Anomaly pseudorange identification method, apparatus and computer-readable medium

Abstract

This application discloses an abnormal pseudorange identification method, apparatus, and computer-readable medium. The method acquires preset carrier observation information obtained from carrier observations, performs prediction processing on the receiver's position and clock bias based on the carrier observation information, and updates the predicted position and clock bias based on the pseudorange observation set of the observed satellite. On this basis, the pseudorange residual vector of the observed satellite is determined according to the measurement update result, and abnormal pseudorange is identified based on the pseudorange residual vector and an abnormal pseudorange identification strategy. This application uses carrier observation information as an aid for abnormal pseudorange identification, enabling GNSS receivers to quickly and accurately identify erroneous / abnormal pseudorange observations when entering complex signal environments, avoiding negative impacts on positioning accuracy. It also avoids a series of problems existing in known technologies when dealing with pseudorange measurement problems in complex signal environments, such as high receiver hardware costs and strong subjectivity.

Description

Technical Field

[0001] This application belongs to the field of satellite navigation technology, and in particular relates to an abnormal pseudorange identification method, device and computer-readable medium. Background Technology

[0002] With the popularization and development of satellite navigation technology, the demand for high-precision positioning services based on the Global Navigation Satellite System (GNSS) has become increasingly important. Typically, the position information of a GNSS receiver can be calculated based on pseudorange observations from four or more satellites and broadcast ephemeris data; therefore, the receiver's positioning accuracy is directly limited by the accuracy of the pseudorange measurement. However, when the receiver is in complex signal environments such as urban canyons or short-term interference, the pseudorange measurements obtained by the receiver often contain a large number of non-systematic measurement errors, and the correct measurement rate of these pseudorange measurements may also be very low.

[0003] To address the above problems, receivers currently employ the following countermeasures:

[0004] One approach is to use a multipath suppression antenna or a dedicated anti-interference radio frequency module, but this will inevitably increase the receiver hardware cost.

[0005] Another approach is to use higher prior information (such as carrier-to-noise ratio, signal lock strength, etc.) thresholds for positioning algorithms. However, this approach is more subjective and will inevitably reduce the availability of receiver positioning.

[0006] Another approach is to use the Receiver Autonomous Integrity Detection (RAIM) algorithm, which utilizes redundant pseudorange observation data for observation consistency detection. However, in addition to requiring high observation redundancy, the RAIM algorithm also has high requirements for the constellation configuration of the correct observations in order to have good observability of errors / abnormal observations. This makes it difficult to guarantee the detection probability of the RAIM algorithm, especially when the receiver is in a multipath signal environment such as an urban canyon. Since the constellation configuration of the actual unobstructed satellites is already very poor, it will further affect the detection probability of the RAIM algorithm. Summary of the Invention

[0007] In view of this, this application provides an abnormal pseudorange identification method, apparatus and computer-readable medium, which are mainly used to solve the problem of abnormal pseudorange identification in complex signal environments, so as to provide technical support for GNSS-based high-precision positioning services, while avoiding at least some of the defects of known technologies in dealing with pseudorange measurement problems in such environments.

[0008] The specific plan is as follows:

[0009] An abnormal pseudorange identification method includes:

[0010] Obtain preset carrier observation information obtained from carrier observation of the observation satellite;

[0011] The receiver's position and clock bias are predicted based on the preset carrier observation information to obtain the prediction results of the receiver's position and clock bias.

[0012] The prediction results are updated by measuring the pseudorange observation set of the observation satellite to obtain the measurement update results of the prediction results.

[0013] Based on the measurement update results, determine the pseudorange residual vector of the observed satellite;

[0014] Based on the pseudorange residual vector and the preset abnormal pseudorange identification strategy, abnormal pseudorange observation values ​​in the pseudorange observation set of the observation satellite are identified.

[0015] Optionally, obtaining the preset carrier observation information obtained from carrier observation of the observation satellite includes:

[0016] Determine whether the carrier-locked observation satellite meets the carrier observation conditions; the carrier observation conditions are the carrier observation conditions required to ensure the effectiveness of the least squares estimation when the vector to be estimated is determined using the least squares estimation method, and the vector to be estimated consists of the receiver's position increment and clock error increment;

[0017] If so, obtain the carrier phase observation values ​​of the observation satellite in the current two adjacent epochs;

[0018] If not, obtain the Doppler observation information of the observation satellite at the current epoch.

[0019] Optionally, the carrier observation conditions include: the number of observation satellites with continuous carrier lock and no carrier cycle slip is not less than 4.

[0020] Optionally, the preset carrier observation information includes the carrier phase observation values ​​of the observation satellite in the current two adjacent epochs; the step of predicting the receiver's position and clock bias based on the preset carrier observation information to obtain the predicted results of the receiver's position and clock bias includes:

[0021] Based on the carrier phase observations of the two current adjacent epochs, the position increment and clock error increment of the receiver are determined using the least squares estimation method;

[0022] Using the receiver position increment and clock error increment, the optimal state estimate information of the receiver position and clock error in the preceding epoch of the adjacent epoch is updated to obtain the prediction result of the receiver position and clock error in the following epoch of the adjacent epoch.

[0023] Optionally, the step of using the receiver's position increment and clock bias increment to update the optimal state estimate information of the receiver's position and clock bias in the preceding epoch of the adjacent epoch, to obtain the prediction result of the receiver's position and clock bias in the subsequent epoch of the adjacent epoch, includes:

[0024] The receiver's sub-prediction model state information corresponding to the previous epoch is obtained as the optimal state estimation information of the receiver's position and clock error in the previous epoch. The sub-prediction model state information includes: the result of extracting sub-prediction models related to the receiver's position and clock error from the pre-built receiver state prediction model, and performing optimal state estimation of the receiver's position and clock error in the previous epoch based on the extracted sub-prediction models.

[0025] The state time of the sub-prediction model is updated using the receiver position increment and clock error increment to obtain the prediction results of the receiver position and clock error in the later epoch.

[0026] Optionally, the preset carrier observation information includes Doppler observation information of the observation satellite at the current epoch; the step of predicting the receiver's position and clock bias based on the preset carrier observation information to obtain the predicted results of the receiver's position and clock bias includes:

[0027] Update the state time of the pre-built receiver state prediction model to the current epoch to obtain the model state of the receiver state prediction model at the current epoch.

[0028] Using the Doppler observation information of the observation satellite at the current epoch, the receiver state prediction model is measured and updated to correspond to the model state at the current epoch.

[0029] From the measurement update results of the receiver state prediction model at the current epoch, extract the sub-prediction model related to the receiver position and clock error, and obtain the prediction results of the receiver position and clock error at the current epoch based on the sub-prediction model.

[0030] Optionally, the receiver state prediction model is a Kalman filter model constructed based on the receiver's position, velocity, clock bias, and clock drift;

[0031] The sub-prediction model is a sub-filter model corresponding to the receiver position and clock error, obtained from the receiver's Kalman filter model.

[0032] Optionally, updating the state time of the pre-built receiver state prediction model to the current epoch includes:

[0033] By modeling the receiver's position as uniform linear motion, its velocity as a random process, and its clock bias and clock drift as second-order random processes, the state time of the receiver state prediction model is updated to the current epoch.

[0034] Optionally, determining the pseudorange residual vector of the observed satellite based on the measurement update result includes:

[0035] Based on the measurement update results, determine the pseudorange prediction value of the observed satellite;

[0036] Based on the pseudorange prediction and pseudorange observation values ​​of the observed satellite, determine the pseudorange residual vector of the observed satellite.

[0037] Optionally, the step of identifying anomalous pseudorange observations in the pseudorange observation set of the observed satellite based on the pseudorange residual vector and a preset anomalous pseudorange identification strategy includes:

[0038] A global detection process is performed on the pseudorange residual vector and the pseudorange observation set. The global detection process includes: determining whether there are abnormal pseudorange observation values ​​of the observation satellite in the corresponding pseudorange observation set based on the preset global detection statistics corresponding to each pseudorange residual in the pseudorange residual vector.

[0039] If the global detection process determines that there are abnormal pseudorange observations in the pseudorange observation set, the abnormal pseudorange observations in the pseudorange observation set are identified according to each pseudorange residual in the pseudorange residual vector.

[0040] Optionally, the pseudorange observation set includes each pseudorange observation value of the observed satellite in the current epoch; the step of identifying anomalous pseudorange observation values ​​in the pseudorange observation set based on each pseudorange residual in the pseudorange residual vector includes:

[0041] The first pseudorange residual that is higher than a preset detection threshold among the pseudorange residuals contained in the pseudorange residual vector is identified, and the pseudorange observation value corresponding to the first pseudorange residual is identified as the first abnormal pseudorange observation value of the observation satellite in the current epoch.

[0042] The first anomalous pseudorange observation value is removed from the pseudorange observation set to obtain the remaining observation set;

[0043] The global detection process is performed on the remaining observation set and its corresponding pseudorange residual vector;

[0044] If, based on the global detection processing, it is determined that there are anomalous pseudorange observations in the residual observation set, the observation with the largest pseudorange residual in the residual observation set is identified as the second anomalous pseudorange observation of the observation satellite in the current epoch.

[0045] Remove the second anomalous pseudorange observation from the remaining observation set;

[0046] The remaining observation set is updated to the observation set obtained after removing the second abnormal pseudorange observation value, and the process is repeated until the global detection processing is performed on the remaining observation set and its corresponding pseudorange residual vector, until it is determined that there are no abnormal pseudorange observation values ​​in the remaining observation set based on the global detection processing.

[0047] An abnormal pseudorange identification device includes:

[0048] The acquisition unit is used to acquire preset carrier observation information obtained from carrier observation of the observation satellite;

[0049] The prediction unit is used to perform prediction processing on the position and clock error of the receiver based on the preset carrier observation information, and obtain the prediction results of the receiver position and clock error;

[0050] The measurement update unit is used to perform measurement update processing on the prediction results based on the pseudorange observation set of the observation satellite to obtain the measurement update results of the prediction results.

[0051] The determining unit is used to determine the pseudorange residual vector of the observed satellite based on the measurement update results;

[0052] The identification unit is used to identify abnormal pseudorange observation values ​​in the pseudorange observation set of the observation satellite according to the pseudorange residual vector and the preset abnormal pseudorange identification strategy.

[0053] A computer-readable medium having a computer program stored thereon, the computer program comprising program code for performing the methods described in any of the preceding descriptions.

[0054] A computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods described in any of the preceding descriptions.

[0055] In summary, the abnormal pseudorange identification method, apparatus, and computer-readable medium provided in this application acquire preset carrier observation information obtained from carrier observation of the observed satellite, perform prediction processing on the receiver's position and clock bias based on the acquired preset carrier observation information, perform measurement update processing on the prediction results of the receiver's position and clock bias based on the pseudorange observation set of the observed satellite, determine the pseudorange residual vector of the observed satellite based on the measurement update results, and identify abnormal pseudorange based on the pseudorange residual vector and the abnormal pseudorange identification strategy.

[0056] As can be seen, this application proposes and implements a carrier-assisted abnormal pseudorange identification scheme. The observation accuracy of carrier observation information is much higher than that of pseudorange observation. At the same time, the distortion rate of carrier observation information when entering a complex signal environment is much smaller than that of pseudorange. This application uses carrier observation information as an aid for abnormal pseudorange identification, which enables GNSS receivers to quickly and accurately identify erroneous / abnormal pseudorange observations when entering a complex signal environment, avoiding negative impacts on positioning accuracy. At the same time, it does not increase the receiver hardware cost, avoiding a series of problems such as high receiver hardware cost, strong subjectivity, low positioning availability, and low detection probability that exist in known technologies when dealing with pseudorange measurement problems in complex signal environments. Attached Figure Description

[0057] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0058] Figure 1 This is a flowchart illustrating the abnormal pseudorange identification method provided in this application;

[0059] Figure 2 This is an exemplary detailed process for anomaly pseudorange identification based on the method of this application;

[0060] Figure 3 This is a structural diagram of the abnormal pseudorange identification device provided in this application. Detailed Implementation

[0061] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0062] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0063] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0064] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0065] This application discloses an abnormal pseudorange identification method, apparatus, computer-readable medium, and computer program product, which are used to solve the problem of abnormal pseudorange identification in complex signal environments based on carrier-assisted design, thereby providing technical support for high-precision positioning services based on GNSS, while avoiding at least some of the defects of known technologies in dealing with pseudorange measurement problems in such environments.

[0066] The applicant's research found that, in addition to providing pseudorange observations directly for positioning, the GNSS receiver signal processor can also provide carrier observation information based on the carrier tracking loop, including but not limited to carrier phase, carrier frequency, and Doppler. Furthermore, the observation accuracy of carrier observation information is much higher than that of pseudorange observation. For example, the observation accuracy of carrier phase and Doppler is two orders of magnitude higher than that of pseudorange. At the same time, the distortion rate of carrier observation information when entering complex signal environments is much lower than that of pseudorange. Based on this, this application proposes and implements a carrier-assisted abnormal pseudorange identification scheme. By using carrier observation information as an aid for abnormal pseudorange identification, the GNSS receiver can quickly and accurately identify erroneous pseudorange observations when entering complex signal environments, thus avoiding negative impacts on positioning accuracy.

[0067] See Figure 1 The flowchart shown illustrates the anomaly pseudorange identification method. The anomaly pseudorange identification method provided in this application includes the following processing flow:

[0068] Step 101: Obtain the preset carrier observation information obtained from carrier observation of the observation satellite.

[0069] This application primarily employs a least-squares estimation method. Based on the carrier phase difference between two adjacent epochs (k to k+1, where k is a positive integer), the receiver's position and clock bias increment between these two epochs are calculated. The results are then used to update the receiver's position and clock bias state over time, thus initially achieving receiver position and clock bias prediction (i.e., pre-hoc prediction). This prediction serves as the basis for subsequent anomaly pseudorange identification. However, due to limitations in the receiver's carrier locking capability, the effectiveness of the least-squares estimation method is not always guaranteed.

[0070] To address this issue, this application sets carrier observation conditions to determine whether the validity of the least squares estimation is satisfied. The set carrier observation conditions are, respectively, the carrier observation conditions required to guarantee the validity of the least squares estimation when the vector to be estimated is determined using the least squares estimation method. The vector to be estimated consists of the receiver's position increment and clock error increment.

[0071] The applicant found that when using the least squares estimation method to solve for the receiver's position increment and clock error increment, the basic condition for the least squares solution to hold is that there must be four (or more) continuously locked satellites and the carrier observations must not have carrier cycle slips. Accordingly, the above carrier observation conditions can be specifically set as follows: the number of observation satellites with continuously locked carriers and no carrier cycle slips is not less than 4.

[0072] Based on this, when carrier observation information is needed, it can be first determined whether the carrier-locked observation satellite meets the aforementioned carrier observation conditions. If the carrier observation conditions are met, the carrier phase observation values ​​of the observation satellite in the current two adjacent epochs (e.g., specifically obtaining the carrier phase observation values ​​of the current k to k+1 epochs) are obtained as auxiliary information to participate in the subsequent anomaly pseudorange identification process. If the carrier observation conditions are not met, the Doppler observation information of the observation satellite in the current epoch is obtained, such as obtaining the Doppler observation information of the current k+1 epoch. The Doppler observation information is then used as alternative auxiliary information for the failure to meet the carrier observation conditions and participates in the subsequent anomaly pseudorange identification process.

[0073] Step 102: Based on the acquired preset carrier observation information, perform prediction processing on the receiver's position and clock bias to obtain the prediction results of the receiver's position and clock bias.

[0074] In particular, for the two cases of whether the carrier-locked observation satellite meets the carrier observation conditions, the position and clock bias of the receiver can be predicted by using the carrier phase observation values ​​of the observation satellite in the current two adjacent epochs, or the Doppler observation information of the observation satellite in the current epoch.

[0075] In this application embodiment, a receiver state prediction model is pre-built to predict the receiver position and clock bias in the above two cases. Optionally, the receiver state prediction model is a receiver Kalman filter model built based on receiver position, velocity, clock bias and clock drift.

[0076] Specifically, the Kalman filter model for the receiver is constructed as follows:

[0077] The state vector is defined as:

[0078] X = [rv δT δf] T (1)

[0079] In equation (1), r represents the position coordinates of the receiver in the ECEF coordinate system, v represents the velocity vector of the receiver in the ECEF coordinate system, and δT and δf represent the clock error and clock drift of the receiver, respectively. ECEF is an abbreviation for Earth Centered Earth Fixed, which represents the Earth-centered coordinate system, or simply the geocentric coordinate system.

[0080] The state covariance matrix is:

[0081]

[0082] In equation (2), P r P represents the variance matrix of the receiver's position change information; v P represents the variance matrix of the receiver's velocity variation information. T P represents the variance matrix of the clock bias variation of the receiver; f P represents the variance matrix of the receiver's clock drift variation; rv P represents the covariance matrix of the changes in position and velocity of the receiver; rT P represents the covariance matrix of the receiver's position change and clock error change; rf P represents the covariance matrix of the receiver's position change and clock drift change; vT P represents the covariance matrix of the receiver's velocity variation and clock bias variation; vf P represents the covariance matrix of the receiver's velocity variation and clock drift variation; Tf Let P be the covariance matrix of the clock bias variation and clock drift variation of the receiver. Since P is a real symmetric matrix, its upper triangular part is ignored here.

[0083] The following further explains the implementation process of predicting receiver position and clock error using the corresponding carrier observation information based on the constructed receiver state prediction model in the above two cases.

[0084] (I) Prediction of receiver position and clock bias based on carrier phase observations

[0085] In this case, the receiver position and clock bias are predicted based on the carrier phase observations of the two adjacent epochs, i.e., epochs k to k+1. The prediction process includes:

[0086] 11) Based on the carrier phase observations of the two adjacent epochs, the position increment and clock error increment of the receiver are determined using the least squares estimation method.

[0087] The observation model for the carrier phase of satellite s is as follows:

[0088]

[0089] In equation (3), r represents the position coordinates of satellite s in epoch k. k This represents the position coordinates of the receiver at epoch k. δT represents the satellite clock bias of satellite s at epoch k. k I represents the clock bias of the receiver at epoch k. s and T s Let N represent the ionospheric and tropospheric delays of satellite s, respectively. Both exhibit strong short-term correlations, and their short-term epoch variations are negligible. s This indicates the initial ambiguity cycle number of the carrier phase, which remains unchanged during continuous carrier locking. This indicates carrier phase measurement noise.

[0090] Let Δr k，k+1 =r k+1 -r k Let ΔδT be the epoch displacement vector of the receiver (i.e., the receiver position increment). k，k+1 =δT k+1 -δT k This represents the epoch clock bias change of the receiver (i.e., the receiver clock bias increment). Let be the change in the orbital position of satellite s. Let be the clock bias change of satellite s. and All of these can be obtained through navigation messages. By calculating the epoch difference in equation (3), ignoring measurement noise and the change in the receiver-satellite unit line-of-sight vector, the carrier phase epoch difference can be obtained:

[0091]

[0092] Further derivation yields:

[0093]

[0094] in, Specifically, representing the carrier phase increment of satellite s from epoch k to k+1, when carrier increment observations from four (or more) satellites are available, the least squares method can be used to model it as follows:

[0095]

[0096] In equation (6), Represents the least squares observation vector. Let G represent the least squares vector to be estimated, which consists of the receiver position increment and clock error increment, and G is the observation matrix.

[0097] Solving equation (6) using least squares yields the vector to be estimated:

[0098] x=[(GT G) -1 G T ]y (7)

[0099] Assuming carrier phase increment The measurement noise variance is According to the least squares estimation theory, the variance matrix of the estimation error of x can be obtained as follows:

[0100]

[0101] It should be noted that the above solution process can also use a weighted algorithm, but the form is similar, so it will not be described again here.

[0102] 12) Using the receiver position increment and clock error increment, update the optimal state estimate information of the receiver position and clock error in the previous epoch in the adjacent epoch, and obtain the prediction results of the receiver position and clock error in the subsequent epoch in the adjacent epoch.

[0103] Specifically, the state information of the sub-prediction model corresponding to the earlier epoch in two adjacent epochs can be obtained as the optimal state estimate information for the receiver position and clock bias in that earlier epoch. Then, using the receiver position increment and clock bias increment, the state time information of the sub-prediction model corresponding to the earlier epoch in two adjacent epochs is updated to obtain the prediction result of the receiver position and clock bias corresponding to the later epoch in two adjacent epochs.

[0104] It's easy to understand that in two adjacent epochs, the earlier epoch is epoch k, and the later epoch is epoch k+1, which is the current epoch.

[0105] The state information of the receiver in the previous epoch in two adjacent epochs includes: the result of extracting the sub-prediction model related to the receiver position and clock error from the receiver state prediction model constructed above, and the result of performing the optimal state estimation of the receiver's position and clock error in the previous epoch (k epoch) based on the extracted sub-prediction model.

[0106] Taking the receiver Kalman filter model constructed above as an example, the sub-filter X1 = [r δT] can be extracted from it. T As a sub-prediction model, its covariance matrix is:

[0107]

[0108] Let the sub-filter state information of epoch k be X. 1，k(Based on the state-optimal estimation of the receiver's position and clock difference at epoch k using X1), the state-time update can be performed using the least squares results obtained from equations (7)-(8), resulting in the predicted position and clock difference of the receiver at epoch k+1 (essentially a priori prediction results):

[0109]

[0110]

[0111] In equations (10)-(11), X 1，k，k+1 This represents the predicted state vector value of the sub-filter at epoch k+1. P represents the state error variance matrix of the sub-filter at epoch k. 1，k，k+1 This represents the predicted value of the state variance matrix of the sub-filter at epoch k+1.

[0112] Due to the observation noise variance of the carrier phase The position and clock error covariance obtained after the above calculation are relatively high, and can still be kept in line with P. 1k At a similar level.

[0113] (II) Doppler-based receiver position and clock bias prediction

[0114] In this case, the receiver position and clock bias are predicted based on the Doppler observation information of the observation satellite at the current epoch (k epoch). The prediction process includes:

[0115] 21) Update the state time of the pre-built receiver state prediction model to the current epoch to obtain the model state of the receiver state prediction model at the current epoch.

[0116] This embodiment updates the state time of the receiver state prediction model to the current epoch, i.e., to the k+1 epoch, by modeling the receiver's position as uniform linear motion, its velocity as a random process, and its clock bias and clock drift as second-order random processes.

[0117] Specifically, for the constructed receiver Kalman filter model, the position is modeled as uniform linear motion, the velocity as a random process, and the clock bias and clock drift as second-order random processes, thus obtaining the filter dynamic model:

[0118]

[0119] In equation (12), W v The random walk noise represents the velocity, and its noise density is Q. v W f Let Q represent the random walk noise of the clock, with noise density Q. f .

[0120] Discretizing equation (12) and letting t be the time interval from k to k+1 epochs, we can obtain the receiver prediction state at epoch k+1:

[0121]

[0122] The corresponding state covariance propagation model (i.e., the prediction result of the state covariance matrix at epoch k+1) is as follows:

[0123]

[0124] In the above formula, Q k The covariance matrix representing the random walk noise in the state equation has the following form:

[0125]

[0126] 22) Use the Doppler observation information of the observation satellite at the current epoch to measure and update the receiver state prediction model at the current epoch.

[0127] The observation model for Doppler observations of satellite s is as follows (ignoring the change in the satellite's unit line-of-sight vector between epochs k and k+1):

[0128]

[0129] In equation (16), v s δf represents the instantaneous velocity of satellite s. s The clock drift of satellite 's' can be obtained from the navigation message. This indicates Doppler observation noise. This represents the position coordinates of satellite s in the k-epoch.

[0130] After linearizing the above equation, the Kalman observation model (Doppler vector observation model) is further constructed as follows:

[0131]

[0132] Based on this formula, the receiver state prediction model (such as the Kalman filter model of the receiver) is updated by measurement at the current epoch (k+1), resulting in the Doppler measurement update model of the receiver state prediction model. The measurement update process is as follows:

[0133] X k+1 =X k，k+1 +K k+1 (Z d，k+1 -H d X k，k+1 (18)

[0134] Pk+1 =(IK k+1 H)P k，k+1 (19)

[0135] in,

[0136] 23) Extract the sub-prediction model related to the receiver's position and clock error from the measurement update results of the receiver's state prediction model at the current epoch, and obtain the prediction results of the receiver's position and clock error at the current epoch based on the sub-prediction model.

[0137] Specifically, the sub-filter X1 = [r δT] can be extracted from the results obtained after the above measurement update. T , It will be used as a sub-prediction model in subsequent steps.

[0138] The receiver position and clock bias prediction process based on carrier phase observation information or Doppler observation information can be found in the following reference. Figure 2 This application provides a detailed exemplary process for identifying anomalous pseudoranges based on the method described in this application. Two Kalman time update methods are provided: the preferred method uses carrier phase epoch differential to directly update the receiver position and clock bias to the current epoch; the alternative is to use dynamic equations for time updates to the current epoch, supplemented by Doppler measurement updates, to improve the prediction accuracy of position and clock bias to a level comparable to the previous epoch. Both methods can transfer the relatively accurate position solution from the adjacent previous epoch to the current epoch, using this as a basis for observing and filtering anomalous pseudoranges, thereby improving the accuracy and efficiency of the filtering process.

[0139] Step 103: Perform measurement update processing on the predicted position and clock error of the receiver based on the pseudorange observation set of the observation satellite to obtain the measurement update result of the predicted result.

[0140] The predicted receiver position and clock bias obtained through the above steps are essentially pre-predicted results for the receiver position and clock bias. This step updates the measurements based on the pseudorange observation set of the observation satellite to obtain the post-predicted results for the receiver position and clock bias. In other words, the measurement update results obtained in this step are essentially post-predicted results for the receiver position and clock bias.

[0141] The measurement update process is described in detail below:

[0142] The pseudorange observation model for satellite s is as follows:

[0143]

[0144] In equation (20), To represent pseudorange measurement noise, let δr = r k+1 -r k，k+1 The prediction error for the receiver position is δδT=δT k+1 -δT k，k+1 Substituting the receiver clock bias prediction error into the above equation and linearizing it, we can obtain the pseudorange observation information as follows:

[0145]

[0146] in, Based on the above formula, the Kalman observation model (i.e., pseudorange innovation vector) is constructed as follows:

[0147]

[0148] Then, following the procedure below, the predicted results of receiver position and clock bias (pre-anniversary prediction results) are updated using pseudorange-based measurements to obtain the updated measurement results of the predicted results of receiver position and clock bias at the current epoch (k+1) (post-anniversary prediction results):

[0149] X 1，k+1 =X 1，k，k+1 +δX1=X 1，k，k+1 +K ρ，k+1 Z ρ，k+1 (twenty three)

[0150]

[0151] Subsequently, the measurement update result (post-hoc prediction result) of the predicted receiver position and clock error at the current epoch (k+1) can be used as the optimal state estimate information of the receiver at the current epoch position and clock error, and participate in the processing of the next adjacent epoch.

[0152] Step 104: Determine the pseudorange residual vector of the observed satellite based on the measurement update results.

[0153] Specifically, the pseudorange prediction value of the observed satellite can be determined based on the measurement update results of the receiver's prediction results at the current epoch position and clock error. This prediction value is essentially the post-hoc pseudorange prediction value of the observed satellite. Then, the pseudorange residual vector of the observed satellite can be determined based on the pseudorange observation value and the pseudorange prediction value (by performing a difference operation between the two).

[0154] Step 105: Identify the abnormal pseudorange observation values ​​in the pseudorange observation set of the observed satellite based on the pseudorange residual vector and the preset abnormal pseudorange identification strategy.

[0155] The pseudorange observation set of the observation satellite includes all pseudorange observations of the observation satellite in the current epoch (k+1). After obtaining the pseudorange residual vector corresponding to the pseudorange observation set, the abnormal pseudorange observations in the pseudorange observation set of the observation satellite are further identified based on the individual pseudorange residuals in the pseudorange residual vector.

[0156] Optionally, this recognition process can be divided into two aspects: global detection and local detection.

[0157] (I) Global Detection

[0158] It is used to determine whether there are abnormal pseudorange observation values ​​in the corresponding pseudorange observation set of the observed satellite, based on the preset global detection statistics corresponding to each pseudorange residual in the pseudorange residual vector.

[0159] Optionally, the preset global detection statistics can be:

[0160] For global detection, this embodiment sets a first threshold as a protection threshold, denoted as T. th .

[0161] In global detection, the global detection statistics corresponding to each pseudorange residual in the pseudorange residual vector can be calculated. It also determines whether the global detection statistic is below the protection threshold. If it is, that is, T < T th This indicates that the observation satellite has no abnormal observations in the pseudorange observations at the current epoch (k+1). If the values ​​are not lower than the specified values, then T≥T th If the result is negative, it indicates the presence of an anomaly, and subsequent local detection will continue to identify the anomaly.

[0162] (II) Local Detection

[0163] If abnormal pseudorange observations are found in the pseudorange observation set of the observed satellite based on global detection processing, the abnormal pseudorange observations in the pseudorange observation set are further identified based on each pseudorange residual in the pseudorange residual vector.

[0164] Optionally, a second threshold can be set as the detection threshold, and pseudoranges with residuals higher than the threshold can be identified as abnormal pseudoranges.

[0165] In summary, this application proposes and implements a carrier-assisted abnormal pseudorange identification scheme. The observation accuracy of carrier observation information is much higher than that of pseudorange observation. At the same time, the distortion rate of carrier observation information when entering a complex signal environment is much smaller than that of pseudorange. This application uses carrier observation information as an aid for abnormal pseudorange identification, which enables GNSS receivers to quickly and accurately identify erroneous / abnormal pseudorange observations when entering a complex signal environment, thus avoiding negative impacts on positioning accuracy.

[0166] Therefore, this application facilitates the timely and rapid identification and elimination of abnormal pseudorange observations when a GNSS receiver transitions from a favorable signal environment to a complex signal environment, thereby improving the positioning accuracy and availability of the GNSS receiver. Simultaneously, it avoids a series of problems inherent in known technologies when dealing with pseudorange measurement in complex signal environments, such as high receiver hardware costs, strong subjectivity, low positioning availability, and low detection probability.

[0167] The following embodiment provides a preferred implementation of anomaly pseudorange identification based on global detection and local detection.

[0168] In this implementation, for local detection, the set second threshold is specifically used as the excessive anomaly detection threshold, expressed as:

[0169] If global detection determines that the observing satellite has abnormal observations at the current epoch, the following processing can be used to further identify abnormal pseudoranges based on a set second threshold:

[0170] 31) Determine the first pseudorange residual that is higher than the preset detection threshold among all pseudorange residuals included in the pseudorange residual vector, identify the pseudorange observation value corresponding to the first pseudorange residual in the pseudorange observation set as the first abnormal pseudorange observation value of the observation satellite in the current epoch; remove the first abnormal pseudorange observation value from the pseudorange observation set to obtain the remaining observation set; and perform the above-mentioned global detection processing on the remaining observation set and its corresponding pseudorange residual vector.

[0171] The preset detection threshold here is the second threshold, which is also the set threshold for detecting excessive anomalies.

[0172] Specifically, it can iterate through and detect all pseudorange residuals in the pseudorange residual vector. All satisfied The pseudorange is removed as an anomaly observation. Then, the remaining observation set after the pseudorange is removed is used to reconstruct the Kalman observation. The pseudorange measurement is updated according to equations (23)-(24), and the pseudorange residual vector is updated based on this. Then, for the remaining observation set and its corresponding pseudorange residual vector, global detection is performed again according to the global detection logic.

[0173] 32) If, based on global detection processing, it is determined that there are anomalous pseudorange observations in the remaining observation set, the observation with the largest pseudorange residual in the remaining observation set is identified as the second anomalous pseudorange observation of the observation satellite in the current epoch; the second anomalous pseudorange observation is removed from the remaining observation set; the remaining observation set is updated to the observation set obtained after removing the second anomalous pseudorange observation, and the process is repeated until the global detection processing is performed on the remaining observation set and its corresponding pseudorange residual vector, until it is determined based on global detection processing that there are no anomalous pseudorange observations in the remaining observation set.

[0174] That is, iterate through all pseudorange residuals in the pseudorange residual vector corresponding to the remaining observation set, find the maximum value and remove the corresponding pseudorange as an abnormal observation, update the remaining observation set based on the remaining pseudorange observation set after removal, and reconstruct the Kalman observations according to equations (23)-(24) to update the pseudorange measurement, and update the pseudorange residual vector based on this, and then perform global detection again according to the global detection logic. Iterate this process until there are no abnormal pseudorange observation values ​​in the remaining observation set.

[0175] The first and second anomalous pseudorange observations identified in the above process are both anomalous pseudorange observations.

[0176] It is worth noting that typical receiver autonomous integrity detection algorithms can only identify one erroneous satellite (abnormal pseudorange) in a single iteration, meaning they can only perform step 32) and not step 31). Otherwise, they may fall into a local optimum, leading to identification errors. This approach obviously suffers from slow identification speed and low efficiency. In contrast, this application, aided by relatively accurate position and clock bias, can significantly reduce the probability of this error. It can first use a coarser threshold for batch detection (step 31), and then further refine the threshold through iterative operations provided by step 32). Thus, this application solves the aforementioned shortcomings of known technologies, effectively improving the detection and identification rate of abnormal pseudoranges while ensuring identification accuracy.

[0177] Corresponding to the above-described abnormal pseudorange identification method, this application also provides an abnormal pseudorange identification device, the structure of which is as follows: Figure 3 As shown, it includes:

[0178] The acquisition unit 10 is used to acquire preset carrier observation information obtained from carrier observation of the observation satellite;

[0179] The prediction unit 20 is used to perform prediction processing on the position and clock error of the receiver based on the preset carrier observation information, and obtain the prediction results of the receiver position and clock error;

[0180] Measurement update unit 30 is used to perform measurement update processing on the prediction result based on the pseudorange observation set of the observation satellite to obtain the measurement update result of the prediction result;

[0181] The determination unit 40 is used to determine the pseudorange residual vector of the observed satellite based on the measurement update result;

[0182] The identification unit 50 is used to identify abnormal pseudorange observation values ​​in the pseudorange observation set of the observation satellite according to the pseudorange residual vector and the preset abnormal pseudorange identification strategy.

[0183] In one embodiment, the acquisition unit 10 is specifically used for:

[0184] Determine whether the carrier-locked observation satellite meets the carrier observation conditions; the carrier observation conditions are the carrier observation conditions required to ensure the effectiveness of the least squares estimation when the vector to be estimated is determined using the least squares estimation method, and the vector to be estimated consists of the receiver's position increment and clock error increment;

[0185] If so, obtain the carrier phase observation values ​​of the observation satellite in the current two adjacent epochs;

[0186] If not, obtain the Doppler observation information of the observation satellite at the current epoch.

[0187] In one embodiment, the carrier observation conditions include: the number of observation satellites with continuous carrier lock and no carrier cycle slip is not less than 4.

[0188] In one embodiment, the preset carrier observation information includes the carrier phase observation values ​​of the observation satellite in the current two adjacent epochs; the prediction unit 20 is specifically used for:

[0189] Based on the carrier phase observations of the two current adjacent epochs, the position increment and clock error increment of the receiver are determined using the least squares estimation method;

[0190] Using the receiver position increment and clock error increment, the optimal state estimate information of the receiver position and clock error in the preceding epoch of the adjacent epoch is updated to obtain the prediction result of the receiver position and clock error in the following epoch of the adjacent epoch.

[0191] In one embodiment, when the prediction unit 20 updates the optimal state estimate information of the receiver position and clock bias in the preceding epoch of the adjacent epoch using the receiver position increment and clock bias increment to obtain the prediction result of the receiver position and clock bias in the subsequent epoch of the adjacent epoch, it is specifically used for:

[0192] The receiver's sub-prediction model state information corresponding to the previous epoch is obtained as the optimal state estimation information of the receiver's position and clock error in the previous epoch. The sub-prediction model state information includes: the result of extracting sub-prediction models related to the receiver's position and clock error from the pre-built receiver state prediction model, and performing optimal state estimation of the receiver's position and clock error in the previous epoch based on the extracted sub-prediction models.

[0193] The state time of the sub-prediction model is updated using the receiver position increment and clock error increment to obtain the prediction results of the receiver position and clock error in the later epoch.

[0194] In one embodiment, the preset carrier observation information includes Doppler observation information of the observation satellite at the current epoch; the prediction unit 20 is specifically used for:

[0195] Update the state time of the pre-built receiver state prediction model to the current epoch to obtain the model state of the receiver state prediction model at the current epoch.

[0196] Using the Doppler observation information of the observation satellite at the current epoch, the receiver state prediction model is measured and updated to correspond to the model state at the current epoch.

[0197] From the measurement update results of the receiver state prediction model at the current epoch, extract the sub-prediction model related to the receiver position and clock error, and obtain the prediction results of the receiver position and clock error at the current epoch based on the sub-prediction model.

[0198] In one embodiment, the receiver state prediction model is a Kalman filter model constructed based on the receiver's position, velocity, clock bias, and clock drift;

[0199] The sub-prediction model is a sub-filter model corresponding to the receiver position and clock error, obtained from the receiver's Kalman filter model.

[0200] In one embodiment, the prediction unit 20, when updating the state time of the pre-built receiver state prediction model to the current epoch, specifically performs the following:

[0201] By modeling the receiver's position as uniform linear motion, its velocity as a random process, and its clock bias and clock drift as second-order random processes, the state time of the receiver state prediction model is updated to the current epoch.

[0202] In one embodiment, the determining unit 40 is specifically used for:

[0203] Based on the measurement update results, determine the pseudorange prediction value of the observed satellite;

[0204] Based on the pseudorange prediction and pseudorange observation values ​​of the observed satellite, determine the pseudorange residual vector of the observed satellite.

[0205] In one embodiment, the identification unit 50 is specifically used for:

[0206] A global detection process is performed on the pseudorange residual vector and the pseudorange observation set. The global detection process includes: determining whether there are abnormal pseudorange observation values ​​of the observation satellite in the corresponding pseudorange observation set based on the preset global detection statistics corresponding to each pseudorange residual in the pseudorange residual vector.

[0207] If the global detection process determines that there are abnormal pseudorange observations in the pseudorange observation set, the abnormal pseudorange observations in the pseudorange observation set are identified according to each pseudorange residual in the pseudorange residual vector.

[0208] In one embodiment, the pseudorange observation set includes each pseudorange observation value of the observed satellite at the current epoch; the identification unit 50, when identifying abnormal pseudorange observation values ​​in the pseudorange observation set based on each pseudorange residual in the pseudorange residual vector, is specifically used for:

[0209] The first pseudorange residual that is higher than a preset detection threshold among the pseudorange residuals contained in the pseudorange residual vector is identified, and the pseudorange observation value corresponding to the first pseudorange residual is identified as the first abnormal pseudorange observation value of the observation satellite in the current epoch.

[0210] The first anomalous pseudorange observation value is removed from the pseudorange observation set to obtain the remaining observation set;

[0211] The global detection process is performed on the remaining observation set and its corresponding pseudorange residual vector;

[0212] If, based on the global detection processing, it is determined that there are anomalous pseudorange observations in the residual observation set, the observation with the largest pseudorange residual in the residual observation set is identified as the second anomalous pseudorange observation of the observation satellite in the current epoch.

[0213] Remove the second anomalous pseudorange observation from the remaining observation set;

[0214] The remaining observation set is updated to the observation set obtained after removing the second abnormal pseudorange observation value, and the process is repeated until the global detection processing is performed on the remaining observation set and its corresponding pseudorange residual vector, until it is determined that there are no abnormal pseudorange observation values ​​in the remaining observation set based on the global detection processing.

[0215] The abnormal pseudorange identification device provided in this application is relatively simple to describe because it corresponds to the abnormal pseudorange identification method provided in the above method embodiments. For related similarities, please refer to the description of the above method embodiments, which will not be described in detail here.

[0216] This application also provides a computer-readable medium having a computer program stored thereon, the computer program comprising program code for performing the abnormal pseudorange identification method as provided in the above method embodiments.

[0217] In the context of this application, a computer-readable medium (machine-readable medium) can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0218] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0219] The aforementioned computer-readable medium may be contained within an electronic device or may exist independently without being assembled into an electronic device.

[0220] This application also provides a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the abnormal pseudorange identification method as provided in the above method embodiments.

[0221] Specifically, according to embodiments of this application, the processes described in the above-described reference flowcharts can be implemented as computer software programs. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments of this application.

[0222] It should be noted that although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

[0223] While several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0224] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. An abnormal pseudo-range identification method, characterized in that, The method comprises: acquiring preset carrier observation information obtained by carrier observation on an observation satellite; wherein the acquisition of the preset carrier observation information obtained by carrier observation on the observation satellite comprises: determining whether a carrier-locked observation satellite meets a carrier observation condition; the carrier observation condition is a carrier observation condition required to ensure the effectiveness of least squares estimation in the case of determining a to-be-estimated vector using a least squares estimation method, the to-be-estimated vector being composed of a position increment of a receiver and a clock bias increment; if yes, acquiring carrier phase observation values of the observation satellite at the current two adjacent epochs; if no, acquiring Doppler observation information of the observation satellite at the current epoch; performing prediction processing on the position and the clock bias of the receiver according to the preset carrier observation information to obtain a prediction result of the position and the clock bias of the receiver; wherein the prediction processing on the position and the clock bias of the receiver according to the preset carrier observation information comprises: performing prediction processing on the position and the clock bias of the receiver by using the carrier phase observation values of the observation satellite at the current two adjacent epochs or by using the Doppler observation information of the observation satellite at the current epoch; performing measurement update processing on the prediction result according to a pseudorange observation set of the observation satellite to obtain a measurement update result of the prediction result; determining a pseudorange residual vector of the observation satellite according to the measurement update result; identifying an abnormal pseudorange observation value in the pseudorange observation set of the observation satellite according to the pseudorange residual vector and a preset abnormal pseudorange identification strategy.

2. The method of claim 1, wherein, The carrier observation condition comprises: the number of observation satellites that are continuously locked and have not occurred carrier cycle slip is not less than 4.

3. The method of claim 1, wherein, The preset carrier observation information comprises carrier phase observation values of the observation satellite at the current two adjacent epochs; and the prediction processing on the position and the clock bias of the receiver according to the preset carrier observation information to obtain a prediction result of the position and the clock bias of the receiver comprises: determining a position increment and a clock bias increment of the receiver using a least squares estimation method according to the carrier phase observation values of the current two adjacent epochs; updating state optimal estimation information of the position and the clock bias of the receiver in a former epoch of the adjacent epochs using the position increment and the clock bias increment of the receiver to obtain a prediction result of the position and the clock bias of the receiver corresponding to a latter epoch of the adjacent epochs.

4. The method of claim 3, wherein, The updating of the state optimal estimation information of the position and the clock bias of the receiver in the former epoch of the adjacent epochs using the position increment and the clock bias increment of the receiver to obtain the prediction result of the position and the clock bias of the receiver corresponding to the latter epoch of the adjacent epochs comprises: acquiring sub-prediction model state information of the receiver corresponding to the former epoch as the state optimal estimation information of the position and the clock bias of the receiver in the former epoch; the sub-prediction model state information comprises: a result obtained by performing state optimal estimation on the position and the clock bias of the receiver in the former epoch based on a sub-prediction model extracted from a receiver state prediction model constructed in advance and related to the position and the clock bias of the receiver. The state time of the receiver state prediction model is updated to the current epoch, to obtain a model state of the receiver state prediction model corresponding to the current epoch.

5. The method of claim 1, wherein, The preset carrier observation information comprises Doppler observation information of an observation satellite at the current epoch. The prediction processing of the position and the clock bias of the receiver according to the preset carrier observation information comprises: The state time of the receiver state prediction model is updated to the current epoch, to obtain a model state of the receiver state prediction model corresponding to the current epoch. The model state of the receiver state prediction model corresponding to the current epoch is measured and updated using the Doppler observation information of the observation satellite at the current epoch. A sub-prediction model related to the position and the clock bias of the receiver is extracted from the measurement update result of the model state of the receiver state prediction model corresponding to the current epoch, to obtain a prediction result of the position and the clock bias of the receiver corresponding to the current epoch based on the sub-prediction model.

6. The method of claim 5, wherein, The receiver state prediction model is a Kalman filter model based on the position, the velocity, the clock bias and the clock drift of the receiver. The sub-prediction model is a sub-filter model corresponding to the position and the clock bias of the receiver based on the Kalman filter model of the receiver.

7. The method of claim 6, wherein, The state time of the receiver state prediction model is updated to the current epoch, to obtain a model state of the receiver state prediction model corresponding to the current epoch. The state time of the receiver state prediction model is updated to the current epoch by modeling the position of the receiver as uniform linear motion, the velocity as a random process, and the clock bias and the clock drift as a second-order random process.

8. The method of claim 1, wherein, The pseudo-range residual error vector of the observation satellite is determined according to the measurement update result, comprising: The pseudo-range prediction value of the observation satellite is determined according to the measurement update result. The pseudo-range residual error vector of the observation satellite is determined according to the pseudo-range prediction value and the pseudo-range observation value.

9. The method of claim 1, wherein, The abnormal pseudo-range observation value in the pseudo-range observation set of the observation satellite is identified according to the pseudo-range residual error vector and a preset abnormal pseudo-range identification strategy, comprising: Global detection processing is performed on the pseudo-range residual error vector and the pseudo-range observation set; the global detection processing comprises: determining whether there is an abnormal pseudo-range observation value in the corresponding pseudo-range observation set of the observation satellite according to a preset global detection statistic quantity corresponding to each pseudo-range residual error in the pseudo-range residual error vector; In a case where it is determined that there is an abnormal pseudo-range observation value in the pseudo-range observation set based on the global detection processing, the abnormal pseudo-range observation value in the pseudo-range observation set is identified according to each pseudo-range residual error in the pseudo-range residual error vector.

10. The method of claim 9, wherein, The pseudo-range observation set comprises each pseudo-range observation value of the observation satellite at the current epoch; the abnormal pseudo-range observation value in the pseudo-range observation set is identified according to each pseudo-range residual error in the pseudo-range residual error vector, comprising: A first pseudo-range residual error higher than a preset detection threshold is determined from each pseudo-range residual error contained in the pseudo-range residual error vector, and a pseudo-range observation value corresponding to the first pseudo-range residual error is identified as a first abnormal pseudo-range observation value of the observation satellite at the current epoch; The first abnormal pseudo-range observation value is eliminated from the pseudo-range observation set, to obtain a remaining observation set. performing the global detection processing on the residual observation set and a corresponding pseudo-range residual error vector thereof; in a case where it is determined based on the global detection processing that there is an abnormal pseudo-range observation value in the residual observation set, identifying an observation value corresponding to a largest pseudo-range residual error in the residual observation set as a second abnormal pseudo-range observation value of the observation satellite at a current epoch; eliminating the second abnormal pseudo-range observation value from the residual observation set; updating the residual observation set to an observation set obtained after eliminating the second abnormal pseudo-range observation value, and looping to the step of performing the global detection processing on the residual observation set and a corresponding pseudo-range residual error vector thereof until it is determined based on the global detection processing that there is no abnormal pseudo-range observation value in the residual observation set.

11. An abnormal pseudo-range identification device characterized by comprising: comprise: an acquisition unit, configured to acquire preset carrier observation information obtained by performing carrier observation on an observation satellite; the acquisition unit is specifically configured to determine whether a carrier-locked observation satellite satisfies a carrier observation condition; the carrier observation condition is a carrier observation condition required to ensure the effectiveness of least squares estimation in a case where a least squares estimation method is used to determine a to-be-estimated vector composed of a position increment of a receiver and a clock error increment; if yes, carrier phase observation values of the observation satellite at two adjacent epochs are acquired; and if no, Doppler observation information of the observation satellite at a current epoch is acquired; a prediction unit, configured to perform prediction processing on a position and a clock error of the receiver according to the preset carrier observation information to obtain a prediction result of the position and the clock error of the receiver; the prediction unit is specifically configured to perform prediction processing on the position and the clock error of the receiver by using the carrier phase observation values of the observation satellite at the two adjacent epochs or by using the Doppler observation information of the observation satellite at the current epoch; a measurement update unit, configured to perform measurement update processing on the prediction result according to a pseudo-range observation set of the observation satellite to obtain a measurement update result of the prediction result; a determination unit, configured to determine a pseudo-range residual error vector of the observation satellite according to the measurement update result; an identification unit, configured to identify an abnormal pseudo-range observation value in the pseudo-range observation set of the observation satellite according to the pseudo-range residual error vector and a preset abnormal pseudo-range identification strategy.

12. A computer readable medium characterized by having a computer program stored thereon, the computer program comprising program code for performing the method of any one of claims 1-10.

13. A computer program product, characterised in that, comprising a computer program carried on a non-transitory computer readable medium, the computer program comprising program code for performing the method of any one of claims 1-10.

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