CN113759407A

CN113759407A - GNSS integer ambiguity fixing method, positioning device and mobile station

Info

Publication number: CN113759407A
Application number: CN202111052820.1A
Authority: CN
Inventors: 赵德力; 林俊; 陶永康; 孙宾姿
Original assignee: Guangdong Huitian Aerospace Technology Co Ltd
Current assignee: Guangdong Huitian Aerospace Technology Co Ltd
Priority date: 2021-09-08
Filing date: 2021-09-08
Publication date: 2021-12-07
Anticipated expiration: 2041-09-08
Also published as: CN113759407B

Abstract

The embodiment of the application discloses a GNSS integer ambiguity fixing method, a positioning device and a mobile station, wherein the method comprises the following steps: acquiring GNSS satellite observation data of a mobile station and a reference station at the current moment; obtaining an initial floating point solution variance matrix at the current moment by utilizing the GNSS satellite observation data and a double-difference observation equation; performing recursive calculation of a least square method according to the integer ambiguity matrix at the previous moment, the target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain a target floating solution ambiguity matrix at the current moment and a target floating solution variance matrix at the current moment; and obtaining the GNSS integer ambiguity matrix at the current moment according to the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment. By implementing the method, the fixing success rate of the integer ambiguity at the current moment can be improved, and accurate positioning is realized.

Description

GNSS integer ambiguity fixing method, positioning device and mobile station

Technical Field

The present application relates to the field of positioning technologies, and in particular, to a GNSS integer ambiguity fixing method, a positioning apparatus, and a mobile station.

Background

When the number of visible satellites is small, the surrounding shielding is severe, or the performance of a Global Navigation Satellite System (GNSS) antenna is poor, the observed quantity (pseudo range, carrier phase, etc.) received by the GNSS has large noise, which makes it difficult to fix the whole-cycle ambiguity in the Real Time Kinematic (RTK) resolving process and makes the accuracy of the RTK locating result poor.

Disclosure of Invention

The embodiment of the application provides a GNSS integer ambiguity fixing method, a GNSS integer ambiguity positioning device and a GNSS integer ambiguity mobile station, which are beneficial to improving the integer ambiguity fixing success rate and realizing accurate positioning.

A first aspect of an embodiment of the present application provides a method for fixing an integer ambiguity of a GNSS, including:

acquiring GNSS satellite observation data of a mobile station and a reference station at the current moment;

obtaining an initial floating point solution variance matrix at the current moment by utilizing the GNSS satellite observation data and a double-difference observation equation;

performing recursive calculation of a least square method according to the integer ambiguity matrix at the previous moment, the target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain a target floating solution ambiguity matrix at the current moment and a target floating solution variance matrix at the current moment;

and obtaining the GNSS integer ambiguity matrix at the current moment according to the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

As an optional implementation manner, in the first aspect of this embodiment of the present application, the method further includes:

acquiring cycle slip information of a visible satellite corresponding to the current moment;

if the cycle slip information indicates that a first visible satellite with cycle slip exists in visible satellites corresponding to the current time, updating the integer ambiguity matrix of the previous time by using the initial floating ambiguity resolution of the first visible satellite in the initial floating ambiguity resolution matrix of the current time, and updating the target floating solution variance matrix of the previous time by using the initial floating solution variance of the first visible satellite in the initial floating solution variance matrix of the current time;

the method for performing recursive calculation of a least square method according to the integer ambiguity matrix at the previous moment, the target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment includes:

and performing recursive calculation of a least square method according to the updated integer ambiguity matrix at the previous moment, the updated target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

As an optional implementation manner, in the first aspect of the embodiment of the present application, the GNSS satellite observation data of the mobile station and the reference station at the current time includes first dual-frequency observation data;

the acquiring cycle slip information of the visible satellite corresponding to the current moment includes:

acquiring second dual-frequency observation data of the mobile station and the reference station at the previous moment;

obtaining an M-W combined observation value and an ionospheric residual observation value according to the first dual-frequency observation data and the second dual-frequency observation data;

and determining cycle slip information of the visible satellite corresponding to the current moment according to the M-W combined observed value and the ionosphere residual error observed value.

acquiring a first visible satellite sequence corresponding to the previous moment and a second visible satellite sequence corresponding to the current moment;

performing recursive calculation of a least square method according to the updated integer ambiguity matrix at the previous moment, the updated target floating solution variance matrix at the previous moment and the updated initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment, including:

if the first visible satellite sequence is the same as the second visible satellite sequence, performing recursive calculation of a least square method by using the updated integer ambiguity matrix corresponding to the first satellite sequence at the previous moment, the updated target floating solution variance matrix corresponding to the first satellite sequence at the previous moment and the initial floating solution variance matrix at the current moment to obtain a target floating solution ambiguity matrix at the current moment and a target floating solution variance matrix at the current moment.

if the first visible satellite sequence is not the same as the second visible satellite sequence, determining a conversion matrix according to the first visible satellite sequence and the second visible satellite sequence;

according to the conversion matrix, converting the updated integer ambiguity matrix corresponding to the last moment of the first visible satellite sequence into the updated integer ambiguity matrix corresponding to the last moment of the second visible satellite sequence, and converting the updated target floating point solution variance matrix corresponding to the last moment of the first visible satellite sequence into the updated target floating point solution variance matrix corresponding to the last moment of the second visible satellite sequence;

and performing recursive calculation of a least square method according to the updated integer ambiguity matrix corresponding to the second visible satellite sequence at the previous moment, the updated target floating solution variance matrix corresponding to the second visible satellite sequence at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

As an optional implementation manner, in the first aspect of the embodiment of the present application, after obtaining the GNSS integer ambiguity matrix at the current time according to the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time, the method further includes:

calculating the credibility corresponding to the GNSS integer ambiguity matrix at the current moment;

and when the credibility is greater than a preset threshold value, determining that the integer ambiguity matrix at the current moment is valid.

when the credibility is smaller than or equal to the preset threshold, if the cycle slip information of the visible satellite corresponding to the current moment indicates that a second visible satellite without cycle slip exists and the number of the second visible satellites is larger than the number threshold, acquiring the integer ambiguity of the second visible satellite from the integer ambiguity matrix of the previous moment;

and updating the GNSS integer ambiguity matrix at the current moment by using the integer ambiguity of the second visible satellite.

A second aspect of the embodiments of the present application provides a positioning apparatus, including:

the acquisition unit is used for acquiring GNSS satellite observation data of the mobile station and the reference station at the current moment;

the equation solving unit is used for obtaining an initial floating point solution variance matrix at the current moment by utilizing the GNSS satellite observation data and the double-difference observation equation;

the recursive unit is used for performing recursive calculation of a least square method according to the integer ambiguity matrix at the previous moment, the target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment;

and the fixing unit is used for obtaining the GNSS integer ambiguity matrix at the current moment according to the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

A third aspect of the embodiments of the present application provides a positioning apparatus, including:

a memory storing executable program code;

and a processor coupled to the memory;

the processor calls the executable program code stored in the memory, and when executed by the processor, the executable program code causes the processor to implement the method according to the first aspect of the embodiments of the present application.

A fourth aspect of embodiments of the present application provides a mobile station, which may include:

a memory storing executable program code;

and a processor coupled to the memory;

A fifth aspect of embodiments of the present application provides a computer-readable storage medium, on which executable program code is stored, and when the executable program code is executed by a processor, the method according to the first aspect of embodiments of the present application is implemented.

A sixth aspect of embodiments of the present application discloses a computer program product, which, when run on a computer, causes the computer to perform any one of the methods disclosed in the first aspect of embodiments of the present application.

A seventh aspect of the embodiments of the present application discloses an application publishing platform, where the application publishing platform is configured to publish a computer program product, where when the computer program product runs on a computer, the computer is caused to execute any one of the methods disclosed in the first aspect of the embodiments of the present application.

According to the technical scheme, the embodiment of the application has the following advantages:

in the embodiment of the application, GNSS satellite observation data of a mobile station and a reference station at the current moment are obtained; obtaining an initial floating point solution variance matrix at the current moment by utilizing the GNSS satellite observation data and a double-difference observation equation; performing recursive calculation of a least square method according to the integer ambiguity matrix at the previous moment, the target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain a target floating solution ambiguity matrix at the current moment and a target floating solution variance matrix at the current moment; and obtaining the GNSS integer ambiguity matrix at the current moment according to the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment. By implementing the method, after the initial floating ambiguity resolution matrix at the current moment is obtained through a double-difference observation equation, the integer ambiguity resolution matrix at the previous moment and the target floating ambiguity resolution variance matrix at the previous moment are utilized to carry out recursive calculation of a least square method, and because the integer ambiguity resolution matrix at the previous moment and the target floating ambiguity resolution variance resolution matrix at the previous moment are greatly related to the integer ambiguity resolution matrix at the current moment, the integer ambiguity resolution matrix at the previous moment and the target floating ambiguity resolution variance resolution matrix at the previous moment are recursion matrixes fixed according to the current moment, the problem of poor precision of the floating ambiguity resolution caused by high noise of the observed quantity at the current moment can be effectively solved, the fixed success rate of the integer ambiguity resolution at the current moment can be improved, and accurate positioning can be realized.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the embodiments and the drawings used in the description of the prior art, and obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained according to the drawings.

FIG. 1 is a flowchart illustrating a GNSS integer ambiguity fixing method according to an embodiment of the present disclosure;

FIG. 2A is a flowchart illustrating another GNSS integer ambiguity fixing method according to an embodiment of the present disclosure;

fig. 2B is a schematic flowchart illustrating a process of converting the updated integer ambiguity matrix of the previous time from a corresponding first visible satellite sequence to a corresponding second visible satellite sequence according to an embodiment of the present application;

FIG. 3 is a schematic structural diagram of a positioning device according to an embodiment of the present disclosure;

fig. 4 is a block diagram of a positioning apparatus according to an embodiment of the present disclosure;

fig. 5 is a block diagram of a mobile station according to an embodiment of the present disclosure.

Detailed Description

For a person skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. The embodiments in the present application shall fall within the protection scope of the present application.

Firstly, the prior integer ambiguity fixing method is introduced: the current integer ambiguity fixing method can be generally divided into two categories: single epoch and multi-epoch methods.

The single epoch method can solve the whole-cycle ambiguity by using an observed value at one moment, but under the influence of errors such as troposphere, ionosphere and multipath, the single epoch method is easy to converge to a local minimum value, and the fixing success rate of the whole-cycle ambiguity is low.

The multi-epoch method is used for resolving by utilizing a plurality of observed values for a long time, so that a more accurate floating point solution can be solved, and further the integer ambiguity fixing rate is improved. However, in practice, the multi-epoch method is commonly used at present in a batch method, a kalman filter method, a sliding window method, and the like. The batch processing method superposes GNSS observation equations of a plurality of epochs, and then solves a floating solution by using least square, so that the calculation amount is overlarge, and the batch processing method cannot be used for real-time estimation of the position. The parameters to be estimated of the kalman filtering method are usually position deviation and integer ambiguity, and the position deviation and the integer ambiguity need to be modeled. In modeling, it is necessary to assume that the positional deviation changes slowly, but in the case of high dynamics, the assumption that the positional deviation changes slowly is not true, and modeling cannot be performed. Although the introduction of velocity and acceleration into the equation of state can solve this problem, the increase in the number of parameters to be estimated also results in a decrease in the fixed success rate of the whole-cycle ambiguity. The sliding window method solves the law equation of ambiguity on the basis of a GNSS double-difference observation equation, but the sliding window method loses satellite information when reducing the satellite and lacks the satellite information when increasing the satellite, and the fixed success rate of the ambiguity in the whole cycle is reduced.

The execution subject of the GNSS integer ambiguity fixing method disclosed in the embodiment of the present invention may be a mobile station, or may be a positioning apparatus provided on the mobile station, and the embodiment of the present invention is not limited thereto.

It is understood that the mobile station may include a general handheld screen electronic terminal device such as a mobile phone, a smart phone, a portable terminal, a Personal Digital Assistant (PDA), a Portable Multimedia Player (PMP) device, a notebook computer, a notebook (NotePad), a wireless broadband (Wibro) terminal, a tablet computer (PC), a smart PC, a point of sale (POS), a car computer, and the like.

The mobile station may also include a wearable device. The wearable device may be worn directly on the user or may be a portable electronic device integrated into the user's clothing or accessory. Wearable equipment is not only a hardware equipment, can realize powerful intelligent function through software support and data interaction, high in the clouds interaction more, for example: the system has the functions of calculation, positioning and alarming, and can be connected with a mobile phone and various terminals. Wearable devices may include, but are not limited to, wrist-supported watch types (e.g., wrist watches, wrist-supported products), foot-supported shoes types (e.g., shoes, socks, or other leg-worn products), head-supported Glass types (e.g., glasses, helmets, headbands, etc.), and various types of non-mainstream products such as smart clothing, bags, crutches, accessories, and the like.

The technical solution of the present application is further described below by way of examples.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for fixing global navigation satellite system ambiguity according to an embodiment of the present application. The method can comprise the following steps:

101. and acquiring GNSS satellite observation data of the mobile station and the reference station at the current moment.

In this embodiment, at the current time, when the mobile station and the reference station observe multiple satellites simultaneously, the mobile station may obtain a first carrier phase observed quantity and a first pseudo-range observed quantity corresponding to each satellite, and the reference station may obtain a second carrier phase observed quantity and a second pseudo-range observed quantity corresponding to each satellite. That is, the GNSS satellite observation data of the mobile station and the reference station at the current time may include a first carrier phase observation, a first pseudorange observation, a second carrier phase observation, and a second pseudorange observation corresponding to each satellite.

102. And obtaining an initial floating point solution variance matrix at the current moment by using the GNSS satellite observation data and the double-difference observation equation.

In the embodiment of the present application, for example, the number of satellites is m +1, and the double-difference observation equation is expressed as:

in this applicationIn the examples, in the formula (1.1)

A double difference carrier phase observation matrix is represented, which can be expressed as:

where λ is the carrier wavelength, i ∈ { i | i ═ 1, 2, m +1, (i ≠ k) },

represented as double-difference carrier-phase observations corresponding to satellite i and satellite k, where,

a first carrier-phase observation representing a satellite i received by the mobile station,

a second carrier-phase observation representing a satellite i received by the reference station; a denotes the integer ambiguity matrix and b denotes the baseline vector.

In the examples of the present application, in the formula (1.2)

A double-differenced pseudorange observations matrix is represented, which may be expressed as:

wherein,

represented as double-differenced pseudorange observations corresponding to satellite i and satellite k,

a first pseudorange observation representing a satellite i received by the mobile station,

a second pseudorange observation representing a satellite i received by the reference station.

A is an m ambiguity design matrix, which can be expressed as:

b is an mx 3 baseline design matrix, which can be expressed as:

wherein,

expressed as unit direction vector of satellite i to mobile station:

rⁱis the position of the satellite i, rⁱCan be derived from broadcast ephemeris r₂Is the position of the mobile station, r₂The value of a single point location can be taken,

the noise may be observed for double differences. It should be noted that the standalone position fix can be obtained from pseudorange observations of the mobile station.

In some embodiments, a least squares solution may be used to solve double-difference observation equations that are substituted into the GNSS satellite observation data to obtain an initial floating solution variance matrix at the current time.

103. And performing recursive calculation of a least square method according to the integer ambiguity matrix at the previous moment, the target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

In some embodiments, performing recursive computation of a least square method according to the integer ambiguity matrix at the previous time, the target floating solution variance matrix at the previous time, and the initial floating solution variance matrix at the current time to obtain the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time may include: acquiring cycle slip information of a visible satellite corresponding to the current moment, a first visible satellite sequence corresponding to the current moment and a second visible satellite sequence corresponding to the last moment; updating the integer ambiguity matrix at the previous moment and the target floating point solution variance matrix at the previous moment according to the cycle slip information, the first visible satellite sequence and the second visible satellite sequence; and performing recursive calculation of a least square method according to the updated integer ambiguity matrix at the previous moment, the updated target floating solution variance matrix at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

In some embodiments, the cycle slip information for the visible satellite corresponding to the current time may include identification information corresponding to the first visible satellite for which the cycle slip occurred.

In some embodiments, the visible satellite sequences (the first visible satellite sequence and the second visible satellite sequence) may indicate the number and arrangement order of the visible satellites, such as {1, 2, 3, 4, 6}, {2, 1, 3, 4, 6}, {1, 2, 3, 4} are three different visible satellite sequences. It is understood that the change of the visible satellite at the current time compared with the previous time can be obtained according to the first visible satellite sequence and the second visible satellite sequence. Wherein the changing of the visible satellite may comprise at least one of a master satellite switching, an increase of the satellite and a decrease of the satellite. The switching of the primary satellite indicates that the reference satellite corresponding to the current time is changed compared with the previous time, and it should be noted that the visible satellite with the largest altitude angle can be used as the reference satellite at any time; increasing satellites indicates that new visible satellites are added at the current time compared to the previous time, and decreasing satellites indicates that visible satellites are decreased at the current time compared to the previous time.

104. And obtaining the GNSS integer ambiguity matrix at the current moment according to the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

In this embodiment of the present application, obtaining the integer ambiguity matrix at the current time according to the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time may include: obtaining a sequential conditional least square ambiguity matrix according to the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment; and constructing a target function and a search space according to the least square ambiguity matrix of the sequential condition, and performing minimum search solving on the target function in the search space to obtain the GNSS whole ambiguity matrix of the previous moment.

Further, in some embodiments, the minimum search solution is performed on the objective function in the search space to obtain the GNSS integer ambiguity matrix at the previous time, which may include, but is not limited to, the following ways:

the method comprises the following steps of 1, utilizing a Fast Ambiguity Resolution Approach (FARA) to search a minimum value solution for a target function in a search space to obtain a GNSS integer Ambiguity matrix of a previous moment;

and 2, searching a minimum value in a search space by using a Least square descent correlation Adjustment method (LAMBDA) to solve the target function to obtain the GNSS whole-cycle Ambiguity matrix at the previous moment.

By implementing the method, after the initial floating ambiguity resolution matrix at the current moment is obtained through a double-difference observation equation, the integer ambiguity resolution matrix at the previous moment and the target floating ambiguity resolution variance matrix at the previous moment are used for carrying out recursive calculation of a least square method, and because the integer ambiguity resolution matrix at the previous moment and the target floating ambiguity resolution variance resolution matrix at the previous moment are in greater association with the integer ambiguity resolution matrix at the current moment, the integer ambiguity resolution matrix at the previous moment and the target floating ambiguity resolution variance resolution matrix at the previous moment are recursive matrices according to the fixed current moment, the problem of poor floating ambiguity resolution precision caused by large noise of observed quantity at the current moment can be effectively solved, and the integer ambiguity resolution fixed success rate at the current moment can be improved, so that accurate positioning is realized.

Referring to fig. 2A, fig. 2A is a flowchart illustrating another GNSS integer ambiguity fixing method according to an embodiment of the present application. May include the steps of:

201. and acquiring GNSS satellite observation data of the mobile station and the reference station at the current moment.

202. And obtaining an initial floating point solution variance matrix at the current moment by using the GNSS satellite observation data and the double-difference observation equation.

In the embodiment of the present application, please refer to step 101 to step 102 shown in fig. 1 for descriptions of step 201 to step 202, which are not described herein again.

203. And acquiring cycle slip information of the visible satellite corresponding to the current moment.

In some embodiments, the GNSS satellite observations of the mobile station and the reference station at the current time may include first dual-frequency observations; the obtaining of the cycle slip information of the visible satellite corresponding to the current time may include: acquiring second dual-frequency observation data of the mobile station and the reference station at the previous time; obtaining an M-W (Melbourne-Wubbena) combined observation value and a ionospheric residual error (GF) observation value according to the first dual-frequency observation data and the second dual-frequency observation data; and determining cycle slip information of the visible satellite corresponding to the current moment according to the M-W combined observation value and the GF observation value. By implementing the method, the cycle slip information is determined based on the M-W combined observation value and the GF observation value, and the accuracy of the cycle slip information is favorably ensured.

The following explains the M-W combined observation and Gf observation:

wherein, subscript 1 in formula 2.1 and formula 2.2 represents frequency point 1, subscript 2 represents frequency point 2, f represents carrier frequency, phi represents carrier phase observed quantity, P represents pseudo range observed quantity, I represents ionosphere error, N₁The ambiguity, N, at frequency 1₂Indicating the ambiguity at frequency point 2. It can be seen that N_GFRepresents Gf observed value, N_MWRepresents the M-W combined observed value, visible, N_GFAnd N_MWBoth are a combination of two frequency point ambiguities.

It can be understood that, by combining the formula (2.1) and the formula (2.2), the M-W combination observation value corresponding to the current time, the M-W combination observation value corresponding to the previous time, the GF observation value corresponding to the current time, and the GF observation value corresponding to the previous time can be solved.

Further, an M-W combination observation difference value of the current time compared with the previous time may be obtained according to the M-W combination observation value corresponding to the current time and the M-W combination observation value corresponding to the previous time, and a GF observation difference value of the current time compared with the previous time may be obtained according to the GF observation value corresponding to the current time and the GF observation value corresponding to the previous time. It should be noted that the difference value of the M-W combined observation value may be obtained by subtracting the M-W combined observation value corresponding to the previous time from the M-W combined observation value corresponding to the current time, or subtracting the M-W combined observation value corresponding to the current time from the M-W combined observation value corresponding to the previous time; similarly, the GF observation value difference value may be obtained by subtracting the GF observation value corresponding to the previous time from the GF observation value corresponding to the current time, or subtracting the GF observation value corresponding to the current time from the GF observation value corresponding to the previous time.

Suppose, with δ N_MWRepresenting the difference of M-W combined observations, by δ N_GFRepresenting GF observed differences by T_MWRepresenting a first difference threshold, T_GFIndicating a second difference threshold if deltaN_GF<T_GFAnd δ N_MW<T_MWThen, the cycle slip information is determined to indicate that the first visible satellite corresponding to the current time does not have the cycle slipAnd if not, determining that the cycle slip information indicates that the first visible satellite with the cycle slip exists in the visible satellites corresponding to the current time.

204. And if the cycle slip information indicates that the first visible satellite with cycle slip exists in the visible satellites corresponding to the current time, updating the integer ambiguity matrix of the previous time by using the initial floating ambiguity resolution of the first visible satellite in the initial floating ambiguity resolution matrix of the current time, and updating the target floating solution variance matrix of the previous time by using the initial floating solution variance of the first visible satellite in the initial floating solution variance matrix of the current time.

In some embodiments, updating the integer ambiguity matrix for the previous time instant with the initial floating solution ambiguity for the first visible satellite in the initial floating solution ambiguity matrix for the current time instant may include: and replacing the integer ambiguity of the first visible satellite in the integer ambiguity matrix at the previous moment with the initial floating ambiguity of the first visible satellite in the initial floating ambiguity resolution matrix at the current moment.

In some embodiments, updating the target floating solution variance matrix at the previous time with the initial floating solution variance of the first satellite in view of the initial floating solution variance matrix at the current time may include: and setting the row and the column corresponding to the first visible satellite in the target floating solution variance matrix at the previous moment to be 0, and replacing the diagonal line corresponding to the first visible satellite with the initial floating solution variance of the first visible satellite in the initial floating solution variance matrix at the current moment.

205. And acquiring a first visible satellite sequence corresponding to the last moment and a second visible satellite sequence corresponding to the current moment.

206. If the first visible satellite sequence is the same as the second visible satellite sequence, performing recursive calculation of a least square method by using the updated integer ambiguity matrix corresponding to the first satellite sequence at the previous moment, the updated target floating solution variance matrix corresponding to the first satellite sequence at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

It should be noted that the indication that the first visible satellite sequence and the second visible satellite sequence are the same indicates the number and the order of the visible satellites at the current time, which are the same as the number and the order of the visible satellites corresponding to the previous time. It can be understood that if the first visible satellite sequence and the second visible satellite sequence are the same, the updated integer ambiguity matrix at the previous time does not need to be converted from the corresponding first visible satellite sequence to the corresponding second visible satellite sequence, and if the first visible satellite sequence and the second visible satellite sequence are not the same, the updated integer ambiguity matrix at the previous time needs to be converted from the corresponding first visible satellite sequence to the corresponding second visible satellite sequence.

In some embodiments, a recursive calculation of least squares may be performed using the metrology equation, wherein,

the measurement equation is as follows: y ═ Hx + epsilon (2.3);

wherein y is a measurement quantity, x is a parameter to be estimated, epsilon is an error, a variance matrix of the error epsilon is R, and R is an initial floating-point solution variance matrix of the current moment

The original formula of the recursive least squares method is:

x_t＝x_t-1+K_t(y_t-H_tx_t-1)

P_t＝(I-K_tH_t)P_t-1 (2.4)

in the formula (2.4), t and t-1 represent the current time and the previous time, respectively, P is a variance matrix of the parameter x to be estimated, and H is a unit observation matrix.

In the embodiment of the present application, if the first visible satellite sequence and the second visible satellite sequence are the same, let y in equation (2.3) be the integer ambiguity corresponding to the updated last time of the first visible satellite sequence

The parameter x to be estimated is the target floating ambiguity a of the current time corresponding to the second satellite sequence_t,tThen the measurement equation is:

by substituting equation (2.5) for equation (2.4), a recursive equation can be obtained:

in the equations (2.5) and (2.6),

a target floating point solution variance matrix representing an updated last time instance corresponding to the first sequence of visible satellites,

Initial floating-point solution variance matrix, a, representing the current time_t,tA target float solution ambiguity matrix representing the current time,

Representing the target floating-point solution variance matrix at the current time. Due to the fact that

And

known as simultaneous

And

the variance matrix of the target floating point solution at the current moment can be obtained

Further, based on the formula (2.5), the target floating solution ambiguity matrix a at the current time can be known_t,tIs the updated integer ambiguity of the last time instant corresponding to the first sequence of visible satellites

207. And obtaining the GNSS integer ambiguity matrix at the current moment according to the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

It should be noted that the integer ambiguity matrix at the current time corresponds to the second visible satellite sequence.

In this embodiment of the application, after step 207, the reliability corresponding to the GNSS integer ambiguity matrix at the current time may also be calculated; and when the reliability is greater than a preset threshold value, determining that the GNSS integer ambiguity matrix at the current moment is valid. It should be noted that the confidence level may represent a ratio of squared residuals combined with optimal ambiguity.

In some embodiments, when the GNSS integer ambiguity matrix at the current time is valid, the GNSS integer ambiguity matrix at the current time may be substituted as a by formula (1.1), so as to solve a baseline vector b, and finally, an accurate positioning result of the mobile station is obtained according to the baseline vector b.

In some embodiments, if the reliability is less than or equal to a preset threshold, and the cycle slip information of the visible satellite corresponding to the current time indicates that there is a second visible satellite without cycle slip, and the number of the second visible satellites is greater than a number threshold, the integer ambiguity of the second visible satellite is obtained from the integer ambiguity matrix of the previous time; updating the GNSS integer ambiguity matrix at the current moment according to the integer ambiguity of the second visible satellite; and performing baseline calculation by using the updated GNSS integer ambiguity matrix at the current moment to obtain a positioning result corresponding to the current moment. Illustratively, the number threshold may be 5, 6, or 8.

It can be understood that, in the embodiment of the present application, the updated GNSS integer ambiguity matrix at the current time may also be substituted into the formula (1.1) to perform baseline solution, so as to obtain a baseline vector b, and finally, according to the baseline vector b, an accurate positioning result of the mobile station is obtained.

Based on the above description, when the GNSS integer ambiguity matrix at the current time is invalid, the GNSS integer ambiguity matrix at the current time may be updated by using the integer ambiguity of the second visible satellite that has not generated a cycle slip at the previous time, so that the fixing success rate of the GNSS integer ambiguity matrix at the current time may be further improved.

In some embodiments, if the first visible satellite sequence and the second visible satellite sequence are different, the updated integer ambiguity matrix at the last time needs to be converted from the corresponding first visible satellite sequence to the corresponding second visible satellite sequence, and the specific conversion step may be as shown in fig. 2B, and may include the following steps:

210. and determining a conversion matrix according to the first visible satellite sequence and the second visible satellite sequence.

The transformation matrix is explained below:

based on the above description, the change condition of the visible satellite may include at least one of a main satellite switching, an increase of the satellite, and a decrease of the satellite. Further, in the embodiment of the present application, a plurality of predetermined transformation matrices may be included, and the plurality of predetermined transformation matrices may include a transformation matrix corresponding to a switching of a primary satellite, a transformation matrix corresponding to an increasing satellite, and a transformation matrix corresponding to a decreasing satellite.

In some embodiments, determining the transformation matrix from the first visible satellite sequence and the second visible satellite sequence may include: and determining a first conversion matrix from the preset conversion matrix according to the first satellite sequence and the second satellite sequence, and obtaining a second conversion matrix according to the first conversion matrix. Wherein the first transformation matrix may include at least one of a transformation matrix corresponding to a master satellite handoff, a transformation matrix for an incremental satellite, and a transformation matrix for a decremental satellite.

In an embodiment of the present application, obtaining the second transformation matrix according to the first transformation matrix may include: when the number of the first conversion matrices is plural, the plural first conversion matrices may be multiplied to obtain the second conversion matrix.

The following description is made for the switching matrix for switching between the main satellite and the satellite, the switching matrix for increasing the satellite, and the switching matrix for decreasing the satellite:

switching matrix of the switching of the main satellite:

assuming that the reference satellite is switched from satellite k to satellite j, the corresponding ambiguity changes accordingly, T_transCan be expressed as:

description of the transformation matrix corresponding to the minus star case: when subtracting the satellite, removing the row of the ambiguity corresponding to the satellite, and converting the matrix T_subCan be as follows:

description of a conversion matrix corresponding to the star increasing case: when increasing the satellite, the ambiguity of the corresponding satellite is set to be 0, and the corresponding transformation matrix is T_add：

Illustratively, if the first transformation matrix includes T_trans、T_addAnd T_subThen the second transformation matrix is represented as

Then:

220. and according to the conversion matrix, converting the updated integer ambiguity matrix corresponding to the last moment of the first visible satellite sequence into the updated integer ambiguity matrix corresponding to the last moment of the second visible satellite sequence, and converting the updated target floating point solution variance matrix corresponding to the last moment of the first visible satellite sequence into the updated target floating point solution variance matrix corresponding to the last moment of the second visible satellite sequence.

In some embodiments, the second transformation matrix may be multiplied by the integer ambiguity matrix corresponding to the updated previous time instant of the first visible satellite sequence to obtain an updated integer ambiguity matrix corresponding to the previous time instant of the second visible satellite sequence; and multiplying the updated target floating point solution variance matrix of the previous moment corresponding to the first visible satellite sequence by using the second conversion matrix and the transpose of the second conversion matrix to obtain the updated target floating point solution variance matrix of the previous moment corresponding to the second visible satellite sequence.

Illustratively, the updated integer ambiguity matrix for the last time instant of the first visible satellite sequence is represented as

The updated target floating point solution variance matrix at the last time instant of the corresponding first sequence of visible satellites is represented as

The updated integer ambiguity matrix for the last time instant corresponding to the second visible satellite sequence is represented as

The updated target floating point solution variance matrix corresponding to the second sequence of visible satellites at the last time may be expressed as

Then

230. And performing recursive calculation of a least square method according to the updated integer ambiguity matrix corresponding to the second visible satellite sequence at the previous moment, the updated target floating solution variance matrix corresponding to the second visible satellite sequence at the previous moment and the initial floating solution variance matrix at the current moment to obtain the target floating solution ambiguity matrix at the current moment and the target floating solution variance matrix at the current moment.

It is understood that in the case that the first visible satellite sequence and the second visible satellite sequence are not identical, let the quantity measurement y in equation (2.3) be the updated integer ambiguity corresponding to the last time instant of the second visible satellite sequence

At this time, the measurement equation is:

by substituting equation (2.7) for equation (2.4), a recursive equation can be obtained:

in the equations (2.7) and (2.8),

a target floating point solution variance matrix representing an updated last time instant corresponding to the second sequence of visible satellites,

And

known as simultaneous

And

Further, based on the formula (2.7), the target floating solution ambiguity matrix a at the current time can be known_t,tFor example, the integer ambiguity matrix corresponding to the updated last time instant of the second visible satellite sequence is represented as

In some embodiments, if T is included in the first transformation matrix_addThen, the initial floating solution ambiguity of the newly added satellite can be obtained from the initial floating solution ambiguity matrix at the current moment, and the initial floating solution ambiguity of the newly added satellite is used for updating

And then use the updated

Carry out the minimizationRecursive calculation of two multiplications. Due to the fact that at T_addThe ambiguity of the newly added satellite is set to be 0, which often causes larger error, and by implementing the method, the initial floating solution ambiguity of the newly added satellite can be used for updating

The fixed success rate of the integer ambiguity matrix at the current moment can be further improved.

It should be noted that, after the step 230, the step 207 may be further executed to perform the fixing operation of the integer ambiguity matrix corresponding to the current time.

By implementing the method, after the initial floating solution ambiguity matrix at the current moment is obtained through a double-difference observation equation, the integer ambiguity matrix at the previous moment and the target floating solution variance matrix at the previous moment are firstly utilized to carry out recursive calculation of a least square method, and the integer ambiguity matrix at the previous moment and the integer ambiguity matrix at the current moment have larger correlation, so that the integer ambiguity matrix at the previous moment is the recursive matrix according to the fixed current moment, the problem of poor precision of the floating solution ambiguity at the current moment caused by larger noise of the observed quantity at the current moment can be effectively solved, the fixed success rate of the integer ambiguity at the current moment can be improved, and accurate positioning can be realized. Furthermore, when the integer ambiguity matrix at the previous moment and the target floating point solution variance matrix at the previous moment are used for recursive calculation, the cycle slip detection is firstly carried out on the visible satellite at the current moment, and when the visible satellite at the current moment has the first visible satellite with cycle slip, the integer ambiguity matrix at the previous moment and the target floating point solution variance matrix at the previous moment are updated, so that the effectiveness of the integer ambiguity matrix at the previous moment and the target floating point solution variance matrix at the previous moment can be ensured, and the fixed success rate of the integer ambiguity at the current moment can be further improved. Furthermore, when the satellite sequence at the current moment is different from the satellite sequence at the previous moment, the integer ambiguity matrix at the previous moment and the target floating point solution variance matrix at the previous moment can be further adjusted through the conversion matrix, so that the validity of the integer ambiguity matrix at the previous moment and the validity of the target floating point solution variance matrix at the previous moment can be further ensured.

Referring to fig. 3, fig. 3 is a schematic structural diagram of a positioning device according to an embodiment of the present disclosure. The positioning device may include: the system comprises an acquisition unit 301, an equation solving unit 302, a recursion unit 303 and a fixing unit 304; wherein:

an obtaining unit 301, configured to obtain GNSS satellite observation data of a mobile station and a reference station at a current time;

an equation solving unit 302, configured to obtain an initial floating point solution variance matrix at the current time by using the GNSS satellite observation data and the double-difference observation equation;

a recursion unit 303, configured to perform recursive computation of a least square method according to the integer ambiguity matrix at the previous time, the target floating solution variance matrix at the previous time, and the initial floating solution variance matrix at the current time, to obtain a target floating solution ambiguity matrix at the current time and a target floating solution variance matrix at the current time;

the fixing unit 304 is configured to obtain the GNSS integer ambiguity matrix at the current time according to the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time.

In some embodiments, the recursion unit 303 is further configured to obtain cycle slip information of a visible satellite corresponding to the current time; and if the cycle slip information indicates that the first visible satellite with the cycle slip exists in the visible satellites corresponding to the current time, updating the integer ambiguity matrix of the previous time by using the initial floating solution ambiguity of the first visible satellite in the initial floating solution ambiguity matrix of the current time, and updating the target floating solution variance matrix of the previous time by using the initial floating solution variance of the first visible satellite in the initial floating solution variance matrix of the current time.

Further, the manner of obtaining the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time by performing the recursive calculation of the least square method according to the integer ambiguity matrix at the previous time, the target floating solution variance matrix at the previous time, and the initial floating solution variance matrix at the current time by the recursive unit 303 may specifically include: and the recursion unit 303 is configured to perform a least square recursion calculation according to the updated integer ambiguity matrix at the previous time, the updated target floating solution variance matrix at the previous time, and the updated initial floating solution variance matrix at the current time, so as to obtain a target floating solution ambiguity matrix at the current time and a target floating solution variance matrix at the current time.

In some embodiments, the GNSS satellite observations of the mobile station and the reference station at the current time may include first dual-frequency observations.

Further, the manner of the recursion unit 303 for acquiring the cycle slip information of the visible satellite corresponding to the current time may specifically include: the recursion unit 303 acquires second dual-frequency observation data of the mobile station and the reference station at the previous time; obtaining an M-W combined observed value and an ionospheric residual observed value according to the first dual-frequency observed data and the second dual-frequency observed data; and determining cycle slip information of the visible satellite corresponding to the current moment according to the M-W combined observed value and the ionosphere residual error observed value.

In some embodiments, the recursion unit 303 is further configured to obtain a first visible satellite sequence corresponding to a previous time and a second visible satellite sequence corresponding to a current time.

Further, in some embodiments, the manner of obtaining the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time by performing the recursive calculation of the least square method according to the updated integer ambiguity matrix at the previous time, the updated target floating solution variance matrix at the previous time, and the updated initial floating solution variance matrix at the current time by the recursive unit 303 may specifically include: the recursion unit 303 is configured to perform a least square recursion calculation by using the updated integer ambiguity matrix of the previous time corresponding to the first satellite sequence, the updated target floating solution variance matrix of the previous time corresponding to the first satellite sequence, and the initial floating solution variance matrix of the current time if the first visible satellite sequence is the same as the second visible satellite sequence, so as to obtain the target floating solution ambiguity matrix of the current time and the target floating solution variance matrix of the current time.

In some embodiments, the recursion unit 303 is further configured to determine a transformation matrix according to the first visible satellite sequence and the second visible satellite sequence if the first visible satellite sequence and the second visible satellite sequence are not the same; and according to the conversion matrix, converting the updated integer ambiguity matrix corresponding to the last moment of the first visible satellite sequence into the updated integer ambiguity matrix corresponding to the last moment of the second visible satellite sequence, and converting the updated target floating solution variance matrix corresponding to the last moment of the first visible satellite sequence into the updated target floating solution variance matrix corresponding to the last moment of the second visible satellite sequence.

Further, the manner of obtaining the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time by performing the recursive calculation of the least square method according to the updated integer ambiguity matrix at the previous time, the updated target floating solution variance matrix at the previous time, and the updated initial floating solution variance matrix at the current time by the recursive unit 303 may specifically include: the recursion unit 303 is configured to perform a least square recursion calculation according to the updated integer ambiguity matrix corresponding to the second visible satellite sequence at the previous time, the updated target floating solution variance matrix corresponding to the second visible satellite sequence at the previous time, and the initial floating solution variance matrix at the current time, so as to obtain the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time.

In some embodiments, the fixing unit 304 is further configured to calculate a reliability corresponding to the GNSS integer ambiguity matrix at the current time after obtaining the GNSS integer ambiguity matrix at the current time according to the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time; and when the reliability value is larger than a preset threshold value, determining that the GNSS integer ambiguity matrix at the current moment is valid.

In some embodiments, the fixing unit 304 is further configured to, when the above-mentioned reliability is less than or equal to a preset threshold, if the cycle slip information of the visible satellite corresponding to the current time indicates that there is a second visible satellite without cycle slip, and the number of the second visible satellites is greater than the number threshold, obtain the integer ambiguity of the second visible satellite from the integer ambiguity matrix of the previous time; and updating the GNSS integer ambiguity matrix at the current moment by using the integer ambiguity of the second visible satellite.

Referring to fig. 4, fig. 4 is a block diagram of a positioning device according to an embodiment of the present disclosure. The method can comprise the following steps:

a memory 401 storing executable program code;

and a processor 402 to which the memory 401 is coupled;

the processor 402 invokes executable program code stored in the memory 401, which when executed by the processor 402, causes the processor 402 to implement the above-described GNSS integer ambiguity fixing method.

Referring to fig. 5, fig. 5 is a block diagram of a mobile station according to an embodiment of the present disclosure. The method can comprise the following steps:

a memory 501 in which executable program code is stored;

and a processor 502 to which the memory 501 is coupled;

the processor 502 invokes executable program code stored in the memory 501, which when executed by the processor 502 causes the processor 502 to implement the above-described GNSS integer ambiguity fixing method.

The embodiment of the application discloses a computer readable storage medium, which stores a computer program, wherein the computer program realizes the method described in the above embodiment when being executed by a processor.

Embodiments of the present application disclose a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program, when executed by a processor, implements the method as described in the embodiments above.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. The storage medium may be a magnetic disk, an optical disk, a ROM, etc.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.

The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (ssd)), among others.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims

1. A method for fixing Global Navigation Satellite System (GNSS) integer ambiguity, which is characterized in that the method comprises the following steps:

2. The method of claim 1, further comprising:

if the cycle slip information indicates that a first visible satellite with cycle slip exists in the visible satellites corresponding to the current time, updating the integer ambiguity matrix of the previous time by using the initial floating ambiguity resolution of the first visible satellite in the initial floating ambiguity resolution matrix of the current time, and updating the target floating solution variance matrix of the previous time by using the initial floating solution variance of the first visible satellite in the initial floating solution variance matrix of the current time;

3. The method of claim 2, wherein the GNSS satellite observations of the mobile station and the reference station at the current time comprise first dual-frequency observations;

4. A method according to claim 2 or 3, characterized in that the method further comprises:

5. The method of claim 4, further comprising:

6. The method according to any one of claims 1-3 and 5, wherein after obtaining the GNSS integer ambiguity matrix at the current time according to the target floating solution ambiguity matrix at the current time and the target floating solution variance matrix at the current time, the method further comprises:

and when the reliability is greater than a preset threshold value, determining that the GNSS integer ambiguity matrix at the current moment is valid.

7. The method of claim 6, further comprising:

8. A positioning device, comprising:

9. A positioning device, comprising:

a memory storing executable program code;

and a processor coupled to the memory;

the processor calls the executable program code stored in the memory, which when executed by the processor causes the processor to implement the method of any one of claims 1-7.

10. A mobile station, comprising:

a memory storing executable program code;

and a processor coupled to the memory;