Multi-scale data segmentation MEMS inertial navigation rapid calibration method and system

Abstract

The invention provides a multi-scale data segmentation MEMS inertial navigation rapid calibration method and system, and relates to the field of MEMS inertial navigation calibration, and the method comprises the steps: carrying out segmentation processing on original data output by an MEMS inertial sensor according to a preset time scale sequence, and generating a plurality of data segments with different time scales, the time lengths of which are sequentially increased; performing weighted fusion on different data segments according to a preset initial weight coefficient, generating a fusion observation vector, performing coarse alignment calculation of navigation positioning, obtaining an initial attitude angle, and calculating a corresponding observation residual error; adjusting the initial weight coefficient according to the observation residual error and performing coarse alignment calculation again until the observation residual error is smaller than a preset threshold value; and determining the corresponding initial attitude angle when the observation residual error is smaller than a preset threshold value as a final MEMS inertial navigation calibration result. By implementing the method, the contradiction between the calibration speed and the calibration precision can be balanced, the time required by the calibration process is shortened on the premise of ensuring the calibration precision as much as possible, and the calibration efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of MEMS inertial navigation calibration, and in particular to a multi-scale data segmentation MEMS inertial navigation fast calibration method and system. Background Art

[0002] With the rapid development of micro-electromechanical system (MEMS) technology, MEMS inertial navigation systems have been widely used in the fields of drones, robots, etc. due to their advantages such as small size and low cost. MEMS inertial navigation systems need to be initially calibrated before actual use to obtain accurate attitude information, which is of great significance to ensure navigation and positioning accuracy.

[0003] In the related art, the calibration of MEMS inertial navigation system mainly adopts the method based on Kalman filtering. This method first filters the raw data output by the MEMS sensor to suppress noise, and then uses a fixed-length data window to perform attitude solution. During the solution process, the attitude angle is iteratively estimated by the Kalman filter algorithm until the estimated value converges and the calibration result is output.

[0004] However, due to the complex time-varying characteristics of the measurement noise of MEMS sensors, it is difficult for a fixed-length data window to take into account both noise immunity and convergence speed. When the data window is short, the measurement noise will significantly affect the calibration accuracy; when the data window is long, although the noise can be effectively suppressed, it will slow down the convergence process and extend the calibration time. In addition, with the diversification of navigation application scenarios, the demand for fast startup and accurate calibration is becoming increasingly prominent. Summary of the invention

[0005] The present application provides a multi-scale data segmentation MEMS inertial navigation fast calibration method and system, which are used to solve the problem of how to shorten the calibration time of the MEMS inertial navigation system while ensuring the calibration accuracy.

[0006] In a first aspect, the present application provides a multi-scale data segmentation MEMS inertial navigation fast calibration method, which is applied to a MEMS inertial navigation system, and the method comprises: The raw data output by the MEMS inertial sensor is stored in real time through a multi-level buffer, wherein the raw data includes angular velocity data and acceleration data; The raw data in the multi-level buffer is processed in segments according to a preset time scale sequence to generate a plurality of data segments with different time scales with increasing time lengths; Performing weighted fusion on the data segments of different time scales according to a preset initial weight coefficient to generate a fused observation vector; Using the fused observation vector to perform a rough alignment calculation for navigation positioning to obtain an initial attitude angle, and calculating an observation residual based on the initial attitude angle, wherein the observation residual is a difference between the initial attitude angle and a corresponding theoretical value; Adjust the initial weight coefficient according to the observation residual and perform rough alignment calculation again until the observation residual is less than a preset threshold; The initial attitude angle corresponding to when the observation residual is less than a preset threshold is determined as the final MEMS inertial navigation calibration result.

[0007] Through the above embodiments, the MEMS inertial navigation system can process short-term and long-term data at the same time through multi-level buffer storage and multi-time scale segmentation, avoiding the problem of long calibration time caused by using a long time window to obtain high precision in the traditional method. The weight coefficient is used for data fusion and the weight is dynamically adjusted according to the observation residual to achieve fast and accurate calibration. Compared with the fixed window method, the calibration time can be greatly shortened under the same accuracy requirements. This solution has a good balance between the contradiction between calibration speed and accuracy, and significantly shortens the time required for the calibration process while ensuring the calibration accuracy as much as possible, thereby improving the calibration efficiency.

[0008] In some embodiments, the step of segmenting the raw data in the multi-level buffer according to a preset time scale sequence specifically includes: Establishing multiple parallel sliding windows for selecting data segments to be processed at different time scales, respectively, wherein the length of each sliding window matches the corresponding time scale; Performing signal quality evaluation on the original data in each sliding window to obtain a window signal characteristic value, wherein the window signal characteristic value includes signal fluctuation and data continuity; Determining the segmentation parameters of each sliding window according to the window signal characteristic value, wherein the segmentation parameters include a window moving step length and a data interception interval, and the window moving step length is related to the overlap degree of adjacent data segments; Segmenting the original data in each sliding window in parallel based on the segmentation parameter to obtain multiple candidate data segments; The candidate data segments are screened and integrated according to the signal consistency criterion to generate final data segments of different time scales.

[0009] Through the above embodiments, the MEMS inertial navigation system uses a parallel sliding window for segmented processing, which can process data of multiple time scales at the same time, avoiding the time delay caused by serial processing. By dynamically adjusting the segmentation parameters through signal quality evaluation, high-quality data segments can be quickly screened out for calibration calculations, reducing the time consumed in processing low-quality data. At the same time, the signal consistency criterion is introduced to ensure data reliability, which improves the processing speed while ensuring the calibration accuracy. Compared with the traditional fixed parameter segmentation method, this solution can reduce data processing time while maintaining a considerable level of accuracy.

[0010] In some embodiments, the step of adjusting the initial weight coefficient according to the observation residual and performing the rough alignment calculation again until the observation residual is less than a preset threshold specifically includes: Establishing a multi-level precision control sequence of multiple observation residual thresholds arranged from large to small, each of the observation residual thresholds corresponds to an iteration stage; Calculating a relaxation factor according to an observation residual threshold value in a current iteration phase, wherein the relaxation factor is positively correlated with the observation residual threshold value; Performing weighted processing on the observation residual according to the relaxation factor to obtain a weight adjustment amount, and updating the initial weight coefficient according to the weight adjustment amount to generate a new weight coefficient combination; Performing rough alignment calculation again according to the new weight coefficient combination to determine whether the obtained observation residual is less than the observation residual threshold of the current iteration stage; If yes, it goes to the next iteration stage; if no, it returns to the relaxation factor calculation step to continue the iteration.

[0011] Through the above embodiments, the MEMS inertial navigation system establishes a multi-level precision control sequence and uses relaxation factors to dynamically adjust the weights, avoiding the problem of blindly pursuing high precision and excessive iteration in traditional methods. By setting a reasonable precision target for each iteration stage and coordinating adaptive iteration step control, rapid convergence of calibration accuracy is achieved and calibration time is shortened.

[0012] In some embodiments, before the step of if yes, then entering the next iteration phase, the process further includes: The temporary attitude angle calculated by the rough alignment is output and displayed, and the number of rough alignment times is recorded.

[0013] Through the above embodiments, the MEMS inertial navigation system displays the temporary attitude angle in real time and records the number of rough alignments, so that the operator can judge the calibration progress in time and immediately terminate the calibration process when the required accuracy is achieved, thus avoiding the time waste caused by over-calibration. At the same time, these records also provide a basis for optimizing the calibration strategy, which helps to further improve the calibration efficiency.

[0014] In some embodiments, the step of calculating the relaxation factor according to the observation residual threshold value of the current iteration stage specifically includes: Calculate the ratio of the current observation residual to the observation residual threshold to obtain a convergence index, wherein the convergence index represents the distance between the current calculation result and the target accuracy; Substituting the convergence index into a preset nonlinear mapping function to obtain a dynamic attenuation rate, wherein the dynamic attenuation rate decreases monotonically with the iteration process; The relaxation factor of the current iteration stage is calculated according to the dynamic decay rate.

[0015] Through the above embodiments, the MEMS inertial navigation system adopts a dynamic attenuation mechanism based on the convergence index, so that the iterative process can adaptively adjust the step size: a large step size is used for rapid approximation when the accuracy is low, and a small step size is used for fine adjustment when the target accuracy is close. This adaptive strategy avoids the repeated oscillation problem in the traditional fixed step size method and speeds up the convergence speed.

[0016] In some embodiments, before the step of segmenting the raw data in the multi-level buffer according to a preset time scale sequence, the method further includes: Extracting features from historical data segments through a preset sliding time window to obtain a data feature sequence, wherein the data feature sequence includes a signal fluctuation feature and a data correlation feature; Building a time series prediction model based on the data feature sequence, wherein the time series prediction model adopts a recursive neural network structure to capture the time series dependency of data segments; Use the time series prediction model to predict the data features of the next time window to obtain a predicted feature value; The sampling frequency and the buffer length are dynamically adjusted according to the predicted characteristic value to generate an optimized acquisition strategy.

[0017] Through the above embodiments, the MEMS inertial navigation system uses a recursive neural network to build a time series prediction model, and predicts the data characteristics of the next time window by learning the characteristics of historical data, thereby realizing intelligent optimization of data acquisition parameters. This prediction-based adaptive acquisition strategy can dynamically adjust the sampling frequency and buffer length according to signal characteristics, while ensuring data quality and reducing storage and computing overhead. Especially in complex dynamic environments, this method shows excellent adaptability and resource utilization efficiency.

[0018] In some embodiments, before the step of using the fused observation vector to perform a rough alignment calculation for navigation positioning to obtain an initial attitude angle, the step further includes: Performing cluster analysis on historical calibration data, extracting error characteristic patterns, and establishing an error pattern library, wherein the error pattern library contains typical error patterns and their corresponding optimal compensation parameters; Calculating a current error characteristic vector according to the fused observation vector, wherein the error characteristic vector includes a drift characteristic and a noise distribution characteristic; Calculate the matching degree between the error feature vector and the typical error pattern in the error pattern library to obtain pattern similarity; An optimal compensation mode is selected according to the mode similarity, and corresponding compensation parameters are extracted to generate an initial compensation scheme for the initial attitude angle during the rough alignment calculation process.

[0019] Through the above embodiments, the MEMS inertial navigation system establishes an error pattern library and performs intelligent matching compensation, which can quickly identify system error characteristics and apply corresponding compensation in the early stage of calibration, avoiding the problem of slow convergence caused by traditional methods that completely rely on iterative calculations. Through the guidance of this prior knowledge, the initial attitude angle can be closer to the true value, thereby reducing the number of subsequent iterations.

[0020] In a second aspect, the present application provides a MEMS inertial navigation system, the MEMS inertial navigation system comprising: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, and the computer program codes include computer instructions. The one or more processors call the computer instructions so that the MEMS inertial navigation system can implement a multi-scale data segmentation MEMS inertial navigation fast calibration method provided in the above embodiment, which will not be repeated here.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a MEMS inertial navigation system, the MEMS inertial navigation system can implement a multi-scale data segmentation MEMS inertial navigation fast calibration method provided in the above-mentioned embodiment, which will not be repeated here.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on a MEMS inertial navigation system, the MEMS inertial navigation system can implement a multi-scale data segmentation MEMS inertial navigation fast calibration method provided in the above embodiment, which will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The multi-level buffer and parallel sliding window are used to process data segments of different time scales at the same time, breaking through the trade-off dilemma between calibration speed and accuracy of the traditional fixed window method. Adaptive fusion is performed through dynamic weight adjustment and signal quality evaluation, which greatly shortens the processing time while ensuring calibration accuracy, and realizes rapid calibration of navigation positioning.

[0024] 2. Establish a multi-level precision control sequence from coarse to fine, and cooperate with the dynamic attenuation mechanism based on the convergence index to achieve rapid approximation of calibration accuracy. By adaptively adjusting the iteration step size, the repeated oscillation problem of the traditional fixed step size method is avoided, thereby greatly shortening the calibration time.

[0025] 3. Combine recursive neural network prediction with error pattern library, optimize acquisition parameters by predicting data features, and use historical experience to quickly identify and compensate for system errors. This intelligent solution can reduce data acquisition time, reduce the iterative calculation amount of the system, and improve calibration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a multi-scale data segmentation MEMS inertial navigation fast calibration method in an embodiment of the present application; Figure 2 It is another flow chart of a multi-scale data segmentation MEMS inertial navigation fast calibration method in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the MEMS inertial navigation system in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of a method for rapid calibration of MEMS inertial navigation with multi-scale data segmentation in an embodiment of the present application.

[0030] S101 , storing raw data output by the MEMS inertial sensor in real time through a multi-level buffer.

[0031] Among them, the multi-level cache is a storage structure composed of multiple cache units of different capacities or levels, which is used to temporarily store the data output by the sensor in real time in chronological order; MEMS inertial sensor refers to accelerometers and gyroscopes that integrate microelectromechanical systems (MEMS) technology, which are used to measure the acceleration and angular velocity information of the carrier and are the core data acquisition components of the inertial navigation system.

[0032] This step is performed after the MEMS inertial navigation system is started and before the calibration process begins. It receives the data stream output by the sensor in real time and stores it continuously, providing a continuous raw data buffer for subsequent segment processing.

[0033] Specifically, the MEMS inertial navigation system establishes a hierarchical data storage mechanism through a multi-level cache at the hardware or software level. The multi-level cache usually contains multiple cache units of different lengths (such as short-term cache, medium-term cache, and long-term cache), which correspond to data storage at different time scales (such as seconds and minutes). The sensor outputs raw data (x / y / z-axis acceleration of the accelerometer, x / y / z-axis angular velocity of the gyroscope) at a fixed frequency (such as 100Hz). These data are written to the multi-level cache in real time in timestamp order to form a continuous data stream storage. The cache supports real-time reading and segmented retrieval of data, providing a basis for subsequent data processing at different time scales.

[0034] Optionally, the system is designed with a three-level FIFO (first-in-first-out) cache queue. The first level is a high-speed cache (capacity of 1 second data) for temporary storage of the latest data; the second level is an intermediate frequency cache (capacity of 10 seconds data) for storing recent data; the third level is a low-speed cache (capacity of 60 seconds data) for long-term storage of historical data. Sensor data is written to the three-level cache in sequence. Each time new data is written, each level of cache is automatically updated, and the old data overflows in chronological order.

[0035] S102, segmenting the original data in the multi-level buffer according to a preset time scale sequence to generate a plurality of data segments of different time scales with increasing time lengths.

[0036] Among them, the preset time scale sequence refers to a set of predefined time lengths (such as 1s, 5s, 10s, 30s), which are used to guide the time span of the original data segmentation, usually arranged in order from small to large to form multi-scale data segments.

[0037] Specifically, the system extracts the original data of the corresponding time length in the multi-level buffer according to the preset time scale sequence (for example, [1s, 5s, 10s, 30s]). For each time scale, the sliding window technology (window length equals the time scale) is used to slide the original data into segments, and the window moving step can be set to a fixed value (such as 50% of the time scale) or dynamically adjusted (according to the signal quality). During the segmentation process, the signal quality of the data in each window is evaluated (such as calculating the signal volatility and data continuity), and high-quality data segments are screened out to avoid segments with excessive noise or missing data. Finally, multiple data segments of different time lengths are generated, such as 1 second segment, 5 second segment, 10 second segment, etc. Each data segment contains a complete original data sequence in the corresponding time for subsequent weighted fusion.

[0038] Optionally, for a preset time scale sequence (such as t1=1s, t2=5s, t3=10s), create sliding windows of the same length as t1, t2, and t3 in a multi-level buffer. Each window slides independently, and the window movement step is set to a fixed value (such as t1 / 2, t2 / 2, t3 / 2) to ensure that adjacent data segments partially overlap (such as 50% overlap). Perform signal quality detection on the data in each window, calculate the volatility (such as the standard deviation of acceleration data) and continuity (check whether there is data missing or timestamp jump), and set a threshold to filter windows with volatility exceeding the threshold or data missing. Perform time alignment and format standardization on the window data that passes the detection to generate data segments of the corresponding time scale, for example, arrange the 1s window data segments in chronological order and store them as a structure containing the starting timestamp and data point sequence.

[0039] Optionally, the average signal fluctuation and continuity at each time scale are statistically analyzed based on historical data to generate initial segmentation parameters (e.g., the default moving step is 30% of the time scale). The signal fluctuation (e.g., the root mean square error of angular velocity data) and continuity index (standard deviation of the interval between data points) of the current window are calculated in real time. If the fluctuation is 1.5 times higher than the historical average, the moving step is automatically reduced (e.g., from 30% to 20%), and the data segment overlap is increased to improve signal stability; if the continuity is good (data missing rate <5%), the moving step is increased to reduce the amount of calculation. The candidate data segments of each time scale are checked for consistency, that is, the data at the same time point in the data segments of different time scales are checked for consistency (a certain time error is allowed), inconsistent segments are eliminated, and finally reliable multi-scale data segments are generated.

[0040] S103 , performing weighted fusion on data segments of different time scales according to a preset initial weight coefficient to generate a fused observation vector.

[0041] Specifically, the system first assigns an initial weight coefficient to each time scale data segment, for example, setting the weight of the 1s data segment =0.3, 5s data segment weight =0.25, 10s data segment weight =0.2, 30s data segment weight =0.25, and the sum of the weights is 1. Then, the characteristic values ​​(such as the mean and variance of acceleration data, and the integral value of angular velocity data) are extracted for each data segment to form a characteristic vector. The characteristic vectors of each scale are weighted and summed according to the weight coefficient to obtain the fused observation vector.

[0042] S104, using the fused observation vector to perform a rough alignment calculation for navigation positioning, obtaining an initial attitude angle, and calculating the observation residual based on the initial attitude angle.

[0043] Among them, the initial attitude angle is used to represent the carrier attitude parameters obtained by coarse alignment calculation, including pitch angle, roll angle, and yaw angle, reflecting the initial orientation of the carrier relative to the navigation coordinate system; the observation residual refers to the difference between the initial attitude angle and the corresponding theoretical value. The theoretical value is usually based on the direction of the gravity field when the carrier is stationary (such as the theoretical values ​​of the pitch angle and roll angle are 0°, and the yaw angle can be based on magnetic north or a preset reference direction).

[0044] Specifically, the MEMS inertial navigation system uses the acceleration and angular velocity information in the fused observation vector to perform rough alignment through the inertial navigation algorithm. First, the acceleration data in the fused observation vector is projected into the gravity field to calculate the attitude matrix between the carrier coordinate system and the navigation coordinate system. For example, assuming that when the carrier is stationary, the accelerometer measurement value should be equal to the gravitational acceleration g. By solving the components of the gravity vector in the carrier coordinate system, the pitch angle and roll angle are obtained. The yaw angle can be determined by integrating the angular velocity or combining with a magnetic compass. After obtaining the initial attitude angle, the difference between it and the theoretical value (such as the pitch angle and roll angle are 0° when stationary) is calculated, which is the observation residual.

[0045] In addition, before using the fused observation vector for rough alignment calculation of navigation positioning, the MEMS inertial navigation system first performs cluster analysis on the historical calibration data. By selecting a suitable clustering algorithm (such as the K-Means algorithm), the historical calibration data is divided into several categories, each of which has similar error characteristics, thereby extracting error feature patterns. These typical error feature patterns and their corresponding optimal compensation parameters are stored in the error pattern library.

[0046] Next, based on the current fused observation vector, its drift characteristics and noise distribution characteristics are calculated to construct the current error characteristic vector. For example, by taking the first-order difference of the acceleration data in the fused observation vector, its drift trend is obtained; by calculating the standard deviation of the acceleration data, its noise distribution characteristics are obtained.

[0047] Then, the cosine similarity method is used to calculate the matching degree between the current error feature vector and the typical error pattern in the error pattern library, and the pattern similarity between each typical error pattern and the current error feature vector is obtained.

[0048] According to the pattern similarity, the typical error pattern with the highest similarity is selected as the optimal compensation pattern, and the compensation parameters corresponding to the pattern are extracted from the error pattern library. According to these compensation parameters, the initial compensation scheme for the initial attitude angle is generated during the rough alignment calculation process.

[0049] After completing the above preparations, the MEMS inertial navigation system uses the acceleration and angular velocity information in the fused observation vector to perform rough alignment through the strapdown inertial navigation algorithm. When calculating the initial attitude angle, the initial compensation scheme is applied to compensate the calculation results. For example, when calculating the pitch angle and roll angle, the acceleration data is adjusted according to the compensation parameters, and then the attitude angle is calculated.

[0050] S105, adjusting the initial weight coefficient according to the observation residual and performing the coarse alignment calculation again until the observation residual is less than a preset threshold.

[0051] Specifically, the system first establishes a multi-level precision control sequence (such as three-level thresholds: θ1=5°, θ2=2°, θ3=0.5°), and iterates from the first level threshold. At the current stage, the ratio of the observation residual to the threshold (convergence index) is calculated, and the dynamic attenuation rate is obtained through a nonlinear mapping function (such as an exponential function), and then the relaxation factor is calculated. For example, if the current residual is 4°, the threshold θ1=5°, and the convergence index is 0.8, the dynamic attenuation rate is 0.9 when substituted into the mapping function, and the relaxation factor α=0.8×0.9=0.72. According to the relaxation factor, the weight coefficient is adjusted (such as increasing the weight of the long-term data segment to suppress noise), a new fused observation vector is generated, and the rough alignment calculation is performed again to obtain a new observation residual. If the residual is less than the current threshold (such as 4°<5°), it enters the next level threshold (θ2=2°), and repeats the above process until the observation residual is less than the final threshold (such as 0.5°).

[0052] S106: Determine the initial attitude angle corresponding to when the observation residual is less than a preset threshold as the final MEMS inertial navigation calibration result.

[0053] This step is executed when the observation residual is less than the preset threshold for the first time (the iteration of step S105 is terminated), marking the end of the calibration process. At this time, the carrier completes the initial calibration and enters the navigation mode. The calibration result is used for subsequent inertial navigation solution and position update.

[0054] Specifically, after a rough alignment calculation, when the observed residuals (such as pitch residual 0.3°, roll residual 0.4°, yaw residual 0.6°) are less than the preset threshold (such as 0.5°), the system immediately terminates the iteration and marks the current initial attitude angle (such as Pitch=0.2°, Roll=0.3°, Yaw=0.5°) as the final calibration result. To ensure the stability of the results, it is possible to verify whether the residuals are continuously less than the threshold (such as 3 consecutive verifications) to avoid misjudgment due to accidental noise. The final result is stored in the system register for the navigation algorithm to call as the initial attitude matrix input for inertial navigation solution.

[0055] In the above embodiment, the MEMS inertial navigation system can process short-term and long-term data simultaneously through multi-level buffer storage and multi-time scale segmentation, avoiding the problem of long calibration time caused by using a long time window to obtain high precision in the traditional method. The weight coefficient is used for data fusion and the weight is dynamically adjusted according to the observation residual to achieve fast and accurate calibration. Compared with the fixed window method, the calibration time can be greatly shortened under the same accuracy requirements. This solution balances the contradiction between calibration speed and accuracy well, significantly shortens the time required for the calibration process while ensuring the calibration accuracy as much as possible, and improves the calibration efficiency.

[0056] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of a multi-scale data segmentation MEMS inertial navigation rapid calibration method in an embodiment of the present application.

[0057] S201. Extract features of historical data segments through a preset sliding time window to obtain a data feature sequence.

[0058] This step is performed after the MEMS inertial navigation system is started and a certain amount of historical data has been accumulated (such as after the first calibration is completed). It belongs to the preprocessing stage of data acquisition optimization.

[0059] Specifically, the system first defines a preset sliding time window (such as window length T=20s, sliding step t=10s), starts sliding from the earliest moment in the historical data, and intercepts a data segment of length T each time. Signal fluctuation characteristics and data correlation characteristics are extracted for each data segment. Among them, the signal fluctuation characteristics can be determined by calculating the standard deviation of the acceleration data (reflecting the noise level), etc.; the data correlation characteristics can be determined by calculating the autocorrelation coefficient of the acceleration or angular velocity at adjacent time points (reflecting the strength of data time series dependence) and the mutual correlation coefficient (reflecting the correlation of the three-axis data).

[0060] It is understandable that other methods may also be used to extract features, such as time-frequency feature extraction based on wavelet transform, feature selection algorithms combined with machine learning (such as recursive feature elimination), etc., which are not limited here.

[0061] S202: construct a time series prediction model based on the data feature sequence, and use the time series prediction model to predict the data features of the next time window to obtain a predicted feature value.

[0062] Specifically, the system can use RNN architecture (such as LSTM network) to build a time series prediction model. Standardize the data feature sequence (such as normalizing to [0,1]), and divide it into a training set (accounting for 80%) and a validation set (accounting for 20%) in chronological order. Each sample is a feature sequence of the past N time windows, and the label is the feature value of the N+1th window. Set up two LSTM layers (128 neurons per layer), with a Dropout layer to prevent overfitting, and use a fully connected layer in the output layer. The number of neurons is equal to the feature dimension (such as 10 dimensions), and the activation function is a linear function (suitable for regression tasks). Then use the Adam optimizer, the loss function is the mean square error (MSE), monitor the validation set loss during training, terminate the training in advance when the loss no longer decreases for 5 consecutive epochs, and save the optimal model parameters to obtain the time series prediction model. This enables the time series prediction model to learn the temporal dependence law of the data feature sequence, for example, high volatility in the current window is usually accompanied by high noise levels in the next time window, thereby realizing the prediction of future features.

[0063] S203: Dynamically adjust the sampling frequency and buffer length according to the predicted characteristic value to generate an optimized acquisition strategy.

[0064] Specifically, the system first inputs the feature sequences of the latest N time windows into the time series prediction model to obtain the predicted feature values ​​of the next time window (such as predicted signal volatility σ=0.8, data correlation ρ=0.6). Then, an optimized acquisition strategy is generated according to the preset adjustment rules. Optionally, if σ exceeds the threshold of 0.7 (indicating that the signal fluctuates violently), the sampling frequency is increased from 100Hz to 200Hz to capture high-frequency motion details; if σ<σ0.3 (indicating that the signal is stable), the sampling frequency is reduced to 50Hz to reduce the amount of data. If ρ>0.5 (indicating strong data correlation), it means that the data timing dependence is obvious, and the length of the long-term cache area can be reduced from 60s to 30s, using correlation to reduce storage requirements; if ρ<0.3 (weak correlation), it is extended to 90s to avoid data loss affecting calibration accuracy.

[0065] The optimized acquisition strategy finally generated is sent to the sensor driving module and cache management module in real time to achieve dynamic optimization of acquisition parameters.

[0066] S204: Establish multiple parallel sliding windows for selecting data segments to be processed on different time scales.

[0067] Specifically, the MEMS inertial navigation system creates an independent sliding window for each time scale in a multi-level buffer according to a preset time scale sequence. The length of each window is equal to the corresponding time scale (such as a 1s window length equals 1s of data volume), and the number of windows is consistent with the number of time scales, supporting parallel operation. For example, a 1s window captures 100 data points at a sampling rate of 100Hz, and a 5s window captures 500 data points. Each window slides synchronously according to the timestamp to ensure that the data segments are aligned in time. When the window slides, it always overwrites the latest data in the buffer to avoid processing outdated information.

[0068] S205: Perform signal quality evaluation on the original data in each sliding window to obtain a window signal characteristic value.

[0069] Specifically, the MEMS inertial navigation system evaluates the raw data in each sliding window from two aspects: signal fluctuation and data continuity. For signal fluctuation, the system calculates statistics such as the standard deviation and root mean square error (RMSE) of the acceleration data and angular velocity data. The larger the standard deviation or RMSE, the more drastic the data fluctuation and the more unstable the signal. For data continuity, the system checks whether there are missing values ​​in the data and whether the timestamp is continuous. If there is a data point loss or a timestamp jump, the data continuity is considered poor.

[0070] S206. Determine a segmentation parameter of each sliding window according to the window signal characteristic value, and perform parallel segmentation on the original data in each sliding window based on the segmentation parameter to obtain multiple candidate data segments.

[0071] Specifically, the MEMS inertial navigation system determines the segmentation parameters of each sliding window according to the window signal characteristic value obtained in step S205. If the data signal fluctuation in the window is large and the data continuity is poor, the system will reduce the window moving step length, increase the overlap of adjacent data segments, and appropriately adjust the data interception interval to avoid data abnormal areas; on the contrary, if the signal fluctuation is small and the data continuity is good, the window moving step length can be increased to reduce overlap and improve processing efficiency. After determining the segmentation parameters, the system uses these parameters to perform parallel segmentation on the original data in each sliding window. For example, for a sliding window with a time scale of 1 second, if the moving step length is determined to be 0.5 seconds and the data interception interval is the entire window length, then starting from the window starting position, a data segment of 1 second in length is intercepted every 0.5 seconds to obtain multiple candidate data segments of 1 second in length, and sliding windows of other time scales are also operated similarly, and finally multiple candidate data segments of different time scales are obtained.

[0072] S207 , screening and integrating the candidate data segments according to the signal consistency criterion to generate final data segments of different time scales.

[0073] Specifically, the MEMS inertial navigation system processes the candidate data segments according to the signal consistency criterion. First, the system compares the signal characteristics of different candidate data segments, such as the changing trends of acceleration and angular velocity. If the changing trends of acceleration and angular velocity of two candidate data segments are too different within the same time range, it is determined that they may be subject to different interferences or have data anomalies, and one or two data segments may be unreliable, and they are marked as data segments to be screened. For the marked data segments, the system further checks whether their data volatility and continuity conform to the characteristic distribution of the overall data. If the volatility is abnormally high or the continuity is poor, it is removed from the candidate data segments. Then, the remaining reliable candidate data segments are integrated according to the time scale. For candidate data segments of the same time scale, if their signal characteristics are similar and the changing trends are consistent, they are merged into a final data segment; if there are partially overlapping data segments, the data of the overlapping parts are weighted averaged or other fusion operations are performed, and finally high-quality data segments of different time scales are generated to prepare for subsequent weighted fusion.

[0074] S208. Calculate the observation residual corresponding to the initial attitude angle based on data segments of different time scales.

[0075] Specifically, the MEMS inertial navigation system first calculates the initial attitude angle using data segments of different time scales. The system uses an inertial navigation algorithm to perform calculations based on the acceleration and angular velocity information in the data segment. For example, by projecting the acceleration data into the gravity field and combining methods such as angular velocity integration, the attitude matrix between the carrier coordinate system and the navigation coordinate system is calculated, and then the initial attitude angles such as pitch angle, roll angle, and yaw angle are obtained. After obtaining the initial attitude angle, the system compares it with the theoretical value to calculate the observation residual. Taking the pitch angle as an example, if the theoretical value is 0° and the calculated initial pitch angle is 0.5°, the observation residual of the pitch angle is 0.5° - 0° = 0.5°. Similarly, the observation residuals of the roll angle and yaw angle are calculated, which will not be repeated here.

[0076] S209, calculating the ratio of the current observation residual to the observation residual threshold to obtain a convergence index, and inputting the convergence index into a preset nonlinear mapping function to obtain a dynamic attenuation rate.

[0077] Specifically, the MEMS inertial navigation system first obtains the current observation residual calculated in step S208 and the pre-set observation residual threshold. The current observation residual is divided by the observation residual threshold to obtain a convergence index. For example, if the current observation residual is 3° and the observation residual threshold is 5°, the convergence index is 3°÷5° = 0.6. Then, this convergence index is used as input and substituted into the preset nonlinear mapping function. Assume that the preset nonlinear mapping function is an exponential function (x is the convergence index, y is the dynamic decay rate), substituting the convergence index 0.6 into the function, the dynamic decay rate is calculated to be 0.936. This dynamic decay rate will be used to calculate the relaxation factor in the subsequent calculation, and then adjust the weight coefficient to optimize the calibration process.

[0078] S210, calculating the relaxation factor of the current iteration stage according to the dynamic decay rate.

[0079] This step has been introduced in step S105 and will not be repeated here.

[0080] S211. Perform weighted processing on the observation residual according to the relaxation factor to obtain a weight adjustment amount, and update the initial weight coefficient according to the weight adjustment amount to generate a new weight coefficient combination.

[0081] Specifically, the MEMS inertial navigation system first performs a weighted operation on the relaxation factor obtained in step S210 and the observation residual calculated in step S208. For example, assuming that the relaxation factor is α and the observation residual is e, the weight adjustment amount can be calculated as Δw=α×e (Δw is the weight adjustment amount). Then, the initial weight coefficient is updated according to the weight adjustment amount.

[0082] S212, performing rough alignment calculation again based on the new weight coefficient combination to determine whether the obtained observation residual is less than the observation residual threshold of the current iteration stage.

[0083] Specifically, the MEMS inertial navigation system uses the new combination of weight coefficients generated in step S211 to perform weighted fusion on data segments of different time scales again to obtain a new fused observation vector. Then, using this new fused observation vector, a coarse alignment calculation is performed again according to the inertial navigation algorithm to obtain a new initial attitude angle and calculate the observation residual. This observation residual is then compared with the observation residual threshold pre-set in the current iteration stage. If the observation residual threshold of the current iteration stage is 0.5°, since 0.4°<0.5°, it is judged that the observation residual obtained by this coarse alignment calculation is less than the observation residual threshold of the current iteration stage; if the observation residual is greater than or equal to the threshold, it is judged that the accuracy requirement of the current stage is not met.

[0084] S213. If yes, enter the next iteration stage; if no, return to the relaxation factor calculation step to continue iteration.

[0085] Specifically, after the MEMS inertial navigation system completes the comparison between the observation residual and the observation residual threshold of the current iteration stage in step S212, it makes a decision based on the comparison result. If the observation residual is less than the observation residual threshold of the current iteration stage, the system enters the next iteration stage. When entering the next iteration stage, the system will update the relevant parameter settings, such as adjusting the observation residual threshold to a stricter value, to prepare for the next round of calibration calculations, and continue to improve the calibration accuracy. If the observation residual is greater than or equal to the observation residual threshold of the current iteration stage, the system returns to step S210. After returning to step S210, the system will recalculate the relaxation factor according to information such as the current convergence index. Because the convergence index will change as the iteration proceeds, the recalculated relaxation factor will also change accordingly, and then different weight adjustment amounts will be obtained, the weight coefficient will be updated, and the rough alignment calculation will be performed again, hoping to obtain a smaller observation residual, so that the calibration result is closer to the true value, and so on. The cycle iteration is repeated until the observation residual is less than the preset final threshold.

[0086] The MEMS inertial navigation system of the embodiment of the present invention is applied to electronic equipment. Figure 3 A schematic diagram of the architecture of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0087] It should be noted that Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0088] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling related hardware through instructions (computer programs), and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein a plurality of instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided in the embodiment of the present invention.

[0089] Specifically, the storage medium and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more signal lines. The storage medium stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, random access storage medium (Random Access Memory, referred to as: RAM), read-only storage medium (Read Only Memory, referred to as: ROM), programmable read-only storage medium (Programmable Read-Only Memory, referred to as: PROM), erasable read-only storage medium (Erasable Programmable Read-Only Memory, referred to as: EPROM), electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, referred to as: EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the program after receiving the execution instruction.

[0090] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps, and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0091] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0092] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-scale data segmentation MEMS inertial navigation fast calibration method, applied to MEMS inertial navigation system, characterized in that: The method comprises: The raw data output by the MEMS inertial sensor is stored in real time through a multi-level buffer, wherein the raw data includes angular velocity data and acceleration data; The raw data in the multi-level buffer is processed in segments according to a preset time scale sequence to generate a plurality of data segments with different time scales with increasing time lengths; Performing weighted fusion on the data segments of different time scales according to a preset initial weight coefficient to generate a fused observation vector; Using the fused observation vector to perform a rough alignment calculation for navigation positioning to obtain an initial attitude angle, and calculating an observation residual based on the initial attitude angle, wherein the observation residual is a difference between the initial attitude angle and a corresponding theoretical value; Adjust the initial weight coefficient according to the observation residual and perform rough alignment calculation again until the observation residual is less than a preset threshold; The initial attitude angle corresponding to when the observation residual is less than a preset threshold is determined as the final MEMS inertial navigation calibration result.

2. The method according to claim 1, characterized in that: The step of segmenting the original data in the multi-level buffer according to a preset time scale sequence specifically includes: Establishing multiple parallel sliding windows for selecting data segments to be processed at different time scales, respectively, wherein the length of each sliding window matches the corresponding time scale; Performing signal quality evaluation on the original data in each sliding window to obtain a window signal characteristic value, wherein the window signal characteristic value includes signal fluctuation and data continuity; Determining the segmentation parameters of each sliding window according to the window signal characteristic value, wherein the segmentation parameters include a window moving step length and a data interception interval, and the window moving step length is related to the overlap degree of adjacent data segments; Segmenting the original data in each sliding window in parallel based on the segmentation parameter to obtain multiple candidate data segments; The candidate data segments are screened and integrated according to the signal consistency criterion to generate final data segments of different time scales.

3. The method according to claim 1, characterized in that The step of adjusting the initial weight coefficient according to the observation residual and performing the rough alignment calculation again until the observation residual is less than a preset threshold specifically includes: Establishing a multi-level precision control sequence of multiple observation residual thresholds arranged from large to small, each of the observation residual thresholds corresponds to an iteration stage; Calculating a relaxation factor according to an observation residual threshold value in a current iteration phase, wherein the relaxation factor is positively correlated with the observation residual threshold value; Performing weighted processing on the observation residual according to the relaxation factor to obtain a weight adjustment amount, and updating the initial weight coefficient according to the weight adjustment amount to generate a new weight coefficient combination; Performing rough alignment calculation again according to the new weight coefficient combination to determine whether the obtained observation residual is less than the observation residual threshold of the current iteration stage; If yes, it goes to the next iteration stage; if no, it returns to the relaxation factor calculation step to continue the iteration.

4. The method according to claim 3, characterized in that Before the step of entering the next iteration phase, the following steps are also included: The temporary attitude angle calculated by the rough alignment is output and displayed, and the number of rough alignment times is recorded.

5. The method according to claim 3, characterized in that: The step of calculating the relaxation factor according to the observation residual threshold value of the current iteration stage specifically includes: Calculate the ratio of the current observation residual to the observation residual threshold to obtain a convergence index, wherein the convergence index represents the distance between the current calculation result and the target accuracy; Substituting the convergence index into a preset nonlinear mapping function to obtain a dynamic attenuation rate, wherein the dynamic attenuation rate decreases monotonically with the iteration process; The relaxation factor of the current iteration stage is calculated according to the dynamic decay rate.

6. The method according to claim 1, characterized in that Before the step of segmenting the original data in the multi-level buffer according to a preset time scale sequence, the method further includes: Extracting features from historical data segments through a preset sliding time window to obtain a data feature sequence, wherein the data feature sequence includes a signal fluctuation feature and a data correlation feature; Building a time series prediction model based on the data feature sequence, wherein the time series prediction model adopts a recursive neural network structure to capture the time series dependency of the data segments; Use the time series prediction model to predict the data features of the next time window to obtain a predicted feature value; The sampling frequency and the buffer length are dynamically adjusted according to the predicted characteristic value to generate an optimized acquisition strategy.

7. The method according to claim 1, characterized in that Before the step of using the fused observation vector to perform rough alignment calculation for navigation positioning to obtain an initial attitude angle, the method further includes: Performing cluster analysis on historical calibration data, extracting error characteristic patterns, and establishing an error pattern library, wherein the error pattern library contains typical error patterns and their corresponding optimal compensation parameters; Calculating a current error characteristic vector according to the fused observation vector, wherein the error characteristic vector includes a drift characteristic and a noise distribution characteristic; Calculate the matching degree between the error feature vector and the typical error pattern in the error pattern library to obtain pattern similarity; An optimal compensation mode is selected according to the mode similarity, and corresponding compensation parameters are extracted to generate an initial compensation scheme for the initial attitude angle during the rough alignment calculation process.

8. A MEMS inertial navigation system, characterized in that: The MEMS inertial navigation system includes: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the MEMS inertial navigation system to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a MEMS inertial navigation system, the MEMS inertial navigation system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on a MEMS inertial navigation system, the MEMS inertial navigation system is enabled to perform the method according to any one of claims 1 to 7.

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