Integrated navigation deep coupling carrier phase quality control and adaptive robustness method

Abstract

The application provides a kind of integrated navigation deep coupling carrier phase quality control and adaptive robustness method, comprising: for the complex situation of frequent discontinuous scene download wave phase precision difference, tracking discontinuous, and carrier phase tracking unstable cycle slip, respectively through carrier phase prediction and cycle slip detection method, effectively improve the carrier phase observation value quality of deep integrated receiver.For the observation quality of GNSS is susceptible to interference and the error divergence performance of INS, using improved adaptive robustness Kalman filtering algorithm, effectively improve the integrated navigation positioning precision.The application effectively weakens the influence of abnormal model deviation and abnormal value, improves the positioning precision of integrated navigation system.

Description

Technical Field

[0001] This invention relates to the field of integrated navigation technology, and in particular to a method for deep coupled carrier phase quality control and adaptive robustness in integrated navigation. Background Technology

[0002] In navigation and positioning systems, due to the fragility of carrier phase tracking, continuous and reliable centimeter-level positioning accuracy can currently only be guaranteed in open environments with good observation conditions. Complex urban environments, such as tree-lined roads, high-rise canyons, overpasses, and tunnels, frequently weaken, obstruct, and reflect GNSS signals, causing a sharp decline in GNSS observation quality. This manifests as deterioration in pseudorange accuracy, frequent carrier phase interruptions, and even complete unavailability. This leads to poorer GNSS positioning accuracy, and the continuity and availability of positioning cannot be guaranteed. Inertial navigation offers high short-time positioning accuracy and is not limited by the environment. However, due to errors in the inertial measurement unit (INS), such as device noise, zero bias, scale factor, axis deviation, and nonlinearity, the positioning accuracy of INS alone diverges exponentially over time. Combining GNSS and INS can provide more accurate, continuous, and reliable navigation information (including position, velocity, and attitude).

[0003] In carrier tracking, increasing the coherent integration time and compressing the loop bandwidth are common methods to improve pseudo-code and carrier phase quality. However, these methods can only improve the accuracy of pseudorange observations and have limited effect; they cannot improve carrier phase observations. Summary of the Invention

[0004] This invention provides a method for integrated navigation deep coupling carrier phase quality control and adaptive robustness to overcome the deficiencies in the prior art.

[0005] In a first aspect, the present invention provides a method for integrated navigation deep-coupled carrier phase quality control and adaptive robustness, comprising:

[0006] The carrier phase prediction method and cycle slip detection method are used to improve the quality of the carrier phase observations of the receiver in the integrated navigation, and the improved carrier phase observations are obtained.

[0007] An improved adaptive robust Kalman filter algorithm is used to enhance the positioning accuracy of integrated navigation, resulting in a more accurate integrated navigation positioning value.

[0008] According to the present invention, a method for deep coupled carrier phase quality control and adaptive robustness in integrated navigation is provided, wherein the carrier phase prediction method includes:

[0009] Identify the normally tracking satellite and use the normally tracking satellite to calculate the Doppler of the receiver crystal oscillator clock drift;

[0010] Based on the common-mode properties of the impact of the receiver crystal oscillator drift on all satellite channels, the Doppler estimate of the crystal oscillator drift of the phase prediction channel is obtained, and the predicted Doppler value of the phase prediction channel is obtained by combining the motion Doppler estimate of the phase prediction channel.

[0011] When any satellite is blocked and enters the phase prediction mode, the carrier numerically controlled oscillator is updated using the predictive Doppler. The Doppler output of the carrier numerically controlled oscillator is integrated to obtain the carrier phase prediction value.

[0012] According to the present invention, a method for integrated navigation deep coupled carrier phase quality control and adaptive robustness is provided, wherein the cycle slip detection method includes:

[0013] Using the high-precision positioning results obtained by fusing RTK, inertial navigation and odometry, the change in satellite-to-ground distance in adjacent epochs is obtained;

[0014] The distance change measured by the carrier phase was detected, and the following was obtained:

[0015]

[0016] in, This represents the distance change measured by the carrier phase. c·Δδt represents the change in distance between the satellite and Earth. u,k,k-n Represents the local clock difference, ΔN k,k-n Δε represents cycle slip, and Δε represents phase error;

[0017] By selecting a reference satellite and performing inter-satellite subtraction to cancel out local clock errors, we obtain:

[0018]

[0019] According to the present invention, a method for integrated navigation deep coupled carrier phase quality control and adaptive robustness is provided, wherein the improved adaptive robust Kalman filter algorithm includes:

[0020] The prediction residual is calculated using GNSS observation information from the Global Navigation Satellite System, and the estimated observation vector covariance matrix is ​​obtained based on the prediction residual.

[0021] The estimated observation vector covariance matrix is ​​compared with the manually set observation vector covariance matrix to obtain a comparison value of the observation vector covariance matrix. If the comparison value of the observation vector covariance matrix is ​​greater than 0, it is determined that there is abnormal GNSS, and the observation vector covariance matrix is ​​adjusted by a scaling factor. Otherwise, there is no need to adjust the observation vector covariance matrix.

[0022] Based on the Mahalanobis distance of the predicted residual, disturbance anomalies are detected, and an adaptive factor is obtained to expand the covariance matrix of the observation vector.

[0023] According to the present invention, a method for deep coupled carrier phase quality control and adaptive robustness in integrated navigation is provided, which calculates prediction residuals using GNSS observation information and obtains an estimated observation vector covariance matrix based on the prediction residuals, including:

[0024]

[0025] Where k-1 and k represent time t respectively. k-1 and t k , Indicates t k The predicted state vector value at time t. Indicates the previous time t k-1 The optimal estimate of the state vector, Φ k,k-1 Indicates t k-1 to t k The state transition matrix at time t.

[0026] According to the present invention, a method for integrated navigation deep coupling carrier phase quality control and adaptive robustness compares the estimated observation vector covariance matrix with the manually set observation vector covariance matrix to obtain a comparison value of the observation vector covariance matrix. If the comparison value of the observation vector covariance matrix is ​​greater than 0, it is determined that there is abnormal GNSS, and the observation vector covariance matrix is ​​adjusted by a scaling factor; otherwise, no adjustment of the observation vector covariance matrix is ​​required. The method includes:

[0027] If t k The observation vector Z at time t is k The condition is valid, and the position accuracy factor is greater than the first preset value C1 or the satellite data N. sat If the value is less than the second preset value C2, and the RTK solution state is fixed, then calculate the current time t. k The predicted value P of the variance-covariance matrix of the state-optimal estimate k,k-1 ;

[0028]

[0029] in, To predict the residual, i.e. the innovation vector, Z k For t k The observation vector at time H k The observation matrix;

[0030]

[0031] in, For tk The covariance matrix of the observation vector at time estimate;

[0032]

[0033] Where, α k For t k The scaling factor for time.

[0034] According to the present invention, a method for deep coupled carrier phase quality control and adaptive robustness in integrated navigation is provided, which detects disturbance anomalies based on Mahalanobis distance of predicted residuals and obtains an adaptive factor to expand the covariance matrix of the observation vector, including:

[0035]

[0036] in, For t k The covariance matrix of the time-information vector;

[0037]

[0038] in, The threshold for the chi-square distribution;

[0039]

[0040] Where, β k (i) is the adaptive factor at time tk;

[0041]

[0042] Among them, K k For t k Time-matrix gain matrix;

[0043] Update system status x:

[0044]

[0045] in, For the previous moment t k The optimal estimate of the state vector;

[0046] Update the error covariance matrix of system state x:

[0047] P k =(IK k H k )P k,k-1 .

[0048] Secondly, the present invention also provides a combined navigation deeply coupled carrier phase quality control and adaptive robustness system, comprising:

[0049] The quality control module is used to improve the quality of the carrier phase observations of the receiver in the integrated navigation by using carrier phase prediction and cycle slip detection methods, so as to obtain the carrier phase observations with improved quality.

[0050] The adaptive robust module is used to improve the positioning accuracy of integrated navigation by utilizing an improved adaptive robust Kalman filter algorithm, and obtain the integrated navigation positioning value with improved accuracy.

[0051] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the integrated navigation deep coupled carrier phase quality control and adaptive robustness method as described above.

[0052] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the integrated navigation deep coupled carrier phase quality control and adaptive robustness method as described above.

[0053] The present invention provides a method for integrated navigation deep coupling carrier phase quality control and adaptive robustness, which improves the continuity of carrier phase through carrier phase prediction and cycle slip detection methods, effectively improving the carrier phase observation quality of deep integrated receivers. Furthermore, the adaptive robustness algorithm effectively weakens the influence of abnormal model bias and outliers in the measurement, thereby improving the positioning accuracy of a single-frequency GNSS integrated navigation system that is vulnerable and does not output observation vector covariance. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the integrated navigation deep coupling carrier phase quality control and adaptive robustness method provided by the present invention;

[0056] Figure 2 This is a block diagram of carrier phase prediction provided by the present invention;

[0057] Figure 3 This is a flowchart of the improved adaptive robust Kalman filter algorithm provided by the present invention;

[0058] Figure 4 This is a schematic diagram of the structure of the integrated navigation deep coupled carrier phase quality control and adaptive robust system provided by the present invention;

[0059] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0061] Figure 1 This is a flowchart illustrating the integrated navigation deep coupling carrier phase quality control and adaptive robustness method provided in this embodiment of the invention, as shown below. Figure 1 As shown, it includes:

[0062] Step 100: Using carrier phase prediction and cycle slip detection methods, the quality of the carrier phase observations of the receiver in the integrated navigation is improved to obtain the improved carrier phase observations;

[0063] Step 200: Use the improved adaptive robust Kalman filter algorithm to improve the positioning accuracy of the integrated navigation system and obtain the improved integrated navigation positioning value.

[0064] To achieve stable carrier phase tracking and high-precision receiver positioning in complex urban environments, this invention provides a method for quality control of GNSS carrier phase observations in complex urban environments and an improved adaptive robust Kalman filter algorithm for single-frequency GNSS / MEMS-IMU deep integrated navigation.

[0065] Specifically, the carrier phase prediction method uses the Doppler of the receiver crystal oscillator to solve the normal tracking satellite. Based on the common-mode nature of the impact of the receiver crystal oscillator's clock drift on all satellite channels, the estimated value of the crystal oscillator's clock Doppler of the phase prediction channel is obtained. Combined with the motion Doppler estimate of the phase prediction channel, the predicted Doppler value of the phase prediction channel is obtained, thereby improving the continuity of the carrier phase.

[0066] The cycle slip detection method uses the high-precision positioning results obtained by fusing RTK, inertial navigation, and odometry to obtain the distance change between satellite and ground in adjacent epochs. It selects a reference satellite to perform inter-satellite difference to cancel the influence of receiver clock error. Based on the inter-satellite difference formula, it determines whether there is a cycle slip value in the carrier phase observation value and repairs it, effectively improving the carrier phase observation value quality of the deep-combined receiver.

[0067] An improved adaptive robust Kalman filter algorithm is proposed. The prediction residual is calculated using GNSS observation information. The estimated observation vector covariance matrix is ​​obtained based on the prediction residual. It is compared with the manually set observation vector covariance matrix. If the estimated observation vector covariance matrix is ​​greater than the set observation vector covariance matrix, the set covariance matrix of the observation vectors is adjusted by a scaling factor to reduce the weight of GNSS in the Kalman filter and effectively weaken the influence of abnormal model bias and outliers in the measurement.

[0068] This invention addresses the challenges of poor carrier phase accuracy, discontinuous tracking, and unstable carrier phase tracking leading to cycle slips in frequently intermittent scenarios. It effectively improves the carrier phase observation quality of deep integrated receivers through carrier phase prediction and cycle slip detection methods. Furthermore, considering the susceptibility of GNSS observation quality to interference and the impact of INS error divergence performance, it utilizes an improved adaptive robust Kalman filter algorithm to effectively enhance the positioning accuracy of integrated navigation.

[0069] Based on the above embodiments, the carrier phase prediction method includes:

[0070] Identify the normally tracking satellite and use the normally tracking satellite to calculate the Doppler of the receiver crystal oscillator clock drift;

[0071] Based on the common-mode properties of the impact of the receiver crystal oscillator drift on all satellite channels, the Doppler estimate of the crystal oscillator drift of the phase prediction channel is obtained, and the predicted Doppler value of the phase prediction channel is obtained by combining the motion Doppler estimate of the phase prediction channel.

[0072] When any satellite is blocked and enters the phase prediction mode, the carrier numerically controlled oscillator is updated using the predictive Doppler. The Doppler output of the carrier numerically controlled oscillator is integrated to obtain the carrier phase prediction value.

[0073] Specifically, such as Figure 2 As shown, the Doppler of the receiver crystal oscillator is calculated using the normally tracked satellite. Based on the common-mode nature of the impact of the receiver crystal oscillator's clock drift on all satellite channels, the estimated value of the crystal oscillator's clock drift Doppler for the phase prediction channel is obtained. Then, the predicted Doppler value for the phase prediction channel is obtained by combining the motion Doppler estimate of the phase prediction channel.

[0074] When a satellite is briefly blocked and enters phase prediction mode, the predicted Doppler will be used directly to update the carrier numerically controlled oscillator. Then, the Doppler output of the carrier numerically controlled oscillator will be integrated to obtain the carrier phase prediction value, thereby improving the continuity of the carrier phase.

[0075] Based on the above embodiments, the cycle slip detection method includes:

[0076] By using the high-precision positioning results obtained by fusing real-time differential positioning (RTK), inertial navigation, and odometry, the change in satellite-to-ground distance in adjacent epochs can be obtained.

[0077] The distance change measured by the carrier phase was detected, and the following was obtained:

[0078]

[0079] in, This represents the distance change measured by the carrier phase. c·Δδt represents the change in distance between the satellite and Earth. u,k,k-n Represents the local clock difference, ΔN k,k-n Δε represents cycle slip, and Δε represents phase error;

[0080] To obtain cycle slip information of a satellite carrier phase observation, a reference satellite is selected for inter-satellite subtraction to cancel the influence of receiver clock bias, and the local clock bias is also canceled, resulting in:

[0081]

[0082] The above formula is used to determine whether there are cycle slip values ​​in the carrier phase observations and to repair them, thereby improving the quality of the carrier phase observations of the deep combined receiver.

[0083] Based on the above embodiments, such as Figure 3 The flowchart shown is for an improved adaptive robust Kalman filter algorithm. It calculates the prediction residual using GNSS observation information and obtains the estimated observation vector covariance matrix based on the prediction residual. This is then compared with a manually set observation vector covariance matrix. If the estimated observation vector covariance matrix is ​​greater than the set one, then an anomalous GNSS exists. Subsequently, a scaling factor is used to adjust the observation vector covariance matrix to reduce the GNSS weight in the Kalman filter. Conversely, if the current GNSS observation quality matches the estimate, no adjustment to the observation vector covariance matrix R is needed. For anomalous observations, the estimated observation vector covariance matrix calculated by the scaling factor becomes unpredictable. Mahalanobis distance based on the prediction residual is introduced to detect disturbance anomalies, and an adaptive factor is obtained to inflate the state prediction covariance matrix. The algorithm effectively mitigates the influence of abnormal model biases and outliers in measurements, improving the positioning accuracy of a single-frequency GNSS integrated navigation system that is vulnerable and does not output the observation vector covariance.

[0084] First, the prediction residuals are calculated using GNSS observation information. Based on the prediction residuals, the estimated observation vector covariance matrix is ​​obtained, including:

[0085]

[0086] Where k-1 and k represent time t respectively. k-1 and t k , Indicates t k The predicted state vector value at time t. Indicates the previous time t k-1 The optimal estimate of the state vector, Φ k,k-1 Indicates t k-1 to t k The state transition matrix at time t.

[0087] Then, the estimated observation vector covariance matrix and the manually set observation vector covariance matrix are compared to obtain a comparison value. If the comparison value is greater than 0, it indicates the presence of abnormal GNSS, and the observation vector covariance matrix is ​​adjusted using a scaling factor. Otherwise, no adjustment is needed.

[0088] If t k The observation vector Z at time t is k The condition is valid, and the position accuracy factor is greater than the first preset value C1 or the satellite data N. sat If the value is less than the second preset value C2, and the RTK solution state is fixed, then calculate the current time t. k The predicted value P of the variance-covariance matrix of the state-optimal estimate k,k-1 ;

[0089]

[0090] in, To predict the residual, i.e. the innovation vector, Z k For t k The observation vector at time H k The observation matrix;

[0091]

[0092] in, For t k The covariance matrix of the observation vector at time estimate;

[0093]

[0094] Where, α k For t k The scaling factor for time.

[0095] Furthermore, based on the Mahalanobis distance of the predicted residuals, perturbation anomalies are detected, and an adaptive factor is obtained to expand the observation vector covariance matrix, including:

[0096]

[0097] in, For t k The covariance matrix of the time-information vector;

[0098]

[0099] in, The threshold for the chi-square distribution;

[0100]

[0101] Where, β k (i) is t k Time-adaptive factor;

[0102]

[0103] Among them, K k For t k Time-matrix gain matrix;

[0104] Update system status x:

[0105]

[0106] in, For the previous moment t k The optimal estimate of the state vector;

[0107] Update the error covariance matrix of system state x:

[0108] P k =(IK k H k )P k,k-1 .

[0109] This invention can improve the continuity of carrier phase, effectively improve the quality of carrier phase observations of deep integrated receivers, and effectively weaken the influence of abnormal model bias and outliers in measurement through an adaptive robustness algorithm, thereby improving the positioning accuracy of a single-frequency GNSS integrated navigation system that is vulnerable and does not output observation vector covariance.

[0110] The following describes the integrated navigation deep-coupled carrier phase quality control and adaptive robust system provided by the present invention. The integrated navigation deep-coupled carrier phase quality control and adaptive robust system described below can be referred to in correspondence with the integrated navigation deep-coupled carrier phase quality control and adaptive robust method described above.

[0111] Figure 4 This is a schematic diagram of the structure of the integrated navigation deeply coupled carrier phase quality control and adaptive robust system provided in an embodiment of the present invention, as shown below. Figure 4As shown, it includes: a quality control module 41 and an adaptive robustness module 42, wherein:

[0112] The quality control module 41 is used to improve the quality of the carrier phase observation value of the receiver in the integrated navigation by using the carrier phase prediction method and the cycle slip detection method, so as to obtain the carrier phase observation value with improved quality; the adaptive robust module 42 is used to improve the positioning accuracy of the integrated navigation by using the improved adaptive robust Kalman filter algorithm, so as to obtain the integrated navigation positioning value with improved accuracy.

[0113] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 510, a communication interface 520, a memory 530, and a communication bus 540. The processor 510, communication interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logic instructions in the memory 530 to execute a deeply coupled carrier phase quality control and adaptive robustness method for integrated navigation. This method includes: using a carrier phase prediction method and a cycle slip detection method to improve the quality of the carrier phase observations of the receiver in integrated navigation, obtaining improved carrier phase observations; and using an improved adaptive robust Kalman filter algorithm to improve the positioning accuracy of integrated navigation, obtaining a more accurate integrated navigation positioning value.

[0114] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the integrated navigation deep-coupled carrier phase quality control and adaptive robustness method provided by the above methods. The method includes: using a carrier phase prediction method and a cycle slip detection method to improve the quality of the carrier phase observation value of the receiver in integrated navigation, to obtain a carrier phase observation value with improved quality; and using an improved adaptive robust Kalman filter algorithm to improve the positioning accuracy of integrated navigation, to obtain a combined navigation positioning value with improved accuracy.

[0116] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for integrated navigation deep-coupled carrier phase quality control and adaptive robustness, characterized in that, include: The carrier phase prediction method and cycle slip detection method are used to improve the quality of the carrier phase observations of the receiver in the integrated navigation, and the improved carrier phase observations are obtained. An improved adaptive robust Kalman filter algorithm is used to improve the positioning accuracy of integrated navigation, and the improved integrated navigation positioning value is obtained. The carrier phase prediction method includes: Identify the normally tracking satellite and use the normally tracking satellite to calculate the Doppler of the receiver crystal oscillator clock drift; Based on the common-mode properties of the impact of the receiver crystal oscillator drift on all satellite channels, the Doppler estimate of the crystal oscillator drift of the phase prediction channel is obtained, and the predicted Doppler value of the phase prediction channel is obtained by combining the motion Doppler estimate of the phase prediction channel. When any satellite is blocked and enters the phase prediction mode, the carrier numerically controlled oscillator is updated using the predictive Doppler. The Doppler output of the carrier numerically controlled oscillator is integrated to obtain the carrier phase prediction value. The improved adaptive robust Kalman filter algorithm includes: The prediction residual is calculated using GNSS observation information from the Global Navigation Satellite System, and the estimated observation vector covariance matrix is ​​obtained based on the prediction residual. The estimated observation vector covariance matrix is ​​compared with the manually set observation vector covariance matrix to obtain the observation vector covariance matrix comparison value. If the observation vector covariance matrix comparison value is greater than 0, it is determined that there is abnormal GNSS, and the observation vector covariance matrix is ​​adjusted by the scaling factor; otherwise, there is no need to adjust the observation vector covariance matrix. Based on the Mahalanobis distance of the predicted residual, disturbance anomalies are detected, and an adaptive factor is obtained to expand the covariance matrix of the observation vector.

2. The method for deep coupled carrier phase quality control and adaptive robustness in integrated navigation according to claim 1, characterized in that, The cycle slip detection method includes: By using the high-precision positioning results obtained by fusing real-time differential positioning (RTK), inertial navigation, and odometry, the change in satellite-to-ground distance in adjacent epochs can be obtained. The distance change measured by the carrier phase was detected, and the following was obtained: in, This represents the distance change measured by the carrier phase. This represents the change in distance between the satellite and the Earth. Indicates local clock difference, Indicates weekly jump, Indicates phase error; By selecting a reference satellite and performing inter-satellite subtraction to cancel out local clock errors, we obtain: 。 3. The method for deep coupled carrier phase quality control and adaptive robustness in integrated navigation according to claim 1, characterized in that, The prediction residual is calculated using GNSS observation information, and the estimated observation vector covariance matrix is ​​obtained based on the prediction residual, including: in, and Representing time respectively and , express The predicted state vector value at time t. Indicates the previous moment The optimal estimate of the state vector. express arrive The state transition matrix at time step.

4. A deeply coupled carrier phase quality control and adaptive robustness system for integrated navigation, based on the deeply coupled carrier phase quality control and adaptive robustness method for integrated navigation as described in any one of claims 1 to 3, characterized in that, include: The quality control module is used to improve the quality of the carrier phase observations of the receiver in the integrated navigation by using carrier phase prediction and cycle slip detection methods, so as to obtain the carrier phase observations with improved quality. The adaptive robust module is used to improve the positioning accuracy of integrated navigation by utilizing an improved adaptive robust Kalman filter algorithm, and obtain the integrated navigation positioning value with improved accuracy.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the integrated navigation deep coupling carrier phase quality control and adaptive robustness method as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the integrated navigation deep coupling carrier phase quality control and adaptive robustness method as described in any one of claims 1 to 3.

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