GNSS and INS tight coupling method and system

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation Satellite System) tight coupling method and system. The method comprises the following steps: acquiring first state distribution of a first target system, wherein the first target system is a GNSS and INS tight coupling system; presetting a first algorithm, and performing state estimation operation based on the first state distribution in combination with the first algorithm; and correcting the first target system according to the state estimation operation result. By adopting the particle filtering technology, the nonlinear noise problem of the GNSS signal in a high electromagnetic interference environment can be effectively solved. Particle filtering serves as a recursive Bayesian filtering technology based on a Monte Carlo method, probability distribution is represented through a group of random samples (particles), and a posterior probability density function in a nonlinear and non-Gaussian noise environment can be well approached. Therefore, even under the condition that the GNSS signal quality is obviously reduced, the method provided by the invention can also provide a more accurate and robust positioning result.

Description

Technical Field

[0001] The present invention relates to the technical field of wire state monitoring, and particularly to a GNSS and INS tight coupling method and system. Background Art

[0002] In a power transmission system, wire state monitoring is crucial for ensuring the stable operation of the power grid and the safety of power transmission. However, due to the presence of strong electromagnetic fields around the wire, such environmental interference usually has a significant impact on the quality of GNSS (Global Navigation Satellite System) signals, thereby resulting in a decrease in positioning accuracy and being unable to meet the requirements of precise monitoring. This electromagnetic interference usually stems from the strong electromagnetic field effect of the high-voltage transmission line itself, which may cause the satellite signals received by the GNSS receiver to be affected by noise, attenuation, or even signal loss. Although some existing methods have been able to achieve relatively ideal GNSS positioning accuracy in a conventional environment, in a high electromagnetic interference environment, the performance of GNSS significantly deteriorates and it is difficult to perform the task of high-precision positioning.

[0003] To solve the above problems, the GNSS / INS (Inertial Navigation System) tight coupling algorithm has been widely applied. The GNSS / INS tight coupling algorithm aims to improve the positioning accuracy and enhance the robustness of the system by fusing the global position information of GNSS with the local attitude and velocity information of INS. In this combination, INS provides high-frequency, short-term positioning and attitude information by measuring acceleration and angular velocity, while GNSS provides absolute position information. However, the traditional GNSS / INS tight coupling algorithm still faces multiple challenges.

[0004] Firstly, the significant degradation of GNSS signal quality is a key issue. In a high electromagnetic interference environment, GNSS signals often exhibit strong noise and multipath effects, resulting in an increase in the error of the received satellite data. This makes it difficult for traditional linear filtering algorithms such as the Kalman filter to effectively process these non-linear noises, thereby affecting the positioning accuracy of the entire system. Although some linear Kalman filters (such as the Extended Kalman Filter or the Unscented Kalman Filter) have been used to solve the state estimation problem of non-linear systems, their robustness to high noise and electromagnetic interference is still limited.

[0005] Secondly, the disadvantages of INS itself are also obvious. The positioning and attitude estimation of INS rely on the accuracy of inertial sensors (such as accelerometers and gyroscopes). However, inertial sensors usually accumulate drift errors over time, especially during long-term operation, and their errors gradually increase, resulting in inaccurate position information provided by the system. To mitigate this problem, it is usually necessary to rely on GNSS signals to correct the estimation results of INS, but this in turn makes the quality problem of GNSS signals a key factor affecting the overall system performance. Summary of the Invention

[0006] The purpose of this section 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 section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the present invention provides a GNSS and INS tight coupling method and system, which can solve the problems mentioned in the background art.

[0009] To solve the above technical problems, the present invention provides the following technical solutions:

[0010] In a first aspect, the present invention provides a GNSS and INS tight coupling method, including:

[0011] Obtaining a first state distribution of a first target system, where the first target system is a GNSS and INS tight coupling system;

[0012] Presetting a first algorithm, and performing a state estimation operation based on the first state distribution in combination with the first algorithm;

[0013] Correcting the first target system according to the result of the state estimation operation.

[0014] As a preferred solution of the GNSS and INS tight coupling method of the present invention, wherein: the obtaining of the first state distribution of the first target system includes:

[0015] According to the first data information of the first target system;

[0016] Performing the acquisition of the first state distribution on the first target system;

[0017] The first state distribution at least includes the initial state distributions of several state acquisition points.

[0018] As a preferred solution of the GNSS and INS tight coupling method of the present invention, wherein: the first algorithm includes:

[0019] The first algorithm is any algorithm for obtaining the result of the state estimation operation according to the first state distribution;

[0020] The result of the state estimation operation at least includes a first prediction operation and a first weight update operation.

[0021] As a preferred solution of the GNSS and INS tight coupling method described in the present invention, wherein: the obtaining of the first state distribution of the first target system further includes:

[0022] Denote several state acquisition points as target particles;

[0023] Configure the initial weights and initial state distribution for the target particles.

[0024] As a preferred solution of the GNSS and INS tight coupling method described in the present invention, wherein: the first prediction operation includes:

[0025] The first prediction operation includes, for each target particle, establishing a first observation function and calculating a predicted value according to the first observation function.

[0026] As a preferred solution of the GNSS and INS tight coupling method described in the present invention, wherein: the first weight update operation includes:

[0027] The first weight update operation includes normalizing the weights of all target particles so that their sum is 1, resampling several target particles from the current target particle set according to the normalized weights to form a new particle set, and resetting the weights.

[0028] As a preferred solution of the GNSS and INS tight coupling method described in the present invention, wherein: the state distribution of the several state acquisition points at least includes the initial position, initial velocity, initial attitude quaternion, initial gyroscope bias, initial accelerometer bias, and initial GNSS error term.

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

[0030] A data acquisition module, configured to acquire the first state distribution of a first target system, where the first target system is a GNSS and INS tight coupling system;

[0031] An operation module, configured to preset a first algorithm and perform a state estimation operation based on the first state distribution in combination with the first algorithm;

[0032] A correction module, configured to correct the first target system according to the result of the state estimation operation.

[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 the processor implements the steps of the method described above when executing the computer program.

[0034] Fourthly, 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, which obtains the first state distribution of a first target system, and the first target system is a GNSS and INS tight coupling system; a first algorithm is preset, and state estimation operations are performed based on the first state distribution in combination with the first algorithm; the first target system is corrected according to the results of the state estimation operations. By adopting the particle filtering technology, the nonlinear noise problem of GNSS signals in a high electromagnetic interference environment can be effectively processed. As a recursive Bayesian filtering technology based on the Monte Carlo method, particle filtering represents the probability distribution through a set of random samples (particles), and can better approximate the posterior probability density function in a non-linear and non-Gaussian noise environment. Therefore, even when the GNSS signal quality significantly deteriorates, the method of the present invention can provide more accurate and robust positioning results. 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 It is a flowchart of a method for a GNSS and INS tight coupling method and system 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 drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts 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, including:

[0041] In the existing related technologies, there are some problems. For example, in the case of signal occlusion or interference, the positioning accuracy of the traditional loosely coupled GNSS and INS system will significantly decrease. In addition, since the loosely coupled system mainly relies on GNSS signals, when the GNSS signal quality is poor, the reliability of the system will also be affected. Although the tightly coupled system improves the positioning robustness to a certain extent, its algorithm complexity is high, the amount of calculation is large, and the requirements for hardware are relatively high, which may bring problems of cost and efficiency in practical applications.

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

[0043] Figure 1 A method flow chart of a GNSS and INS tight coupling method and system is shown, including:

[0044] S101, obtaining the first state distribution of the first target system, where the first target system is a GNSS and INS tight coupling system;

[0045] In an alternative embodiment, the GNSS and INS tight coupling system is implemented through the following steps: First, satellite signals are obtained through a GNSS receiver, and the dynamic information of the carrier is obtained through an inertial navigation system (INS).

[0046] In an alternative embodiment, data fusion technology is used to fuse GNSS signals and INS information to obtain a more accurate and stable positioning result. During this process, the system will monitor the quality of GNSS signals in real time, and when the signal quality decreases, the weights of the fusion algorithm will be automatically adjusted to ensure positioning accuracy.

[0047] In another alternative embodiment, the system also has an adaptive filtering function, which can dynamically adjust the filtering parameters according to environmental changes and the motion state of the carrier, further improving the robustness and reliability of the system.

[0048] In the embodiment of this application, obtaining the first state distribution of the first target system includes:

[0049] According to the first data information of the first target system;

[0050] Performing the acquisition of the first state distribution on the first target system;

[0051] The first state distribution includes at least the initial state distributions of several state acquisition points.

[0052] In an alternative embodiment, the first data information is satellite signal data continuously collected by a GNSS receiver within a specific time interval. This data information includes, but is not limited to, the pseudorange, carrier phase, Doppler shift of the satellite, and the ephemeris data of the satellite. Through this data, the position, velocity, and time information of the vehicle in space can be calculated.

[0053] In another alternative embodiment, the first data information may also include the measurement data of the accelerometer and gyroscope provided by the INS system, which reflects the dynamic changes of the vehicle. By comprehensively analyzing this data, a more accurate vehicle state estimation can be obtained, thereby achieving a tight coupling between GNSS and INS.

[0054] In the embodiments of the present application, the first data information is not restricted, and those skilled in the art can select appropriate sensors and data acquisition methods according to actual needs. For example, in addition to the GNSS receiver and INS system, other sensors such as wheel speed sensors, magnetic compasses, and barometric altimeters can also be integrated to enhance the environmental adaptability and measurement accuracy of the system.

[0055] It should be noted that by fusing these multi-source data, the estimation accuracy of the first target system state can be further improved, ensuring stable and reliable measurement results in various complex environments.

[0056] In an alternative embodiment, the first state distribution acquisition can be achieved through a Kalman filter. The Kalman filter is an effective recursive filter that can estimate the state of a dynamic system from a series of measurements containing noise.

[0057] In an alternative embodiment, the Kalman filter is used to process the data provided by the GNSS and INS systems to estimate the state information such as the position, velocity, and attitude of the vehicle. By establishing a mathematical model to describe the dynamic behavior of the vehicle and combining the actual measurement data, the Kalman filter can predict and correct the state estimation, thereby providing more accurate and stable output results.

[0058] It should be noted that the parameters of the filter can be adjusted according to the actual application scenario to adapt to different dynamic conditions and measurement noise levels.

[0059] In an alternative embodiment, the first state distribution acquisition can also be achieved through a particle filter. The particle filter is a recursive Bayesian filtering technique based on the Monte Carlo method. It represents the probability distribution through a set of random samples (particles) and approximates the posterior probability through resampling and weight updating.

[0060] In an alternative embodiment, a particle filter is used to process data from GNSS and INS systems to improve the accuracy and robustness of the carrier state estimation. The particle filter is particularly suitable for state estimation problems in non-linear and non-Gaussian noise environments. It can handle more complex dynamic system models and can adapt to changes in the system model and noise statistical characteristics.

[0061] It should be noted that by appropriately selecting the number of particles and the resampling strategy, the computational complexity and estimation accuracy can be balanced to meet the requirements of real-time applications.

[0062] In the embodiment of the present application, a method based on particle filtering is selected to obtain the first state distribution;

[0063] In the embodiment of the present application, obtaining the first state distribution of the first target system further includes:

[0064] Denote a number of state acquisition points as target particles;

[0065] Configure the initial weights and initial state distribution for the target particles.

[0066] In the embodiment of the present application, the state distribution of a number of state acquisition points at least includes the initial position, initial velocity, initial attitude quaternion, initial gyroscope bias, initial accelerometer bias, and initial GNSS error term.

[0067] Exemplarily, sample N particles from the initial state distribution p(x 0 ) and set the initial weights and set the initial weights

[0068] where the state vector of the particle is:

[0069]

[0070] where, represents the initial position of the i-th particle, represents the initial velocity of the i-th particle, represents the initial attitude quaternion of the i-th particle, represents the initial gyroscope bias of the i-th particle, represents the initial accelerometer bias of the i-th particle, represents the initial GNSS error term of the i-th particle.

[0071] It should be noted that to obtain the first state distribution of the first target system, the particle filter technology can be used to handle non-linear problems and enhance the robustness of the system. This method can update the system state in real time and adapt to the dynamically changing environment. Through the tightly coupled method, the dependence of the system on a single sensor can be reduced, and the overall reliability can be improved. This method can also estimate and compensate for sensor errors, further improving the accuracy of positioning.

[0072] S102, preset the first algorithm, and perform a state estimation operation based on the first state distribution in combination with the first algorithm;

[0073] In the embodiment of the present application, the first algorithm includes:

[0074] The first algorithm is any algorithm that obtains the state estimation operation result according to the first state distribution;

[0075] The state estimation operation result includes at least a first prediction operation and a first weight update operation.

[0076] In an optional embodiment, the first algorithm may be a Kalman filter algorithm. This algorithm estimates the system state recursively by establishing a mathematical model of the system state.

[0077] In an optional embodiment, at each time step, the Kalman filter first predicts the state at the next moment, and then corrects the predicted value according to the actual measurement value. In this way, the Kalman filter can effectively fuse data from different sensors, reduce the influence of noise and errors, and thus provide a more accurate system state estimation.

[0078] In an optional embodiment, the first algorithm may also be a particle filter algorithm. This algorithm represents the probability distribution through a series of random samples (particles), and each particle represents a possible state of the system. At each time step, the particle filter updates the weights of the particles according to the system model and the observation data, and then selects the particles with higher weights through the resampling process to approximate the posterior probability distribution.

[0079] It should be noted that the particle filter is particularly suitable for dealing with non-linear and non-Gaussian noise problems, and can provide a more flexible and robust state estimation. In addition, it can also handle multimodal distributions and is suitable for state estimation of complex dynamic systems.

[0080] In the embodiment of the present application, the first prediction operation includes:

[0081] The first prediction operation includes establishing a first observation function for each target particle and calculating the predicted value according to the first observation function.

[0082] In the embodiment of the present application, the first weight update operation includes:

[0083] The first weight update operation includes normalizing the weights of all target particles so that their sum is 1, resampling a number of target particles from the current set of target particles according to the normalized weights to form a new set of particles, and resetting the weights.

[0084] Exemplarily, for each particle, predict the state at the next moment according to the state transition function of the INS, the IMU measurement value, and the process noise;

[0085] Furthermore, for each particle, calculate the predicted GNSS observation value using the observation function, and update the particle weight according to the actual GNSS observation value;

[0086] Furthermore, normalize the weights of all particles so that their sum is 1;

[0087] Furthermore, resample N particles from the current set of particles according to the normalized weights to form a new set of particles, and reset the weights;

[0088] In an optional embodiment, the method for predicting the state at the next moment for each particle according to the state transition function of the INS, the IMU measurement value, and the process noise is specifically as follows:

[0089] Attitude update:

[0090]

[0091] Velocity update:

[0092]

[0093] Position update:

[0094]

[0095] Gyroscope bias update:

[0096]

[0097] Accelerometer bias:

[0098]

[0099] GNSS error term update:

[0100]

[0101] Aggregate state vector:

[0102]

[0103] Wherein, Denote the process noise of the attitude quaternion of the $i$-th particle at time $k$; Denote the process noise of the velocity vector of the $i$-th particle at time $k$; Denote the process noise of the position vector of the $i$-th particle at time $k$; Denote the process noise of the gyroscope bias of the $i$-th particle at time $k$; Denote the process noise of the accelerometer bias of the $i$-th particle at time $k$; Denote the process noise of the GNSS error term of the $i$-th particle at time $k$, which follows a Gaussian distribution; Denote the skew-symmetric matrix formed by the angular velocity Denote the quaternion corresponding rotation matrix, which is used to transform the body-frame acceleration to the navigation frame; $g$ represents the gravitational acceleration; $\Delta t$ represents the time step; Denote the attitude quaternion of the $i$-th particle at time $k$; Denote the velocity vector of the $i$-th particle at time $k$.

[0104] In an alternative embodiment, for each particle, the method of calculating the predicted GNSS observation value using the observation function and updating the particle weight according to the actual GNSS observation value is as follows:

[0105] Predicted observation value:

[0106]

[0107] where

[0108] Weight update:

[0109]

[0110] where Denote the predicted value of the $i$-th particle at time $k$; $z$ k Denote the actual GNSS observation value at time $k$; is the measurement noise of the $i$-th particle at time $k$, which follows a Gaussian distribution, and $R$ k is the covariance matrix of the measurement noise at time $k$.

[0111] In an alternative embodiment, the method of normalizing the weights of all particles so that their sum is 1 is as follows:

[0112]

[0113] where is the weight of the $i$-th particle at time $k$ after normalization.

[0114] In an optional embodiment, the method of resampling N particles from the current particle set according to the normalized weights to form a new particle set and resetting the weights is as follows:

[0115] Calculate the cumulative distribution function of the particle weights;

[0116] Generate a uniformly distributed random number;

[0117] For each new particle Calculate the sampling point:

[0118]

[0119] For all resampled particles, set the weights:

[0120]

[0121] It should be noted that by presetting the first algorithm and performing state estimation operations based on the first state distribution and optimizing through the first algorithm, a faster convergence speed can be achieved, so as to quickly respond and update the state estimation in a dynamically changing environment. The introduction of the first algorithm can also reduce the consumption of computing resources because it optimizes the data processing flow and improves the computing efficiency. This method can provide users with more accurate and reliable navigation and positioning services, meeting the needs of high-precision applications.

[0122] S103. Correct the first target system according to the result of the state estimation operation.

[0123] In an optional embodiment, correcting the first target system according to the result of the state estimation operation can be achieved by adjusting the parameters of the first target system to ensure the accuracy and stability of the system state. For example, the error model parameters of the inertial navigation system (INS) can be adjusted, or the signal processing algorithm of the GNSS receiver can be fine-tuned.

[0124] In an optional embodiment, machine learning techniques can also be used to train a model through historical data to predict and compensate for system errors, thereby further improving the performance of the navigation system. Through these methods, it can be ensured that the system can provide continuous and accurate positioning information in various dynamic environments.

[0125] In summary, the present invention proposes a GNSS and INS tight coupling method to obtain the first state distribution of the first target system, where the first target system is a GNSS and INS tight coupling system; a first algorithm is preset, and state estimation operations are performed based on the first state distribution in combination with the first algorithm; the first target system is corrected according to the results of the state estimation operations. By adopting the particle filtering technology, the nonlinear noise problem of GNSS signals in a high electromagnetic interference environment can be effectively processed. Particle filtering, as a recursive Bayesian filtering technology based on the Monte Carlo method, represents the probability distribution through a set of random samples (particles), and can better approximate the posterior probability density function in a nonlinear and non-Gaussian noise environment. Therefore, even when the GNSS signal quality significantly deteriorates, the method of the present invention can provide more accurate and robust positioning results.

[0126] Embodiment 2

[0127] In a preferred embodiment, the following specific method steps are designed:

[0128] Step 1: Sample N particles from the initial state distribution p(x 0 ) and set the initial weights and

[0129] Step 2: For each particle, predict the state at the next moment according to the state transition function of the INS, the IMU measurement value, and the process noise;

[0130] Step 3: For each particle, calculate the predicted GNSS observation value using the observation function, and update the particle weight according to the actual GNSS observation value;

[0131] Step 4: Normalize the weights of all particles so that their sum is 1;

[0132] Step 5: According to the normalized weights, resample N particles from the current particle set to form a new particle set, and reset the weights;

[0133] Step 6: Calculate the state estimation using the resampled particle set;

[0134] Step 7: Use the resampled particles as the input for the next moment, and repeat steps 2 to 6.

[0135] In an alternative embodiment, the state vector of the particle is:

[0136]

[0137] where represents the initial position of the i-th particle, represents the initial velocity of the i-th particle, Represents the initial attitude quaternion of the i-th particle, Represents the initial gyroscope bias of the i-th particle,

[0138] Represents the initial accelerometer bias of the i-th particle, Represents the initial GNSS error term of the i-th particle.

[0139] In an optional embodiment, for each particle, the method of predicting the state at the next moment according to the state transition function of the INS, the IMU measurement value, and the process noise is specifically as follows:

[0140] Attitude update:

[0141]

[0142] Velocity update:

[0143]

[0144] Position update:

[0145]

[0146] Gyroscope bias update:

[0147]

[0148] Accelerometer bias:

[0149]

[0150] GNSS error term update:

[0151]

[0152] Aggregate state vector:

[0153]

[0154] Among them, Represents the process noise of the attitude quaternion of the i-th particle at time k; Represents the process noise of the velocity vector of the i-th particle at time k; Represents the process noise of the position vector of the i-th particle at time k; Represents the process noise of the gyroscope bias of the i-th particle at time k,; Represents the process noise of the accelerometer bias of the i-th particle at time k; Represents the process noise of the GNSS error term of the i-th particle at time k, which follows a Gaussian distribution; Represents the angular velocity An anti-symmetric matrix formed; Denotes a quaternion The corresponding rotation matrix, used to convert the body-frame acceleration to the navigation frame; g represents the gravitational acceleration; Δt represents the time step; Denotes the attitude quaternion of the i-th particle at time k; Denotes the velocity vector of the i-th particle at time k.

[0155]

[0156]

[0157] In an optional embodiment, for each particle, the method of calculating the predicted GNSS observation value using the observation function and updating the particle weight according to the actual GNSS observation value is as follows:

[0158] Predicted observation value:

[0159]

[0160] Wherein,

[0161] Weight update:

[0162]

[0163] Wherein, Denotes the predicted value of the i-th particle at time k; z k Denotes the actual GNSS observation value at time k; Is the measurement noise of the i-th particle at time k, following a Gaussian distribution, R k Is the covariance matrix of the measurement noise at time k.

[0164] In an optional embodiment, the method of normalizing the weights of all particles so that their sum is 1 is as follows:

[0165]

[0166] Wherein, Is the weight of the i-th particle at time k after normalization.

[0167] As the filtering process progresses, the weights of most particles tend to zero, and only the weights of a few particles are significantly larger. To solve this problem, the resampling step not only redistributes the particles but also needs to reset the weights.

[0168] In an optional embodiment, according to the normalized weights, resample N particles from the current particle set to form a new particle set and reset the weights as follows:

[0169] S01. Calculate the cumulative distribution function of the particle weights;

[0170] S02. Generate a uniformly distributed random number;

[0171] S03. For each new particle Calculate the sampling point:

[0172]

[0173] S04. For all resampled particles, set the weights:

[0174]

[0175] It should be noted that the above steps reduce or eliminate low-weight particles by replicating high-weight particles, concentrating the computing resources in the regions that are more likely to represent the true state. Ensure that the particle set can cover the high-probability regions in the state space and avoid the particles being overly concentrated in certain specific regions. Through resampling, a sufficient number of effective particles are maintained to prevent the exacerbation of the particle degeneracy problem.

[0176] In an optional embodiment, the method for calculating the state estimate using the resampled particle set is:

[0177]

[0178] Wherein, is the state estimate at time k.

[0179] It should be noted that multiple particles are generated through particle filtering to represent the state distribution of the system, and the state of the system is estimated by iteratively updating the particle weights and states. Compared with the traditional Kalman filter, particle filtering is a non-parametric filter based on the Monte Carlo method, which can effectively handle the state estimation problems of non-linear and non-Gaussian systems. Therefore, in complex and high-noise environments, the particle filter exhibits superior performance;

[0180] In this application, particle filtering can better capture the true state distribution of the system by introducing a large number of randomly sampled particles, has a higher tolerance for outliers and noise, and enhances the robustness of the system; it can process the observation data severely contaminated by noise that is difficult to effectively handle by traditional filters in the case of strong electromagnetic interference and significant degradation of GNSS signal quality;

[0181] This application tightly couples GNSS data through particle filtering. Even in the case of poor GNSS signal quality, it can still use a small amount of effective GNSS information to correct the drift of the INS and maintain the positioning accuracy of the system;

[0182] In this application, the particle filter can adjust the particle weights and distributions according to real-time observation data to adapt to a dynamically changing environment. Especially when the GNSS signal quality fluctuates greatly, the particle filter can adaptively adjust, perform key sampling on important particles, and maintain the stability of the system;

[0183] The particle filter of this application does not require linearization and directly processes the nonlinear model of the system, having the ability of global optimization and avoiding the problem of local optimal solutions;

[0184] The framework of the particle filter used in this application allows the introduction of more complex system models and noise characteristics, and can be customized for specific application scenarios, improving the applicability and scalability of the algorithm.

[0185] Embodiment 3

[0186] This embodiment also provides a GNSS and INS tightly coupled system, including:

[0187] A data acquisition module, configured to acquire the first state distribution of a first target system, where the first target system is a GNSS and INS tightly coupled system;

[0188] An operation module, configured to preset a first algorithm and perform a state estimation operation based on the first state distribution in combination with the first algorithm;

[0189] A correction module, configured to correct the first target system according to the result of the state estimation operation.

[0190] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above respective modules.

[0191] This embodiment also provides a computer device, which can 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. The computer program, when executed by the processor, implements a GNSS and INS tight coupling method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering 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.

[0192] 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:

[0193] Obtain the first state distribution of the first target system, where the first target system is a GNSS and INS tight coupling system;

[0194] Preset a first algorithm, and perform a state estimation operation based on the first state distribution in combination with the first algorithm;

[0195] Correct the first target system according to the result of the state estimation operation.

[0196] 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 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 by the scope of the claims of the present invention.

[0197] 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 complete hardware embodiment, a complete 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 storage, CD-ROM, optical storage, etc.) that contain 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, etc.

[0198] 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, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows Figure 1 or a plurality of flows and / or blocks

[0199] 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, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one or more flows Figure 1 or a plurality of flows and / or blocks

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed 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 Figure 1 one or more flows Figure 1 or a plurality of flows and / or blocks

[0201] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0202] Obviously, those skilled in the art can make various changes and modifications 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 cover these changes and modifications.

Claims

1. A method for tightly coupling GNSS and INS, characterized in that: include: Acquire a first state distribution of a first target system, where the first target system is a tightly coupled system of GNSS and INS; Preset a first algorithm, and perform a state estimation operation based on the first state distribution in combination with the first algorithm; The first target system is modified according to the state estimation operation result.

2. The GNSS and INS tight coupling method according to claim 1, characterized in that: The obtaining of the first state distribution of the first target system comprises: According to the first data information of the first target system; Acquiring a first state distribution of the first target system; The first state distribution includes at least initial state distributions of a plurality of state acquisition points.

3. The GNSS and INS tight coupling method as claimed in claim 2, characterized in that: The first algorithm comprises: The first algorithm is any algorithm that obtains a state estimation operation result according to the first state distribution; The state estimation operation result includes at least a first prediction operation and a first weight update operation.

4. The GNSS and INS tight coupling method as claimed in claim 3, characterized in that: The obtaining of the first state distribution of the first target system further comprises: Record several state collection points as target particles; Configure the initial weight and initial state distribution of the target particles.

5. The GNSS and INS tight coupling method as claimed in claim 4, characterized in that: The first prediction operation includes: The first prediction operation includes establishing a first observation function for each target particle, and calculating a prediction value according to the first observation function.

6. The method for tightly coupling GNSS and INS as claimed in claim 5, characterized in that: The first weight updating operation includes: The first weight updating operation includes normalizing the weights of all target particles so that their sum is 1, resampling a number of target particles from the current target particle set according to the normalized weights to form a new particle set, and resetting the weights.

7. The GNSS and INS tight coupling method according to claim 6, characterized in that: The state distribution of the plurality of state acquisition points includes at least an initial position, an initial velocity, an initial attitude quaternion, an initial gyroscope bias, an initial accelerometer bias and an initial GNSS error term.

8. A GNSS and INS tightly coupled system, characterized in that: include: A data acquisition module, used to acquire a first state distribution of a first target system, where the first target system is a tightly coupled system of GNSS and INS; An operation module, configured to preset a first algorithm and perform a state estimation operation based on the first state distribution in combination with the first algorithm; A correction module is used to correct the first target system according to the state estimation operation result.

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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