CN112833919A - Management method and system for redundant inertial measurement data - Google Patents
Management method and system for redundant inertial measurement data Download PDFInfo
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Abstract
The invention discloses a management method and a system of redundant inertial measurement data, wherein the management method comprises the following steps: acquiring multi-path IMU data acquired by a plurality of IMU units; generating detection data by carrying out multi-dimensional fault assessment on the multi-path IMU data; extracting optimal IMU data contained in the detection data; and taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment. According to the method, the IMU data are subjected to combined voting grading through multi-dimensional fault assessment, the fault detection accuracy is effectively improved, meanwhile, the IMU data applied at the current moment are gradually corrected to be the same as the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate through simulating the trend of the IMU data with the highest combined voting grading at the current moment, and the problem that the IMU data to be applied are large in switching amplitude and further the motion stability of the device is seriously influenced is solved.
Description
Technical Field
The invention relates to the technical field of inertial navigation systems, in particular to a management method and a management system of redundant inertial measurement data.
Background
The IMU is called an Inertial Measurement Unit, and the Inertial Measurement Unit enables the rear-end data processing Unit to work out the pose of the measured object by detecting and measuring acceleration and rotary motion. Currently, IMU is widely applied to devices requiring precise displacement estimation by attitude and motion control, for example: inertial navigation devices for automobiles, robots, submarines, airplanes, missiles, and spacecraft, and the like.
The IMU is used as key data acquisition equipment for controlling the motion of the device, and if a fault occurs in the using process, effective data information cannot be provided, so that serious potential safety hazards can be caused to the controlled device, and huge economic loss can be caused. Therefore, most of the existing devices using the IMU are designed for IMU redundancy, and fault detection is continuously performed on each IMU to isolate a faulty IMU and switch between IMU data to be applied of different channels.
However, the conventional method for detecting the failure of the inertial measurement data has the problems of false detection and missed detection due to a single criterion, which may cause the false switching of the channel to which the IMU data is to be applied subsequently, and when the IMU data to be applied of different channels has a large difference, the conventional method for switching the data of the inertial measurement data has the problem of an excessively large switching range, which may seriously affect the motion stability of the apparatus.
In summary, the conventional management method for the inertial measurement data has the problems of poor fault detection accuracy and large switching amplitude of the IMU data to be applied.
Disclosure of Invention
In view of the above, the present invention provides a management method and a system for redundant inertial measurement data, wherein a multidimensional fault assessment is used to perform a joint voting score on IMU data, so as to effectively improve the fault detection accuracy, and simultaneously, by simulating a trend of IMU data with the highest joint voting score at a current time, IMU data applied at the current time is gradually corrected to be the same as IMU data acquired by a first simulated IMU unit at a next time according to a linear or nonlinear adjustment rate, so that a problem that the switching amplitude of IMU data to be applied is large and further the motion stability of a device is seriously affected is avoided, and problems of poor fault detection accuracy and large switching amplitude of IMU data to be applied in a conventional management method for inertial measurement data are solved.
In order to solve the above problems, the technical solution of the present invention is specifically a method for managing redundant inertial measurement data, including: acquiring multi-path IMU data acquired by a plurality of IMU units; generating detection data by performing multi-dimensional fault assessment on the multi-path IMU data; extracting optimal IMU data contained in the detection data; and taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, wherein when a second IMU unit corresponding to the IMU data applied at the current moment is different from the first IMU unit, the IMU data applied at the current moment is gradually corrected to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit.
Optionally, the method for simulating the trend of the IMU data acquired by the first IMU unit with the IMU data applied at the current time includes: continuously extracting the optimal IMU data as target adjusting data in a time interval between the current moment and the next moment, so that the IMU data applied at the current moment gradually approaches to the optimal IMU data; and gradually correcting the IMU data applied at the current moment to be the same as the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate.
Optionally, the method for managing the inertial measurement data further includes: in the time interval between the current time and the next time and in the process that the IMU data applied at the current time is gradually corrected to be overlapped with the IMU data acquired by the first IMU unit at the next time in a mode of simulating the trend of the IMU data acquired by the first IMU unit, continuously acquiring the multi-path IMU data acquired by the plurality of IMU units and calculating the IMU unit corresponding to the optimal IMU data to be used as a first IMU unit to be applied at the next time, if the plurality of first IMU units continuously generated in the time interval are the same, gradually correcting the IMU data applied at the current time to be overlapped with the IMU data acquired by the first IMU unit at the next time continuously in a mode of simulating the trend of the IMU data acquired by the first IMU unit, and if the plurality of first IMU units continuously generated in the time interval are different for the first time, taking the time of generating the first different first IMU unit as the current time, and gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the updated first IMU unit at the next moment according to a mode of simulating the trend of the updated IMU data acquired by the updated first IMU unit.
Optionally, generating detection data based on the multiple IMU data includes: calculating first evaluation data of the multi-path IMU data based on a preset navigation information estimation model; calculating second evaluation data of the multi-path IMU data based on the data statistical characteristics; and jointly voting the first evaluation data and the second evaluation data based on a preset fault voting rule and generating the detection data.
Optionally, extracting the optimal IMU data included in the detection data includes: extracting the IMU data with the highest score in the joint vote as the optimal IMU data.
Accordingly, the present invention provides a system for managing redundant inertial measurement data, comprising: the IMU units are used for acquiring multi-path IMU data; the system comprises an upper computer unit, a first IMU unit and a second IMU unit, wherein the upper computer unit generates detection data by carrying out multi-dimensional fault assessment on the multi-path IMU data and extracts the optimal IMU data contained in the detection data, the IMU unit corresponding to the optimal IMU data is used as the first IMU unit to be applied at the next moment, the second IMU unit corresponding to the IMU data applied by the upper computer unit is different from the first IMU unit at the current moment, and the IMU data applied at the current moment is gradually corrected to be coincident with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit.
Optionally, the upper computer unit includes a data quality evaluation module, where, when the upper computer unit receives the multiple paths of IMU data, the data quality evaluation module calculates first evaluation data of the multiple paths of IMU data based on a preset navigation information estimation model, and calculates second evaluation data of the multiple paths of IMU data based on a data statistical characteristic, and then the data quality evaluation module performs joint voting on the first evaluation data and the second evaluation data based on a preset fault voting rule to generate the detection data.
Optionally, the upper computer unit further includes a data switching module, where the data switching module gradually corrects the IMU data applied at the current time to be the same as the IMU data acquired by the first IMU unit at the next time according to a linear or nonlinear adjustment rate.
Optionally, in a time interval between a current time and a next time and during a process that the data switching module gradually corrects the IMU data applied at the current time to coincide with the IMU data acquired by the first IMU unit at the next time in a manner of simulating a trend of the IMU data acquired by the first IMU unit, the plurality of IMU units continuously acquire the multi-path IMU data, the data quality evaluation module continuously calculates the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next time, and if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are the same, the data switching module gradually corrects the IMU data applied at the current time to coincide with the IMU data acquired by the first IMU unit at the next time in a manner of simulating a trend of the IMU data acquired by the first IMU unit, if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are different for the first time, the time when the first IMU units different for the first time are generated by the data quality evaluation module is taken as the current time, and the data switching module gradually corrects the IMU data applied at the current time to be overlapped with the IMU data acquired by the updated first IMU unit at the next time in a manner of simulating the trend of the updated IMU data acquired by the updated first IMU unit.
Optionally, the upper computer unit further includes an attitude calculation module, and the attitude calculation module can generate attitude data based on IMU data applied at the current time.
The method has the advantages that the multi-dimensional fault assessment is used for carrying out combined voting grading on IMU data, so that the fault detection accuracy is effectively improved, meanwhile, the trend of the IMU data with the highest combined voting grading at the current moment is simulated, the IMU data applied at the current moment are gradually corrected to be the same as the IMU data acquired by the simulated first IMU unit at the next moment according to the linear or nonlinear adjustment rate, so that the data among different channels are smoothly switched, the problem that the switching amplitude of the IMU data to be applied is large and the movement stability of the device is seriously influenced is solved, and the problems of poor fault detection accuracy and large switching amplitude of the IMU data to be applied existing in the traditional management method of the inertial measurement data are solved.
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FIG. 1 is a simplified flow diagram of a method of managing redundant inertial measurement data of the present invention;
fig. 2 is a simplified block diagram of the present invention method of managing redundant inertial measurement data.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for managing redundant inertial measurement data includes: acquiring multi-path IMU data acquired by a plurality of IMU units; generating detection data by performing multi-dimensional fault assessment on the multi-path IMU data; extracting optimal IMU data contained in the detection data; and taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, and gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit when a second IMU unit corresponding to the IMU data applied at the current moment is different from the first IMU unit. Wherein extracting the optimal IMU data included in the detection data includes: extracting the IMU data with the highest score in the joint vote as the optimal IMU data. The present time and the next time are qualitative limitations for explaining the method of managing the redundancy inertia measurement data according to the present invention, and are not quantitative limitations. Specifically, the time interval between the current time and the next time may be 0.1s, 0.2s, and the influencing parameters include: the data transmission frame rate, the attitude calculation precision requirement, the stability of the IMU unit and the like between the IMU unit and the upper computer unit, and a user can set the time interval between the current moment and the next moment according to an actual application scene and the requirements of the actual application scene.
According to the method, the IMU data are subjected to combined voting grading through multi-dimensional fault assessment, the fault detection accuracy is effectively improved, meanwhile, the IMU data applied at the current moment are gradually corrected to be the same as the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate through simulating the trend of the IMU data with the highest combined voting grading at the current moment, so that data between different channels are smoothly switched, the problem that the movement stability of the device is seriously influenced due to the large switching amplitude of the IMU data to be applied is solved, and the problems of poor fault detection accuracy and large switching amplitude of the IMU data to be applied existing in the traditional inertial measurement data management method are solved.
Further, the method for simulating the trend of the IMU data acquired by the first IMU unit with the IMU data applied at the current time includes: continuously extracting the optimal IMU data as target adjusting data in a time interval between the current moment and the next moment, so that the IMU data applied at the current moment gradually approaches to the optimal IMU data; and gradually correcting the IMU data applied at the current moment to be the same as the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate.
Furthermore, the method for simulating the trend of the IMU data acquired by the first IMU unit with the IMU data applied at the current time may further be: extracting a plurality of historical IMU data acquired by the first IMU unit in a last preset time period; after the plurality of historical IMU data are arranged according to time sequence information, simulating the trend of the IMU data acquired by the first IMU unit, and calculating the IMU data acquired by the first IMU unit at the next moment; and gradually correcting the IMU data applied at the current moment to be the same as the IMU data acquired by the first simulated IMU unit at the next moment according to a linear or nonlinear adjustment rate. The preset time period may be the same as the time interval between the current time and the next time, or the preset time period may be increased more accurately and appropriately for simulation, that is, more historical IMU data is called as a simulation sample, and a user may set the time period according to an actual application scenario and a requirement thereof. The method for simulating the trend of the IMU data acquired by the first IMU unit may be a preset navigation information estimation model, a pre-trained neural network model, or the like.
Furthermore, the management method of the inertial measurement data further comprises: in the time interval between the current time and the next time and in the process that the IMU data applied at the current time is gradually corrected to be overlapped with the IMU data acquired by the first IMU unit at the next time in a mode of simulating the trend of the IMU data acquired by the first IMU unit, continuously acquiring the multi-path IMU data acquired by the plurality of IMU units and calculating the IMU unit corresponding to the optimal IMU data to be used as a first IMU unit to be applied at the next time, if the plurality of first IMU units continuously generated in the time interval are the same, gradually correcting the IMU data applied at the current time to be overlapped with the IMU data acquired by the first IMU unit at the next time continuously in a mode of simulating the trend of the IMU data acquired by the first IMU unit, and if the plurality of first IMU units continuously generated in the time interval are different for the first time, taking the time of generating the first different first IMU unit as the current time, and gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the updated first IMU unit at the next moment according to a mode of simulating the trend of the updated IMU data acquired by the updated first IMU unit.
According to the invention, through continuously carrying out multi-dimensional fault evaluation and generating a plurality of first IMU units in the process of switching the first IMU unit to be applied by the data switching module, and continuously iterating the target first IMU unit, the problem that the motion stability of the device is seriously influenced due to instantaneous fault of the target first IMU unit at the initial moment of data switching by the data switching module in the data switching process is effectively avoided. Also, by way of protection of the invention: the IMU data applied at the current moment is gradually corrected to be the same as the IMU data acquired by the first IMU unit at the next moment according to the linear or nonlinear adjustment rate, so that the data switching methods for smoothly switching the data among different channels are matched, the problems that the device is seriously shaken and the like due to frequent instantaneous switching of an upper computer unit and the fact that IMU data with larger difference degree are used as data for posture resolving and using, and the like when the data switching is frequent due to frequent instantaneous faults of one or more IMU units can be effectively avoided, and the movement stability of the device is guaranteed to the maximum extent.
Furthermore, when the IMU unit represented by the IMU data of one or more channels is judged to be a fault IMU unit, the upper computer unit isolates the IMU data acquired by the fault IMU unit so that the IMU data does not participate in subsequent attitude calculation, and meanwhile, the weight degradation is carried out on the fault IMU unit, so that the frame rate when the IMU data acquired by the fault IMU unit subsequently is transmitted to the data quality evaluation module is reduced, and the frame rate when the IMU data acquired by the fault IMU unit subsequently is transmitted to the data quality evaluation module is restored to the initial value until the IMU data acquired by the fault IMU unit subsequently is judged to be out of fault when the data quality evaluation module carries out multi-dimensional fault evaluation. According to the method, the situation that the upper computer unit continuously has large and useless computational load due to the fact that invalid IMU data are continuously output by the failed IMU unit is effectively avoided by setting a weight degradation method. Meanwhile, the loss of potential optimal IMU data caused by directly and completely isolating IMU data output by the faulty IMU unit by the upper computer unit when the faulty IMU unit recovers after generating an instantaneous fault is avoided.
Further, generating detection data based on the plurality of IMU data, comprising: calculating first evaluation data of the multi-path IMU data based on a preset navigation information estimation model; calculating second evaluation data of the multi-path IMU data based on the data statistical characteristics; and jointly voting the first evaluation data and the second evaluation data based on a preset fault voting rule and generating the detection data. The preset fault voting rule may be a fault correspondence table with a preset score.
Further, the preset navigation information estimation model may be a kalman filter algorithm (KF) model, an Extended Kalman Filter (EKF) model, or the like, which can solve the navigation information of the moving object, and includes a linear system state prediction equation X, for example, using the kalman filter algorithm (KF) modelkAnd linear system observation equation ZkThe overall model expression is as follows:
a is an n × n order state transfer coefficient matrix, B is a gain matrix of the optional control input, w is process excitation noise, and XkIs the true value of the predicted state at time k, Xk-1Is the true value of the state at time k-1, ZkTo observe true values (i.e., data from other sensors or processed navigation information, other sensor types may be gps, barometer, etc.), uk-1C is a measuring coefficient matrix of m multiplied by n orders, and v is observation noise. Further based on the above overall model expression, the following can be obtained:
wherein the content of the first and second substances,
the data quality K of a certain path of IMU data in the first evaluation datakIn order to be the kalman gain factor,
is based on xk-1The observed value obtained by estimation is represented by the formula
And after the data quality of the IMU data of all channels is finished based on traversal of the model, packaging to generate the first evaluation data.
Further, calculating second evaluation data of the multi-path IMU data based on the data statistical characteristics, comprising: any type of statistical characteristic index is extracted based on the multi-channel IMU data, the data quality of the IMU data of all channels is generated based on the preset type of statistical characteristic index threshold, and then the second evaluation data can be generated by packaging. Wherein the statistical property index is defined as: calculating the difference between the IMU data of different paths, and then calculating the approximate standard deviation data of the difference data, for describing the fluctuation condition of the difference data, for example, the method of calculating the statistical characteristic index may be: firstly, difference data among multiple paths of IMU data are extracted, N x (N-1)/2 paths of difference data are obtained on the assumption that N paths of IMU data exist, and then smoothing processing is carried out on each path of difference sequence to obtain a group of statistical indexes of approximate mean values of the difference sequence. And finishing judging whether the IMU data corresponding to the difference value is in fault according to a preset threshold value of the preset type index.
Accordingly, as shown in fig. 2, the present invention provides a system for managing redundant inertial measurement data, comprising: the IMU units are used for acquiring multi-path IMU data; the system comprises an upper computer unit, a first IMU unit and a second IMU unit, wherein the upper computer unit generates detection data by carrying out multi-dimensional fault assessment on the multi-path IMU data and extracts the optimal IMU data contained in the detection data, the IMU unit corresponding to the optimal IMU data is used as the first IMU unit to be applied at the next moment, the second IMU unit corresponding to the IMU data applied by the upper computer unit is different from the first IMU unit at the current moment, and the IMU data applied at the current moment is gradually corrected to be coincident with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit. The upper computer unit further comprises an attitude calculation module, and the attitude calculation module can generate attitude data based on IMU data applied at the current moment so that the upper computer unit can control the motion state of the device based on the attitude data. The upper computer unit can be any kind of data processing unit capable of sending control commands/performing data processing, for example: when the unmanned aerial vehicle is applied to the unmanned aerial vehicle, the upper computer unit can be a flight control unit; when the invention is applied to a vehicle, the upper computer unit can be a Vehicle Control Unit (VCU). The type of the upper computer unit is not particularly limited in the present invention.
Further, the upper computer unit comprises a data quality evaluation module, wherein when the upper computer unit receives the multiple paths of IMU data, the data quality evaluation module calculates first evaluation data of the multiple paths of IMU data based on a preset navigation information estimation model, calculates second evaluation data of the multiple paths of IMU data based on data statistical characteristics, and then jointly votes the first evaluation data and the second evaluation data based on a preset fault voting rule to generate the detection data.
Further, the upper computer unit further includes a data switching module, where the data switching module gradually corrects the IMU data applied at the current time to be the same as the IMU data acquired by the first IMU unit at the next time according to a linear or nonlinear adjustment rate.
Furthermore, in a time interval between a current time and a next time and during a process that the data switching module gradually corrects the IMU data applied at the current time to be overlapped with the IMU data acquired by the first IMU unit at the next time in a manner of simulating a trend of the IMU data acquired by the first IMU unit, the plurality of IMU units continuously acquire the multi-path IMU data, the data quality evaluation module continuously calculates the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next time, and if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are the same, the data switching module gradually corrects the IMU data applied at the current time to be overlapped with the IMU data acquired by the first IMU unit at the next time in a manner of simulating a trend of the IMU data acquired by the first IMU unit, if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are different for the first time, the time when the first IMU units different for the first time are generated by the data quality evaluation module is taken as the current time, and the data switching module gradually corrects the IMU data applied at the current time to be overlapped with the IMU data acquired by the updated first IMU unit at the next time in a manner of simulating the trend of the updated IMU data acquired by the updated first IMU unit.
The method and system for managing redundant inertial measurement data according to the embodiments of the present invention are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Claims (10)
1. A method for managing redundant inertial measurement data, comprising:
acquiring multi-path IMU data acquired by a plurality of IMU units;
generating detection data by performing multi-dimensional fault assessment on the multi-path IMU data;
extracting optimal IMU data contained in the detection data;
taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, wherein,
and when a second IMU unit corresponding to the IMU data applied at the current moment is different from the first IMU unit, gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the first IMU unit at the next moment according to a mode of simulating the trend of the IMU data acquired by the first IMU unit.
2. The method for managing inertial measurement data according to claim 1, characterized in that the method for simulating the IMU data applied at the current moment to the trend of IMU data acquired by the first IMU unit comprises:
continuously extracting the optimal IMU data as target adjusting data in a time interval between the current moment and the next moment, so that the IMU data applied at the current moment gradually approaches to the optimal IMU data;
and gradually correcting the IMU data applied at the current moment to be the same as the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate.
3. The method for managing inertial measurement data according to claim 1, further comprising:
in the time interval between the current moment and the next moment and in the process that the IMU data applied at the current moment is gradually corrected to be overlapped with the IMU data acquired by the first IMU unit at the next moment according to the mode of simulating the trend of the IMU data acquired by the first IMU unit,
continuously acquiring the multi-path IMU data acquired by the IMU units and calculating the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment,
if the plurality of first IMU units continuously generated in the time interval are the same, the IMU data applied at the current moment is gradually corrected to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit,
if the plurality of first IMU units continuously generated in the time interval are different for the first time, the time of generating the first IMU units different for the first time is taken as the current time, and IMU data applied at the current time are gradually corrected to be overlapped with IMU data acquired by the first IMU units at the next time after updating according to a mode of simulating the trend of the IMU data acquired by the first IMU units after updating.
4. The method of managing inertial measurement data according to claim 1, wherein generating detection data based on the multiplexed IMU data comprises:
calculating first evaluation data of the multi-path IMU data based on a preset navigation information estimation model;
calculating second evaluation data of the multi-path IMU data based on the data statistical characteristics;
and jointly voting the first evaluation data and the second evaluation data based on a preset fault voting rule and generating the detection data.
5. The method for managing inertial measurement data according to claim 3, wherein extracting the optimal IMU data included in the detection data comprises:
extracting the IMU data with the highest score in the joint vote as the optimal IMU data.
6. A system for managing redundant inertial measurement data, comprising:
the IMU units are used for acquiring multi-path IMU data;
the system comprises an upper computer unit, a first IMU unit and a second IMU unit, wherein the upper computer unit generates detection data by carrying out multi-dimensional fault assessment on the multi-path IMU data and extracts the optimal IMU data contained in the detection data, the IMU unit corresponding to the optimal IMU data is used as the first IMU unit to be applied at the next moment, the second IMU unit corresponding to the IMU data applied by the upper computer unit is different from the first IMU unit at the current moment, and the IMU data applied at the current moment is gradually corrected to be coincident with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit.
7. The system for managing inertial measurement data according to claim 6, characterized in that said upper computer unit comprises a data quality evaluation module, wherein,
under the condition that the upper computer unit receives the multi-path IMU data, the data quality evaluation module calculates first evaluation data of the multi-path IMU data based on a preset navigation information estimation model, and calculates second evaluation data of the multi-path IMU data based on data statistical characteristics, and then the data quality evaluation module carries out combined voting on the first evaluation data and the second evaluation data based on a preset fault voting rule and generates detection data.
8. The system for managing inertial measurement data according to claim 6, wherein said upper computer unit further comprises a data switching module, wherein,
and the data switching module gradually corrects the IMU data applied at the current moment to be the same as the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate.
9. The system for managing inertial measurement data according to claim 8, wherein during a time interval between a current time and a next time and during a process in which the data switching module gradually modifies the IMU data applied at the current time to coincide with the IMU data acquired by the first IMU unit at the next time in a manner of simulating a trend of the IMU data acquired by the first IMU unit, the plurality of IMU units continuously acquire the multi-path IMU data, the data quality evaluation module continuously calculates the IMU unit corresponding to the optimal IMU data as the first IMU unit to be applied at the next time,
if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are the same, the data switching module gradually corrects the IMU data applied at the current moment to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit,
if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are different for the first time, the time when the first IMU units different for the first time are generated by the data quality evaluation module is taken as the current time, and the data switching module gradually corrects the IMU data applied at the current time to be overlapped with the IMU data acquired by the updated first IMU unit at the next time in a manner of simulating the trend of the updated IMU data acquired by the updated first IMU unit.
10. The system for managing inertial measurement data according to claim 6, characterized in that the upper computer unit further comprises an attitude calculation module capable of generating attitude data based on IMU data applied at the present time.
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