that:
1. the invention provides a Beidou three-frequency ambiguity initialization method and a Beidou three-frequency ambiguity initialization system between INS auxiliary dynamic targets, which realize advantage complementation through Beidou and INS data fusion and are suitable for complex time-varying shielding environments.
2. According to the invention, the INS is used for constructing a virtual baseline observation value, so that the floating solution precision of the three-frequency ambiguity is improved, the search space of the ambiguity is reduced, and the fixing of the three-frequency ambiguity is accelerated.
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FIG. 1 is a flow chart of a method of an embodiment of the present invention.
Fig. 2 is a flowchart of a beidou tri-frequency ambiguity fixing method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a Beidou three-frequency ambiguity initialization method and a Beidou three-frequency ambiguity initialization system between INS auxiliary dynamic targets, which utilize Beidou/INS fusion to calculate a real-time high-frequency dynamic reference position (step 1); determining a baseline vector deviation and constructing a virtual baseline vector observation value (step 2); estimating a Beidou three-frequency ambiguity floating solution, and searching and fixing (step 3); and finally, determining a real-time relative position result between the dynamic targets by least square solution baseline (step 4). Referring to fig. 1 and fig. 2, the method and the system for initializing the beidou tri-frequency ambiguity between INS-assisted dynamic targets provided in the embodiment of the present invention specifically include the following steps:
step 1, a dynamic reference target and a mobile target respectively calculate a dynamic reference position through Beidou/INS fusion to obtain self real-time high-frequency dynamic reference position information, which specifically comprises the following steps: carrying out
Step 1.1, respectively carrying out inter-epoch difference on a dynamic reference target and a mobile target by using a carrier phase observation value, obtaining the position variation quantity from the current moment to the next moment by least square solution, and recursively estimating the dynamic position of the next moment according to the position of the current moment;
step 1.1, recursively estimating the dynamic position of the dynamic reference target at the next moment, which is specifically as follows:
wherein A represents a dynamic reference target,representation->Dynamic position of time dynamic reference object, +.>Representation->The dynamic position of the time of day dynamic reference target recursion,representation->Time to->The amount of change in position between moments>The moment indicates the current moment,/->The moment indicates the next moment,/->And indicating the Beidou data observation interval.
And step 1.1, recursively estimating the dynamic position of the dynamic flow target at the next moment, wherein the dynamic position is as follows:
wherein B represents a dynamic flow target,representation->Dynamic position of the dynamic flow object at the moment, +.>Representation->Dynamic position of the dynamic flow target recursion at the moment,representation->Time to->Amount of positional change between time instants.
And 1.2, respectively performing loose combination calculation on the dynamic position and the inertial navigation predicted position result at the next moment in the step 1.1 by the dynamic reference target and the flowing target, and respectively obtaining the dynamic reference position results of the target A and the target B at the next moment by the extended Kalman filtering.
The dynamic reference position of the dynamic reference target at the next moment in the step 1.2 is specifically as follows:
wherein A represents a dynamic reference target,representation->Dynamic reference position of the time-of-day dynamic reference object, +.>The time indicates the next time.
The dynamic reference position of the dynamic flow target at the next moment in the step 1 is specifically as follows:
wherein,Brepresenting the dynamic flow object(s),representation->Dynamic reference position of the time-of-day dynamic flow object, +.>The time indicates the next time.
Step 2, calculating the baseline vector deviation, and constructing a virtual baseline vector observation value, which specifically comprises the following steps:
step 2.1, at the next moment, according to the Beidou real-time dynamic positioning solution, obtaining a relative position result between the targets A and B, and combining the dynamic reference position results of the targets A and B in the step 1, determining that the baseline vector deviation between the targets is as follows:
wherein,representation->The relative position between time targets a and B results,representation->Time base line vector bias,/->The time indicates the next time.
Correcting the dynamic reference position difference value between targets according to the baseline vector deviation, and constructing a virtual baseline vector result at the next moment:
wherein,representation->The moment of time virtual baseline vector is calculated,representation->Dynamic reference position of the time-of-day dynamic reference object, +.>Representation->Dynamic reference position of the time-of-day dynamic flow object, +.>The time indicates the next time.
Step 2.2, determining the variance of the virtual baseline vector at the next moment according to the covariance of the dynamic reference position results of the targets A and B in the step 1 and the relative position variance in the Beidou real-time dynamic positioning:
wherein,representation->The covariance value of the dynamic reference position result of time reference target a,representation->A value of the covariance of the dynamic reference position result of the time-of-day flow object B,/->Representation->Time relative position variance value, +.>Representation->Variance value of time virtual baseline vector, +.>The time indicates the next time.
Step 3, obtaining a Beidou three-frequency original ambiguity floating solution through least square estimation by utilizing a Beidou pseudo-range and carrier phase double-difference observed value and combining the virtual baseline vector observed value and the variance of the Beidou pseudo-range and carrier phase double-difference observed value in the step 2, and searching and fixing the floating solution, wherein the method specifically comprises the following steps:
step 3.1, using the virtual baseline vector observation value constructed in the step 2 as an additional constraint, and expressing an observation equation as follows:
wherein,representation->Baseline vector of time of day>The time indicates the next time.
The three-frequency original ambiguity floating solution is obtained by combining the Beidou pseudo-range and carrier phase double-difference observation equation, wherein the three-frequency dynamic ambiguity solving equation is as follows:
wherein,and->Representing double-difference pseudo-range observations (m) and double-difference carrier-phase observations (m), and +.>Represents the double-difference distance (m) between the ground and the earth and is +.>、IAnd->Respectively representing coefficient matrix, identity matrix and zero matrix, < ->And->Wavelength (m) and double difference original ambiguity (week) representing original observations, respectively, +.>And->Representing double-difference pseudo-range and carrier observation noise and error, respectively, +.>Representation->Baseline vector of time of day.
And 3.2, searching and fixing the Beidou three-frequency original ambiguity floating solution in the step 3.1. Referring to fig. 2, first, an ultra-wide lane ambiguity fixing solution is determined, then the wide lane ambiguity is calculated by combining the observation value with the wide lane observation value, and finally the original ambiguity is calculated by combining the wide lane observation value with the correctly fixed ambiguity with the original observation value, comprising the following sub-steps,
and 3.2.1, selecting reference satellites in the Beidou 2 and the Beidou 3 systems respectively, and determining the double-difference ultra-wide lane ambiguity of each system. For the Beidou 2 system, B3I and B2I are utilized to determine double-difference ultra-wide lane ambiguity; for the Beidou 3 system, the B1C and B1I and the B3I and B2a are respectively utilized to determine the double-difference ultra-wide lane ambiguity, and the ultra-wide lane ambiguity is determined by directly rounding, so that the calculation expression is as follows:
in the method, in the process of the invention,and->Respectively representing ultra-wide lane ambiguity (week) and wavelength (m) thereof, < ->And->Respectively represent frequency bandsmSum frequency bandnCorresponding frequency (Hz), ->And->Representing frequency bandsmSum frequency bandnCorresponding double difference pseudo-range observations (m),>and->Respectively represent frequency bandsmSum frequency bandnCorresponding double difference carrier phase observations (m),>and (c) observation noise (m) representing the linear combination observation caused by the environmental factors.
Step 3.2.2, the ultra-wide lane ambiguity integer value and the B1I-B3I wide lane carrier phase observation equation of the simultaneous step 3.2.1 are used for estimating the wide lane ambiguity floating solution by least squares, the floating ambiguity is searched and fixed by an LAMBDA method, and the wide lane ambiguity fixed solution is obtained, wherein the error equation is as follows:
in the method, in the process of the invention,and->Residual vectors representing ultra-wide lane ambiguity fixed value and wide lane carrier-phase observation, respectively, ++>AndIrespectively represent coefficient matrix and identity matrix, < >>Correction representing coordinates of the flow object, +.>Correction vector representing ultra-wide lane ambiguity fixed value, < ->Correction vector representing wide-lane carrier-phase observations,/->And->The wavelength and ambiguity of the wide-lane carrier-phase observations are represented, respectively.
And 3.2.3, combining the phase observation equation of the widelane ambiguity of the step 3.2.2 and the double-difference original ambiguity floating solution calculated in the step 3.1, searching and fixing by using an LAMBDA method, wherein an error equation is as follows:
in the method, in the process of the invention,and->The residual vectors representing the exact wide-lane and original double-difference carrier-phase observations respectively,correction vector representing wide-lane carrier-phase observations,/->Representing the original double difference carrier phase observations +.>Correction vector of->And->Respectively representing the original double-difference carrier phase observation value +.>Wavelength (m) and ambiguity (weeks).
When (when)After the ambiguity fixed value of +.>And->According to the known ultra-wide lane, the fixed value of the widelane ambiguity and +.>The linear relation of the ambiguity is determined, and a specific calculation formula is as follows:
wherein,、/>and->Respectively representing the original observation value of double-difference carrier phase +.>、/>And->Corresponding original integer ambiguity.
Finally utilizeN 1 、N 2 AndN 3 and (3) verifying the reliability of the ambiguity fixing by linear relation and Ratio test between the ambiguities:
in the middle ofδRepresents a threshold value of the difference limit, the range is 0.1 to 0.5,kandbis a constant value, and is used for the treatment of the skin,Mrepresenting the Ratio check threshold.
And 4, carrying out baseline calculation by using the ambiguity fixing result in the step 3, and determining the relative position between the dynamic targets, wherein the method specifically comprises the following steps:
after fixing with step 3.2.3 ambiguityBaseline solution is carried out on the double-difference carrier phase observed value to obtain a baseline vector between targets, and the position of the flowing target relative to the reference target is determined according to a baseline vector result:
wherein,representation->The moment target utilizes Beidou real-time dynamic positioning to calculate a baseline vector result between A and B, and the moment target is->Representation->The relative position of the targets is dynamically flowed at the moment.
In specific implementation, the above processes can be implemented by using computer software technology to realize automatic operation processes, and the system device for operating the process of the method of the invention is also within the protection scope of the invention.
The invention provides an INS auxiliary dynamic inter-target Beidou three-frequency ambiguity rapid initialization system, which comprises the following modules:
the reference position resolving module utilizes Beidou/INS fusion to resolve real-time high-frequency dynamic reference positions, and the real-time high-frequency dynamic reference positions comprise dynamic reference positions of dynamic reference targets and dynamic flowing targets;
the baseline vector determining module is used for determining baseline vector deviation and constructing virtual baseline vector observation according to the dynamic reference position result;
the ambiguity acquisition module is used for obtaining a Beidou three-frequency original ambiguity floating solution through least square estimation by combining a Beidou pseudo-range and carrier phase double-difference observation value with a virtual baseline vector observation value and a variance thereof, and searching and fixing the Beidou three-frequency original ambiguity floating solution;
and the relative position determining module is used for carrying out baseline calculation by using the ambiguity fixing result and determining a real-time relative position result between the dynamic targets.
The specific implementation manner of each module is the same as that of each step, and the invention is not written.
According to the technical scheme of the invention, the INS auxiliary Beidou three-frequency ambiguity test statistical result in the table 1 is obtained, wherein MAX represents the maximum value of the baseline vector error in the E/N/U direction. The results in Table 1 show that the INS-assisted Beidou three-frequency ambiguity has higher baseline vector accuracy and ambiguity fixation rate, and the baseline vector error difference constant value is smaller.
Table 1 INS assisted Beidou tri-frequency ambiguity test statistics
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.

Claims (8)

1. The method for initializing the Beidou three-frequency ambiguity between the INS auxiliary dynamic targets is characterized by comprising the following steps of:
step 1, resolving a real-time high-frequency dynamic reference position by Beidou/INS fusion, wherein the real-time high-frequency dynamic reference position comprises a dynamic reference target and a dynamic reference position of a dynamic flowing target;
step 2, determining baseline vector deviation and constructing a virtual baseline vector observation value according to the dynamic reference position result in the step 1;
the specific implementation method of the step 2 is as follows:
step 2.1, at the next moment, according to the Beidou real-time dynamic positioning solution, obtaining a relative position result between the dynamic reference target A and the dynamic flow target B, and combining the dynamic reference position results of the targets A and B in the step 1, determining that the baseline vector deviation between the targets is as follows:
wherein,representation->The relative position between time targets a and B results,representation->Dynamic reference position of time dynamic reference target a, +.>Representation->Dynamic reference position of time dynamic flow object B, +.>Representation->Time base line vector bias,/->Time of day indication->Is the next time of (a);
correcting the dynamic reference position difference value between targets according to the baseline vector deviation, and constructing a virtual baseline vector result at the next moment:
wherein,representation->Time virtual baseline vector,/->Representation->Dynamic reference position of the time-of-day dynamic reference object, +.>Representation->Dynamic reference position of the time-of-day dynamic flow object, +.>Time of day indication->Is the next time of (a);
step 2.2, determining the variance of the virtual baseline vector at the next moment according to the covariance of the dynamic reference position results of the targets A and B in the step 1 and the relative position variance in the Beidou real-time dynamic positioning:
wherein,representation->The covariance value of the dynamic reference position result of time reference target a,representation->A value of the covariance of the dynamic reference position result of the time-of-day flow object B,/->Representation->Time relative position variance value, +.>Representation->A variance value of the virtual baseline vector at the moment;
step 3, obtaining a Beidou three-frequency original ambiguity floating solution by least square estimation by utilizing the Beidou pseudo-range and carrier phase double-difference observed value and combining the virtual baseline vector observed value and the variance of the virtual baseline vector observed value in the step 2, and searching and fixing;
and step 4, carrying out baseline calculation by using the ambiguity fixing result in the step 3, and determining a real-time relative position result between the dynamic targets.
2. The method for initializing Beidou tri-frequency ambiguity between INS-aided dynamic targets according to claim 1, wherein the method is characterized by comprising the following steps: the specific implementation method of the step 1 is as follows:
step 1.1, a dynamic reference target and a mobile target respectively utilize carrier phase observation values to conduct inter-epoch difference, the position change quantity from the current moment to the next moment is obtained through least square solution, and the dynamic position of the next moment is recursively calculated according to the position of the current moment;
and step 1.2, respectively performing loose combination calculation on the dynamic position and the inertial navigation predicted position result at the next moment in the step 1.1 by the dynamic reference target and the flow target, and respectively obtaining the dynamic reference position results of the dynamic reference target A and the dynamic flow target B at the next moment by the extended Kalman filtering.
3. The method for initializing Beidou tri-frequency ambiguity between INS-aided dynamic targets according to claim 2, wherein the method is characterized by comprising the following steps: in step 1.1, the dynamic position of the dynamic reference target at the next moment is recursively calculated, which is as follows:
wherein A represents a dynamic reference target,representation->Dynamic position of time dynamic reference object, +.>Representation->The dynamic position of the time of day dynamic reference target recursion,representation->Time to->The amount of change in position between moments>The moment indicates the current moment,/->Time of day indication->Is the next moment of->Representing the Beidou data observation interval;
in step 1.1, the dynamic position of the dynamic flow target at the next moment is recursively calculated, which is as follows:
wherein B represents a dynamic flow target,representation->Dynamic position of the dynamic flow object at the moment, +.>Representation->Dynamic position of the dynamic flow target recursion at the moment,representation->Time to->Amount of positional change between time instants.
4. The method for initializing Beidou tri-frequency ambiguity between INS-aided dynamic targets according to claim 1, wherein the method is characterized by comprising the following steps: the specific implementation mode of the step 3 is as follows;
step 3.1, using the virtual baseline vector observation constructed in step 2 as an additional constraint, the observation equation expression is:
wherein,representation->Baseline vector of time of day>The time represents the next time;
the three-frequency original ambiguity floating solution is obtained by combining the Beidou pseudo-range and carrier phase double-difference observation equation, wherein the three-frequency dynamic ambiguity solving equation is as follows:
wherein,and->Representing double-difference pseudo-range observations and double-difference carrier-phase observations, +.>The distance between the two different grounds is represented,、Iand->Respectively representing coefficient matrix, identity matrix and zero matrix, < ->And->Wavelength and double difference original ambiguity representing original observations, respectively, +.>And->Representing double-difference pseudoranges and carrier observed noise and errors respectively,representation->A baseline vector of time;
step 3.2, searching and fixing the Beidou three-frequency original ambiguity floating solution in step 3.1: firstly, determining an ultra-wide lane ambiguity fixing solution, then, combining the observation value with a wide lane observation value to calculate the wide lane ambiguity, and finally, combining the wide lane observation value with the correctly fixed ambiguity with an original observation value to calculate the original ambiguity.
5. The INS-assisted inter-dynamic target Beidou three-frequency ambiguity initialization method disclosed by claim 4 is characterized by comprising the following steps of: the specific implementation mode of the step 3.2 is as follows;
step 3.2.1, respectively selecting reference satellites in the Beidou 2 and the Beidou 3 systems, determining double-difference ultra-wide lane ambiguity of each system, and determining double-difference ultra-wide lane ambiguity of the Beidou 2 system by using B3I and B2I; for the Beidou 3 system, the B1C and B1I and the B3I and B2a are respectively utilized to determine the double-difference ultra-wide lane ambiguity, and the ultra-wide lane ambiguity is determined by directly rounding, so that the calculation expression is as follows:
in the method, in the process of the invention,indicating ultra-wide lane ambiguity, < >>And->Respectively represent frequency bandsmSum frequency bandnCorresponding frequency, ++>Andrepresenting frequency bandsmSum frequency bandnCorresponding double-difference pseudo-range observations, +.>And->Respectively represent frequency bandsmSum frequency bandnCorresponding double difference carrier phase observations, +.>Wavelength indicative of a linear combination observation, +.>Observation noise representing the linear combined observation caused by environmental factors;
and 3.2.2, combining the ultra-wide lane ambiguity integer value and the B1I-B3I wide lane carrier phase observation equation of the step 3.2.1, estimating a wide lane ambiguity floating solution by using least square, and searching and fixing the floating ambiguity by using an LAMBDA method to obtain a wide lane ambiguity fixed solution, wherein the error equation is as follows:
in the method, in the process of the invention,and->Residual vectors representing the ultra-wide lane ambiguity fixed value and the wide lane carrier-phase observations respectively,andIrespectively represent coefficient matrix and identity matrix, < >>Correction representing coordinates of the flow object, +.>Correction vector representing ultra-wide lane ambiguity fixed value, < ->Correction vector representing wide-lane carrier-phase observations,/->And->The wavelength and the ambiguity of the wide-lane carrier phase observation value are respectively represented;
step 3.2.3, combining the wide-lane ambiguity phase observation equation of step 3.2.2 and the double-difference original ambiguity floating solution calculated in step 3.1, searching and fixing by using an LAMBDA method, wherein the error equation is as follows:
in the method, in the process of the invention,and->Residual vectors representing accurate wide-lane and original double-difference carrier-phase observations, respectively, +.>Correction vector representing wide-lane carrier-phase observations,/->Representing the original double difference carrier phase observations +.>Correction vector of->And->Respectively representing the original double-difference carrier phase observation value +.>Wavelength and ambiguity of (a);
when (when)After the ambiguity fixed value of +.>And->According to the known ultra-wide lane, the fixed value of the widelane ambiguity and +.>The linear relation of the ambiguity is determined, and a specific calculation formula is as follows:
wherein,、/>and->Respectively representing the original observation value of double-difference carrier phase +.>、/>And->Corresponding original integer ambiguity;
finally utilizeN 1 、N 2 AndN 3 and (3) verifying the reliability of the ambiguity fixing by linear relation and Ratio test between the ambiguities:
in the middle ofδRepresents a threshold value of the difference limit, the range is 0.1 to 0.5,kandbis a constant value, and is used for the treatment of the skin,Mrepresenting the Ratio check threshold.
6. The INS-aided inter-dynamic target Beidou three-frequency ambiguity initialization method of claim 5 is characterized by comprising the following steps: the specific implementation mode of the step 4 is as follows;
after fixing with step 3.2.3 ambiguityThe double-difference carrier phase observation value is subjected to baseline solution to obtain a baseline vector between targets, and the position of the flowing target relative to the reference target is determined according to a baseline vector result;
wherein,representation->Baseline vector results between time dynamic reference target a and dynamic flow target B, +.>Representation->The dynamic reference position of the time of day dynamic reference target,representation->The relative position of the targets is dynamically flowed at the moment.
7. The Beidou three-frequency ambiguity rapid initialization system between INS auxiliary dynamic targets is characterized by comprising the following modules:
the reference position resolving module utilizes Beidou/INS fusion to resolve real-time high-frequency dynamic reference positions, and the real-time high-frequency dynamic reference positions comprise dynamic reference positions of dynamic reference targets and dynamic flowing targets;
the baseline vector determining module is used for determining baseline vector deviation and constructing virtual baseline vector observation according to the dynamic reference position result;
the specific implementation method of the baseline vector determination module is as follows:
step 2.1, at the next moment, according to the Beidou real-time dynamic positioning solution, obtaining a relative position result between a dynamic reference target A and a dynamic flow target B, and combining the dynamic reference position results of the targets A and B, determining the baseline vector deviation between the targets as follows:
wherein,representation->The relative position between time targets a and B results,representation->Dynamic reference position of time dynamic reference target a, +.>Representation->Dynamic reference position of time dynamic flow object B, +.>Representation->Time base line vector bias,/->Time of day indication->Is the next time of (a);
correcting the dynamic reference position difference value between targets according to the baseline vector deviation, and constructing a virtual baseline vector result at the next moment:
wherein,representation->Time virtual baseline vector,/->Representation->Dynamic reference position of the time-of-day dynamic reference object, +.>Representation->Dynamic reference position of the time-of-day dynamic flow object, +.>Time of day indication->Is the next time of (a);
step 2.2, determining the variance of the virtual baseline vector at the next moment according to the covariance of the dynamic reference position results of the middle targets A and B and the relative position variance in the Beidou real-time dynamic positioning:
wherein,representation->The covariance value of the dynamic reference position result of time reference target a,representation->A value of the covariance of the dynamic reference position result of the time-of-day flow object B,/->Representation->Time relative position variance value, +.>Representation->A variance value of the virtual baseline vector at the moment;
the ambiguity acquisition module is used for obtaining a Beidou three-frequency original ambiguity floating solution through least square estimation by combining a Beidou pseudo-range and carrier phase double-difference observation value with a virtual baseline vector observation value and a variance thereof, and searching and fixing the Beidou three-frequency original ambiguity floating solution;
and the relative position determining module is used for carrying out baseline calculation by using the ambiguity fixing result and determining a real-time relative position result between the dynamic targets.
8. The INS-aided fast inter-dynamic inter-target beidou tri-frequency ambiguity initialization system of claim 7, wherein: the specific implementation mode of the ambiguity acquisition module is as follows;
step 3.1, using the constructed virtual baseline vector observation as an additional constraint, the observation equation expression is:
wherein,representation->Baseline vector of time of day>The time represents the next time;
the three-frequency original ambiguity floating solution is obtained by combining the Beidou pseudo-range and carrier phase double-difference observation equation, wherein the three-frequency dynamic ambiguity solving equation is as follows:
wherein,and->Representing double-difference pseudo-range observations and double-difference carrier-phase observations, +.>The distance between the two different grounds is represented,、Iand->Respectively representing coefficient matrix, identity matrix and zero matrix, < ->And->Wavelength and double difference original ambiguity representing original observations, respectively, +.>And->Representing double-difference pseudoranges and carrier observed noise and errors respectively,representation->A baseline vector of time;
step 3.2, searching and fixing the Beidou three-frequency original ambiguity floating solution in step 3.1: firstly, determining an ultra-wide lane ambiguity fixing solution, then, combining the observation value with a wide lane observation value to calculate the wide lane ambiguity, and finally, combining the wide lane observation value with the correctly fixed ambiguity with an original observation value to calculate the original ambiguity.
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