Beidou and inertial navigation fusion positioning navigation method and system

Abstract

The invention relates to the technical field of fusion navigation, in particular to a Beidou and inertial navigation fusion positioning navigation method and system, and the method comprises the following steps: collecting Beidou signals and inertial navigation data, fusing the data, formatting a Beidou carrier phase observation value and the inertial navigation data, removing errors and abnormal values, normalizing the data, and obtaining the Beidou and inertial navigation fusion positioning data. And an initialized data fusion result is obtained. According to the method, high-quality basic data is provided for subsequent processing by collecting and optimizing Beidou and inertial navigation data, removing abnormal values and performing normalization, a Markov decision process is introduced to analyze and optimize a fusion navigation path, and the adaptive capacity and prediction precision in the face of dynamic change are enhanced; the dynamic navigation adjustment can respond to the path change in real time, ensure the consistency of the navigation instruction and the actual driving path, enhance the continuous operation capability in the signal unstable environment, and effectively maintain the positioning accuracy and stability.

Description

Technical Field

[0001] The present invention relates to the field of fusion navigation technology, and in particular to a navigation method and system for fusion positioning of Beidou and inertial navigation. Background Art

[0002] The field of fused navigation technology involves the integration of multiple navigation systems, such as satellite navigation systems and inertial navigation systems. Its core goal is to improve positioning accuracy and reliability, especially in environments where satellite signals are unstable or lost. Through the collaborative work of algorithms and hardware, fused navigation technology optimizes the integration of multiple navigation information sources, enabling the system to maintain efficient and stable navigation performance in a variety of environments. This overall technical area encompasses multiple sub-areas, including signal reception, data processing, algorithm optimization, and hardware design, to ensure the continuity and accuracy of the navigation system in dynamic and complex usage scenarios.

[0003] The Beidou and inertial navigation fusion positioning method refers to a technical approach that combines China's Beidou satellite navigation system and an inertial navigation system. The topic focuses on how to use the inertial navigation system to maintain and improve navigation accuracy when Beidou satellite signals are interfered with or blocked. This involves fusing sensor data from the inertial navigation system with signal data from Beidou satellites, processing this information through specific algorithms to compensate for the shortcomings of each system alone. This primarily involves the application of data fusion technology and algorithms, enabling inertial navigation to continue providing position information when Beidou signals are unavailable.

[0004] Existing technologies often exhibit certain limitations when dealing with signal interruptions or instability, primarily due to their reliance on a single data source or simple data processing techniques. This singleness makes it difficult for the system to independently maintain high accuracy and continuity through inertial navigation equipment when the Beidou signal is interfered with or completely lost, impacting the overall navigation effectiveness. The lack of dynamic adaptation and real-time optimization mechanisms results in system response lags or large path deviations in complex or rapidly changing navigation environments, further limiting the scope and effectiveness of navigation technology in changing environments. These technical limitations not only reduce the user experience but also impact navigation safety at critical moments. Summary of the Invention

[0005] In order to solve the problem that the existing technology often shows certain limitations when processing signal interruptions or instability, mainly because it relies on a single data source or simple data processing technology. This singleness makes it difficult for the system to independently maintain high accuracy and continuity through inertial navigation equipment when the Beidou signal is interfered with or completely lost, affecting the overall navigation effect. The lack of dynamic adaptation and real-time optimization mechanisms makes the system exhibit response lag or large path deviation in complex or rapidly changing navigation environments, further limiting the application scope and effect of navigation technology in a changing environment. The technical limitations not only reduce the user experience, but also affect the technical problems of navigation safety at critical moments. The embodiment of the present invention provides a navigation method and system for Beidou and inertial navigation fusion positioning. The technical solution is as follows:

[0006] On the one hand, a navigation method for Beidou and inertial navigation fusion positioning is provided, the method comprising:

[0007] S1: Collect BeiDou signals and inertial navigation data, fuse the data, format BeiDou carrier phase observations and inertial navigation data, remove errors and outliers, normalize the data, and obtain the initial data fusion results;

[0008] S2: Based on the initialization data fusion results, the Markov decision process is used to analyze the state transition probability and expected return of the Beidou and inertial navigation fusion positioning path, define the estimated behavior for each state, calculate the state transition probability and corresponding return value caused by each behavior, and generate an optimized navigation solution;

[0009] S3: When performing dynamic navigation adjustment based on the optimized navigation solution and Beidou and inertial navigation fusion positioning, the difference between the real-time path and the predetermined path is monitored, and the navigation command is adjusted to match the path change to obtain a dynamic navigation adjustment result;

[0010] S4: using the dynamic navigation adjustment result, when the Beidou signal is interrupted, using historical positioning data and inertial navigation data, estimating the position during the breakpoint, filling the time series of position information, and generating a positioning reconstruction analysis result;

[0011] S5: Compare the positioning reconstruction analysis results with the real-time data of Beidou and inertial navigation fusion positioning, update the navigation status and parameters, identify and correct the navigation position deviation by comparing the estimated and real-time data, and obtain the navigation verification result.

[0012] As a further solution of the present invention, the initialization data fusion results include data integration quality assessment, timestamp correction status, and abnormal data screening records; the optimized navigation plan includes recommended path selection, estimated schedule, and traffic condition adaptive adjustment; the dynamic navigation adjustment results include path deviation measurement, implemented navigation command adjustment, and real-time path update records; the positioning reconstruction analysis results include estimated breakpoint locations and time series integrity recovery status; and the navigation verification results include error correction success rate, data matching test, and navigation stability assessment.

[0013] As a further solution of the present invention, the steps of collecting Beidou signals and inertial navigation data, fusing the data, formatting Beidou carrier phase observations and inertial navigation data, removing errors and outliers, normalizing the data, and obtaining the initialization data fusion result are specifically as follows:

[0014] S101: Collect BeiDou signals and inertial navigation data, compare timestamps, time synchronize data from different sources, verify data alignment through spatial position correction, and obtain time synchronization and spatial calibration data;

[0015] S102: Using the time synchronization and spatial calibration data, performing data formatting, unifying the Beidou carrier phase observation value and the inertial navigation data structure, removing noise and outliers, and obtaining a formatted data set;

[0016] S103: performing a data normalization operation on the formatted data set, adjusting the data range and scale to a unified standard, and obtaining an initialized data fusion result.

[0017] As a further solution of the present invention, based on the initialization data fusion result, the Markov decision process is used to analyze the state transition probability and expected return of the Beidou and inertial navigation fusion positioning path, define an estimated behavior for each state, calculate the state transition probability and corresponding return value caused by each behavior, and generate an optimized navigation solution. Specifically, the steps are as follows:

[0018] S201: Based on the initialization data fusion result, define the state space in the Markov decision process, including the estimated Beidou and inertial navigation fusion positioning path state, to obtain a state space definition;

[0019] S202: Using the state space definition, calculate the transition probability between states, evaluate the expected returns of multiple states based on the historical performance of inertial navigation and BeiDou data, record the behavior choices and potential impacts of each state, and construct a state transition probability matrix;

[0020] S203: Calculate the state transition probabilities and reward values corresponding to multiple behaviors through the state transition probability matrix, analyze the navigation path in each state, and obtain an optimized navigation solution.

[0021] As a further solution of the present invention, the formula for calculating the transition probability between the states is:

[0022]

[0023] Among them, P ij represents the transition probability from state i to state j, α k represents the influence weight of the k-th state, s i and s j They represent the numerical representation of state i and state j respectively, β is the adjustment coefficient, abs is the function for finding the absolute value, and n is the number of states.

[0024] As a further solution of the present invention, according to the optimized navigation scheme, when dynamic navigation adjustment is performed based on Beidou and inertial navigation fusion positioning, the difference between the real-time path and the predetermined path is monitored, and the navigation command is adjusted to match the path change. The steps of obtaining the dynamic navigation adjustment result are specifically as follows:

[0025] S301: Using the optimized navigation solution, monitoring the real-time path of Beidou and inertial navigation fusion positioning in real time, comparing it with the predetermined navigation path, identifying the deviation between the two, and obtaining path deviation monitoring data;

[0026] S302: Analyze the causes and characteristics of the differences between the real-time path and the planned path based on the path deviation monitoring data, adjust the navigation command according to the size and direction of the differences, match the real-time path status, and obtain a navigation command adjustment record;

[0027] S303: Analyze the matching degree between each navigation command adjustment and the path change requirement through the navigation command adjustment record, identify the consistency between the path and the predetermined navigation solution, and obtain a dynamic navigation adjustment result.

[0028] As a further solution of the present invention, the steps of utilizing the dynamic navigation adjustment result and using historical positioning data and inertial navigation data to estimate the position during the breakpoint when the Beidou signal is interrupted, filling the time series of the position information, and generating the positioning reconstruction analysis result are specifically as follows:

[0029] S401: Based on the dynamic navigation adjustment result, using historical positioning data and real-time inertial navigation data as input, through time series analysis, identifying the exact time point and duration of Beidou signal interruption, and obtaining signal interruption time analysis data;

[0030] S402: using the signal interruption time analysis data to estimate the position during the Beidou signal interruption, calculating the continuity of the position before and after the interruption through an interpolation algorithm, verifying the seamless connection of the position information, and obtaining position interpolation estimation data;

[0031] S403: Using the position interpolation estimation data, filling in the time series of position information, identifying continuous positioning information during the Beidou signal interruption period, verifying the continuous operation of navigation, and generating positioning reconstruction analysis results.

[0032] As a further solution of the present invention, the positioning reconstruction analysis result is compared with the real-time data of Beidou and inertial navigation fusion positioning, the navigation status and parameters are updated, and the navigation position deviation is identified and corrected by comparing the estimated and real-time data to obtain the navigation verification result. Specifically, the steps are as follows:

[0033] S501: Based on the positioning reconstruction analysis result, collect and compare the real-time data of Beidou and inertial navigation fusion positioning, analyze the position deviation and the cause, and obtain position deviation comparison data;

[0034] S502: Using the position deviation comparison data and a Kalman filter algorithm, state estimation is performed, and navigation parameters are optimized by updating the position error covariance matrix in real time to obtain a navigation state update record;

[0035] S503: Compare the estimated position with the real-time position through the navigation status update record, correct the deviation of the navigation path, verify the reliability of the navigation, and generate a navigation verification result.

[0036] As a further solution of the present invention, the formula of the Kalman filter algorithm is as follows:

[0037] x k|k =x k|k-1 +K k (y k -H k x k|k-1 );

[0038] Among them, x k|k represents the real-time state estimation, x k|k-1 Represents the predicted state, K k represents the Kalman gain, y k represents the real-time observation value, H k Represents the observation model.

[0039] On the other hand, an electric vehicle state monitoring system is provided, wherein the electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method, and the system includes:

[0040] The data recording module collects BeiDou carrier phase observations and inertial navigation data, performs data processing, eliminates outliers, performs data normalization, and obtains the initialization data fusion results;

[0041] The path optimization module uses the initialization data fusion result to calculate the state transition probability and expected reward, defines the optimal behavior according to each state, calculates the new state probability and reward, and obtains the optimized navigation solution;

[0042] The deviation identification module monitors the deviation between the real-time path and the preset path based on the optimized navigation solution, adjusts the navigation instructions to match the path changes, and obtains dynamic navigation adjustment results;

[0043] The position reconstruction module uses the dynamic navigation adjustment results and, when the Beidou signal is interrupted, utilizes historical positioning data and inertial data to estimate the position during the breakpoint, fill in the position information time series, and generate a positioning reconstruction analysis result;

[0044] The navigation status update module compares the positioning reconstruction analysis result with the real-time Beidou and inertial navigation fusion data, updates the navigation status and parameters, corrects the position deviation, and obtains the navigation verification result.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] By collecting and optimizing Beidou and inertial navigation data, removing outliers and normalizing them, high-quality basic data is provided for subsequent processing. A Markov decision process is introduced to analyze and optimize the fused navigation path, enhancing adaptability and prediction accuracy in the face of dynamic changes. Dynamic navigation adjustments can respond to path changes in real time, ensuring consistency between navigation instructions and the actual driving path. By comparing historical and real-time data, position information is effectively compensated when Beidou signals are interrupted, enhancing the ability to continue operating in unstable signal environments. Multi-dimensional data processing and real-time feedback mechanisms significantly improve navigation performance in various environments, especially in signal-limited situations, effectively maintaining positioning accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0048] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0049] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0050] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0051] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0052] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0053] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0056] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0057] See also Figure 1 The embodiment of the present invention provides a navigation method for Beidou and inertial navigation fusion positioning. The processing flow of the method may include the following steps:

[0058] S1: Collect BeiDou signals and inertial navigation data, fuse the data, format BeiDou carrier phase observations and inertial navigation data, remove errors and outliers, normalize the data, and obtain the initial data fusion results;

[0059] S2: Based on the initial data fusion results, the Markov decision process is used to analyze the state transition probability and expected return of the Beidou and inertial navigation fusion positioning path. The estimated behavior is defined for each state, and the state transition probability and corresponding reward value caused by each behavior are calculated to generate an optimized navigation solution.

[0060] S3: Based on the optimized navigation scheme, when dynamic navigation adjustment is performed according to the Beidou and inertial navigation fusion positioning, the difference between the real-time path and the planned path is monitored, and the navigation command is adjusted to match the path change to obtain the dynamic navigation adjustment result;

[0061] S4: Using the dynamic navigation adjustment results, when the Beidou signal is interrupted, the historical positioning data and inertial navigation data are used to estimate the position during the interruption period, fill in the time series of position information, and generate positioning reconstruction analysis results;

[0062] S5: Compare the positioning reconstruction analysis results with the real-time data of Beidou and inertial navigation fusion positioning, update the navigation status and parameters, and identify and correct the navigation position deviation by comparing the estimated and real-time data to obtain the navigation verification result.

[0063] The initialization data fusion results include data integration quality assessment, timestamp correction status, and abnormal data screening records. The optimized navigation plan includes recommended path selection, estimated schedule, and traffic condition adaptability adjustment. The dynamic navigation adjustment results include path deviation measurement, implemented navigation command adjustment, and real-time path update records. The positioning reconstruction analysis results include estimated breakpoint locations and time series integrity recovery status. The navigation verification results include error correction success rate, data matching test, and navigation stability assessment.

[0064] See also Figure 2 , collect BeiDou signals and inertial navigation data, fuse the data, format BeiDou carrier phase observations and inertial navigation data, remove errors and outliers, normalize the data, and obtain the initialization data fusion results. The specific steps are:

[0065] S101: Collect BeiDou signals and inertial navigation data, compare timestamps, synchronize data from different sources, verify data alignment through spatial position correction, and obtain time synchronization and spatial calibration data. The execution process is as follows;

[0066] When collecting Beidou signals and inertial navigation data, the system performs timestamp comparisons, a process involving sophisticated signal processing techniques and data comparison algorithms. During the collection process, the Beidou and inertial navigation system data must be accurately synchronized to ensure timestamp consistency. This synchronization is achieved through advanced time correction algorithms. The system receives raw timestamp data from each sensor, including satellite positioning signals and dynamic data from the inertial measurement unit. After preliminary data screening, a detailed timestamp comparison is performed to verify the receipt and recording time of each data packet. A dynamic time warping algorithm is used to adjust the time series, ensuring accurate temporal alignment of data from different sources. This algorithm adjusts the data alignment by minimizing the cumulative distance between time series. Spatial position correction technology is used to verify the spatial alignment of the data. This technology relies on a high-precision geographic information system and complex spatial mapping algorithms to ensure data accuracy and reliability. This technology achieves complete temporal synchronization and spatial alignment of Beidou and inertial navigation data, resulting in time-synchronized and spatially calibrated data.

[0067] S102: Using time synchronization and spatial calibration data, perform data formatting, unify the Beidou carrier phase observation values and the inertial navigation data structure, remove noise and outliers, and obtain the formatted data set. The execution process is as follows;

[0068] Unify the BeiDou carrier phase observation value and the inertial navigation data structure according to the formula:

[0069] Δφ=φ BD -φ INS ;

[0070] Calculate the difference Δφ of the carrier phase observation value;

[0071] Where, φ BD Represents the carrier phase of the Beidou signal, φ INS Represents the phase of the inertial navigation system;

[0072] The system needs to collect corresponding phase data from BeiDou satellites and inertial navigation systems. At a specific measurement time, the carrier phase received from the BeiDou satellite is set to 355 degrees.

[0073] The phase measured from the inertial navigation system is 345 degrees. The phase difference between the two can be calculated using the formula:

[0074] Δφ=355-345=10;

[0075] This difference is a key indicator for data alignment and subsequent processing, and is used to guide noise filtering and the removal of data outliers. By accurately calculating the phase difference, the two types of data can be effectively integrated, improving the overall quality and availability of the dataset. It also helps to optimize system performance and improve the accuracy of data applications by reducing errors in the data processing process.

[0076] S103: Perform data normalization on the formatted data set to adjust the data range and scale to a unified standard, and obtain the initialization data fusion result. The execution process is as follows;

[0077] When performing data normalization, the system adjusts the data range and scale to a unified standard. The system performs a detailed normalization process on data from different sensors to ensure that all data can be compared and analyzed on the same basis. This process involves mapping the maximum and minimum data values to a range between 0 and 1 to achieve normalization. For example, if a sensor's output range is set from -100 to 100, each value can be converted to a range of 0 to 1 by subtracting -100 and dividing by 200. Normalization also involves identifying and removing outliers in the data, which are caused by sensor errors or external interference. These technical steps ensure the overall quality of the dataset, providing a stable and reliable foundation for subsequent data analysis and decision support. This normalization process not only unifies the data in terms of dimensionality but also ensures its quality, significantly improving data processing efficiency and the accuracy of analysis results, ultimately resulting in an initialized data fusion result.

[0078] See also Figure 3 Based on the initial data fusion results, the Markov decision process is used to analyze the state transition probability and expected return of the Beidou and inertial navigation fusion positioning path, define the estimated behavior for each state, calculate the state transition probability and corresponding return value caused by each behavior, and generate the optimized navigation solution in the following steps:

[0079] S201: Based on the initialization data fusion results, the state space in the Markov decision process is defined, including the estimated Beidou and inertial navigation fusion positioning path state. The execution flow of the state space definition is as follows;

[0080] Defining the state space in the Markov decision process is a key step. This process involves estimating the path state of Beidou and inertial navigation fusion positioning. It is necessary to extract key features from the data fusion results. The features will be used to define different states of the state space. Each state represents an estimated position or navigation path state. The definition process requires in-depth analysis and understanding of the fusion data to ensure that the state space fully covers all estimated navigation situations. It will provide a clear framework for subsequent decision-making processes, which is crucial for achieving efficient and accurate navigation decisions, help improve the navigation performance of the system, and ensure that the navigation system can make the best navigation decisions under various environmental conditions to obtain the state space definition.

[0081] S202: Using the state space definition, calculate the transition probability between states, combine the historical performance of inertial navigation and BeiDou data to evaluate the expected returns of multiple states, record the behavior choices and potential impacts of each state, and construct the state transition probability matrix. The execution process is as follows;

[0082] The formula for calculating the transition probability between states is:

[0083]

[0084] Among them, P ij represents the transition probability from state i to state j, α k represents the influence weight of the k-th state, s i and s j They represent the numerical representation of state i and state j respectively, β is the adjustment coefficient, abs is the function for finding the absolute value, and n is the number of states;

[0085] Parameter meaning and setting value:

[0086] α k is the weight of state k, which reflects the importance of the state in the historical performance. The weight is dynamically adjusted based on the successful execution rate of the state to ensure that the model can reflect the importance of each state in real time;

[0087] s i and s j are the numerical representations of state i and state j, respectively. The numerical values are derived from real-time data monitoring of the corresponding states, such as position information, speed, or key performance indicators;

[0088] β is the adjustment coefficient, which is used to control the adjustment of the model's sensitivity to state differences. This coefficient is determined based on the overall variability to balance the impact of differences between different states and ensure that the probability calculation does not rely too much on small differences;

[0089] There are three states set, state values s1 = 0.5, s3 = 0.7;

[0090] Weights α1 = 0.4, α3 = 0.3;

[0091] The adjustment coefficient β = 2, the value of which is derived from actual monitoring and historical performance data. For example, the weight coefficient is calculated based on the successful execution rate of each state in the past year;

[0092] Substitute the parameters into the formula for calculation:

[0093] Calculate the transition probability P from state 1 to state 2 12 ,

[0094] Computing molecules

[0095]

[0096]

[0097] Calculate the denominator

[0098]

[0099] Calculate P 12

[0100]

[0101] The results show that the transition probability from state 1 to state 2 is 50%, indicating that under the current setting, the probability of transition from state 1 to state 2 is equal, which means that state 1 and state 2 have the same transition tendency in the defined model. The decision strategy can be further optimized or the state weight can be adjusted based on this result.

[0102] S203: Calculate the state transition probabilities and reward values corresponding to multiple behaviors through the state transition probability matrix, analyze the navigation path in each state, and obtain the optimized navigation solution. The execution process is as follows;

[0103] The process of calculating the state transition probabilities and reward values corresponding to multiple behaviors includes detailed mathematical calculations and model analysis. In order to analyze the navigation path in each state, it is necessary to establish a probability matrix containing all states and behaviors. The algorithm is used to calculate the transition probability from one state to another, as well as the reward value obtained for each transition. The calculation is based on historical data and prediction models to ensure the accuracy and reliability of the calculation. Through the data, each state can be evaluated, and the navigation behavior that produces the best results under given conditions can be selected to guide actual navigation operations, ensuring that the navigation system can provide the most effective path selection and navigation decisions under different conditions, and obtain an optimized navigation solution.

[0104] See also Figure 4 According to the optimized navigation scheme, when dynamic navigation adjustment is performed based on Beidou and inertial navigation fusion positioning, the difference between the real-time path and the planned path is monitored, and the navigation command is adjusted to match the path change. The specific steps to obtain the dynamic navigation adjustment result are as follows:

[0105] S301: Using the optimized navigation solution, monitor the real-time path of the Beidou and inertial navigation fusion positioning in real time, compare it with the predetermined navigation path, identify the deviation between the two, and obtain the path deviation monitoring data. The execution process is as follows;

[0106] When monitoring the real-time path generated by the fusion of Beidou and inertial navigation, the system continuously acquires and compares deviations between the real-time path and the planned navigation path. This process involves advanced real-time data processing and path analysis technologies. Real-time monitoring is performed through the integrated Beidou and inertial navigation systems, which provide high accuracy and stability. The system acquires path data, including position coordinates and movement speed, through high-frequency sampling. The system compares the real-time path with the planned path using an advanced deviation identification algorithm. This algorithm accurately calculates the distance and direction deviations between the paths and updates the information in real time. By analyzing the real-time data stream, the system can detect even the smallest path deviations and immediately provide feedback to the navigation control center. This monitoring process ensures accurate path monitoring and immediate response, which is crucial for navigation adjustments in dynamic environments. This efficient monitoring not only improves navigation accuracy but also significantly enhances safety and reliability by acquiring path deviation monitoring data.

[0107] S302: Based on the path deviation monitoring data, the causes and characteristics of the differences between the real-time path and the planned path are analyzed. According to the size and direction of the differences, the navigation command is adjusted to match the real-time path status. The execution process of obtaining the navigation command adjustment record is as follows;

[0108] Analyze the reasons for the difference between the real-time path and the planned path according to the formula:

[0109]

[0110] Calculate the path deviation value δ;

[0111] Where G t and G t Represents the coordinates of the real-time path, G p and G p Coordinates representing the intended path;

[0112] At a certain moment, the position provided by the real-time monitoring Beidou and inertial navigation system is (G t ,G t )=(100,150);

[0113] The corresponding position of the predetermined path is (G p ,G p )=(95,148);

[0114] Apply the above formula to calculate the Euclidean distance between two locations, and the path deviation value is

[0115]

[0116] The results provide the specific numerical deviation between the real-time path and the planned path, providing accurate data support for navigation system adjustments, so that navigation commands can be adjusted promptly and accurately to match the actual path status, ensuring navigation accuracy and optimal path adjustment.

[0117] S303: Analyze the matching degree between each navigation command adjustment and the path change requirement through the navigation command adjustment record, identify the consistency between the path and the predetermined navigation solution, and obtain the dynamic navigation adjustment result. The execution process is as follows;

[0118] The system records the details of each navigation command adjustment in detail, including the time, reason, operation performed, and result of the adjustment. Through in-depth analysis of the records, the system can evaluate the effectiveness of navigation command adjustments and the adaptability of path changes. For example, the system will analyze whether the adjusted path is closer to the planned path and whether the adjusted command effectively reduces path deviation. The analysis also includes an assessment of the consistency of the path with the planned navigation plan, which is achieved by comparing the changes in the path before and after the adjustment. Through analysis, the system can not only optimize navigation commands and improve navigation accuracy and responsiveness, but also ensure the stable operation and efficiency of the entire navigation system. It provides important data support for future path optimization, enhances the system's adaptability to complex environments and overall navigation performance, and obtains dynamic navigation adjustment results.

[0119] See also Figure 5 ,Utilizing the dynamic navigation adjustment results, when the BeiDou signal is interrupted,,the historical positioning data and inertial navigation data are used to,estimate the position during the breakpoint, fill the time series of position information, and generate the,positioning reconstruction analysis results.,Specific steps are as follows:

[0120] S401: Based on the dynamic navigation adjustment results, using historical positioning data and real-time inertial navigation data as input, through time series analysis, identify the exact time point and duration of the Beidou signal interruption, and obtain the signal interruption time analysis data. The execution process is as follows;

[0121] The exact time point and duration of the Beidou signal interruption are identified through complex data processing algorithms. During this process, the system collects and integrates data from the Beidou system and the inertial navigation system. The historical positioning data provides positioning information before the signal interruption, while the real-time inertial navigation data fills the information gap during the signal interruption. By applying time series analysis technology, the system can accurately detect the start and end time points of the signal interruption, as well as the specific duration of the signal interruption. The analysis relies on advanced statistical methods and algorithms, such as the autoregressive moving average (ARMA) model, which can extract key features of breakpoints and duration periods from time series data. It not only enhances the navigation system's response to signal interruption, but also improves the robustness and reliability of the overall navigation solution, and obtains signal interruption time analysis data.

[0122] S402: Using the signal interruption time analysis data, the position during the Beidou signal interruption is estimated. The continuity of the position before and after the interruption is calculated through the interpolation algorithm to verify the seamless connection of the position information. The execution process of obtaining the position interpolation estimation data is as follows;

[0123] Estimate the position during BeiDou signal interruption according to the formula:

[0124]

[0125] Where R O is the estimated position during the signal interruption period, R O-1 Represents the last position point before the signal is interrupted, R O+1 Represents the first position point after signal recovery;

[0126] The last position point before the Beidou signal is interrupted is R O-1 =(50,75);

[0127] The first position after signal recovery is R O+1 =(55,80);

[0128] The position interpolation is estimated by the above formula, and the estimated position during the breakpoint period is calculated as:

[0129]

[0130] The results provide continuous position information during BeiDou signal interruption, so that the position information can still maintain a certain degree of continuity and accuracy during the signal interruption, which is crucial to ensuring the stable operation and navigation accuracy of the navigation system during the signal interruption.

[0131] S403: Using position interpolation estimation data, filling the time series of position information, identifying continuous positioning information during Beidou signal interruption, verifying the continued operation of navigation, and generating positioning reconstruction analysis results. The execution process is as follows;

[0132] The system fills in the time series of location information and identifies continuous positioning information during Beidou signal outages. During this process, the system utilizes a combination of data interpolation techniques to ensure that position information remains intact during signal outages. Through a sophisticated position interpolation algorithm, the system generates a continuous time series of positions. Although the position data is estimated without Beidou signal support, its accuracy is significantly improved through intelligent algorithm optimization. For example, using linear or cubic interpolation methods, the system analyzes the error between the interpolated points and the actual measured points and adjusts algorithm parameters to minimize the error. It also verifies the navigation system's continued operational capability. Once the Beidou signal is restored, the interpolated data can be quickly merged with new real-time data, ensuring a seamless transition and efficient performance. This not only verifies the system's adaptability to complex situations but also provides strong data support and solutions for future predicted signal outages, generating positioning reconstruction analysis results.

[0133] See also Figure 6 , compare the positioning reconstruction analysis results with the real-time data of Beidou and inertial navigation fusion positioning, update the navigation status and parameters, and identify and correct the navigation position deviation by comparing the estimated and real-time data. The specific steps to obtain the navigation verification results are as follows:

[0134] S501: Based on the positioning reconstruction analysis results, collect and compare the real-time data of Beidou and inertial navigation fusion positioning, analyze the position deviation and its cause, and obtain the position deviation comparison data. The execution process is as follows;

[0135] In the process of collecting real-time data of Beidou and inertial navigation fusion positioning, it is necessary to update and compare the position data at different time points in real time. The data will be used to analyze position deviations and causes. The real-time monitoring system will capture the Beidou signal and the data output of the inertial navigation system, compare the data at each time point, and identify position deviations. The analysis process involves a variety of data processing technologies, including data filtering, signal processing, and statistical analysis to ensure the accuracy and reliability of the data, and display the position differences at each time point in detail, providing a basis for further system optimization and accuracy improvement. The data can also help identify system errors or external interference factors, take corresponding improvement measures, and obtain position deviation comparison data.

[0136] S502: Using the position deviation comparison data and the Kalman filter algorithm, state estimation is performed, and the navigation parameters are optimized by updating the position error covariance matrix in real time to obtain the navigation state update record. The execution process is as follows;

[0137] The formula of the Kalman filter algorithm is as follows:

[0138] x k|k =x k|k-1 +K k (y k -H k x k|k-1 );

[0139] Among them, x k|k represents the real-time state estimation, x k|k-1 Represents the predicted state, K k represents the Kalman gain, y k represents the real-time observation value, H k represents the observation model;

[0140] Parameter meaning and setting value:

[0141] x k|k-1 It represents the predicted state, which is predicted from the sensor data at the previous moment and has a value of 2.0, reflecting prior knowledge and historical behavior;

[0142] K k represents the Kalman gain, the specific value is set to 0.1, which is based on the ratio of the error covariance to the observation noise and reflects the weight of the error estimate;

[0143] y k is the current observation value, which is obtained through real-time measurement by the sensor and is set to 3.5, reflecting the actual state at the current moment;

[0144] H k is the observation model parameter, and its value is set to 1.0, indicating that the state variables are directly converted to observation values;

[0145] Substitute the parameters into the formula for calculation:

[0146] x k|k =2.0+0.1×(3.5-1.0×2.0)=2.0+0.1×(3.5-2.0)

[0147] =2.0+0.1×1.5=2.15;

[0148] The results show that the state estimate at the current moment is fine-tuned after taking into account the latest observation data. This adjustment helps to improve the accuracy and response speed of navigation, enabling better adaptation to environmental changes.

[0149] S503: Compare the estimated position with the real-time position through the navigation status update record, correct the deviation of the navigation path, verify the reliability of the navigation, and generate the navigation verification result. The execution process is as follows;

[0150] The steps for comparing the estimated position with the real-time position include collecting the output data of the navigation system, such as the estimated and actual navigation paths, using data analysis tools to compare the path data in detail, and analyzing the deviations and causes of the paths. This process not only relies on high-precision measurement technology, but also requires complex algorithms to ensure accurate comparison and deviation analysis of the data. Through comparison and analysis, necessary adjustments can be made to the navigation commands to correct any deviations on the path. The steps for verifying the reliability of navigation include testing the performance of the system under various operating conditions to ensure that the system can provide accurate navigation information even in complex or changing environmental conditions. This will prove the stability and reliability of the system, ensure that end users can trust the performance of the navigation system, and generate navigation verification results.

[0151] On the other hand, an electric vehicle state monitoring system is provided. The electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method. The system includes:

[0152] The data recording module collects BeiDou carrier phase observations and inertial navigation data, performs data processing, eliminates outliers, performs data normalization, and obtains the initialization data fusion results;

[0153] The path optimization module uses the initialization data fusion results to calculate the state transition probability and expected reward, defines the optimal behavior for each state, calculates the new state probability and reward, and obtains the optimized navigation plan;

[0154] The deviation identification module monitors the deviation between the real-time path and the preset path based on the optimized navigation solution, adjusts the navigation instructions to match the path changes, and obtains dynamic navigation adjustment results;

[0155] The position reconstruction module uses the dynamic navigation adjustment results. When the Beidou signal is interrupted, it uses historical positioning data and inertial data to estimate the position during the breakpoint, fill in the position information time series, and generate positioning reconstruction analysis results;

[0156] The navigation status update module compares the positioning reconstruction analysis results with the real-time Beidou and inertial navigation fusion data, updates the navigation status and parameters, corrects the position deviation, and obtains the navigation verification results.

[0157] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A navigation method for Beidou and inertial navigation fusion positioning, characterized in that: The following steps are involved: S1: Collect BeiDou signals and inertial navigation data, fuse the data, format BeiDou carrier phase observations and inertial navigation data, remove errors and outliers, normalize the data, and obtain the initial data fusion results; S2: Based on the initialization data fusion results, the Markov decision process is used to analyze the state transition probability and expected return of the Beidou and inertial navigation fusion positioning path, define the estimated behavior for each state, calculate the state transition probability and corresponding return value caused by each behavior, and generate an optimized navigation solution; S3: When performing dynamic navigation adjustment based on the optimized navigation solution and Beidou and inertial navigation fusion positioning, the difference between the real-time path and the predetermined path is monitored, and the navigation command is adjusted to match the path change to obtain a dynamic navigation adjustment result; S4: using the dynamic navigation adjustment result, when the Beidou signal is interrupted, using historical positioning data and inertial navigation data, estimating the position during the breakpoint, filling the time series of position information, and generating a positioning reconstruction analysis result; S5: Compare the positioning reconstruction analysis results with the real-time data of Beidou and inertial navigation fusion positioning, update the navigation status and parameters, identify and correct the navigation position deviation by comparing the estimated and real-time data, and obtain the navigation verification result.

2. The navigation method of Beidou and inertial navigation fusion positioning according to claim 1, characterized in that: The initialization data fusion results include data integration quality assessment, timestamp correction status, and abnormal data screening records. The optimized navigation plan includes recommended path selection, estimated schedule, and traffic condition adaptive adjustment. The dynamic navigation adjustment results include path deviation measurement, implemented navigation command adjustment, and real-time path update records. The positioning reconstruction analysis results include estimated breakpoint locations and time series integrity recovery status. The navigation verification results include error correction success rate, data matching test, and navigation stability assessment.

3. The navigation method of Beidou and inertial navigation fusion positioning according to claim 1, characterized in that: The steps for collecting BeiDou signals and inertial navigation data, fusing the data, formatting BeiDou carrier phase observations and inertial navigation data, removing errors and outliers, and normalizing the data to obtain the initial data fusion results are as follows: S101: Collect BeiDou signals and inertial navigation data, compare timestamps, time synchronize data from different sources, verify data alignment through spatial position correction, and obtain time synchronization and spatial calibration data; S102: Using the time synchronization and spatial calibration data, performing data formatting, unifying the Beidou carrier phase observation value and the inertial navigation data structure, removing noise and outliers, and obtaining a formatted data set; S103: performing a data normalization operation on the formatted data set, adjusting the data range and scale to a unified standard, and obtaining an initialized data fusion result.

4. The navigation method of Beidou and inertial navigation fusion positioning according to claim 1, characterized in that: Based on the initialization data fusion results, the Markov decision process is used to analyze the state transition probability and expected return of the Beidou and inertial navigation fusion positioning path, define the estimated behavior for each state, calculate the state transition probability and corresponding return value caused by each behavior, and generate the optimized navigation solution in the following steps: S201: Based on the initialization data fusion result, define the state space in the Markov decision process, including the estimated Beidou and inertial navigation fusion positioning path state, to obtain a state space definition; S202: Using the state space definition, calculate the transition probability between states, evaluate the expected returns of multiple states based on the historical performance of inertial navigation and BeiDou data, record the behavior choices and potential impacts of each state, and construct a state transition probability matrix; S203: Calculate the state transition probabilities and reward values corresponding to multiple behaviors through the state transition probability matrix, analyze the navigation path in each state, and obtain an optimized navigation solution.

5. The navigation method of Beidou and inertial navigation fusion positioning according to claim 4 is characterized in that: The formula for calculating the transition probability between the states is: Among them, P ij represents the transition probability from state i to state j, α k represents the influence weight of the k-th state, s i and s j They represent the numerical representation of state i and state j respectively, β is the adjustment coefficient, abs is the function for finding the absolute value, and n is the number of states.

6. The navigation method of Beidou and inertial navigation fusion positioning according to claim 1, characterized in that: According to the optimized navigation scheme, when dynamic navigation adjustment is performed based on Beidou and inertial navigation fusion positioning, the steps of monitoring the difference between the real-time path and the predetermined path, adjusting the navigation command to match the path change, and obtaining the dynamic navigation adjustment result are as follows: S301: Using the optimized navigation solution, monitoring the real-time path of Beidou and inertial navigation fusion positioning in real time, comparing it with the predetermined navigation path, identifying the deviation between the two, and obtaining path deviation monitoring data; S302: Analyze the causes and characteristics of the differences between the real-time path and the planned path based on the path deviation monitoring data, adjust the navigation command according to the size and direction of the differences, match the real-time path status, and obtain a navigation command adjustment record; S303: Analyze the matching degree between each navigation command adjustment and the path change requirement through the navigation command adjustment record, identify the consistency between the path and the predetermined navigation solution, and obtain a dynamic navigation adjustment result.

7. The navigation method of Beidou and inertial navigation fusion positioning according to claim 1, characterized in that: The steps of using the dynamic navigation adjustment results, using historical positioning data and inertial navigation data to estimate the position during the breakpoint when the Beidou signal is interrupted, filling the time series of position information, and generating positioning reconstruction analysis results are as follows: S401: Based on the dynamic navigation adjustment result, using historical positioning data and real-time inertial navigation data as input, through time series analysis, identifying the exact time point and duration of Beidou signal interruption, and obtaining signal interruption time analysis data; S402: using the signal interruption time analysis data to estimate the position during the Beidou signal interruption, calculating the continuity of the position before and after the interruption through an interpolation algorithm, verifying the seamless connection of the position information, and obtaining position interpolation estimation data; S403: Using the position interpolation estimation data, filling in the time series of position information, identifying continuous positioning information during the Beidou signal interruption period, verifying the continuous operation of navigation, and generating positioning reconstruction analysis results.

8. The navigation method of Beidou and inertial navigation fusion positioning according to claim 1, characterized in that: The positioning reconstruction analysis results are compared with the real-time data of Beidou and inertial navigation fusion positioning, the navigation status and parameters are updated, and the navigation position deviation is identified and corrected by comparing the estimated and real-time data to obtain the navigation verification results. Specifically, the steps are as follows: S501: Based on the positioning reconstruction analysis result, collect and compare the real-time data of Beidou and inertial navigation fusion positioning, analyze the position deviation and the cause, and obtain position deviation comparison data; S502: Using the position deviation comparison data and a Kalman filter algorithm, state estimation is performed, and navigation parameters are optimized by updating the position error covariance matrix in real time to obtain a navigation state update record; S503: Compare the estimated position with the real-time position through the navigation status update record, correct the deviation of the navigation path, verify the reliability of the navigation, and generate a navigation verification result.

9. The navigation method of Beidou and inertial navigation fusion positioning according to claim 8, characterized in that: The formula of the Kalman filter algorithm is as follows: x k|k =x k|k-1 +K k (y k -H k x k|k-1 ); Among them, x k|k represents the real-time state estimation, x k|k-1 Represents the predicted state, K k represents the Kalman gain, y k represents the real-time observation value, H k Represents the observation model.

10. A navigation system integrating Beidou and inertial navigation positioning, characterized in that: The navigation method for Beidou and inertial navigation fusion positioning according to any one of claims 1 to 9, wherein the system comprises: The data recording module collects BeiDou carrier phase observations and inertial navigation data, performs data processing, eliminates outliers, performs data normalization, and obtains the initialization data fusion results; The path optimization module uses the initialization data fusion result to calculate the state transition probability and expected reward, defines the optimal behavior according to each state, calculates the new state probability and reward, and obtains the optimized navigation solution; The deviation identification module monitors the deviation between the real-time path and the preset path based on the optimized navigation solution, adjusts the navigation instructions to match the path changes, and obtains dynamic navigation adjustment results; The position reconstruction module uses the dynamic navigation adjustment results and, when the Beidou signal is interrupted, utilizes historical positioning data and inertial data to estimate the position during the breakpoint, fill in the position information time series, and generate a positioning reconstruction analysis result; The navigation status update module compares the positioning reconstruction analysis result with the real-time Beidou and inertial navigation fusion data, updates the navigation status and parameters, corrects the position deviation, and obtains the navigation verification result.

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