GNSS and INS tight coupling method and system based on sparse matrix optimization

Abstract

The invention discloses a sparse matrix optimization-based GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System) tight coupling method and system. The method comprises the following steps of: acquiring first target data, and establishing a first state model according to the first target data; presetting first correction judgment, wherein the first correction judgment is used for judging whether first correction operation is carried out or not; and performing optimization correction on the first state model in combination with a first state estimation algorithm and the first correction judgment. Through the sparse matrix optimization technology, the calculation complexity is significantly reduced, the real-time performance of the system is improved, and the high-precision positioning performance can still be kept in a complex environment. According to the method, the data of the GNSS and the INS can be effectively fused, and even if the GPS signal is weakened or lost, continuous and stable positioning can be realized through the assistance of the INS. Through preset correction judgment and optimization correction of the sparse matrix, accumulation of system errors is reduced, and the problem of positioning drift caused by long-term use is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of wire state monitoring, and in particular, to a GNSS and INS tightly coupled method and system based on sparse matrix optimization. Background Art

[0002] Wire state monitoring plays a crucial role in the safe operation of power systems. By monitoring the spatial position and state of wires in real time, potential faults can be detected and prevented in a timely manner, ensuring the stability and reliability of power transmission. High-precision spatial positioning data is the basis for achieving accurate monitoring. However, in practical applications, wires are often in complex and variable geographical environments, such as mountainous areas, forests, and densely built-up urban areas. These environmental factors can cause GPS signals to be blocked, reflected, or affected by multipath effects, resulting in signal weakening or even loss, and unable to provide reliable positioning information.

[0003] Traditional positioning methods mainly rely on the GPS system, but in the above complex environments, single GPS positioning is difficult to meet the high-precision requirements. To improve positioning accuracy, the proposed solution is to add other sensors, such as an inertial navigation system (INS). However, limited by physical installation conditions, cost, and the number of sensors, it is impossible to completely compensate for the deficiencies of GPS signals by simply adding sensors. In addition, although INS has high positioning accuracy in a short period of time, its errors will accumulate over time, resulting in positioning drift during long-term use. To solve the IMU drift problem, existing GPS / INS fusion algorithms usually adopt loose coupling or tight coupling methods, but when dealing with high-precision positioning problems in complex environments, there are still problems such as high computational complexity, insufficient real-time performance, and limited positioning accuracy. These problems severely restrict the performance of wire state monitoring systems and cannot meet the requirements of practical applications. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the specification of this application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a GNSS and INS tightly coupled method and system based on sparse matrix optimization, which can solve the problems mentioned in the background art.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a GNSS and INS tight coupling method based on sparse matrix optimization, including:

[0009] Obtain first target data, and establish a first state model according to the first target data;

[0010] Preset a first correction determination, which is used to determine whether to perform a first correction operation;

[0011] Optimize and correct the first state model by combining a first state estimation algorithm and the first correction determination.

[0012] As a preferred solution of the GNSS and INS tight coupling method based on sparse matrix optimization according to the present invention, wherein: the establishing of the first state model according to the first target data includes:

[0013] Establish a first state transition model and a first observation model according to the first target data;

[0014] The first state model at least includes the first state transition model and the first observation model.

[0015] As a preferred solution of the GNSS and INS tight coupling method based on sparse matrix optimization according to the present invention, wherein: the first state estimation algorithm includes:

[0016] The first state estimation algorithm is any algorithm for performing state estimation calculation on the first state model through the first target data;

[0017] The state at least includes a position state, a velocity state, and an attitude angle state.

[0018] As a preferred solution of the GNSS and INS tight coupling method based on sparse matrix optimization according to the present invention, wherein: the first state estimation algorithm further includes:

[0019] Preset a first sparse matrix;

[0020] Optimize and correct the first state model according to the first sparse matrix.

[0021] As a preferred solution of the GNSS and INS tight coupling method based on sparse matrix optimization according to the present invention, wherein: the preset first correction determination includes:

[0022] Preset a first correction threshold, and detect the first signal quality according to the first correction threshold;

[0023] Obtain the signal-to-noise ratio of the first signal, and compare the signal-to-noise ratio with the first correction threshold;

[0024] If the comparison result is not satisfied, a first calibration operation is performed.

[0025] As a preferred solution of the GNSS and INS tight coupling method based on sparse matrix optimization according to the present invention, wherein: the first sparse matrix includes:

[0026] When the comparison result is not satisfied, linearly increase the value of the observation noise covariance matrix;

[0027] Based on the increased observation noise covariance matrix, combine the predicted state vector of the INS to obtain an optimized and calibrated state vector.

[0028] As a preferred solution of the GNSS and INS tight coupling method based on sparse matrix optimization according to the present invention, wherein: the first observation model is established through an observation matrix, measurement vectors of GPS and INS sensors, and measurement values of GPS and INS sensors.

[0029] In a second aspect, the present invention provides a GNSS and INS tight coupling system based on sparse matrix optimization, including:

[0030] A model establishment module, configured to obtain first target data and establish a first state model according to the first target data;

[0031] A judgment module, configured to preset a first calibration judgment, and the first calibration judgment is used to judge whether to perform a first calibration operation;

[0032] A calibration module, configured to optimize and calibrate the first state model by combining a first state estimation algorithm and the first calibration judgment.

[0033] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a GNSS and INS tight coupling method and system based on sparse matrix optimization, obtains first target data, and establishes a first state model according to the first target data; a first calibration determination is preset, and the first calibration determination is used to determine whether to perform a first calibration operation; the first state model is optimized and calibrated by combining a first state estimation algorithm and the first calibration determination. Through the sparse matrix optimization technology, the computational complexity is significantly reduced, the real-time performance of the system is improved, and high-precision positioning performance can still be maintained in complex environments. This method can effectively fuse the data of GNSS and INS, and even when the GPS signal is weakened or lost, continuous and stable positioning can be achieved through the assistance of INS. Through the preset calibration determination and the optimized calibration of the sparse matrix, the accumulation of system errors is reduced, and the positioning drift problem caused by long-term use is avoided. The system and method are applicable to various complex environments, such as mountainous areas, forests, and densely built-up urban areas, providing reliable technical support for conductor state monitoring. The system and method of the present invention not only improve the positioning accuracy, but also have advantages in terms of cost and physical installation conditions, and are easy to integrate and apply in existing conductor monitoring systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts. Among them:

[0037] Figure 1 FIG. is a flowchart of a method for a GNSS and INS tight coupling method and system based on sparse matrix optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0039] Embodiment 1

[0040] Referring to Figure 1 , which is the first embodiment of the present invention, this embodiment provides a GNSS and INS tight coupling method and system based on sparse matrix optimization, including:

[0041] In the existing related technologies, there are some problems. For example, GNSS signals are easily blocked in environments such as urban canyons, tunnels, or indoors, resulting in a decrease in positioning accuracy. At the same time, although the INS system can provide continuous positioning information, its cumulative error will increase over time, affecting the accuracy of positioning. In addition, the traditional tight coupling method of GNSS and INS has high computational complexity and insufficient real-time performance when dealing with a large amount of data, making it difficult to meet the real-time positioning requirements in high-dynamic environments.

[0042] This application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail on how to implement the tight coupling method of GNSS and INS based on sparse matrix optimization;

[0043] Figure 1 The method flow chart of a tight coupling method and system of GNSS and INS based on sparse matrix optimization is shown, including:

[0044] S101, obtain the first target data, and establish a first state model according to the first target data;

[0045] In an optional embodiment, the first target data may include, but is not limited to, the pseudorange of GNSS satellite signals, Doppler frequency shift, satellite ephemeris information, and the accelerometer and gyroscope data provided by the INS system. Through these data, an initial state estimation model can be constructed, which will serve as the basis for subsequent optimization and filtering processes.

[0046] In an optional embodiment, the establishment of the first state model needs to consider various factors, such as signal propagation delay, atmospheric refraction, earth rotation correction, etc., to ensure the accuracy and reliability of the model.

[0047] In an optional embodiment, the first state model of wire detection can be established by different methods. For example, a Kalman filter can be used to optimize the state transition model, and a particle filter can be used to process the non-linear observation model. In this way, GNSS and INS data can be effectively fused to improve the overall performance of the system.

[0048] In another optional embodiment, in order to further improve the accuracy of state estimation, sparse matrix optimization technology can be introduced, which can reduce computational complexity and improve the efficiency of the filtering algorithm.

[0049] In another optional embodiment, this optimization method can ensure that in a dynamically changing environment, the system can still provide stable and accurate positioning information.

[0050] In an alternative embodiment, the first state model for wire detection can further improve the accuracy of state estimation by incorporating machine learning algorithms. For example, support vector machines (SVMs) or neural networks can be utilized to identify and classify patterns in the data, enabling more accurate predictions of the state transition model.

[0051] In another alternative embodiment, through an ensemble learning method, the prediction results of multiple models can be fused to obtain a more robust estimation result.

[0052] It should be noted that the introduction of these methods can not only enhance the model's adaptability to complex environments but also improve the system's robustness, ensuring reliable positioning information under various conditions.

[0053] In the embodiment of the present application, establishing the first state model based on the first target data includes:

[0054] Establishing a first state transition model and a first observation model based on the first target data;

[0055] The first state model includes at least the first state transition model and the first observation model.

[0056] In the embodiment of the present application, the first observation model is established through an observation matrix, the measurement vectors of GPS and INS sensors, and the measurement values of GPS and INS sensors.

[0057] Exemplarily, the first state transition model is:

[0058] x k =F k x k-1 +B k u k +w k

[0059]

[0060] The first observation model is:

[0061] z k =H k x k +v k

[0062] where k represents the time; x k represents the state vector; F k is the state transition matrix derived from the kinematic equations of the INS, B k is the control input model matrix, mapping the influence of the control input u k on the state; p k is the position vector, v kis the velocity vector, θ k is the attitude angle vector; H k represents the observation matrix that maps the state vector to the observation space; z k represents the measurement vector of the GPS and INS sensors, containing the measurement values of the GPS and INS sensors.

[0063] It should be noted that obtaining the first target data and establishing the first state model based on the first target data can more accurately estimate and predict the dynamic behavior of the system. By constructing the first state model, the state of the system can be updated in real time, thereby improving the accuracy and reliability of the navigation system.

[0064] It should also be noted that the model can effectively fuse data from different sensors, such as GPS and INS, to reduce the errors and uncertainties of a single sensor. This fusion technology is particularly important for maintaining positioning accuracy in complex environments or signal occlusion situations. Finally, by optimizing the sparse matrix, the computational efficiency can be further improved and resource consumption can be reduced, which is a significant advantage for a real-time navigation system.

[0065] S102, preset the first correction determination, and the first correction determination is used to determine whether to perform the first correction operation;

[0066] In an alternative embodiment, the first correction determination can be based on specific threshold conditions. For example, when the difference between the observed data and the predicted value of the state model exceeds a preset error range, the first correction operation is triggered. This correction operation can include adjusting the position, velocity, or attitude angle vector in the state vector to reduce the prediction error and improve the overall performance of the system. In addition, the first correction determination can also consider environmental factors, such as terrain changes or signal interference, which may affect the measurement accuracy of the sensors. By dynamically adjusting the correction strategy, the system can adapt to changing external conditions and ensure the continuity and accuracy of navigation.

[0067] In an alternative embodiment, the first correction determination can also

[0068] be based on time factors. For example, correction is automatically performed after a specific time interval to calibrate the accumulated errors. This periodic correction can ensure that the system still maintains high accuracy after long-term operation.

[0069] In an alternative embodiment, machine learning algorithms can also be integrated to learn from historical data and predict the best correction timing to further optimize the correction strategy. Such an adaptive mechanism enables the system to more intelligently handle various complex scenarios and improve the robustness and reliability of the navigation system.

[0070] In the embodiment of the present application, the preset first correction determination includes:

[0071] Presetting a first correction threshold, and detecting the first signal quality according to the first correction threshold;

[0072] Acquire a signal-to-noise ratio of the first signal, and compare the signal-to-noise ratio with a first correction threshold;

[0073] If the comparison result is not satisfied, the first correction operation is performed.

[0074] In an optional embodiment, the first correction threshold can be dynamically set by real-time monitoring of system performance. For example, the system can analyze the signal quality in real time and dynamically adjust the first correction threshold according to the changing trend of the signal quality. When the signal quality drops to a certain level, the system will automatically lower the correction threshold to ensure timely correction and prevent the accumulation of navigation errors.

[0075] In another optional embodiment, if the signal quality remains stable or improves, the system can appropriately increase the correction threshold and reduce unnecessary correction operations, thereby optimizing resource usage and extending the service life of the device. This adaptive correction threshold setting mechanism enables the system to maintain optimal navigation performance in different environments and conditions.

[0076] Exemplarily, when the GPS signal is limited, a first correction operation is performed relying on the prediction of the INS.

[0077] It should be noted that the preset first correction judgment, which is used to determine whether to perform the first correction operation, can improve the response speed of the system and ensure that measures can be taken quickly when the signal quality decreases. Reduce the risk of misoperation, and avoid unnecessary corrections when the signal quality is acceptable through preset judgment criteria. Enhance the stability of the system, and through reasonable correction threshold setting, the system can maintain stable performance in various environments. Optimize resource allocation, and through dynamic adjustment of the correction threshold, system resources are reasonably utilized and the service life of the equipment is extended. Improve navigation accuracy, and through accurate first correction judgment, ensure that the system can provide accurate navigation information under various complex conditions.

[0078] S103, optimizing and correcting the first state model in combination with the first state estimation algorithm and the first correction judgment.

[0079] In the embodiment of the present application, the first state estimation algorithm includes:

[0080] The first state estimation algorithm is any algorithm for performing state estimation calculation on the first state model through the first target data;

[0081] The state includes at least the position state, the velocity state and the attitude angle state.

[0082] In an alternative embodiment, the first state estimation algorithm may employ a Kalman filter, which improves the accuracy of state estimation by fusing the measurement data of GNSS and INS. The Kalman filter can handle noisy data and adaptively adjust according to the statistical characteristics of the system dynamics and measurement data.

[0083] It should be noted that the prediction and update steps of the Kalman filter can ensure that the system can provide accurate state estimation in real time under different environmental and motion conditions. In this way, the first state estimation algorithm can not only improve the estimation accuracy of position, speed, and attitude angle states, but also reduce the estimation bias caused by environmental changes or sensor errors.

[0084] In an alternative embodiment, the first state estimation algorithm may also employ a particle filter, which represents the probability distribution through a series of random samples (particles) to perform state estimation for a non-linear non-Gaussian system. The particle filter is particularly suitable for dealing with complex dynamic systems and can adapt to rapid changes in the system state through resampling and weight update.

[0085] In an alternative embodiment, the particle filter can provide a more flexible and robust solution than the traditional Kalman filter, especially when facing strong noise or non-linear problems. In addition, the parallel processing ability of the particle filter makes it have higher computational efficiency on multi-core processors, which is an important advantage for real-time systems.

[0086] In the embodiment of the present application, state estimation is performed using Kalman filtering by presetting a first sparse matrix, and the first sparse matrix is introduced for optimization when calculating the Kalman gain;

[0087] In the embodiment of the present application, the first state estimation algorithm further includes:

[0088] Presetting a first sparse matrix;

[0089] Optimally correcting the first state model according to the first sparse matrix.

[0090] In the embodiment of the present application, the first sparse matrix includes:

[0091] When the comparison result is not satisfied, linearly increase the value of the observation noise covariance matrix;

[0092] Based on the increased observation noise covariance matrix, obtain an optimally corrected state vector by combining the predicted state vector of INS.

[0093] Exemplarily, when using Kalman filtering for state estimation and introducing sparse matrix optimization when calculating the Kalman gain, it specifically includes the following steps:

[0094] A priori state estimation:

[0095] Furthermore, the a priori estimation covariance:

[0096] Calculate the Kalman gain matrix and introduce sparse matrix optimization:

[0097]

[0098] Furthermore, the posterior state estimation:

[0099] Furthermore, the posterior estimation covariance: P k|k =(I - K k H k )P k|k-1 ;

[0100] Wherein, represents the a priori state estimation at time k, represents the posterior state estimation at time k, represents the posterior state estimation at time k - 1; P k|k-1 represents the a priori covariance matrix at time k, P k-1|k-1 represents the posterior estimation covariance matrix at time k - 1, P k|k represents the posterior covariance matrix at time k; K k represents the Kalman gain matrix at time k; I represents the identity matrix, used to maintain the consistency of matrix dimensions; S represents the sparse matrix; λ represents the regularization parameter, controlling the degree of sparsity; R k represents the observation noise covariance matrix at time k.

[0101] In an alternative embodiment, the calculation method of the sparse matrix is as follows:

[0102]

[0103] In an alternative embodiment, the calculation method of λ is as follows:

[0104]

[0105] Wherein, λ 0 is the initial value of λ.

[0106] In an alternative embodiment, the method for solving the sparse optimization problem to obtain the corrected state vector is:

[0107] Step 1: Set the objective function as:

[0108] Step 2: Set the initial value

[0109] Step 3, iterative update: For n = 0, 1, 2, …, iteratively calculate:

[0110]

[0111] Step 4, when ∥∥x (n+1) - x (n) ∥∥ is less than the set threshold, stop the iteration, and take Otherwise, jump to Step 3;

[0112] Wherein, μ is the step size parameter,

[0113]

[0114] -τ = μλS

[0115] In an alternative embodiment, SNR max represents the maximum value of the signal-to-noise ratio of GNSS, SNR min represents the minimum value of the signal-to-noise ratio of GNSS, SNR current represents the current GNSS signal-to-noise ratio.

[0116] In summary, the present invention proposes a tightly coupled method for GNSS and INS based on sparse matrix optimization, obtains first target data, and establishes a first state model according to the first target data; preset a first calibration determination, and the first calibration determination is used to determine whether to perform a first calibration operation; optimize and correct the first state model by combining the first state estimation algorithm and the first calibration determination. Through the sparse matrix optimization technology, the computational complexity is significantly reduced, the real-time performance of the system is improved, and high-precision positioning performance can still be maintained in complex environments. This method can effectively fuse the data of GNSS and INS, and even when the GPS signal is weakened or lost, continuous and stable positioning can be achieved through the assistance of INS. Through the preset calibration determination and the optimized calibration of the sparse matrix, the accumulation of system errors is reduced, and the positioning drift problem caused by long-term use is avoided. The system and method are applicable to various complex environments, such as mountainous areas, forests, and urban high-rise dense areas, providing reliable technical support for conductor state monitoring. The system and method of the present invention not only improve the positioning accuracy, but also have advantages in terms of cost and physical installation conditions, and are easy to integrate and apply in existing conductor monitoring systems.

[0117] Embodiment 2

[0118] In a preferred embodiment, the following specific implementation steps can be designed according to the method in the above embodiment:

[0119] S01. Establish the state transition equations and observation equations of GPS and INS to form a state space model;

[0120] S02. Use Kalman filtering for state estimation and introduce sparse matrix optimization when calculating the Kalman gain;

[0121] S03. When the GPS signal is limited, rely on the prediction of INS and correct the error through sparse optimization. In an optional embodiment, the state transition equation is:

[0122] x k = F k x k-1 + B k u k + w k

[0123]

[0124] The observation equation is:

[0125] z k = H k x k + v k

[0126] where k represents the time, F k is the state transition matrix derived from the kinematic equation of INS, B k is the control input model matrix, mapping the influence of the control input u k on the state; p k is the position vector, v k is the velocity vector, θ k is the attitude angle vector; H k represents the observation matrix, mapping the state vector to the observation space; z k represents the measurement vector of GPS and INS sensors, including the measurement values of GPS and INS sensors.

[0127] In an optional embodiment, using Kalman filtering for state estimation and introducing sparse matrix optimization specifically includes the following steps:

[0128] S02.1. Prior state estimation:

[0129] S02.2. Prior estimation covariance:

[0130] S02.3. Calculate the Kalman gain matrix and introduce sparse matrix optimization:

[0131]

[0132] S02.4, Posterior State Estimation:

[0133] S02.5, Posterior Estimation Covariance: P k|k = (I - K k H k )P k|k-1 ;

[0134] Wherein, represents the prior state estimate at time k, represents the posterior state estimate at time k, represents the posterior state estimate at time k - 1; P k|k-1 represents the prior covariance matrix at time k, P k-1|k-1 represents the posterior estimation covariance matrix at time k - 1, P k|k represents the posterior covariance matrix at time k; K k represents the Kalman gain matrix at time k; I represents the identity matrix, used to maintain the consistency of matrix dimensions; S represents the sparse matrix; λ represents the regularization parameter, controlling the degree of sparsity; R k represents the observation noise covariance matrix at time k.

[0135] In an alternative embodiment, when the GPS signal is limited, relying on the prediction of the INS and correcting the error through sparse optimization includes the following steps:

[0136] S03.1, Detect the GPS signal quality, and determine whether the GPS signal is limited by the signal-to-noise ratio index;

[0137] S03.2, Adjust the observation noise covariance matrix R k , when the GPS signal is limited, increase the value of R k ;

[0138] When the GPS signal is limited, by increasing the value of R k (γ k > 1), the filter will reduce the trust in the observed value during the update phase and rely more on the prediction of the INS. This is because the increase in the observation noise covariance matrix means an increase in the unreliability of the observed value.

[0139] S03.3, Rely on the predicted state vector of the INS;

[0140] S03.4, Solve the following sparse optimization problem to obtain the corrected state vector,

[0141]

[0142] Wherein, γ kRepresents the amplification factor when the signal quality deteriorates, reflecting the change in GPS signal quality; Represents the observation noise covariance matrix under normal conditions; ∥·∥ 2 Represents the two-norm, calculating the Euclidean distance of the vector; ∥·∥ 1 Represents the one-norm, calculating the sum of the absolute values of the vector elements, promoting the sparsity of the solution. Two-norm ∥·∥ 2 : Used to measure the difference between the observed value and the predicted value, minimizing the sum of the squares of the residuals. Specifically, Reflects the error between the state x predicted by the model and the actual observation z k One-norm ∥·∥ 1 : Used to promote the sparsity of the solution. By adding λ∥Sx∥ to the objective function 1 , encouraging the solution x to be zero in certain dimensions, thereby suppressing the cumulative error and improving the robustness of the model. It considers both fitting the observed data and suppressing the cumulative error.

[0143] In an alternative embodiment, the calculation method of the sparse matrix is as follows:

[0144]

[0145] In an alternative embodiment, the calculation method of λ is as follows:

[0146]

[0147] where λ 0 is the initial value of λ.

[0148] In an alternative embodiment, the method for solving the sparse optimization problem to obtain the corrected state vector is:

[0149] Step 1. Set the objective function as:

[0150] Step 2. Set the initial value

[0151] Step 3. Iterative update: For n = 0, 1, 2,..., iteratively calculate:

[0152]

[0153] Step 4. When ∥∥x (n+1) -x (n) ∥∥ is less than the set threshold, stop the iteration and take Otherwise, jump to Step 3;

[0154] where, μ is the step size parameter,

[0155]

[0156] -τ = μλS

[0157] In an alternative embodiment, SNR max represents the maximum value of the signal-to-noise ratio of GNSS, and SNR min represents the minimum value of the signal-to-noise ratio of GNSS, and SNR current represents the current GNSS signal-to-noise ratio.

[0158] Comparison results between Table 1 and the prior art

[0159]

[0160]

[0161] It should be noted that Table 1 shows the comparison results between the present technical solution and the traditional method in multiple performance indicators. Through comparison, it can be seen that the present technical solution has been improved in terms of positioning accuracy, anti-occlusion ability, computational complexity, robustness, and adaptability. Especially in the case of limited GPS signals, the present technical solution can still maintain a high positioning accuracy, which benefits from the sparse matrix optimization technology it adopts. This technology can effectively handle signal occlusion and noise interference problems. In addition, the computational complexity of the present technical solution is low, which means there is a significant advantage in real-time performance. It can quickly respond to environmental changes and dynamically adjust filtering parameters, thereby improving the overall performance of the system.

[0162] Embodiment 3

[0163] This embodiment also provides a tightly coupled system of GNSS and INS based on sparse matrix optimization, including:

[0164] A model establishment module, configured to obtain first target data and establish a first state model according to the first target data;

[0165] A judgment module, configured to preset a first calibration judgment, and the first calibration judgment is used to judge whether to perform a first calibration operation;

[0166] A calibration module, configured to optimize and calibrate the first state model by combining a first state estimation algorithm and the first calibration judgment.

[0167] The above-mentioned respective unit modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above respective modules.

[0168] This embodiment also provides a computer device, which may be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a GNSS and INS tight coupling method based on sparse matrix optimization. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0169] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0170] Obtain first target data and establish a first state model according to the first target data;

[0171] Preset a first correction determination, and the first correction determination is used to determine whether to perform a first correction operation;

[0172] Optimize and correct the first state model by combining the first state estimation algorithm and the first correction determination.

[0173] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0174] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript can be used.

[0175] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0176] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0178] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0179] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A GNSS and INS tight coupling method based on sparse matrix optimization, characterized in that: include: Acquire first target data, and establish a first state model according to the first target data; Preset a first calibration judgment, wherein the first calibration judgment is used to determine whether to perform a first calibration operation; The first state model is optimized and corrected in combination with a first state estimation algorithm and the first correction judgment.

2. The GNSS and INS tight coupling method based on sparse matrix optimization according to claim 1, characterized in that: The establishing of the first state model according to the first target data comprises: Establishing a first state transition model and a first observation model according to the first target data; The first state model at least includes the first state transition model and a first observation model.

3. The GNSS and INS tight coupling method based on sparse matrix optimization as claimed in claim 2, characterized in that: The first state estimation algorithm comprises: The first state estimation algorithm is any algorithm that performs state estimation calculation on the first state model through the first target data; The state at least includes a position state, a speed state and an attitude angle state.

4. The GNSS and INS tight coupling method based on sparse matrix optimization as claimed in claim 3, characterized in that: The first state estimation algorithm also includes: Preset a first sparse matrix; The first state model is optimized and corrected according to the first sparse matrix.

5. The GNSS and INS tight coupling method based on sparse matrix optimization as claimed in claim 4, characterized in that: The preset first calibration judgment includes: Presetting a first correction threshold, and detecting the first signal quality according to the first correction threshold; Acquire a signal-to-noise ratio of the first signal, and compare the signal-to-noise ratio with the first correction threshold; If the comparison result is not satisfied, the first correction operation is performed.

6. The GNSS and INS tight coupling method based on sparse matrix optimization as claimed in claim 5, characterized in that: The first sparse matrix includes: When the comparison result is not satisfied, the value of the observation noise covariance matrix is ​​linearly increased; Based on the increased observation noise covariance matrix, the optimized and corrected state vector is obtained in combination with the predicted state vector of INS.

7. The GNSS and INS tight coupling method based on sparse matrix optimization as claimed in claim 6, characterized in that: The first observation model is established by an observation matrix, measurement vectors of GPS and INS sensors, and measurement values ​​of GPS and INS sensors.

8. A GNSS and INS tightly coupled system based on sparse matrix optimization, characterized in that: include: A model building module, used for acquiring first target data and building a first state model according to the first target data; A judgment module, used for presetting a first calibration judgment, wherein the first calibration judgment is used for judging whether to perform a first calibration operation; A correction module is used to optimize and correct the first state model in combination with a first state estimation algorithm and the first correction judgment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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