Positioning method and device based on satellite state parameters

Abstract

The invention provides a positioning method and device based on satellite state parameters, and relates to the technical field of satellite navigation. The method comprises the following steps: acquiring observation values of a plurality of satellites at the current moment; aiming at the observation value of each satellite in the plurality of satellites, inputting the observation value into a Kalman filter associated with each satellite, and performing state estimation on the input observation value by the Kalman filter based on a state transition equation and an observation equation of the Kalman filter, a candidate state parameter at a previous moment and an error covariance matrix; obtaining candidate state parameters and an error covariance matrix at the current moment; and performing state fusion on each candidate state parameter at the current moment, and positioning a target state parameter at the current moment. According to the invention, the positioning calculation precision and accuracy of the state parameters can be improved, and the estimation result error is reduced through the fusion of the state parameters when a value with poor quality exists in the satellite observation value.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation technology, and in particular to a positioning method and device based on satellite status parameters. Background Art

[0002] High-orbit and low-orbit joint positioning refers to the integration of the high-orbit global navigation satellite system GNSS constellation and the low-orbit satellite constellation, combining the large amount of observations brought by the advantage of the number of low-orbit satellites and the advantage of the wide airspace coverage of high-orbit satellites, and combining low-orbit and high-orbit satellites to provide enhanced positioning services. This method has gradually become a new development direction of ground terminal positioning technology.

[0003] In the related technology, in the application of multi-sensor positioning and target tracking, each sensor has different measurement values ​​and noise characteristics. It is necessary to select an algorithm to fuse the measurement values ​​of multiple sensors in order to obtain better state parameters. The state parameters to be estimated include the receiver's 3D position coordinates, receiver clock error, zenith tropospheric wet delay, ionospheric delay in the station-satellite line of sight direction, and ambiguity parameters. In the high- and low-orbit joint positioning, the different orbital characteristics of high- and low-orbit satellites lead to different measurement values ​​and noise characteristics.

[0004] Therefore, how to improve the positioning accuracy and precision of state parameters, reduce the estimation result error when there are poor quality values ​​in satellite observations, and improve the positioning efficiency of state parameters in complex scenarios has become one of the important research directions. Summary of the invention

[0005] The present application aims to solve one of the technical problems in the related art at least to a certain extent. To this end, one purpose of the present application is to propose a positioning method based on satellite state parameters.

[0006] The second objective of the present application is to provide a positioning device based on satellite status parameters.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of the present application is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a positioning method based on satellite state parameters, including:

[0011] Get the observation values ​​of multiple satellites at the current moment;

[0012] For the observation values ​​of each satellite among the multiple satellites, the observation values ​​are input into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation values ​​based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, and obtains candidate state parameters and error covariance matrix at the current moment;

[0013] The state of each candidate state parameter at the current moment is fused to locate the target state parameter at the current moment.

[0014] The embodiments of the present application can improve the positioning solution precision and accuracy of the state parameters, and when there are values ​​of poor quality in the satellite observation values, reduce the error of the estimation result by fusing the state parameters.

[0015] In the embodiment of the present application, it is applicable to the positioning situation of the combination of pseudorange observation value and carrier phase observation value. The method proposed in the embodiment of the present application is applicable to the joint positioning of high and low orbits, which makes full use of the low orbit satellite resources and can have higher positioning accuracy than pure high orbit satellite positioning.

[0016] To achieve the above-mentioned purpose, the second aspect of the present application proposes a positioning device based on satellite state parameters, including:

[0017] The first acquisition module is used to obtain the observation values ​​of multiple satellites at the current moment;

[0018] The second acquisition module is used for inputting the observation values ​​of each satellite in the multiple satellites into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation values ​​based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, and obtains candidate state parameters and error covariance matrix at the current moment;

[0019] The state fusion module is used to perform state fusion on each candidate state parameter at the current moment and locate the target state parameter at the current moment.

[0020] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including:

[0021] at least one processor; and

[0022] a memory communicatively connected to at least one processor; wherein,

[0023] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the positioning method based on satellite status parameters provided in the embodiment of the first aspect of the present application.

[0024] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium on which computer instructions are stored, wherein the computer instructions are used to enable a computer to execute a positioning method based on satellite status parameters provided in the first aspect embodiment of the present application.

[0025] To achieve the above-mentioned purpose, the fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the positioning method based on satellite status parameters provided in the first aspect embodiment of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart of a positioning method based on satellite state parameters according to an embodiment of the present application;

[0027] Figure 2 is a flowchart of a positioning method based on satellite state parameters according to an embodiment of the present application;

[0028] Figure 3 is a flowchart of a positioning method based on satellite state parameters according to an embodiment of the present application;

[0029] Figure 4 is a schematic diagram of a positioning method based on satellite state parameters according to an embodiment of the present application;

[0030] Figure 5 is a structural block diagram of a positioning device based on satellite state parameters according to an embodiment of the present application;

[0031] Figure 6 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0033] The following describes a positioning method and device based on satellite status parameters according to an embodiment of the present application in conjunction with the accompanying drawings.

[0034] Figure 1 FIG. 1 is a flowchart of a positioning method based on satellite status parameters according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0035] S101, obtaining observation values ​​of multiple satellites at the current moment.

[0036] Optionally, the embodiment of the present application may be executed by a receiver on which multiple Kalman filters are deployed.

[0037] In the embodiment of the present application, the satellite observation value is the data obtained by the satellite observing the earth or other celestial bodies.

[0038] In some embodiments, the multiple satellites are low-orbit satellites, in some embodiments, the multiple satellites are high-orbit satellites, and in some embodiments, the multiple satellites include low-orbit satellites and high-orbit satellites, which is not limited in the embodiments of the present application.

[0039] In the embodiment of the present application, since the ground receiver can simultaneously receive observation values ​​sent by high-orbit satellites and / or low-orbit satellites, and then perform subsequent positioning of satellite status parameters, high-orbit and low-orbit joint positioning can be achieved.

[0040] S102, for the observation values ​​of each satellite among the multiple satellites, the observation values ​​are input into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation values ​​based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, and obtains candidate state parameters and error covariance matrix at the current moment.

[0041] The time in the embodiment of the present application is the epoch time. Taking the high-orbit joint precise point positioning as an example, the state parameter (that is, the state vector) of the receiver is considered to be x, and the state parameter to be estimated includes the 3D position coordinates r of the receiver. r , receiver clock error dt r , and can also include the zenith tropospheric wet delay ZPD r , ionospheric delay in the station-satellite line of sight and the fuzziness parameter etc., expressed as

[0042]

[0043] Wherein, j is the frequency identifier; m represents the number of observable satellites, which also corresponds to the number of observation equations; represents the ambiguity of observation value i; (......) T represents the matrix transpose, Represents the M×1 set of real numbers.

[0044] and P0 represent the initial values ​​of the preset candidate state parameters and their initial error covariance matrix, respectively, as follows

[0045]

[0046]

[0047] Among them, the 3D position coordinates of the receiver r r and the receiver clock difference dt r The initial value of and its covariance matrix can be obtained by the standard single point positioning SPP solution based on pseudorange; the ionospheric delay can be calculated by using the difference between the dual-frequency pseudorange observations, the initial value of the ambiguity parameter is the difference between the carrier phase observation and the pseudorange observation, and the natural tropospheric wet delay can be initialized to 0.

[0048] In the embodiment of the present application, when obtaining the observation values ​​of m satellites at the current time, m Kalman filters are used, and the observation values ​​of each satellite at each time are processed by a Kalman filter respectively, and an estimated value of a state parameter at the current epoch is output, that is, a candidate state parameter at the current time. Since the number of satellites observable by the receiver changes at different epochs, m is a variable. This means that different numbers of Kalman filters will be used at different epochs.

[0049] It should be noted that for the sake of convenience, in the embodiment of the present application, the Kalman filter associated with satellite i is the i-th Kalman filter, that is, Kalman filter i, and the observation value sent by satellite i to the receiver is observation value i.

[0050] Since the ambiguity parameters of the input observation values ​​of each Kalman filter are different, the Kalman filter only needs to estimate the ambiguity parameters of its own input observation values. Therefore, in the embodiment of the present application, the candidate state parameter x of the above receiver is considered to be decomposed into where x i represents the candidate state parameter of the i-th Kalman filter. In addition, when fusing the estimated values ​​of the candidate state parameters, it is only necessary to merge the position coordinate r r , receiver clock error dt r and Zenith Tropospheric Wet Delay ZPD r Parameters are integrated.

[0051] Taking the current epoch as the kth epoch and the previous epoch as the k-1th epoch as an example, the state space equation of Kalman filter i is as follows, and the state transfer equation is:

[0052]

[0053] in represents the state estimate of the candidate state parameter at the known k-1 epoch time in Kalman filter i, Φ k is the preset state transfer matrix, It means that the covariance matrix is ​​Q k Gaussian distributed system noise, Q k are known parameters.

[0054]

[0055] In the above formula are the system noise variances of receiver position, clock error, ZPD, ionospheric delay and phase deviation respectively.

[0056] Optionally, the observation equation includes two types: pseudorange observation equation and carrier phase observation equation, which are respectively expressed as

[0057]

[0058]

[0059] In the above formula, i represents the i-th satellite, that is, satellite i, is the distance between the satellite and the earth, dT s is the satellite clock error, k j is the ionospheric frequency factor, is the projection function on the signal propagation path, λ j is the carrier wavelength, w Lk and w Pk The observation noise of pseudo-range measurement and carrier phase measurement respectively obeys the covariance R Lk and R Pk Gaussian distribution, c is the speed of light, d r,j represents the code phase deviation of the receiver, Represents the eccentricity vector of the satellite transmitting antenna.

[0060] In the embodiment of the present application, the observation equation is first linearized to calculate the Jacobian matrix of the pseudorange observation equation or the carrier phase observation equation as follows:

[0061]

[0062]

[0063] In the formula, represents the candidate state parameters after fusion at epoch k-1, express The first three elements of .

[0064] Secondly, the candidate state parameters of each sub-Kalman filter are calculated. Among them:

[0065]

[0066]

[0067]

[0068] The candidate state parameters at the current k-th moment are:

[0069]

[0070] The error covariance matrix at the current kth moment is:

[0071]

[0072] in, is the state prediction vector; P k|k-1 is the prediction error covariance matrix; is the actual pseudorange observation value or carrier phase observation value obtained by the receiver. Expressed as or According to the actual observation value, select the pseudo-range observation value or the carrier phase observation value. i (·) represents the pseudorange or carrier phase observation equation, which is given by Formula 8 or Formula 9, represents the middle value, Represents the covariance of pseudorange or carrier phase observation noise, and its specific value is R Lk or R Pk .

[0073] Among them, the estimated values ​​of the candidate state parameters at the initial time, that is, at the time of k = 0 epoch are and its initial error covariance matrix P0 as preset parameters, and let

[0074] That is, in some embodiments, when the satellite observation value is a pseudo-range observation value, the observation equation of the associated Kalman filter is a pseudo-range observation equation. In some embodiments, when the satellite observation value is a carrier phase observation value, the observation equation of the associated Kalman filter is a carrier phase observation equation.

[0075] S103, performing state fusion on each candidate state parameter at the current moment to locate the target state parameter at the current moment.

[0076] In the embodiment of the present application, the candidate state parameters at the current epoch are fused to obtain the positioning solution result of the target, and the target state parameters at the current moment are obtained.

[0077] In an embodiment of the present application, for the observation values ​​of each satellite among multiple satellites, the observation values ​​are input into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation values ​​based on its own state transfer equation and observation equation, candidate state parameters at the previous moment, and the error covariance matrix to obtain the candidate state parameters and the error covariance matrix at the current moment; state fusion is performed on each candidate state parameter at the current moment to locate the target state parameters at the current moment. The embodiment of the present application can improve the positioning solution precision and accuracy of the state parameters. When there are values ​​of poor quality in the satellite observation values, the error of the estimation result can be reduced by fusing the state parameters.

[0078] In the embodiment of the present application, it is applicable to the positioning situation of the combination of pseudorange observation value and carrier phase observation value. The method proposed in the embodiment of the present application is applicable to the joint positioning of high and low orbits, which makes full use of the low orbit satellite resources and can have higher positioning accuracy than pure high orbit satellite positioning.

[0079] Figure 2 FIG. 1 is a flowchart of a positioning method based on satellite status parameters according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0080] S201, obtaining observation values ​​of multiple satellites at the current moment.

[0081] S202, for the observation values ​​of each satellite among the multiple satellites, the observation values ​​are input into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation values ​​based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, and obtains candidate state parameters and error covariance matrix at the current moment.

[0082] For the introduction of step S201 to step S202, reference may be made to the relevant contents in the above embodiment, which will not be repeated here.

[0083] S203, determining a fusion vector matrix according to the number of observations.

[0084] In the embodiment of the present application, the fusion vector matrix a is calculated. i for

[0085]

[0086] Wherein, 13 represents a 1×3 all-1 vector. In the embodiment of the present application, for a i There is no restriction on the value of .

[0087] It should be noted that 1 represents a vector of all ones.

[0088] S204, fusing the candidate state parameters at the current moment according to the fusion vector matrix to locate the target state parameter at the current moment.

[0089] The candidate state parameters include position coordinates, receiver clock errors, zenith tropospheric wet delay, ionospheric delay and ambiguity parameters. The position coordinates at the current moment are weighted according to the fusion vector matrix to obtain the target position coordinates, the receiver clock errors at the current moment are weighted to obtain the target receiver clock errors, the zenith tropospheric wet delays at the current moment are weighted to obtain the target zenith tropospheric wet delay, the ionospheric delay sequence is obtained according to the ionospheric delays at the current moment, the ambiguity parameter sequence is obtained according to the ambiguity parameters at the current moment, and the target position coordinates, target receiver clock error, target zenith tropospheric wet delay, ionospheric delay sequence and ambiguity parameter sequence are determined as the target state parameters at the current moment.

[0090] In the embodiment of the present application, for each candidate state parameter vector at the current moment After fusion, obtain the target state parameters at the current moment for:

[0091]

[0092] in, Respectively The 1st to 5th elements of (where the target position coordinates correspond to three elements, plus the target receiver clock error and the target zenith tropospheric wet delay, a total of 5 elements).

[0093] S205, synchronizing the error covariance matrix at the current moment to the Kalman filter.

[0094] In the embodiment of the present application, after locating the target state parameters at the current moment, the target state parameters at the next moment are located, that is, the error covariance matrix and the candidate state parameters are updated, and further iterative parameter estimation is performed between epochs.

[0095] S206, updating the candidate state parameters at the current moment according to the target position coordinates, the target receiver clock error, and the target zenith tropospheric wet delay, and synchronizing the updated candidate state parameters to the Kalman filter.

[0096] In some embodiments, the position coordinates, receiver clock error, and zenith tropospheric wet delay in the candidate state parameters at the current moment are updated to the fused target position coordinates, target receiver clock error, and target zenith tropospheric wet delay, and the updated candidate state parameters are synchronized to the Kalman filter.

[0097] In some embodiments, directly using Replace each Kalman filter state estimate The first 5 elements of , then:

[0098]

[0099] in, and Represents vectors The 6th and Mth elements of .

[0100] In the embodiment of the present application, the fusion vector matrix is ​​determined according to the number of observation values, and the candidate state parameters at the current moment are fused according to the fusion vector matrix to locate the target state parameters at the current moment. The present application takes into account that individual satellites may have different observation noises, and different fusion weight coefficients can be flexibly set for each filter according to different observation noises. The amount of calculation of each sub-Kalman filter is small, and the calculation speed can be improved in complex scenarios. The error covariance matrix at the current moment is synchronized to the Kalman filter, and the candidate state parameters at the current moment are updated according to the target position coordinates, the target receiver clock error, and the target zenith tropospheric wet delay, and the updated candidate state parameters are synchronized to the Kalman filter. By performing iterative parameter estimation between epochs, the positioning solution precision and accuracy of the state parameters can be improved.

[0101] Figure 3 FIG. 1 is a flowchart of a positioning method based on satellite status parameters according to an embodiment of the present application. Figure 3 As shown, the method comprises the following steps:

[0102] S301, obtaining observation values ​​of multiple satellites at the current moment.

[0103] For the introduction of step S301, please refer to the relevant introduction in the above embodiment, which will not be repeated here.

[0104] S302: In response to the satellite not having an associated Kalman filter, determine a first Kalman filter that does not have an associated relationship from a plurality of Kalman filters.

[0105] In an embodiment of the present application, a plurality of Kalman filters are deployed on the receiver. After receiving the observation value of the satellite, if the satellite does not have an associated Kalman filter, a first Kalman filter that does not have an associated relationship is determined from the plurality of Kalman filters.

[0106] S303, establishing an association relationship between the satellite and the first Kalman filter, and determining the candidate state parameters and error covariance matrix of the first Kalman filter at the previous moment as preset candidate state parameters and error covariance matrix.

[0107] An association relationship is established between the satellite and the first Kalman filter so that there is a one-to-one association relationship between the satellite and the Kalman filter. For the first Kalman filter with a newly established association relationship, the candidate state parameters and error covariance matrix at its initial moment are determined to be the preset candidate state parameters and error covariance matrix.

[0108] S304, obtaining a second Kalman filter having an associated relationship among the multiple Kalman filters, and determining a target satellite associated with the second Kalman filter.

[0109] In some implementations, in order to avoid wasting resources, it is necessary to determine at regular intervals whether the associated second Kalman filter is continuously processing the observation values ​​of the target satellite.

[0110] S305: In response to not acquiring the observation value of the target satellite within a preset time threshold, releasing the association between the second Kalman filter and the target satellite.

[0111] If the observation value of the target satellite is not obtained within the preset time threshold, it means that the second Kalman filter has not performed the state estimation calculation process. The association relationship between the second Kalman filter and the target satellite can be released so that a new association relationship can be established for the second Kalman filter when the observation value of a new satellite is received later.

[0112] S306, for the observation values ​​of each satellite among the multiple satellites, the observation values ​​are input into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation values ​​based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, and obtains candidate state parameters and error covariance matrix at the current moment.

[0113] S307, performing state fusion on each candidate state parameter at the current moment to locate the target state parameter at the current moment.

[0114] For the introduction of step S306 to step S307, reference may be made to the relevant contents in the above embodiment, which will not be repeated here.

[0115] In an embodiment of the present application, in response to a satellite not having an associated Kalman filter, a first Kalman filter that does not have an associated relationship is determined from multiple Kalman filters, an association relationship is established between the satellite and the first Kalman filter, and in response to not obtaining the observation value of the target satellite within a preset time threshold, the association relationship between the second Kalman filter and the target satellite is released, which can avoid waste of resources and hardware and improve the positioning efficiency of state parameters in complex scenarios.

[0116] Figure 4FIG. 1 is a schematic diagram of a positioning method based on satellite status parameters according to an embodiment of the present application. Figure 4 As shown, in the embodiment of the present application, m Kalman filters (Kalman filter 1, Kalman filter 2, ..., Kalman filter m) are deployed on the receiver, and the receiver obtains the observation values ​​of m satellites at the current moment (y1, y2, ...y m ), the Kalman filter and the satellite have a one-to-one corresponding relationship as an example for explanation, for the observation value of each satellite, the observation value is input into the Kalman filter associated with the satellite, and the Kalman filter performs state estimation on the observation value based on its own state transfer equation and observation equation, the candidate state parameters and error covariance matrix of the previous moment, and obtains the candidate state parameters and error covariance matrix of the current moment; the receiver performs state fusion on each candidate state parameter of the current moment, locates the target state parameter of the current moment, and then updates the candidate state parameters and error covariance matrix of the previous moment according to the error covariance matrix of the current moment and the target state parameter of the current moment, continues to obtain the observation value of each satellite at the next moment, and continues to locate the target state parameter of the next moment. The embodiment of the present application can improve the positioning solution precision and accuracy of the state parameters.

[0117] Figure 5 is a structural diagram of a positioning device based on satellite state parameters according to an embodiment of the present disclosure, such as Figure 6 As shown, the positioning device 500 based on satellite state parameters includes:

[0118] A first acquisition module 510 is used to acquire observation values ​​of multiple satellites at the current moment;

[0119] The second acquisition module 520 is used to input the observation value of each satellite in the multiple satellites into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation value based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, and obtains candidate state parameters and error covariance matrix at the current moment;

[0120] The state fusion module 530 is used to perform state fusion on each candidate state parameter at the current moment and locate the target state parameter at the current moment.

[0121] In some implementations, the state fusion module 530 is further configured to:

[0122] Determine the fusion vector matrix according to the number of observations;

[0123] The candidate state parameters at the current moment are fused according to the fusion vector matrix to locate the target state parameters at the current moment.

[0124] In some embodiments, the candidate state parameters include position coordinates, receiver clock error, zenith tropospheric wet delay, ionospheric delay, and ambiguity parameters. The state fusion module 530 is further configured to:

[0125] According to the fusion vector matrix, each position coordinate at the current moment is weighted to obtain the target position coordinate, each receiver clock difference at the current moment is weighted to obtain the target receiver clock difference, each zenith tropospheric wet delay at the current moment is weighted to obtain the target zenith tropospheric wet delay;

[0126] Acquire an ionospheric delay sequence according to each ionospheric delay at the current moment, and acquire an ambiguity parameter sequence according to each ambiguity parameter at the current moment;

[0127] The target position coordinates, target receiver clock error, target zenith tropospheric wet delay, ionospheric delay sequence and ambiguity parameter sequence are determined as the target state parameters at the current moment.

[0128] In some implementations, the positioning device 500 based on satellite state parameters further includes an iterative update module 540, which is used to:

[0129] Synchronize the error covariance matrix at the current moment to the Kalman filter;

[0130] The candidate state parameters at the current moment are updated according to the target position coordinates, the target receiver clock error, and the target zenith tropospheric wet delay, and the updated candidate state parameters are synchronized to the Kalman filter.

[0131] In some implementations, the observation values ​​of the satellite are pseudo-range observation values, and the observation equation of the Kalman filter associated with the satellite is a pseudo-range observation equation.

[0132] In some implementations, the observation values ​​of the satellite are carrier phase observation values, and the observation equation of the Kalman filter associated with the satellite is a carrier phase observation equation.

[0133] In some implementations, the first acquisition module 510 is further configured to:

[0134] In response to the satellite not having an associated Kalman filter, determining a first Kalman filter from a plurality of Kalman filters that does not have an associated relationship;

[0135] An association relationship between the satellite and the first Kalman filter is established, and the candidate state parameters and the error covariance matrix of the first Kalman filter at the previous moment are determined as the preset candidate state parameters and the error covariance matrix.

[0136] In some implementations, the first acquisition module 510 is further configured to:

[0137] Acquire a second Kalman filter having an associated relationship among the multiple Kalman filters, and determine a target satellite associated with the second Kalman filter;

[0138] In response to failure to obtain an observation value of the target satellite within a preset time threshold, the association relationship between the second Kalman filter and the target satellite is released.

[0139] The embodiments of the present application can improve the positioning solution precision and accuracy of the state parameters, and when there are values ​​of poor quality in the satellite observation values, reduce the error of the estimation result by fusing the state parameters.

[0140] In the embodiment of the present application, it is applicable to the positioning situation of the combination of pseudorange observation value and carrier phase observation value. The method proposed in the embodiment of the present application is applicable to the joint positioning of high and low orbits, which makes full use of the low orbit satellite resources and can have higher positioning accuracy than pure high orbit satellite positioning.

[0141] Based on the same application concept, an embodiment of the present application also provides an electronic device.

[0142] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 600 includes a memory 601, a processor 602, and a computer program product stored in the memory 601 and executable on the processor 602. When the processor executes the computer program, the aforementioned positioning method based on satellite status parameters is implemented.

[0143] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the positioning method based on satellite status parameters provided in the above embodiments.

[0144] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which, when executed by a processor, implements the positioning method based on satellite state parameters provided in the above embodiments.

[0145] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0146] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0147] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0148] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0149] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0150] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0151] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0152] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A positioning method based on satellite state parameters, characterized in that: include: Get the observation values ​​of multiple satellites at the current moment; For the observation value of each satellite among the multiple satellites, the observation value is input into the Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation value based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, and obtains candidate state parameters and error covariance matrix at the current moment; The state of each candidate state parameter at the current moment is fused to locate the target state parameter at the current moment.

2. The method according to claim 1, characterized in that: The performing state fusion on each of the candidate state parameters at the current moment to locate the target state parameter at the current moment includes: Determine a fusion vector matrix according to the number of observations; The candidate state parameters at the current moment are fused according to the fusion vector matrix to locate the target state parameters at the current moment.

3. The method according to claim 2, characterized in that The candidate state parameters include position coordinates, receiver clock error, zenith tropospheric wet delay, ionospheric delay and ambiguity parameters, and the step of fusing the candidate state parameters at the current moment according to the fusion vector matrix to locate the target state parameters at the current moment includes: According to the fusion vector matrix, each position coordinate at the current moment is weighted to obtain the target position coordinate, each receiver clock difference at the current moment is weighted to obtain the target receiver clock difference, each zenith tropospheric wet delay at the current moment is weighted to obtain the target zenith tropospheric wet delay; Acquire an ionospheric delay sequence according to each of the ionospheric delays at the current moment, and acquire an ambiguity parameter sequence according to each of the ambiguity parameters at the current moment; The target position coordinates, the target receiver clock error, the target zenith tropospheric wet delay, the ionospheric delay sequence and the ambiguity parameter sequence are determined as the target state parameters at the current moment.

4. The method according to claim 3, characterized in that After locating the target state parameters at the current moment, the method further includes: Synchronize the error covariance matrix at the current moment to the Kalman filter; The candidate state parameters at the current moment are updated according to the target position coordinates, the target receiver clock error, and the target zenith tropospheric wet delay, and the updated candidate state parameters are synchronized to the Kalman filter.

5. The method according to any one of claims 1 to 4, characterized in that: Also includes: The observation value of the satellite is a pseudo-range observation value, then the observation equation of the Kalman filter associated with the satellite is a pseudo-range observation equation; or The observation value of the satellite is a carrier phase observation value, and the observation equation of the Kalman filter associated with the satellite is a carrier phase observation equation.

6. The method according to any one of claims 1 to 4, characterized in that: The method is executed by a receiver, wherein a plurality of Kalman filters are deployed on the receiver, and before the observation value is input into the Kalman filter associated with each satellite, the method further includes: In response to the satellite not having an associated Kalman filter, determining a first Kalman filter from the plurality of Kalman filters that does not have an associated relationship; An association relationship between the satellite and the first Kalman filter is established, and the candidate state parameters and the error covariance matrix of the first Kalman filter at a previous moment are determined as preset candidate state parameters and the error covariance matrix.

7. The method according to any one of claims 1 to 4, characterized in that: The method is executed by a receiver, wherein a plurality of Kalman filters are deployed on the receiver. After obtaining the observation values ​​of a plurality of satellites at the current moment, the method further includes: Acquire a second Kalman filter having an associated relationship among the multiple Kalman filters, and determine a target satellite associated with the second Kalman filter; In response to failure to obtain an observation value of the target satellite within a preset time threshold, the association relationship between the second Kalman filter and the target satellite is released.

8. A positioning device based on satellite state parameters, characterized in that: include: The first acquisition module is used to obtain the observation values ​​of multiple satellites at the current moment; A second acquisition module is used for inputting the observation value of each satellite in the multiple satellites into a Kalman filter associated with each satellite, and the Kalman filter performs state estimation on the input observation value based on its own state transfer equation and observation equation, candidate state parameters and error covariance matrix at the previous moment, to obtain candidate state parameters and error covariance matrix at the current moment; The state fusion module is used to perform state fusion on each of the candidate state parameters at the current moment to locate the target state parameter at the current moment.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the steps of the method according to any one of claims 1-7.

Images