COA attitude resolving method based on MEMS IMU auxiliary search

Abstract

The invention provides a COA (Cooti Optimization Algorithm) attitude resolving method based on MEMS (Micro Electro Mechanical System) IMU (Inertial Measurement Unit) auxiliary search. The method comprises the following steps of: 1, acquiring IMU and BDS (Bidirectional Digital Subscriber) dual-antenna data in urban streets with weak GNSS (Global Navigation Satellite System) signals; 2, calculating a pitch angle and a yaw angle through MEMS IMU output data, and determining an optimization algorithm search boundary; comprising the following steps: 3, defining a fitness function to be optimized according to a GNSS carrier phase observation model and an attitude observation model; 4, introducing an adaptive dynamic parameter adjustment mechanism to accelerate the convergence process of the algorithm and effectively avoid falling into a local optimal solution; comprising the following steps of: providing a step length self-adaptive adjustment mechanism aiming at the problem of overlarge leap or slower convergence speed in a solution space so as to improve the efficiency and the precision of an optimization process; and 6, in a space range determined by the inertial navigation system, carrying out attitude search and optimization by using the improved COA, and solving the accurate attitude of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of GNSS high-precision positioning and attitude determination, and in particular to a COA attitude solution method based on MEMS IMU-assisted search. Background Art

[0002] In complex urban environments, GNSS (Global Navigation Satellite System) signals face many interferences and obstacles, especially in urban streets with tall buildings, narrow tunnels, and rural roads with dense trees. These challenges are even more severe. Signal propagation is greatly tested in these areas, and the signal quality is significantly reduced, and even loss of lock occurs, making it extremely difficult to rely solely on GNSS signals for positioning and attitude estimation. Taking urban streets as an example, it is almost inevitable to drive through densely populated areas with tall buildings, small tunnels, and even underground parking lots without skylights. In these environments, the quality of satellite signals often deteriorates rapidly, and even complete loss of lock may occur, resulting in a significant decrease in navigation accuracy. To cope with this situation, the use of inertial navigation systems (INS) in conjunction with GNSS has become an effective means to improve positioning and attitude accuracy and reliability.

[0003] The inertial navigation system (INS) can provide multi-dimensional navigation information such as position, speed, and attitude, is not interfered by external signals, and has good concealment and autonomy, making it an ideal independent navigation system. However, the inertial navigation system also has certain limitations. Its errors will gradually accumulate over time, which means that long-term use may lead to a decrease in accuracy. In addition, traditional inertial sensors are large in size, high in power consumption, and have certain applicability restrictions for different types of carriers. Nevertheless, with the continuous advancement of technology, the micro-electromechanical system inertial measurement unit (MEMS IMU) has been able to provide higher-precision measurements, and its cost has been significantly reduced, making high-precision inertial navigation systems gradually have a wider application prospect.

[0004] The GNSS attitude measurement system can provide basic information such as the heading, track, attitude, position, and speed of the carrier. However, in traditional attitude measurement, one method is to use the characteristics of the baseline vector to determine the attitude parameters of the carrier coordinate system relative to the reference coordinate system. This method is mainly implemented in two ways: one is to construct a cost function related to the baseline vector to estimate the optimal attitude solution, thereby converting it into the classic Wahba problem. However, this method has certain difficulties in attitude accuracy analysis. Another implementation method is to use the baseline vector as a measurement value and obtain the attitude parameters of the carrier by direct solution or least squares estimation. However, the existing attitude measurement method based on the positioning domain processes the baseline vector and the attitude parameter solution separately without considering the correlation between them, which may affect the reliability of attitude measurement to a certain extent. This is easily affected by signal interference and occlusion in complex environments, and the above problems may be further amplified, resulting in a decrease in attitude determination accuracy or failure. Especially in urban areas with high-rise buildings, tunnels or dense trees, the quality of GNSS signals is often greatly challenged, thus affecting the stability and accuracy of attitude estimation.

[0005] Therefore, there is an urgent need for a method that can combine the characteristics of urban environments and organically integrate the attitude angle information provided by the inertial measurement unit (IMU) with the GNSS attitude measurement to improve the accuracy and robustness of attitude estimation. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the purpose of the present invention is to propose a COA attitude solution method based on MEMS IMU assisted search, which utilizes the short-term accurate angle characteristics provided by MEMS IMU, solves the pitch angle and yaw angle to limit the search boundary of COA, and provides high-precision attitude information by searching the fitness function.

[0007] To achieve the above-mentioned purpose and other related purposes, the present invention provides a COA attitude solution method based on MEMS IMU assisted search, the method comprising the following steps:

[0008] Step 1: Build a MEMS IMU and BDS dual-antenna data acquisition platform, as follows Figure 3 As shown, in Figure 2 The data collected on the city streets shown in the figure include acceleration, gyroscope, magnetometer and BDS dual-antenna observation data. The IMU uses JY901B, and the BDS dual-antenna data collection platform is built with TAU1312 and BT-800D. The device is magnetically fixed on the top of the car to collect dual-antenna data and inertial navigation data when the vehicle is driving on the street.

[0009] Step 2: Use MEMS IMU to collect inertial data to obtain pitch angle and yaw angle, which are used to determine the search boundary of the COA attitude optimization algorithm, including:

[0010] 2-1 First, read the IMU raw data and preprocess it to convert the angular velocity and acceleration into appropriate units. Although the car is driving on city streets, the IMU itself is not affected by urban environmental factors such as high-rise buildings, tunnels, and tree shades, so its data quality remains stable. The main source of error comes from the IMU itself. The errors in inertial measurement data mainly include three categories: Bias and Noise, Scale errors, and Axis misalignments.

[0011] 2-2 To solve the error problem in inertial measurement data, the following methods are adopted: First, for noise and deviation problems, the extended Kalman filter (EKF) is used to perform dynamic error correction to improve the smoothness and accuracy of the data. Secondly, for scale factor errors, the scale factors of the IMU are corrected through a systematic calibration process. Finally, the deviation between the axes is reduced through precise coordinate system alignment and IMU calibration.

[0012] 2-3 Perform attitude calculation through inertial data to determine the algorithm search boundary. Assume that the yaw, pitch, and roll angles are z, y, and x respectively; then the quaternion calculation method is expressed as follows:

[0013]

[0014] 2-4 When using the extended Kalman filter (EKF) to process IMU data, the system makes predictions based on the current state at each time step and uses the measurement data to correct and update the predictions. This method can effectively suppress the influence of noise, thereby providing a smoother and more accurate attitude estimation. Accurate attitude estimation helps to narrow the search space of the attitude optimization algorithm and speed up convergence.

[0015] Step 3: Define the fitness function to be optimized based on the GNSS carrier phase observation model and attitude observation model.

[0016] Furthermore, step three specifically includes:

[0017] 3-1 Dual-antenna data acquisition model Figure 4 As shown, the sky satellites are distributed i and j; A and B are the positions of two receivers. is the baseline vector; p and h are the pitch angle and yaw angle of the baseline vector; and Ω are the elevation and azimuth of satellite j relative to the carrier plane of the baseline.

[0018] 3-2 As described in 3-1, specifically, considering various noises in the GNSS signal propagation process, the carrier phase observation equation is modeled as:

[0019] φ=λ -1 (r+c(dt-dT)-IT)+N+ε φ

[0020] Where φ is the carrier phase observation value, λ is the carrier wavelength, r is the satellite-to-ground geometric distance, N is the integer ambiguity, I and T are the ionosphere and troposphere errors, dt and dT are the receiver clock error and satellite clock error, respectively, and ε φ is the carrier phase observation noise.

[0021] 3-3 Single difference is performed on the carrier phase observation value. Since the baseline in the dual-antenna observation model is short, the influence of tropospheric and ionospheric errors can be reasonably ignored, thereby simplifying the error model and improving the solution efficiency and accuracy. The single difference observation equation is:

[0022]

[0023] in, is the single-difference carrier phase observation value, f is the carrier frequency, δt ur is the receiver clock error, The single-difference integer ambiguity. Two receivers perform differential analysis on the same satellite at the same time to eliminate the satellite clock error.

[0024] 3-4 Further differentiate the single difference data obtained in step 3-3, that is, the two receivers perform further differentiation on the two satellites at the same time. The double difference can further eliminate the receiver clock error. The double difference observation equation is:

[0025]

[0026] in, is the double-difference carrier phase observation value, and are the main antenna’s line of sight direction vector to the satellite, b ur is the baseline vector, is the double-difference integer ambiguity. By double-difference processing of the carrier phase observation value, most errors can be eliminated, which is beneficial to the subsequent solution process.

[0027] 3-5 According to the dual-antenna observation model and double-difference observation values, the satellite with the maximum elevation angle is selected as the reference satellite, and the fitness function of the double-difference COA attitude optimization algorithm is constructed. The specific definition is as follows:

[0028]

[0029] When h and p are taken as the true heading angle and pitch angle respectively, the fitness function reaches its global maximum. At this point, the solution of the attitude angle is transformed into the optimal value search problem in a given interval. Since the fitness function of the attitude angle presents multi-peak characteristics, there may be multiple local optimal solutions in the search interval. Therefore, in the search process, it is necessary to effectively suppress the interference of the local optimal solution on the global optimal solution to ensure the accuracy and reliability of the attitude.

[0030] Step 4: Introduce an adaptive dynamic parameter adjustment mechanism to accelerate the convergence process of the COA algorithm and avoid falling into a local optimal solution.

[0031] Furthermore, step four specifically includes:

[0032] 4-1 The basic idea of ​​COA is to simulate two natural behaviors of coatis: (i) hunting and attacking iguanas, and (ii) escaping from predators. The implementation steps of COA are divided into two stages: exploration and exploitation. The following is a detailed description of the exploration stage through a mathematical model.

[0033] In the initial stage of COA implementation, the position of the coati in the search space is initialized by the following formula:

[0034] X i :x i,j =lb j +r·(ub j -lb j ),i=1,2,...,N,j=1,2,...,m,

[0035] F is the vector of objective function values ​​obtained, F i is the objective function value obtained based on i coatis:

[0036]

[0037] The optimal value of the adaptive function is found by searching. In the exploration phase, the N coatis are divided into two groups, each with N / 2. The following two formulas are used to represent their positions and determine whether to update the positions.

[0038]

[0039] The calculated new position of each coati will be accepted for update if the objective function value improves, otherwise the coati will remain at its previous position. j represents the target value of decision variable j relative to the target value, is the new position calculated by the i-th coati, if The location is updated.

[0040] 4-2 The process of the second stage is mathematically modeled based on the natural behavior of coatis escaping from their current position when encountering a predator. In this strategy, the coati adjusts its position to approach the current state and finally reaches a safe area, thus reflecting the exploitation ability of COA in local search.

[0041]

[0042] where, is the new position calculated for the i-th coati in the second stage, and are the local upper bound and local lower bound of the j-th decision variable respectively. If F i P1 <F is satisfied, then update the position

[0043] 4-3 To improve the search accuracy of the algorithm in the later stage, the amplitude of random perturbation gradually decreases during the operation of the algorithm.

[0044]

[0045]

[0046] where, α t is the amplitude of random perturbation at the t-th iteration, and α0 is the initial amplitude of random perturbation.

[0047] Step Five: To address the problems of excessive jumping or slow convergence speed in the solution space, a step-size adaptive adjustment mechanism is proposed. The step size is increased in the early stage of the algorithm to promote global search and decreased in the later stage to improve local exploitation accuracy.

[0048] Furthermore, Step Five specifically includes:

[0049] 5-1 The dynamic adjustment strategy of the step size in each iteration is as follows:

[0050] Step t =β t ·(ub-lb)

[0051]

[0052] where, Step t is the step size at the t-th iteration; β t is the step-size factor, and β0 is the initial step-size ratio, which can be adjusted as needed.

[0053] 5-2 As described in 5-1, the step size is dynamically adjusted in each iteration and flexibly applied in different stages of the algorithm (such as the exploration stage) to optimize the performance of the search process:

[0054]

[0055] Step 6: In step 3, the fitness function is defined according to the carrier phase observation model and the attitude observation model. The decision variables are p and h. The search range is obtained by solving the inertial data. The improved COA is used for attitude search and optimization.

[0056] Furthermore, step six specifically includes:

[0057] The fitness function to be processed is:

[0058]

[0059] By determining the upper and lower bounds of the search based on the range of p and h solved based on the inertial data, the convergence speed is accelerated so that the attitude can be solved quickly and effectively.

[0060] Compared with the prior art, the advantages of the present invention are:

[0061] The present invention proposes a COA attitude solution method based on MEMS IMU assisted search, which makes full use of the characteristics of MEMS IMU in providing high-precision attitude information in a short time, provides a reliable search space for the search process of the COA algorithm, thereby significantly improving the search efficiency and achieving fast and efficient attitude solution. This method defines a fitness function for attitude solution search, and proposes a step size adaptive adjustment mechanism and a dynamic parameter adjustment strategy to address the problems of large jumps and slow convergence speed that may exist in the solution space. This method provides a fast and reliable attitude determination solution for the navigation of the carrier in a complex urban environment, and has broad application prospects and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to further illustrate the contents described in the present invention, the specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. It should be understood that these drawings are only used as typical examples and should not be regarded as limiting the scope of the present invention.

[0063] Figure 1 It is a flowchart of the COA attitude solution method based on MEMS IMU assisted search.

[0064] Figure 2 This is a scene diagram of collecting data in an urban environment with weak GNSS signals.

[0065] Figure 3 It is a schematic diagram of the specific implementation of collecting MEMS IMU and BDS dual antenna data.

[0066] Figure 4 It is a diagram of the spatial geometric relationship between the dual-antenna receiver and the satellite.

[0067] Figure 5 It is a search iteration diagram of the COA attitude solution method based on MEMS IMU assisted search. DETAILED DESCRIPTION

[0068] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. The specific embodiments of the present invention are described below:

[0069] An embodiment of the present invention provides a COA attitude solution method based on MEMS IMU assisted search, and a specific embodiment includes the following steps:

[0070] The core idea of ​​the present invention is to use MEMS IMU to collect inertial navigation data, obtain attitude information by processing the data collected by IMU, and use it as the upper and lower bounds of the COA algorithm search. Based on the spatial geometric relationship between the dual-antenna receiver and the satellite, combined with the GNSS carrier phase observation model and the attitude observation model, a fitness function to be optimized is constructed. In order to accelerate the convergence process of the COA algorithm and avoid falling into the local optimal solution, an adaptive dynamic parameter adjustment mechanism is introduced. In addition, in order to solve the problem of slow convergence speed caused by large jumps in the solution space, a step size adaptive adjustment strategy is proposed. Within the spatial range determined by the inertial navigation system, an improved COA algorithm is used for attitude search and optimization, thereby significantly improving the solution efficiency and attitude accuracy.

[0071] like Figure 1 As shown, it is a flowchart of the COA attitude solution method based on MEMS IMU assisted search. The specific implementation process of the present invention is as follows:

[0072] In order to solve the above problems existing in the prior art, the purpose of the present invention is to propose a COA attitude solution method based on MEMS IMU assisted search, which utilizes the short-term accurate angle characteristics provided by MEMS IMU, solves the pitch angle and yaw angle to limit the search boundary of COA, and provides high-precision attitude information by searching the fitness function.

[0073] To achieve the above-mentioned purpose and other related purposes, the present invention provides a COA attitude solution method based on MEMS IMU assisted search, the method comprising the following steps:

[0074] Step 1: Build a MEMS IMU and BDS dual-antenna data acquisition platform, as follows Figure 3 As shown, in Figure 2 The data collected on the city streets shown in the figure include acceleration, gyroscope, magnetometer and BDS dual-antenna observation data. The IMU uses JY901B, and the BDS dual-antenna data collection platform is built with TAU1312 and BT-800D. The device is magnetically fixed on the top of the car to collect dual-antenna data and inertial navigation data when the vehicle is driving on the street.

[0075] Step 2: Use MEMS IMU to collect inertial data to obtain pitch angle and yaw angle, which are used to determine the search boundary of the COA attitude optimization algorithm, including:

[0076] 2-1 First, read the IMU raw data and preprocess it to convert the angular velocity and acceleration into appropriate units. Although the car is driving on city streets, the IMU itself is not affected by urban environmental factors such as high-rise buildings, tunnels, and tree shades, so its data quality remains stable. The main source of error comes from the IMU itself. The errors in inertial measurement data mainly include three categories: Bias and Noise, Scale errors, and Axis misalignments.

[0077] 2-2 To solve the error problem in inertial measurement data, the following methods are adopted: First, for noise and deviation problems, the extended Kalman filter (EKF) is used to perform dynamic error correction to improve the smoothness and accuracy of the data. Secondly, for scale factor errors, the scale factors of the IMU are corrected through a systematic calibration process. Finally, the deviation between the axes is reduced through precise coordinate system alignment and IMU calibration.

[0078] 2-3 Perform attitude calculation through inertial data to determine the algorithm search boundary. Assume that the yaw, pitch, and roll angles are z, y, and x respectively; then the quaternion calculation method is expressed as follows:

[0079]

[0080] 2-4 When using the extended Kalman filter (EKF) to process IMU data, the system makes predictions based on the current state at each time step and uses the measurement data to correct and update the predictions. This method can effectively suppress the influence of noise, thereby providing a smoother and more accurate attitude estimation. Accurate attitude estimation helps to narrow the search space of the attitude optimization algorithm and speed up convergence.

[0081] Step 3: Define the fitness function to be optimized based on the GNSS carrier phase observation model and attitude observation model.

[0082] Furthermore, step three specifically includes:

[0083] 3-1 Dual-antenna data acquisition model Figure 4 As shown, the sky satellites are distributed i and j; A and B are the positions of two receivers. is the baseline vector; p and h are the pitch angle and yaw angle of the baseline vector; and Ω are the elevation and azimuth of satellite j relative to the carrier plane of the baseline.

[0084] 3-2 As described in 3-1, specifically, considering various noises in the GNSS signal propagation process, the carrier phase observation equation is modeled as:

[0085] φ=λ -1 (r+c(dt-dT)-IT)+N+ε φ

[0086] Where φ is the carrier phase observation value, λ is the carrier wavelength, r is the satellite-to-ground geometric distance, N is the integer ambiguity, I and T are the ionosphere and troposphere errors, dt and dT are the receiver clock error and satellite clock error, respectively, and ε φ is the carrier phase observation noise.

[0087] 3-3 Single difference is performed on the carrier phase observation value. Since the baseline in the dual-antenna observation model is short, the influence of tropospheric and ionospheric errors can be reasonably ignored, thereby simplifying the error model and improving the solution efficiency and accuracy. The single difference observation equation is:

[0088]

[0089] in, is the single-difference carrier phase observation value, f is the carrier frequency, δt ur is the receiver clock error, The single-difference integer ambiguity. Two receivers perform differential analysis on the same satellite at the same time to eliminate the satellite clock error.

[0090] 3-4 Further differentiate the single difference data obtained in step 3-3, that is, the two receivers perform further differentiation on the two satellites at the same time. The double difference can further eliminate the receiver clock error. The double difference observation equation is:

[0091]

[0092] in, is the double-difference carrier phase observation value, and are the main antenna’s line of sight direction vector to the satellite, b ur is the baseline vector, is the double-difference integer ambiguity. By double-difference processing of the carrier phase observation value, most errors can be eliminated, which is beneficial to the subsequent solution process.

[0093] 3-5 According to the dual-antenna observation model and double-difference observation values, the satellite with the maximum elevation angle is selected as the reference satellite, and the fitness function of the double-difference COA attitude optimization algorithm is constructed. The specific definition is as follows:

[0094]

[0095] When h and p are taken as the true heading angle and pitch angle respectively, the fitness function reaches its global maximum. At this point, the solution of the attitude angle is transformed into the optimal value search problem in a given interval. Since the fitness function of the attitude angle presents multi-peak characteristics, there may be multiple local optimal solutions in the search interval. Therefore, in the search process, it is necessary to effectively suppress the interference of the local optimal solution on the global optimal solution to ensure the accuracy and reliability of the attitude.

[0096] Step 4: Introduce an adaptive dynamic parameter adjustment mechanism to accelerate the convergence process of the COA algorithm and avoid falling into a local optimal solution.

[0097] Furthermore, step four specifically includes:

[0098] 4-1 The basic idea of ​​COA is to simulate two natural behaviors of coatis: (i) hunting and attacking iguanas, and (ii) escaping from predators. The implementation steps of COA are divided into two stages: exploration and exploitation. The following is a detailed description of the exploration stage through a mathematical model.

[0099] In the initial stage of COA implementation, the position of the coati in the search space is initialized by the following formula:

[0100] X i :x i,j =lb j +r·(ub j -lb j ),i=1,2,...,N,j=1,2,...,m,

[0101] F is the vector of objective function values ​​obtained, F i is the objective function value obtained based on i coatis:

[0102]

[0103] The optimal value of the adaptive function is found by searching. In the exploration phase, the N coatis are divided into two groups, each with N / 2. The following two formulas are used to represent their positions and determine whether to update the positions.

[0104]

[0105] The newly calculated position of each coati will be accepted for update if the objective function value is improved; otherwise, the coati will remain at the previous position. Among them, j represents p and h of the fitness function corresponding to the decision variable, Iguana j represents the objective value corresponding to the decision variable j, is the newly calculated position of the i-th coati. If it satisfies F i P1 < F, the position is updated.

[0106] 4-2 The process of the second stage is mathematically modeled based on the natural behavior of coatis to flee from the current position when encountering predators. In this strategy, the coati adjusts its position to approach the current state and finally reaches a safe area, thus reflecting the exploitation ability of COA in local search.

[0107]

[0108] Among them, is the newly calculated position of the i-th coati in the second stage of, and are the local upper bound and local lower bound of the j-th decision variable respectively. If it satisfies F i P1 < F, the position is updated

[0109] 4-3 To improve the search accuracy of the algorithm in the later stage, the amplitude of random perturbation gradually decreases during the running of the algorithm.

[0110]

[0111]

[0112] Among them, α t is the amplitude of random perturbation at the t-th iteration, and α0 is the initial amplitude of random perturbation.

[0113] Step Five: To address the problems of excessive jumping or slow convergence speed in the solution space, a step-size adaptive adjustment mechanism is proposed. The step size is increased in the early stage of the algorithm to promote global search and decreased in the later stage to improve local exploitation accuracy.

[0114] Furthermore, Step Five specifically includes:

[0115] 5-1 The dynamic adjustment strategy of the step size in each iteration is as follows:

[0116] Step t =β t ·(ub - lb)

[0117]

[0118] Among them, Step t is the step size of the tth iteration; β t is the step size factor, β0 is the initial step size ratio, which can be adjusted as needed.

[0119] 5-2 As described in 5-1, the step size is dynamically adjusted in each iteration and flexibly applied in different stages of the algorithm (such as the exploration stage) to optimize the performance of the search process:

[0120]

[0121] Step 6: In step 3, the fitness function is defined according to the carrier phase observation model and the attitude observation model. The decision variables are p and h. The search range is obtained by solving the inertial data. The improved COA is used for attitude search and optimization.

[0122] Furthermore, step six specifically includes:

[0123] The fitness function to be processed is:

[0124]

[0125] By determining the upper and lower bounds of the search based on the range of p and h solved based on the inertial data, the convergence speed is accelerated so that the attitude can be solved quickly and effectively.

[0126] Compared with the prior art, the advantages of the present invention are:

[0127] The present invention proposes a COA attitude solution method based on MEMS IMU assisted search, which makes full use of the characteristics of MEMS IMU in providing high-precision attitude information in a short time, provides a reliable search space for the search process of the COA algorithm, thereby significantly improving the search efficiency and achieving fast and efficient attitude solution. This method defines a fitness function for attitude solution search, and proposes a step size adaptive adjustment mechanism and a dynamic parameter adjustment strategy to address the problems of large jumps and slow convergence speed that may exist in the solution space. This method provides a fast and reliable attitude determination solution for the navigation of the carrier in a complex urban environment, and has broad application prospects and practical value.

Claims

1. A COA attitude solution method based on MEMSIMU assisted search, characterized in that: The method comprises the following steps: Step 1: Build a MEMSIMU and BDS dual-antenna data acquisition platform to collect observation data including acceleration, gyroscope, magnetometer and BDS dual-antenna; Step 2: Use the data collected by the IMU (such as accelerometer, gyroscope and magnetometer) to solve the attitude as the boundary of the COA algorithm search; Step 3: Define the fitness function to be optimized based on the GNSS carrier phase observation model and attitude observation model; Step 4: Introduce an adaptive dynamic parameter adjustment mechanism to accelerate the convergence process of the COA algorithm and avoid falling into a local optimal solution; Step 5: To address the problem of too large jumps in the solution space and slow convergence, a step-size adaptive adjustment mechanism is proposed; Step 6: Within the attitude space determined by the inertial navigation system, apply the improved COA to perform attitude search and optimization to improve the efficiency and accuracy of attitude solution.

2. The COA attitude solution method based on MEMSIMU assisted search according to claim 1 is characterized in that: Step 2 is as follows: 2-1 The AHRS attitude solution algorithm is used for attitude calculation, where the yaw, pitch and roll angles are represented by z, y, and x respectively, and are expressed in quaternion calculation as follows: 2-2 The IMU data is filtered through the extended Kalman filter (EKF). At each time step, the EKF predicts the system state and updates the state estimate and covariance matrix in combination with the measurement data, thereby effectively reducing the impact of noise and providing a smoother and more accurate attitude solution result.

3. The COA attitude solution method based on MEMSIMU assisted search according to claim 1 is characterized in that: The specific steps are: 3-1 According to the GNSS carrier phase observation model and attitude observation model, the fitness function carrier phase double difference observation equation to be optimized is defined as: 3-2 According to the dual-antenna observation model and double-difference observation values, the satellite with the maximum elevation angle is selected as the reference satellite, and the fitness function of the double-difference COA attitude optimization algorithm is constructed: Among them, h and p represent the yaw angle and pitch angle, respectively, which are used as decision variables.

4. The COA attitude solution method based on MEMSIMU assisted search according to claim 1 is characterized in that: Step 4 is as follows: The introduction of adaptive dynamic parameter adjustment mechanism accelerates the convergence process of COA algorithm and avoids falling into local optimal solution. The basic idea of ​​COA is to simulate two natural behaviors of coatis: (i) hunting and attacking iguanas, and (ii) escaping from predators. The implementation steps of COA are divided into two stages: exploration and exploitation. In the second phase, the location update strategy is: in, The second phase of is the new position calculated for the i-th coati, and are the local upper and lower bounds of the j-th decision variable respectively. If Update location To improve the search accuracy of the algorithm in the later stages, the amplitude of the random perturbation is gradually reduced during the operation of the algorithm.

5. The COA attitude solution method based on MEMSIMU assisted search according to claim 1 is characterized in that: Step 5 is as follows: To solve the problem of excessive jumps in the solution space or slow convergence, a step-size adaptive adjustment mechanism is proposed to increase the step-size in the early stage of the algorithm to promote global search. The specific strategies are as follows: in, Step t =β t ·(ub-lb), Step t is the step size of the tth iteration; β t is the step size factor, β0 is the initial step size ratio, which can be adjusted as needed.

6. The COA attitude solution method based on MEMSIMU assisted search according to claim 1 is characterized in that: Step 6 is as follows: In step three, a fitness function is constructed based on the carrier phase observation model and the attitude observation model, where the decision variables are p and h, and the search range is obtained by solving the inertial navigation data. Within the attitude space range determined by the inertial navigation system, the improved COA algorithm is used for attitude search and optimization, which effectively accelerates the convergence process of the algorithm search and achieves fast and efficient attitude solution while ensuring high accuracy.

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