Navigation data processing method and device, computer storage medium and terminal

Abstract

According to the navigation data processing method and device, the computer storage medium and the terminal, the second pseudo-range approximate residual errors of the satellites are determined according to the first pseudo-range approximate residual errors of all the frequency points of the satellites, the abnormal satellites are determined based on the difference values of the second pseudo-range approximate residual errors, multi-frequency observed quantity information is fully utilized, and the navigation data processing efficiency is improved. The missed detection probability of the satellite is reduced; second pseudo-range approximate residual errors of all satellites to be detected are applied to satellite anomaly detection, and the probability of missing detection and false detection of a single satellite is reduced. Second pseudo-range general residual errors are sorted, so that ordered distribution of all satellites with related and similar space errors is ensured, and then the second pseudo-range general residual errors between two adjacent satellites are subtracted, so that the common part of the space-related errors is ensured to be fully eliminated; the detection of the abnormal satellite is realized by using the residual difference value and the detection threshold value; through the nonlinear analysis of the residual difference value, the abnormal satellite can be efficiently identified with a small operand, and the detection sensitivity and accuracy are improved.

Description

Technical Field

[0001] This document relates to navigation and positioning technologies, and particularly to a navigation data processing method, apparatus, computer storage medium, and terminal. Background Art

[0002] The Global Navigation Satellite System (GNSS) is an important space infrastructure that provides real-time all-weather position, velocity, and time information services for social production activities. User receivers obtain the pseudorange, carrier, Doppler observables, and navigation message information of each satellite in real time by receiving the navigation signals broadcast by the satellites, calculate the satellite coordinates, satellite velocity, and satellite clock offset based on the satellite orbit and clock offset information provided by the broadcast ephemeris, and use the observables of at least 4 effective satellites to calculate the position and velocity of the user receiver in real time; the satellite orbit and clock offset information serves as the spatio-temporal reference, and its stability and accuracy are the prerequisites for achieving high-precision GNSS services.

[0003] The navigation message broadcast by the satellite will mark whether the set of ephemeris is healthy, and the user selects the healthy ephemeris for position and velocity calculation based on the identifier; however, in actual use, the satellite ephemeris marked as healthy may also exhibit abnormal conditions, such as the instability of the true orbit and clock offset of the satellite during orbit maneuvers, especially when some system failures take some time for the ground monitoring station to detect and identify; in addition, when the user is in a poor signal environment such as signal occlusion and interference, it will also cause abnormal broadcast ephemeris parameters, and the incorrect broadcast ephemeris parameters will lead to errors in the calculated satellite information and have varying degrees of impact on the calculated user position and velocity results. For the differential mode calculation of the inter-station single difference and the inter-station - inter-satellite double difference between two receivers performing synchronous observations, the error caused by the satellite ephemeris can be completely eliminated by differencing, and its position result is not affected by the ephemeris error. For current single-station receiver users of multi-system and multi-frequency, the RAIM algorithm is generally used to detect and identify relatively large magnitudes of ephemeris errors through redundant range observables. However, for an application that uses the distance change between consecutive epochs, such as carrier phase differencing and Doppler velocity measurement between consecutive epochs, relatively large magnitudes of ephemeris errors are eliminated by differencing between consecutive epochs, leaving only the changing part of the ephemeris error between consecutive epochs, whose magnitude is generally in the centimeter to decimeter range, which is the same or similar to the accuracy of the observables used. After the abnormal satellite participates in the calculation, it will cause an error of a similar magnitude to the calculation result, and the existing RAIM algorithm is difficult to detect and identify this magnitude of error data in real time.

[0004] Currently, for the evaluation of broadcast ephemeris accuracy, most are based on post-precision ephemeris for real-time evaluation of broadcast ephemeris orbits and clock errors, which cannot be used by real-time GNSS users. For the detection of abnormal ephemeris with large real-time errors, the methods in related technologies have a relatively small overall computational load, but accurate target reference station coordinates and reference station observations must be obtained; these methods are only for static reference station users and solve the problem of single-signal abnormality after calculating residuals based on accurate positions, without considering the coupling problem between position errors and signal abnormalities; these methods are not applicable to rover users without reference station data, and for dynamic reference station users and network reference station users, since the target reference station may change at any time and its correct reference station coordinates cannot be received by rover users in time, directly using these methods will result in false detections and affect the normal position solution of rover user receivers. For the complex and changeable application scenarios of dynamic rover users in practice, since the prior position of rover users cannot be accurately obtained, especially in the scenario where the prior coordinate error of rover users with high dynamics or complex environments is large, the problem of identifying abnormal satellites relying only on approximate prior coordinates with an accuracy ranging from meters to hundreds of meters needs to be solved. At this time, if rover users directly use the residual statistical method of static reference stations, the residuals will be contaminated by position errors, resulting in missed detection of abnormal satellites and misdetection of normal satellites. If real-time iterative solution is first carried out to correct the position and then the detection of abnormal satellites is carried out, a complex data operation process is required and the position will also be contaminated by abnormal satellites during the iterative solution, resulting in a large error in the converged real-time coordinates and causing more serious misjudgments in the follow-up. Existing methods only directly detect satellite abnormalities through residuals and cannot solve the problem of detecting abnormal satellites when position errors and signal abnormalities are coupled. Moreover, the detection statistical method in residual detection only focuses on the overall distribution of the residual sequence, and the overall distribution of residuals varies greatly in different environmental signal scenarios, with low sensitivity and accuracy of abnormal detection, and the threshold value effective for detection in one scenario has very limited detection effect even through simple adjustment in the complex and changing dynamic user environment. In addition, the methods in related technologies directly select and discard the identified abnormal satellites without distinguishing the impact of ephemeris errors on different GNSS positioning and velocity measurement modes and adopting a more reasonable scheme. If there are multiple misjudged satellites, it will directly lead to a sharp reduction in the available satellites of the user receiver, resulting in a continuous decrease in user position accuracy and even the problem of failed solution.

[0005] In summary, how to achieve efficient detection of abnormal satellites applicable to different scenarios has become a problem to be solved. Summary of the Invention

[0006] An embodiment of the present application provides a navigation data processing method, including:

[0007] For each satellite, determine the second approximate pseudorange residual of the satellite according to the first approximate pseudorange residuals of all frequency points tracked by the satellite;

[0008] Sort the second approximate pseudorange residuals of all satellites to be detected according to their values, and calculate the residual differences between adjacent second approximate pseudorange residuals respectively;

[0009] Determine the abnormal satellites according to the calculated residual differences and a preset detection threshold value.

[0010] On the other hand, an embodiment of the present application further provides a computer storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above navigation data processing method is implemented.

[0011] On yet another aspect, an embodiment of the present application further provides a terminal, including: a memory and a processor, and a computer program is stored in the memory; wherein,

[0012] The processor is configured to execute the computer program in the memory;

[0013] When the computer program is executed by the processor, the above navigation data processing method is implemented.

[0014] On still another aspect, an embodiment of the present application further provides a navigation data processing device, including: an approximate residual determination unit, a difference calculation unit, and a judgment and processing unit; wherein,

[0015] The approximate residual determination unit is configured to: for each satellite, determine the second approximate pseudorange residual of the satellite according to the first approximate pseudorange residuals of all frequency points tracked by the satellite;

[0016] The difference calculation unit is configured to: sort the second approximate pseudorange residuals of all satellites to be detected according to their values, and calculate the residual differences between adjacent second approximate pseudorange residuals respectively;

[0017] The judgment and processing unit is configured to: determine the abnormal satellites according to the calculated residual differences and a preset detection threshold value.

[0018] In the embodiments of the present disclosure, for any single-frequency or multi-frequency receiver, the second approximate pseudorange residual of a satellite is determined according to the first approximate pseudorange residuals of all frequency points of the satellite. Furthermore, based on the difference of the second approximate pseudorange residuals, the abnormal satellite is determined. That is, the satellite anomaly detection applies the observations of all frequency points of each satellite, making full use of the information of multi-frequency observations and reducing the probability of missed detection of satellites. In addition, the satellite anomaly detection applies the second approximate pseudorange residuals of all satellites to be detected, making full use of the multi-frequency observations of multiple satellites and reducing the probability of missed detection and false detection of a single satellite. In the process of determining the abnormal satellite, the second approximate pseudorange residuals are sorted first to ensure the orderly distribution of all satellites with spatially correlated and similar errors. Then, the difference is taken between the second approximate pseudorange residuals of two adjacent satellites, fully eliminating the common part of other relevant errors except for ephemeris errors and large-scale measurement gross errors, ensuring that the common part of the spatially correlated errors is fully eliminated, and obtaining smaller-magnitude differences and uniform numerical distributions corresponding to each satellite. The embodiments of the present application consider that based on satellites with ephemeris anomalies or large-scale gross errors, there will be significant differences in the residual difference distribution corresponding to them and normal satellites. By using the residual difference and the detection threshold value, the detection of abnormal satellites with abnormal ephemeris parameters or significant gross errors in measurement values is realized. Compared with the linear global statistical method for the residual sequence in the related art, through the non-linear analysis of the residual difference, abnormal satellites can be effectively and quickly identified with a small amount of computation, which can be effectively applied to the dynamic and complex rover user scenarios, improving the detection sensitivity and accuracy of abnormal satellites.

[0019] Other features and advantages of the present application will be described in the subsequent specification, and part of them will become obvious from the specification, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained through the solutions described in the specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings are used to provide an understanding of the technical solutions of the present application, and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solutions of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0021] Figure 1 It is a flowchart of the navigation data processing method according to the embodiments of the present disclosure;

[0022] Figure 2 It is a structural block diagram of the navigation data processing device according to the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] This application describes multiple embodiments, but the description is exemplary rather than restrictive, and it will be apparent to those of ordinary skill in the art that there can be more embodiments and implementation solutions within the scope of the embodiments described in this application. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically restricted, any feature or element of any embodiment can be used in combination with any other feature or element in any other embodiment, or can replace any other feature or element in any other embodiment.

[0024] This application includes and contemplates combinations with features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application can also be combined with any conventional features or elements to form a unique inventive solution. Any feature or element of any embodiment can also be combined with features or elements from other inventive solutions to form another unique inventive solution. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented alone or in any suitable combination. Therefore, the embodiments are not subject to other restrictions except those made in accordance with the appended claims and their equivalents. In addition, various modifications and changes can be made within the scope of the appended claims.

[0025] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not depend on the specific order of the steps described herein, the method or process should not be limited to the specific order of steps described. As will be understood by those of ordinary skill in the art, other step sequences are possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation on the claims. In addition, the claims directed to the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can easily understand that these orders can vary and still remain within the spirit and scope of the embodiments of this application.

[0026] Figure 1 For the flowchart of the navigation data processing method according to the embodiments of the present disclosure, as Figure 1 shown, it includes:

[0027] Step 101: For each satellite, determine the second approximate pseudorange residual of the satellite according to the first approximate pseudorange residuals of all frequency points tracked by the satellite;

[0028] Step 102: Sort the second approximate pseudorange residuals of all satellites to be detected according to the value, and calculate the residual differences between adjacent second approximate pseudorange residuals respectively;

[0029] Step 103: Determine the abnormal satellites according to the calculated residual difference and the preset detection threshold value.

[0030] In the embodiment of the present disclosure, for any single-frequency or multi-frequency receiver, the second approximate pseudorange residual of a satellite is determined according to the first approximate pseudorange residuals of all frequency points of the satellite, and then the abnormal satellites are determined based on the difference of the second approximate pseudorange residuals. That is, the satellite anomaly detection applies the observations of all frequency points of each satellite, makes full use of the information of multi-frequency observations, and reduces the probability of missed detection of satellites. In addition, the satellite anomaly detection applies the second approximate pseudorange residuals of all satellites to be detected, makes full use of the multi-frequency observations of multiple satellites, and reduces the probability of missed detection and false detection of a single satellite. In the process of determining abnormal satellites, the second approximate pseudorange residuals are sorted first to ensure the orderly distribution of all satellites with spatially correlated and similar errors. Then, the difference is taken between the second approximate pseudorange residuals of two adjacent satellites, which fully eliminates the common part of other related errors except for ephemeris errors and large-scale measurement gross errors, ensures that the common part of spatially correlated errors is fully eliminated, and obtains smaller magnitude differences and uniform numerical distributions corresponding to each satellite. The embodiment of the present application considers that based on satellites with ephemeris anomalies or large-scale gross errors, there will be significant differences in the corresponding residual difference distributions compared with normal satellites. By using the residual difference and the detection threshold value, the detection of abnormal satellites with ephemeris parameter anomalies or significant gross errors in measurement values is realized. Compared with the linear global statistical method for the residual sequence in the related art, through the non-linear analysis of the residual difference, abnormal satellites can be effectively and quickly identified with a small amount of computation, which can be effectively applied to the dynamic and complex rover user scenarios, and improves the detection sensitivity and accuracy of abnormal satellites.

[0031] In an exemplary example, the above processing of the embodiment of the present disclosure can be executed by a receiver including a GNSS receiver, or can be executed by a device communicating with the receiver. The embodiment of the present disclosure does not limit this.

[0032] In an exemplary example, the first approximate pseudorange residual of frequency point L j is Δρ Lj , and the expression of Δρ Lj is:

[0033]

[0034] In the formula, r represents the geometric distance from the receiver to the satellite. For the i-th satellite, the geometric distance r is calculated from the satellite coordinates of the i-th satellite and the approximate coordinates of the receiver. represents the pseudorange observation value of frequency point L j ,, represents the satellite clock error of frequency point L j .

[0035] In an exemplary example, for each satellite, according to the first approximate pseudorange residual of all frequency points tracked by the satellite, determining the second approximate pseudorange residual of the satellite may include:

[0036] For each satellite, after taking the absolute value of the first approximate pseudorange residual of all frequency points tracked by the satellite respectively, select the first approximate pseudorange residual with the smallest absolute value as the second approximate pseudorange residual.

[0037] In the embodiments of the present disclosure, the absolute value of the approximate pseudorange residual of each frequency point of the i-th satellite is denoted as ……、 After taking the absolute value processing, select ……、 The item with the smallest value in is used as the second approximate pseudorange residual value Δρ of the i-th satellite i , and the expression is:

[0038]

[0039] In an exemplary example, in the embodiments of the present disclosure, the second approximate pseudorange residuals of all satellites to be detected are sorted according to the value, and the residual differences between adjacent second approximate pseudorange residuals are calculated respectively, including:

[0040] Sort the second approximate pseudorange residual values of all n satellites to be detected in descending order;

[0041] Calculate the difference between the two adjacent second approximate pseudorange residuals in the descending sequence to obtain the residual difference.

[0042] In the embodiments of the present disclosure, it is assumed that the second approximate pseudorange residual values of all n satellites to be detected are Δρ 1 、……、Δρ n ; the sequence after descending order is denoted as Δρ max_1 、Δρ max_2 ……、Δρ max_n-1 、Δρ max_n ; the expression for calculating the residual difference is:

[0043] Δ(Δρ k )=Δρ max_k -Δρ max_k+1 (1≤k≤n - 1) (2)

[0044] In the embodiments of the present disclosure, after sorting the second approximate pseudorange residuals, the residual difference is calculated, and the satellites with abnormal ephemeris parameters are identified by using the residual difference. This not only solves the problem that the global statistical method in the related technology fails in the position error and satellite anomaly coupling in the rover application scenario, but also avoids the problem that the residual statistics are easily masked by the overall residual distribution, resulting in poor detection effect, and can more effectively detect and identify the satellites with residual mutations or local anomalies.

[0045] In an exemplary example, the detection threshold T in the embodiments of the present disclosure can be dynamically adjusted according to the reliability level, prior position, and motion state information of the user receiver, and can also be adjusted according to the information including the confidence level; if the error of the approximate coordinates in the embodiments of the present disclosure is small, the detection threshold T only needs to correctly reflect the error difference related to the propagation path. If the error of the approximate coordinates is large, at this time, it is necessary to appropriately increase the detection threshold T. However, due to the spatial distribution of the satellites, the residual difference Δ(Δρ k ) calculated in the embodiments of the present disclosure has also eliminated the same error part caused by two satellites with similar space. At this time, the magnitude of the difference Δ(Δρ k ) corresponding to each satellite is also much smaller than the error caused by abnormal ephemeris parameters, and there are obvious statistical distribution characteristics in the numerical distribution. The abnormal satellites can still be detected according to the residual difference.

[0046] In an exemplary example, the embodiments of the present disclosure determine the abnormal satellites according to the calculated residual difference and the preset detection threshold, which may include:

[0047] The residual differences calculated by sorting the second pseudorange approximate residuals in descending order are used to form a target detection quantity sequence;

[0048] Determine the x1th residual difference in the formed target detection quantity sequence that is the last one greater than the preset detection threshold, and determine the satellite corresponding to the x1th second pseudorange approximate residual and all the satellites corresponding to the second pseudorange approximate residuals ranked before it as abnormal satellites.

[0049] The embodiments of the present disclosure construct a target detection quantity sequence with the residual differences adjacent to the second approximate pseudorange residuals, and use the distribution characteristics of the target detection quantity sequence to quickly detect and identify single or multiple satellites with abnormal ephemeris parameters. While solving the problem that the global statistical method in the related technology fails in the position error and satellite anomaly coupling in the rover application scenario, it avoids the problem that the residual statistics are easily masked by the overall residual distribution, resulting in poor detection effect.

[0050] In the embodiments of the present disclosure, for satellites determined to be abnormal, fault identification of the abnormal satellites can be performed with reference to related technologies.

[0051] The embodiments of the present disclosure can be applicable to static, dynamic, high-speed, and high-dynamic user receivers without being restricted by the user's motion state and user coordinate error. By adjusting a reasonable detection threshold T, it can be used for both static and dynamic user receivers, as well as high-speed and high-dynamic user receivers. And by adjusting the target detection quantity sequence and calculating a reasonable detection threshold T, it can also be used to detect satellites with obvious gross errors locally, not limited to detecting satellites with ephemeris anomalies.

[0052] From the characteristics of GNSS pseudorange observables and related error terms, it can be known that the target detection quantity sequence obtained in the embodiments of the present disclosure can significantly distinguish the numerical distributions of pseudorange residuals corresponding to normal ephemeris and abnormal ephemeris. The calculation amount in the embodiments of the present disclosure is small and the method is simple and effective. It should be noted that after obtaining the target detection quantity sequence in the embodiments of the present disclosure, statistical detection methods in related technologies can be used to determine abnormal satellites. The above methods are only optional examples of the embodiments of the present disclosure, and the embodiments of the present disclosure do not make any limitations in this regard. For other expansions based on the method of the embodiments of the present disclosure, for the second pseudorange approximate residual value Δρ 1 、……、Δρ n Methods for other classification and sorting, or methods for obtaining the residual difference sequence Δ(Δρ) by using other similar calculation methods, or related methods for further performing high-order difference processing on the residual difference sequence Δ(Δρ) repeatedly, all fall within the protection scope of the embodiments of the present disclosure.

[0053] In an exemplary example, the navigation data processing in the embodiments of the present disclosure further includes:

[0054] For each satellite, calculate the first pseudorange approximate residual of each frequency point.

[0055] In the embodiments of the present disclosure, before the position and velocity solution of the GNSS receiver in the current epoch, only approximate receiver position information is required. By fully utilizing the pseudorange observables of multiple frequency points of each satellite and deducting the geometric distance and satellite clock error terms, the approximate pseudorange residuals of each satellite can be obtained.

[0056] The embodiments of the present disclosure fully consider the problem of the coupling of receiver position error and satellite anomaly under different user motion states, and propose and design the single-variable residual statistics problem in related technologies as a multi-variable joint processing, which can be used for both static reference station users and dynamic rover users at the same time.

[0057] In an exemplary example, calculating the first pseudorange approximate residual of each frequency point in the embodiments of the present disclosure includes:

[0058] Obtain the current epoch T kThe pseudorange measurements, carrier measurements, and Doppler measurements of each satellite are obtained, and the ephemeris information of each satellite is acquired simultaneously;

[0059] Take the latest set of valid positions as the approximate position of the receiver at the current moment In the embodiments of the present disclosure, the valid position can be the receiver position directly solved successfully using the measurements at the previous moment, or the current rough position obtained by velocity recursion calculation based on the position information at the previous moment, or the user position information that was not finally solved successfully at the previous moment but can be approximately converged through multiple position iterations.

[0060] For the pseudorange measurements of multiple frequency points of each satellite being tracked simultaneously, the pseudorange of the i-th satellite at frequency point L j The pseudorange observation equation is as follows:

[0061]

[0062] Among them, represents the pseudorange observation value at frequency point L j ; r represents the geometric distance from the receiver to the satellite; c represents the speed of light; represents the receiver clock error at frequency point L j ; represents the satellite clock error at frequency point L j ; represents the ionospheric error at frequency point L j ; T represents the tropospheric error; represents the measurement noise at frequency point L j ;

[0063] In the embodiments of the present disclosure, after subtracting the geometric distance and the satellite clock error from equation (3) , the first pseudorange approximate residual Δρ at frequency point L j can be obtained: Lj :

[0064]

[0065] Among them, the geometric distance r is calculated from the satellite coordinates of the i-th satellite and the approximate coordinates of the receiver. The satellite coordinates and the satellite clock error are both calculated according to the broadcast ephemeris of the i-th satellite;

[0066]

[0067] Perform a linearized expansion on r i , which is expressed as:

[0068]

[0069] Among them, represent the position difference between the approximate coordinates of the receiver and the true coordinates as a vector Δr i represents the geometric distance error of the i-th satellite; if the receiver has low dynamics or the receiver is in a stationary state, the obtained approximate coordinate error is small, and the geometric distance error term Δr i is small, and its influence on the first approximate pseudorange residual is small and can be ignored; however, for users with high-speed movement or large dynamics, the error of the approximate coordinates obtained based on prior information may be large. At this time, Δr i has a significant influence on the first approximate pseudorange residual . If it is directly ignored, the influence of the ephemeris error part in the first approximate pseudorange residual is not obvious, and the abnormal ephemeris parameters cannot be effectively identified.

[0070] For the pseudorange observations of multiple frequency points L1, ……, L n simultaneously tracked by the i-th satellite in the embodiments of the present disclosure, the first approximate pseudorange residuals of each frequency point can be calculated according to the above method ……,

[0072] In an exemplary example, after determining the abnormal satellite, the navigation data processing method of the embodiments of the present disclosure further includes:

[0073] According to the position and velocity solution mode, perform a weight reduction process on the observations of the satellite determined to be abnormal;

[0074] Perform position and velocity solution according to the observations of the abnormal satellite after the weight reduction process.

[0075] In an exemplary example, the weight value for performing the weight reduction process on the observations of the satellite determined to be abnormal in the embodiments of the present disclosure is

[0076] where W i is the weight value of the observations of the i-th satellite, and K i represents the coefficient of the weight value of the observations of the i-th satellite determined to be abnormal, is to calculate the mean value of the second approximate pseudorange residual values Δρ 1 、……、Δρ n1 for all n1 satellites (n1 < n) other than the satellites determined to be abnormal.

[0077] In an exemplary instance, the weight value W of the embodiments of the present disclosure i can be the reciprocal of the measurement variance of the observable quantity, and the measurement variance of the observable quantity of the satellites at each frequency point can be a function of its satellite measurement noise, elevation angle, CN0, continuous tracking time, typical model error value, measurement residual, etc. The weight value W i can also be calculated by other methods in the related art, which will not be elaborated in the embodiments of the present disclosure.

[0078] In an exemplary instance, for the abnormal satellites excluded during the position and velocity solution process in the embodiments of the present disclosure, the weight value for reducing the weight processing For the abnormal satellites that need to be retained during the positioning solution process, the weight value for reducing the weight processing

[0079] In an exemplary instance, for normal satellites in the embodiments of the present disclosure, the weight value can be set

[0080] In the embodiments of the present disclosure, for the satellites determined to be abnormal, according to the mode of position and velocity solution, a more reasonable method for reducing the weight of abnormal satellites is proposed. The position and velocity solution in the embodiments of the present disclosure includes the following modes:

[0081] Mode 1: In the differential mode position solution of synchronous observation, since the error caused by satellite ephemeris can be eliminated by differencing when two receivers in synchronous observation perform single-difference between stations, this part of the influence can be ignored, that is Or reduce the weight of the determined abnormal satellites for use, and the weight value for reducing the weight is

[0082] Mode 2: In the position solution of pseudorange single-point positioning and precise single-point positioning in the single-station mode, since the residual magnitude caused by ephemeris error is relatively large, the faulty satellites are directly excluded, that is Reduce the probability that the position is contaminated in the single-point position solution step. At this time, there is no need to increase additional RAIM and repeated iteration operations, reduce the probability of repeated RAIM satellites and iteration operations in different solution modules during the solution process, and avoid the problem that the position and velocity results are contaminated by gross error observations and cause deviation during the solution process, as well as the deviation is difficult to recover in subsequent solutions or the solution fails after multiple RAIM false detections, which can effectively maintain the accuracy and stability of the user's position solution;

[0083] Mode 3. For a type of applications that use the change amount of the distance between epochs, such as Doppler velocity measurement, carrier or pseudorange difference between epochs, the difference between epochs eliminates most of the ephemeris errors, and only the difference part of the ephemeris errors between epochs remains. Its magnitude is generally in the centimeter to decimeter level, which is equivalent to the accuracy level of the observables used in the solution; in this type of application, for the first application with pre-determined high reliability or when the number of valid satellites is greater than the preset quantity threshold, the observables of the satellites determined to be abnormal are directly excluded, that is For the second application other than the first application or when the number of valid satellites is less than the preset quantity threshold, the satellites determined to be abnormal are used with reduced weight, that is, the weight value for setting the reduced weight processing for the observables of the satellites determined to be abnormal is At this time, the continuity of the solution service can be maintained, and at the same time, the influence of abnormal satellites on the position and velocity measurement results is effectively reduced, and the accuracy and reliability of the position and velocity solution of the user receiver are effectively improved.

[0084] The embodiment of the present disclosure also provides a computer storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above navigation data processing method is implemented.

[0085] The embodiment of the present disclosure also provides a terminal, including: a memory and a processor, and a computer program is stored in the memory;

[0086] Wherein,

[0087] The processor is configured to execute the computer program in the memory;

[0088] When the computer program is executed by the processor, the above navigation data processing method is implemented.

[0089] Figure 2 It is a structural block diagram of the navigation data processing device according to the embodiment of the present disclosure, as Figure 2 shown, including: a rough residual determination unit, a difference calculation unit, and a judgment processing unit; wherein,

[0090] The rough residual determination unit is set to: for each satellite, determine the second pseudorange rough residual of the satellite according to the first pseudorange rough residuals of all frequency points tracked by the satellite;

[0091] The difference calculation unit is set to: sort the second pseudorange rough residuals of all satellites to be detected according to the value, and calculate the residual differences of adjacent second pseudorange rough residuals respectively;

[0092] The judgment processing unit is set to: determine the abnormal satellites according to the calculated residual differences.

[0093] In an exemplary instance, the embodiment of the present disclosure determines that the approximate residual unit is further configured to calculate the frequency point L based on the following expression j of the first pseudo-range approximate residual Δρ Lj :

[0094]

[0095] where r represents the geometric distance from the receiver to the satellite. For the i-th satellite, the geometric distance r is calculated from the satellite coordinates of the i-th satellite and the approximate coordinates of the receiver; represents the pseudo-range observation value of the frequency point L j , represents the satellite clock error of the frequency point L j . In an exemplary instance, the embodiment of the present disclosure determines that the approximate residual unit is configured to:

[0096] For each satellite, after taking the absolute value of the first pseudo-range approximate residuals of all the frequency points tracked by the satellite respectively, select the first pseudo-range approximate residual with the smallest absolute value as the second pseudo-range approximate residual.

[0097] In an exemplary instance, the embodiment of the present disclosure determines that the difference calculation unit is configured to:

[0098] Arrange the second pseudo-range approximate residual values of all n satellites to be detected in descending order;

[0099] For the descending sequence, calculate the difference between two adjacent second pseudo-range approximate residuals before and after to obtain the residual difference.

[0100] In an exemplary instance, the embodiment of the present disclosure determines that the judgment processing unit is configured to:

[0101] Form a target detection quantity sequence with the residual differences calculated after arranging the second pseudo-range approximate residual values in descending order from large to small;

[0102] Determine the x1-th residual difference that is the last one greater than a preset detection threshold value in the formed target detection quantity sequence, and determine the satellite corresponding to the x1-th second pseudo-range approximate residual and all the satellites corresponding to the second pseudo-range approximate residuals ranked before it as abnormal satellites.

[0103] In an exemplary instance, the embodiment of the present disclosure determines that the judgment processing unit is further configured to:

[0104] According to the position and velocity solution mode, perform a weight reduction process on the observation values of the satellites determined to be abnormal;

[0105] Perform position and velocity solution according to the observation values of the abnormal satellites after the weight reduction process.

[0106] In an exemplary instance, the weight value for reducing the weight of the observation values of the satellites determined to be abnormal in the embodiments of the present disclosure is

[0107] where W i is the weight value of the observation value of the i-th satellite, and K i represents the coefficient of the weight value of the observation value of the i-th satellite determined to be abnormal. For all n1 satellites (n1 < n) other than the satellites determined to be abnormal, the mean value of the second pseudo-range approximate residual values Δρ 1 ,..., Δρ n1 is calculated. Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations. In the hardware implementation, the division between the functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be executed by several physical components in cooperation. Some or all components can be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term "computer storage medium" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

Claims

1. A navigation data processing method, characterized in that: include: For each satellite, determining an approximate second pseudorange residual of the satellite according to the approximate first pseudorange residuals of all frequency points tracked by the satellite; The approximate second pseudorange residuals of all satellites to be detected are sorted according to their values, and the residual difference values ​​of adjacent approximate second pseudorange residuals are calculated respectively; The abnormal satellite is determined based on the calculated residual difference and the preset detection threshold.

2. The navigation data processing method according to claim 1, characterized in that: Frequency L j The approximate residual error of the first pseudorange is Δρ Lj , Δρ Lj The expression is: Where r represents the geometric distance from the receiver to the satellite. For the i-th satellite, the geometric distance r is calculated by the satellite coordinates of the i-th satellite and the approximate coordinates of the receiver. Indicates frequency L j The pseudorange observation value of Indicates frequency L j The satellite clock error.

3. The navigation data processing method according to claim 1, characterized in that: The step of determining, for each satellite, an approximate second pseudorange residual of the satellite according to the approximate first pseudorange residuals of all frequency points tracked by the satellite comprises: For each satellite, after taking the absolute values ​​of the first pseudorange approximate residuals of all frequency points tracked by the satellite, the first pseudorange approximate residual with the smallest absolute value is selected as the second pseudorange approximate residual.

4. The navigation data processing method according to claim 1, characterized in that: The second pseudorange approximate residuals of all satellites to be detected are sorted according to the value size, and the residual difference values ​​of adjacent second pseudorange approximate residuals are calculated respectively, including: Arrange the approximate residual values ​​of the second pseudoranges of all n satellites to be detected in descending order; For the sequence arranged in descending order, the difference between two adjacent approximate residuals of the second pseudorange is calculated to obtain the residual difference.

5. The navigation data processing method according to any one of claims 1 to 4, characterized in that: The step of determining an abnormal satellite based on the calculated residual difference value and a preset detection threshold value includes: The residual difference values ​​calculated after sorting the approximate residual values ​​of the second pseudorange in descending order form a target detection amount sequence; The last x1-th residual difference value in the target detection amount sequence that is greater than the detection threshold value is determined, and the satellite corresponding to the x1-th second pseudorange approximate residual and the satellites corresponding to all the second pseudorange approximate residuals that are arranged in front are determined as abnormal satellites.

6. The navigation data processing method according to any one of claims 1 to 4, characterized in that: After determining the abnormal satellite, the navigation data processing method further includes: According to the position and velocity solution mode, the observations of the satellites determined to be abnormal are weighted down; The position and velocity are calculated based on the observations of the abnormal satellite after weight reduction.

7. The navigation data processing method according to claim 6, characterized in that: The weight value for reducing the weight of the observation value of the satellite determined to be abnormal is where, W i is the weight value of the observation of the i-th satellite, and K i represents the coefficient of the weight value of the observation of the i-th satellite determined to be abnormal, for all n1 satellites (n1 < n) except the satellites determined to be abnormal, calculate the mean value of the second approximate pseudorange residual values Δρ 1 ,..., Δρ n1 .

8. A computer storage medium, wherein a computer program is stored in the computer storage medium, and when the computer program is executed by a processor, the navigation data processing method according to any one of claims 1 to 7 is implemented.

9. A terminal, comprising: A memory and a processor, wherein the memory stores a computer program; wherein, The processor is configured to execute the computer program in the memory; When the computer program is executed by the processor, the navigation data processing method according to any one of claims 1 to 7 is implemented.

10. A navigation data processing device, comprising: Determine the rough residual unit, the difference calculation unit and the judgment processing unit; wherein, The approximate residual determination unit is set as follows: for each satellite, according to the approximate residuals of the first pseudoranges of all frequency points tracked by the satellite, the approximate residuals of the second pseudoranges of the satellite are determined; The difference calculation unit is set to: sort the approximate second pseudorange residuals of all satellites to be detected according to the value size, and respectively calculate the residual difference values ​​of adjacent approximate second pseudorange residuals; The judgment processing unit is configured to determine an abnormal satellite according to a residual difference value obtained by calculation and a preset detection threshold value.

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