Inertial navigation positioning method, device, equipment, storage medium and computer product

Abstract

The invention discloses an inertial navigation positioning method, device and equipment, a storage medium and a computer product, and relates to the technical field of inertial navigation, the method comprises the following steps: obtaining synchronous measurement data of a high-speed target drone in a flight process, the synchronous measurement data at least comprising inertial measurement data and satellite measurement data; and determining inertial output data according to a preset error prediction model and the inertial measurement data, and determining an attitude measurement positioning result of the high-speed target drone according to the inertial output data and the actual state identifier corresponding to the satellite measurement data. The invention aims to improve the accuracy of the inertial navigation system in the high-speed target drone application.

Description

Technical Field

[0001] The present application relates to the technical field of inertial navigation, and particularly relates to an inertial navigation positioning method, device, equipment, storage medium, and computer product. Background Art

[0002] With the continuous development of inertial navigation technology, inertial navigation systems play a core role in the attitude measurement and positioning of high-speed target drones, and users' requirements for the accuracy of inertial navigation systems are also increasing day by day.

[0003] In practical applications, when a high-speed target drone is launched from a ground launch rack, it is required that the navigation equipment quickly complete initial alignment and maintain static performance before system detection. However, the vibration noise generated by the booster rocket during the launch process will interfere with inertial navigation data and affect the navigation accuracy during the boost phase. In addition, the differences in noise characteristics under different maneuvering conditions during flight, the rapid change of ambient temperature, and the short-term occlusion of satellite signals during large-angle maneuvers will further increase the uncertainty and complexity of inertial navigation measurement noise and reduce the attitude and positioning accuracy.

[0004] Therefore, how to improve the accuracy of inertial navigation systems in the application of high-speed target drones is a technical problem that needs to be solved urgently at present.

[0005] The above content is only used to assist in understanding the technical solution of the present application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of the present application is to provide an inertial navigation positioning method, device, equipment, storage medium, and computer product, aiming to improve the accuracy of inertial navigation systems in the application of high-speed target drones.

[0007] To achieve the above purpose, the present application proposes an inertial navigation positioning method, and the inertial navigation positioning method includes: Obtain synchronous measurement data of a high-speed target drone during flight, where the synchronous measurement data at least includes inertial measurement data and satellite measurement data; Determine inertial output data based on a preset error prediction model and the inertial measurement data, and determine the attitude and positioning result of the high-speed target drone based on the inertial output data and the actual status identifier corresponding to the satellite measurement data.

[0008] In an embodiment, the step of determining inertial output data based on a preset error prediction model and the inertial measurement data includes: Perform model noise reduction processing on the inertial measurement data according to a preset error prediction model to obtain inertial noise-reduced data; Determine error prediction data based on the error prediction model and the inertial noise reduction data, and determine the true error data of the high-speed target drone during flight. Determine the inertial output data of the error prediction model based on the error prediction data and the true error data.

[0009] In one embodiment, the step of determining the inertial output data of the error prediction model based on the error prediction data and the true error data includes: Determine the error data difference between the error prediction data and the true error data, and determine an error prediction evaluation result based on the error data difference and a preset error tolerance threshold; Determine the inertial output data of the error prediction model based on the error prediction evaluation result and the satellite measurement data.

[0010] In one embodiment, the step of determining the inertial output data of the error prediction model based on the error prediction evaluation result and the satellite measurement data includes: In response to the error prediction evaluation result being a valid prediction, perform error correction on the inertial noise reduction data based on the error prediction data to obtain inertial correction data, and determine whether the satellite measurement data is satellite valid measurement data; If the satellite measurement data is satellite valid measurement data, determine that the inertial output data of the error prediction model is the inertial noise reduction data carrying a satellite valid identifier; If the satellite measurement data is satellite invalid measurement data, determine that the inertial output data of the error prediction model is the inertial correction data carrying a satellite invalid identifier.

[0011] In one embodiment, the step of determining the attitude and position measurement result of the high-speed target drone based on the inertial output data and the actual status identifier corresponding to the satellite measurement data includes: Perform strapdown solution on the inertial output data to obtain inertial solution data, and confirm whether the actual status identifier corresponding to the satellite measurement data is a satellite valid identifier; If the actual status identifier is a satellite valid identifier, construct a fusion filtering solution model based on the satellite measurement data, and obtain the attitude and position measurement result of the high-speed target drone based on the fusion filtering solution model and the inertial solution data.

[0012] In one embodiment, the synchronous measurement data further includes atmospheric measurement data. After the step of confirming whether the actual status identifier corresponding to the satellite measurement data is a satellite valid identifier, the inertial navigation and positioning method includes: If the actual status identifier is a satellite invalid identifier, determine the barometric altitude of the high-speed target aircraft during flight based on the atmospheric measurement data, and perform damping processing on the inertial solution data based on the barometric altitude to obtain the attitude and position determination result of the high-speed target aircraft.

[0013] In addition, to achieve the above object, the present application also proposes an inertial navigation and positioning device, which includes: An acquisition module, configured to acquire synchronous measurement data of the high-speed target aircraft during flight, where the synchronous measurement data includes at least inertial measurement data and satellite measurement data; An attitude and position determination module, configured to determine inertial output data based on a preset error prediction model and the inertial measurement data, and determine the attitude and position determination result of the high-speed target aircraft based on the inertial output data and the actual status identifier corresponding to the satellite measurement data.

[0014] Each functional module of the inertial navigation and positioning device of the present application implements the steps of the inertial navigation and positioning method of the present application as described above when running.

[0015] In addition, to achieve the above object, the present application also proposes an inertial navigation and positioning device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the inertial navigation and positioning method as described above.

[0016] In addition, to achieve the above object, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the steps of the inertial navigation and positioning method as described above.

[0017] In addition, to achieve the above object, the present application also proposes a computer product, which includes a computer program, and the computer program includes computer program code means stored on a computer-readable medium or a carrier wave, and the computer program code means is configured to cause a computer or a processor to implement the steps of the inertial navigation and positioning method as described above when executed.

[0018] The embodiment of the present application provides an inertial navigation and positioning method. By obtaining the synchronous measurement data including inertial measurement data and satellite measurement data during the flight of a high-speed target drone in real time, accurate and reliable data support is provided for a subsequent preset error prediction model. Next, using the preset error prediction model to process the inertial measurement data to determine the inertial output data, and combining the actual state identifier corresponding to the satellite measurement data, the attitude and position measurement result of the high-speed target drone can be accurately obtained, thus effectively coping with challenges such as the vibration noise generated by the booster rocket during the launch process, the difference in noise characteristics under different maneuvering conditions during the flight process, the rapid change of the environmental temperature, and the short-term occlusion of satellite signals during large-angle maneuvers, realizing high-precision and high-stability attitude and position measurement of the high-speed target drone, and thus significantly improving the accuracy of the inertial navigation system in the application of high-speed target drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flowchart of the first embodiment of the inertial navigation and positioning method of the present application; Figure 2 It is a schematic flowchart of the LSTM model prediction and correction involved in the solution of the present application embodiment; Figure 3 It is a real-time solution flowchart of the attitude and position measurement involved in the solution of the present application embodiment; Figure 4 It is a module schematic diagram of the inertial navigation and positioning device of the present application; Figure 5 It is a schematic diagram of the device structure of the hardware operating environment involved in the device of the present application.

[0020] The realization, functional characteristics and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, the following will be described in detail with reference to the accompanying drawings of the specification and the specific embodiments.

[0023] The high-speed target drone has a high speed and strong maneuverability, and has relatively high requirements for the real-time performance and accuracy of navigation. The inertial navigation method has the characteristics of strong autonomy and anti-interference ability, good dynamic performance and high short-term accuracy, and has a core advantage in the attitude and position measurement of high-speed target drones. To solve the problem of the accumulation of inertial navigation errors over time, generally, navigation methods such as satellite / vision are used for fusion, and the error is estimated and corrected through Kalman filtering.

[0024] When a high-speed target drone is launched using a "ground launch rack", it is generally required that the navigation equipment complete the initial alignment before system detection and maintain the performance statically. When launching, a booster rocket is used to provide the initial speed for the target drone. The vibration and noise during the operation of the booster rocket will be transmitted to the inertial measurement data, thus affecting the navigation performance during the boost phase. During the flight, the noise characteristics under different maneuvering conditions are also not the same, which exacerbates the uncertainty and complexity of the inertial measurement noise. The rapid change of the environmental temperature during the flight will also cause a rapid change in the temperature-related inertial measurement error, stimulating a larger cumulative error. When the target drone maneuvers at a large angle, the satellite signal will be blocked for a short time, and the inertial cumulative error cannot be corrected and compensated, reducing the attitude and position measurement accuracy during this period.

[0025] Compared with fiber optic or laser inertial navigation, MEMS inertial navigation has significant advantages such as low power consumption, small size, light weight, and low price, and is widely used in the navigation and positioning fields of various unmanned aerial vehicles. However, the measurement noise of MEMS inertial navigation is easily affected by environmental noise, its calibration and compensation process is simplified, and there is a lack of refined calibration and compensation for related inertial measurement errors (such as zero bias drift, scale factor change, etc.), resulting in strong temperature sensitivity of the measurement information. These problems are the key bottlenecks restricting the application of MEMS inertial navigation in the attitude and position measurement field of high-speed target drones.

[0026] Therefore, based on the deficiencies of the above inertial navigation and positioning solutions, the inertial navigation and positioning method of this application is proposed. The solution of the embodiment of this application is: by obtaining the synchronous measurement data including inertial measurement data and satellite measurement data of the high-speed target drone during flight in real time, accurate and reliable data support is provided for the subsequent preset error prediction model; next, using the preset error prediction model to process the inertial measurement data to determine the inertial output data, and combining the actual status identifier corresponding to the satellite measurement data, the attitude and position measurement result of the high-speed target drone can be accurately obtained, thus effectively coping with challenges such as the vibration and noise generated by the booster rocket during the launch process, the difference in noise characteristics under different maneuvering conditions during flight, the rapid change of the environmental temperature, and the short-term occlusion of the satellite signal during large-angle maneuvers, realizing high-precision and high-stability attitude and position measurement of the high-speed target drone, and thus significantly improving the accuracy of the inertial navigation system in the application of high-speed target drones.

[0027] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a device capable of implementing the above functions, an inertial navigation and positioning device (such as an optoelectronic system), etc. Hereinafter, taking the inertial navigation and positioning device as an example, this embodiment and the following embodiments will be described.

[0028] Based on this, the embodiment of this application provides an inertial navigation and positioning method, referring toFigure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the inertial navigation and positioning method of the present application.

[0029] Referring to Figure 1 , the present application provides an inertial navigation and positioning method. In the first embodiment of the inertial navigation and positioning method, the inertial navigation and positioning method includes steps S10 to S20.

[0030] Step S10: Obtain synchronous measurement data during the flight of the high-speed target drone. The synchronous measurement data includes at least inertial measurement data and satellite measurement data.

[0031] In this embodiment, when the high-speed target drone is launched from the ground launcher, it triggers the inertial measurement unit (IMU), satellite positioning module, and atmospheric sensor to synchronously collect IMU data, GPS data, and atmospheric data at the same timestamp, and perform data preprocessing on the IMU data, GPS data, and atmospheric data to obtain synchronous measurement data including inertial measurement data, satellite measurement data, and atmospheric measurement data.

[0032] It should be noted that the data preprocessing includes at least dimension unification, gross error rejection, and time synchronization processing. Exemplarily, by performing dimension unification on the IMU data, GPS data, and atmospheric data, the actual sensor units corresponding to the IMU data, GPS data, and atmospheric data are uniformly converted into the model training units corresponding to the preset error prediction model. For example, assuming that the model training unit is the International System of Units (SI), the unit conversion rules for the IMU data, GPS data, and atmospheric data respectively refer to Tables 1 to 3 below.

[0033]

[0034] Table 1 is the dimension unification conversion of IMU data

[0035] Table 2 is the dimension unification conversion of GPS data

[0036] Table 3 is the dimension unification conversion of atmospheric data After the dimensions of IMU data, GPS data and atmospheric data are unified, the IMU data, GPS data and atmospheric data are respectively subjected to gross error elimination. Exemplarily, taking the acceleration in IMU data as an example, a fixed time window is used, for example, an acceleration data segment is divided every 1 second (i.e., there are multiple accelerations); next, whether the acceleration in the current time window is in the preset normal speed threshold interval is traversed step by step according to the time sequence, if there is an acceleration that is not in the normal speed threshold interval, then the acceleration that is not in the normal speed threshold interval is determined to be marked as an abnormal value, and the abnormal value is deleted (for example, the abnormal value is directly eliminated or the abnormal value is updated to any threshold value in the normal speed threshold interval); until all accelerations in the current time window are traversed, the next fixed time window of the current time window is updated to the next current time window, and the step of traversing the acceleration in the current time window step by step according to the time sequence is returned to determine whether it is in the preset normal speed threshold interval until the gross error elimination of acceleration is completed. The angular velocity in the IMU data is consistent with the gross error elimination of acceleration, and this application will not be repeated here.

[0037] In a specific embodiment, taking the inertial device temperature in the IMU data as an example, a fixed time window is used, for example, an inertial device temperature data segment is divided every 1 second, and the device temperature average value (i.e. value) and the device temperature standard deviation value (i.e. value); then detect Is it greater than 3? ,in, The first segment of the inertial device temperature data in the current time window. Inertial device temperature , if exists ≤3 , then the inertial device temperature Mark as a valid value; if present >3 , then the inertial device temperature The corresponding timestamp is marked as an abnormal point, and the inertial device temperature at the abnormal point is Replace it with the previous valid value, so as to eliminate the acceleration of abnormal values; next, update the next fixed time window of the current time window to the next current time window, and return to execute the steps of the device temperature mean and device temperature standard deviation of the inertial device temperature data segment respectively, until the gross error elimination of the inertial device temperature is completed. In addition, the gross error elimination of GPS data and atmospheric data is consistent with that of the inertial device temperature, which will not be elaborated in this application.

[0038] It should be noted that the time length corresponding to the fixed time window can be 1 second or can be customized according to application requirements, and the present application does not make any restrictions here.

[0039] After the gross error rejection of the IMU data, GPS data, and atmospheric data is completed, the IMU data, GPS data, and atmospheric data (i.e., inertial measurement data, satellite measurement data, and atmospheric measurement data) with the same timestamp are used as the synchronous measurement data of the high-speed target drone during flight, thereby realizing the synchronization of data acquisition.

[0040] Step S20: Determine the inertial output data according to the preset error prediction model and the inertial measurement data, and determine the attitude and position measurement result of the high-speed target drone according to the inertial output data and the actual state identifier corresponding to the satellite measurement data.

[0041] In this embodiment, by performing model noise reduction processing on the inertial measurement data according to the preset error prediction model, more accurate inertial noise reduction data can be obtained; next, the inertial noise reduction data is used as the model input of the preset error prediction model, and the inertial noise reduction data is trained according to this error prediction model, and the error prediction data can be accurately obtained and compared with the real error data during the flight of the high-speed target drone. Finally, the inertial output data of this error prediction model can be accurately obtained, thereby significantly improving the accuracy and reliability of the flight error prediction of the high-speed target drone.

[0042] It should be noted that the preset error prediction model includes an LSTM (Long Short-Term Memory) noise reduction model and an LSTM error prediction model that have passed training, testing, and evaluation.

[0043] In summary, the embodiment of the present application provides an inertial navigation and positioning method. By real-time obtaining the synchronous measurement data including inertial measurement data and satellite measurement data during the flight of the high-speed target drone, accurate and reliable data support is provided for the subsequent preset error prediction model; next, the preset error prediction model is used to process the inertial measurement data to determine the inertial output data, and combined with the actual state identifier corresponding to the satellite measurement data, the attitude and position measurement result of the high-speed target drone can be accurately obtained, thereby effectively coping with challenges such as vibration noise generated by the booster rocket during the launch process, noise characteristic differences under different maneuvering conditions during flight, rapid changes in environmental temperature, and short-term occlusion of satellite signals during large-angle maneuvers, realizing high-precision and high-stability attitude and position measurement of the high-speed target drone, and thus significantly improving the accuracy of the inertial navigation system in the application of high-speed target drones.

[0044] Further, based on the first embodiment of the present application above, a second embodiment of the inertial navigation and positioning method of the present application is proposed. The above step S20: determining the inertial output data according to the preset error prediction model and the inertial measurement data may further include the following implementation steps S201 to step 202.

[0045] Step S201: Perform model noise reduction processing on the inertial measurement data according to the preset error prediction model to obtain inertial noise-reduced data.

[0046] In this embodiment, referring to Figure 2 , Figure 2 is a schematic diagram of the LSTM model prediction and correction process involved in the solution of the embodiment of the present application. In view of the complexity of external interference noise under different environments and maneuvering conditions of the high-speed target drone flight, the inertial measurement data formed by the angular rate, acceleration, and inertial device temperature after data preprocessing is subjected to model noise reduction processing using a qualified LSTM noise reduction model through training, testing, and evaluation, and the inertial noise-reduced data formed by the noise-reduced angular rate, acceleration, and inertial device temperature can be accurately obtained.

[0047] Step S202: Determine error prediction data according to the error prediction model and the inertial noise-reduced data, and determine the true error data of the high-speed target drone during flight. Determine the inertial output data of the error prediction model according to the error prediction data and the true error data.

[0048] In this embodiment, referring to Figure 2 , the noise-reduced angular rate, acceleration, and inertial device temperature (i.e., inertial noise-reduced data) are used as the model input of the LSTM error prediction model, so that the LSTM error prediction model can perform model training on the inertial noise-reduced data, and the drift error values of the gyroscope and accelerometer (i.e., error prediction data) can be effectively predicted; next, determine the true error data of the high-speed target drone during flight (i.e., the fused and filtered drift error estimation values of the gyroscope and accelerometer), and then perform system evaluation according to the error prediction data and the true error data to obtain an error prediction evaluation result. This prediction evaluation result is used to evaluate whether the prediction of the LSTM error prediction model is effective, so that the prediction performance of the LSTM error prediction model can be accurately evaluated; next, according to the error prediction evaluation result and satellite measurement data, the inertial output data of the error prediction model can be accurately obtained, thereby significantly improving the accuracy and reliability of the high-speed target drone flight error prediction.

[0049] Further, in some other feasible embodiments, the above step S202: determining the inertial output data of the error prediction model according to the error prediction data and the true error data may further include the following implementation steps S2021 to step S2022.

[0050] Step S2021: Determine the error data difference between the error prediction data and the true error data, and determine the error prediction evaluation result according to the error data difference and a preset error tolerance threshold.

[0051] In this embodiment, determine the error data difference between the error prediction data and the true error data, and detect whether the error data difference exceeds the preset error tolerance threshold. If the error data difference does not exceed the preset error tolerance threshold, determine that the error prediction evaluation result is a valid prediction; if the error data difference exceeds the preset error tolerance threshold, determine that the error prediction evaluation result is an invalid prediction.

[0052] Step S2022: Determine the inertial output data of the error prediction model according to the error prediction evaluation result and the satellite measurement data.

[0053] In this embodiment, in response to the error prediction evaluation result being a valid prediction (i.e., Figure 2 characterized by the passing arrow), perform LSTM error correction on the denoised angular rate and acceleration according to the error prediction data to obtain inertial correction data formed by the corrected angular rate and acceleration; subsequently, refer to Figure 2 , determine whether the satellite measurement is valid according to the satellite measurement data to determine whether the inertial output data of the error prediction model is inertial denoised data or inertial correction data.

[0054] Further, in some feasible embodiments, the above step S2023: Determine the inertial output data of the error prediction model according to the inertial correction data and the actual status identifier corresponding to the satellite measurement data may further include the following implementation steps A10 to step A30.

[0055] Step A10: In response to the error prediction evaluation result being a valid prediction, perform error correction on the inertial denoised data according to the error prediction data to obtain inertial correction data, and determine whether the satellite measurement data is satellite valid measurement data.

[0056] In this embodiment, in response to the error prediction evaluation result being a valid prediction (i.e., Figure 2 characterized by the passing arrow), perform LSTM error correction on the denoised angular rate and acceleration according to the error prediction data to obtain inertial correction data formed by the corrected angular rate and acceleration; subsequently, refer to Figure 2 , determine whether the satellite signal transmission of the satellite positioning module is normal by determining whether the satellite measurement data is satellite valid measurement data.

[0057] Step A20: If the satellite measurement data is valid satellite measurement data, determine that the inertial output data of the error prediction model is inertial noise-reduced data carrying a valid satellite identifier.

[0058] In this embodiment, if the satellite measurement data is valid satellite measurement data, that is, the satellite signal transmission of the satellite positioning module is normal, the noise-reduced angular rate and acceleration in the inertial noise-reduced data carrying a valid satellite identifier can be used as the inertial output data of the error prediction model, so as to ensure that when the satellite measurement data is reliable, the accurate attitude and position measurement results can be directly calculated using the noise-reduced angular rate and acceleration.

[0059] Step A30: If the satellite measurement data is invalid satellite measurement data, determine that the inertial output data of the error prediction model is inertial correction data carrying an invalid satellite identifier.

[0060] In this embodiment, if the satellite measurement data is invalid satellite measurement data, that is, the satellite signal transmission of the satellite positioning module is abnormal (i.e., the satellite signal transmission is unstable), that is, when the satellite signal is briefly lost, the inertial correction data carrying an invalid satellite identifier is used as the inertial output data of the error prediction model, so as to effectively reduce the inertial recursive cumulative error of the inertial measurement unit and maintain the accuracy of the system's attitude and position measurement.

[0061] Further, in some other feasible embodiments, the above step S20: Determine the attitude and position measurement result of the high-speed target aircraft according to the inertial output data and the actual status identifier corresponding to the satellite measurement data may further include the following implementation steps B10 to B20.

[0062] Step B10: Perform strapdown solution according to the inertial output data to obtain inertial solution data, and confirm whether the actual status identifier corresponding to the satellite measurement data is a valid satellite identifier.

[0063] In this embodiment, refer to Figure 3 , Figure 3 is the real-time solution flowchart of attitude and position measurement involved in the embodiment solution of this application. By performing LSTM error prediction and correction on the inertial measurement data in the synchronous measurement data through a preset error prediction model, and performing strapdown solution according to the obtained inertial output data, the inertial solution data can be accurately obtained. The inertial solution data at least includes information such as the position, speed, and attitude of the high-speed target aircraft after inertial solution during flight; Next, by confirming whether the actual status identifier corresponding to the satellite measurement data is a valid satellite identifier, the effective judgment of the satellite positioning module can be realized.

[0064] Step B20: If the actual status flag is the satellite valid flag, construct a fusion filter solution model based on the satellite measurement data, and obtain the attitude and position determination result of the high-speed target aircraft based on the fusion filter solution model and the inertial solution data.

[0065] In this embodiment, referring to Figure 3 , if the actual status flag corresponding to the satellite measurement data is the satellite valid flag, the satellite measurement data may include the satellite measurement position and the satellite measurement speed of the high-speed target aircraft during flight; next, construct a fusion filter solution model for the high-speed target aircraft during flight based on the satellite measurement position and the satellite measurement speed; subsequently, the attitude and position determination result of the high-speed target aircraft can be accurately obtained based on the fusion filter solution model and the inertial solution data.

[0066] It should be noted that the satellite measurement position may include the eastward, northward, and upward position measurement vectors; the satellite measurement speed may include the eastward, northward, and upward speed measurement vectors.

[0067] The fusion filter solution model is as follows:

[0068]

[0069]

[0070]

[0071]

[0072] Wherein, represents the error state vector of the high-speed target aircraft during flight measured by the inertial navigation system, represents the platform error angle of the high-speed target aircraft during flight transpose, represents the speed error of the high-speed target aircraft during flight transpose, represents the position error of the high-speed target aircraft during flight transpose; represents the gyro random constant drift value of the high-speed target aircraft during flight transpose; represents the accelerometer random constant drift of the high-speed target aircraft during flight transpose. represents the differential of the error state of the high-speed target aircraft during flight measured by the inertial navigation system; is the state one-step transfer matrix, is the system state noise distribution matrix, and are the white noises of the gyro angular rate and accelerometer specific force measurements respectively; is the measurement vector, is the measurement matrix, is the system measurement noise vector, 、 and are the eastward, northward and upward position measurement vectors, 、 and are the eastward, northward and upward velocity measurement vectors.

[0073] In a specific embodiment, if the actual status identifier corresponding to the satellite measurement data is the satellite valid identifier (i.e., when the satellite measurement data is valid), fusion filtering calculation is performed according to the constructed fusion filtering calculation model to estimate the error status and use the estimated values of the gyro and accelerometer drift errors after fusion filtering (i.e., Figure 3 the estimated values of the gyro and accelerometer drift shown) as the true error data and input it into the LSTM prediction and correction module (i.e., the preset error prediction model). At the same time, the inertial calculation data is processed by fusion filtering according to the constructed fusion filtering calculation model to use the information such as position, velocity and attitude after fusion filtering as the attitude and position measurement result of the high-speed target aircraft.

[0074] Further, in some other feasible embodiments, the synchronous measurement data further includes atmospheric measurement data. After the above step B10: confirming whether the actual status identifier corresponding to the satellite measurement data is the satellite valid identifier, the inertial navigation positioning method may further include the following implementation step C10.

[0075] Step C10: If the actual status identifier is the satellite invalid identifier, determine the barometric altitude of the high-speed target aircraft during flight according to the atmospheric measurement data, and perform damping processing on the inertial calculation data according to the barometric altitude to obtain the attitude and position measurement result of the high-speed target aircraft.

[0076] In this embodiment, referring to Figure 3 , if the actual status identifier corresponding to the satellite measurement data is the satellite invalid identifier, that is, when the satellite measurement information is invalid, the inertial calculation altitude channel is damped according to the barometric altitude calculated from the barometric measurement data, and the inertial calculation position, velocity and attitude information after damping by the barometric altitude is output as the attitude and position measurement result of the high-speed target aircraft, so as to effectively suppress the rapid divergence of the inertial calculation altitude channel error.

[0077] In summary, to balance the cost and performance requirements of a high-speed target drone, this application uses satellite measurement information and other auxiliary means to assist the rapid alignment of MEMS inertial navigation; uses an LSTM (Long Short-Term Memory) neural network model for noise reduction processing to extract effective inertial noise reduction data (i.e., the angular rate and acceleration after noise reduction), and performs fusion filtering with satellite measurement information (i.e., satellite measurement position and satellite measurement speed) to estimate and compensate for inertial measurement error terms in real time; uses an LSTM neural network model to predict and correct the drift errors of gyroscopes and accelerometers, and when the satellite signal is briefly unlocked, reduces the cumulative error of inertial recursion to maintain the accuracy of the system's attitude and position measurement.

[0078] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the inertial navigation and positioning method of this application. Based on this technical concept, more forms of simple transformations are within the protection scope of this application.

[0079] This application also provides an inertial navigation and positioning device. The inertial navigation and positioning device includes an inertial navigation and positioning controller, an information acquisition unit, and a thermal management unit. The inertial navigation and positioning controller is connected to the information acquisition unit and the thermal management unit. Please refer to Figure 4 , and the inertial navigation and positioning controller includes: An acquisition module H01, configured to acquire synchronous measurement data during the flight of the high-speed target drone, where the synchronous measurement data includes at least inertial measurement data and satellite measurement data; An attitude and position measurement module H02, configured to determine inertial output data based on a preset error prediction model and the inertial measurement data, and determine the attitude and position measurement result of the high-speed target drone based on the inertial output data and the actual status identifier corresponding to the satellite measurement data.

[0080] The inertial navigation and positioning device provided by this application adopts the inertial navigation and positioning method in the above embodiment, and can solve the technical problem of poor inertial navigation and positioning effect. Compared with the prior art, the beneficial effects of the inertial navigation and positioning device provided by this application are the same as those of the inertial navigation and positioning method provided by the above embodiment, and other technical features in the inertial navigation and positioning device are the same as those disclosed in the above embodiment method, and will not be elaborated here.

[0081] This application provides an inertial navigation and positioning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the inertial navigation and positioning method in Embodiment 1 above.

[0082] Next, refer to Figure 5, which shows a schematic structural diagram of an inertial navigation and positioning device suitable for implementing the embodiments of the present application. The inertial navigation and positioning device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The shown inertial navigation and positioning device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0083] As Figure 5 shown, the inertial navigation and positioning device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the inertial navigation and positioning device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following devices may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the inertial navigation and positioning device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an inertial navigation and positioning device with various devices, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be implemented or had alternatively.

[0084] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium. The computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0085] The inertial navigation and positioning device provided by the present application adopts the inertial navigation and positioning method in the above-mentioned embodiment, and can solve the technical problem of poor inertial navigation and positioning effect. Compared with the prior art, the beneficial effects of the inertial navigation and positioning device provided by the present application are the same as those of the inertial navigation and positioning method provided by the above-mentioned embodiment, and other technical features in the inertial navigation and positioning device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0086] It should be understood that each part disclosed in the present application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0087] As mentioned above, the above are only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0088] The present application provides a computer-readable storage medium, which has computer-readable program instructions (i.e., computer programs) stored thereon. The computer-readable program instructions are used to execute the inertial navigation and positioning method in the above-mentioned embodiment.

[0089] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, apparatuses, or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution device, apparatus, or component. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0090] The above computer-readable storage medium can be included in an inertial navigation and positioning device; it can also exist separately without being assembled into the inertial navigation and positioning device.

[0091] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the inertial navigation and positioning device, the inertial navigation and positioning device is caused to: Obtain the initial text data to be processed, where the text initial data includes the initial text format and the processing format requirements; Determine the processing mode according to the processing format requirements and the initial text format, where the processing mode includes a first processing mode for changing the text format and a second processing mode for not changing the text format; When the processing mode is the first processing mode, perform text display according to the initial text format and the processing format requirements; When the processing mode is the second processing mode, perform text display according to the initial text format.

[0092] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN: Local Area Network) or a wide area network (WAN: Wide Area Network), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of apparatuses, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based device for performing the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0094] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0095] Refer to Figure 5 , Figure 5Schematic diagram of the storage medium structure involved in the inertial navigation and positioning method of the present application. The computer-readable storage medium provided by the present application stores computer-readable program instructions for executing the above inertial navigation and positioning method (i.e., computer program, which is an over-the-top tracking program), and can solve the technical problem of poor inertial navigation and positioning effect. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the inertial navigation and positioning method provided in the above embodiment, and will not be elaborated here.

[0096] The present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the inertial navigation and positioning method as described above are implemented.

[0097] The computer program product provided by the present application can solve the technical problem of poor inertial navigation and positioning effect. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the inertial navigation and positioning method provided in the above embodiment, and will not be elaborated here.

[0098] The above are only partial embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structural transformation made by using the content of the specification and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. An inertial navigation and positioning method, characterized in that, The inertial navigation and positioning method includes: Obtaining synchronous measurement data during the flight of a high-speed target drone, where the synchronous measurement data at least includes inertial measurement data and satellite measurement data; Determining inertial output data based on a preset error prediction model and the inertial measurement data, and determining the attitude and positioning result of the high-speed target drone based on the inertial output data and the actual status identifier corresponding to the satellite measurement data.

2. The inertial navigation and positioning method according to claim 1, wherein The step of determining inertial output data based on a preset error prediction model and the inertial measurement data includes: Performing model noise reduction processing on the inertial measurement data according to the preset error prediction model to obtain inertial noise-reduced data; Determining error prediction data based on the error prediction model and the inertial noise-reduced data, and determining the true error data during the flight of the high-speed target drone, and determining the inertial output data of the error prediction model based on the error prediction data and the true error data.

3. The inertial navigation and positioning method according to claim 2, wherein The step of determining the inertial output data of the error prediction model based on the error prediction data and the true error data includes: Determining the error data difference between the error prediction data and the true error data, and determining the error prediction evaluation result based on the error data difference and a preset error tolerance threshold; Determining the inertial output data of the error prediction model based on the error prediction evaluation result and the satellite measurement data.

4. The inertial navigation and positioning method according to claim 3, characterized in that, The step of determining the inertial output data of the error prediction model based on the error prediction evaluation result and the satellite measurement data includes: In response to the error prediction evaluation result being a valid prediction, performing error correction on the inertial noise-reduced data based on the error prediction data to obtain inertial corrected data, and determining whether the satellite measurement data is satellite valid measurement data; If the satellite measurement data is satellite valid measurement data, determining that the inertial output data of the error prediction model is the inertial noise-reduced data carrying a satellite valid identifier; If the satellite measurement data is satellite invalid measurement data, determining that the inertial output data of the error prediction model is the inertial corrected data carrying a satellite invalid identifier.

5. The inertial navigation and positioning method according to claim 1, characterized in that The step of determining the attitude and positioning result of the high-speed target drone based on the inertial output data and the actual status identifier corresponding to the satellite measurement data includes: Performing strapdown solution on the inertial output data to obtain inertial solution data, and confirming whether the actual status identifier corresponding to the satellite measurement data is a satellite valid identifier; If the actual status identifier is a satellite valid identifier, constructing a fusion filtering solution model based on the satellite measurement data, and obtaining the attitude and positioning result of the high-speed target drone based on the fusion filtering solution model and the inertial solution data.

6. The inertial navigation and positioning method according to claim 5, characterized in that, The synchronous measurement data further includes atmospheric measurement data. After the step of confirming whether the actual status identifier corresponding to the satellite measurement data is a satellite valid identifier, the inertial navigation and positioning method includes: If the actual status identifier is a satellite invalid identifier, determine the barometric altitude of the high-speed target aircraft during flight based on the atmospheric measurement data, and perform damping processing on the inertial solution data based on the barometric altitude to obtain the attitude and position measurement result of the high-speed target aircraft.

7. An inertial navigation and positioning device, characterized in that, The inertial navigation and positioning device includes: An acquisition module, configured to acquire synchronous measurement data of the high-speed target aircraft during flight, where the synchronous measurement data includes at least inertial measurement data and satellite measurement data; An attitude and position measurement module, configured to determine inertial output data based on a preset error prediction model and the inertial measurement data, and determine the attitude and position measurement result of the high-speed target aircraft based on the inertial output data and the actual status identifier corresponding to the satellite measurement data.

8. An inertial navigation and positioning device, characterized in that, The inertial navigation and positioning device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the inertial navigation and positioning method according to any one of claims 1 to 6.

9. A storage medium, the storage medium being a computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the steps of the inertial navigation and positioning method according to any one of claims 1 to 6.

10. A computer product, the computer product comprising a computer program, characterized in that, The computer program includes computer program code means stored on a computer-readable medium or carrier wave, and the computer program code means are configured to cause a computer or a processor to implement the steps of the inertial navigation and positioning method according to any one of claims 1 to 6 when executed.

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