CN112525190A - Inertial navigation method and device - Google Patents
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Abstract
The embodiment of the application provides an inertial navigation method and device, after acceleration data of a mobile terminal are obtained by measurement of an acceleration measuring device of the mobile terminal, the acceleration data are processed based on a first Kalman filter to obtain error data corresponding to the acceleration data, the acceleration data are processed by the error data to obtain processed target acceleration data, then the target acceleration data are processed based on a second Kalman filter to obtain motion data of the mobile terminal, wherein the first Kalman filter is a zeroth-order Kalman filter. In other words, in the embodiment of the application, after the acceleration data of the mobile terminal is measured, the error of the acceleration data is eliminated by using the first kalman filter, and then the motion data of the mobile terminal is calculated by using the second kalman filter, so that the real-time elimination of the integral error can be realized in the inertial navigation process, and the accuracy of the inertial navigation is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of inertial navigation, in particular to an inertial navigation method and device.
Background
An Inertial Navigation System (INS) uses newton's law of mechanics as a working principle, uses an Inertial measurement element mounted on a carrier to measure acceleration information of the carrier, and obtains Navigation parameters such as displacement and speed of the carrier through integral operation.
The inertial navigation has complete autonomy and interference resistance, and has high precision in a short time, however, due to the influence of an actual measurement environment, some errors are inevitably introduced in the whole integral operation process, and the errors are accumulated along with time, so that the precision of long-time navigation work is poor.
How to reduce the integral error in inertial navigation in real time and improve the inertial navigation precision needs to be solved at present.
Disclosure of Invention
The embodiment of the application provides an inertial navigation method and device, which can effectively reduce integral errors in inertial navigation and improve the accuracy of inertial navigation.
In a first aspect, an embodiment of the present application provides an inertial navigation method, which is applied to a mobile terminal, and the method includes:
measuring acceleration data of the mobile terminal by using an acceleration measuring device of the mobile terminal;
processing the acceleration data based on a first Kalman filter to obtain error data corresponding to the acceleration data, wherein the first Kalman filter is a zeroth-order Kalman filter;
processing the acceleration data based on the error data to obtain processed target acceleration data;
and processing the target acceleration data based on a second Kalman filter to obtain the motion data of the mobile terminal.
In a possible design, the processing the acceleration data based on the error data to obtain processed target acceleration data includes:
calculating a difference between the acceleration data and the error data, determining the difference as the target acceleration data.
In a possible design manner, the processing the target acceleration data based on the second kalman filter to obtain the motion data of the mobile terminal includes:
and performing integral operation on the target acceleration data based on the second Kalman filter to obtain speed data and displacement data of the mobile terminal.
In one possible design, the second kalman filter is a second order kalman filter.
In a second aspect, an embodiment of the present application provides an inertial navigation device, which is applied to a mobile terminal, and the device includes:
the measuring module is used for measuring acceleration data of the mobile terminal by using an acceleration measuring device of the mobile terminal;
the first processing module is used for processing the acceleration data based on a first Kalman filter to obtain error data corresponding to the acceleration data, and processing the acceleration data based on the error data to obtain processed target acceleration data, wherein the first Kalman filter is a zeroth-order Kalman filter;
and the second processing module is used for processing the target acceleration data based on a second Kalman filter to obtain the motion data of the mobile terminal.
In one possible embodiment, the first processing module is configured to:
calculating a difference between the acceleration data and the error data, determining the difference as the target acceleration data.
In one possible embodiment, the second processing module is configured to:
and performing integral operation on the target acceleration data based on the second Kalman filter to obtain speed data and displacement data of the mobile terminal.
In one possible design, the second kalman filter is a second order kalman filter.
In a third aspect, an embodiment of the present application provides a mobile terminal, including at least one processor and a memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory, causing the at least one processor to perform the inertial navigation method as provided by the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the inertial navigation method according to the first aspect is implemented.
In a fifth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for inertial navigation according to the first aspect is implemented.
According to the inertial navigation method and the inertial navigation device, after acceleration data of the mobile terminal are obtained through measurement by an acceleration measuring device of the mobile terminal, the acceleration data are processed based on a first Kalman filter to obtain error data corresponding to the acceleration data, the acceleration data are processed by the error data to obtain processed target acceleration data, then the target acceleration data are processed based on a second Kalman filter to obtain speed data and displacement data of the mobile terminal, and the first Kalman filter is a zeroth-order Kalman filter. In other words, in the embodiment of the application, after the acceleration data of the mobile terminal is obtained through measurement, the first kalman filter is used for processing the acceleration data, so that the error of the acceleration data can be eliminated in real time, and then the second kalman filter is used for calculating the motion data of the mobile terminal, so that the integral error can be eliminated in real time in the inertial navigation process, and the accuracy of the inertial navigation is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments of the present application or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive exercise.
Fig. 1 is a schematic structural diagram of an inertial navigation processing system provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating an inertial navigation method according to an embodiment of the present disclosure;
fig. 3 is a schematic graph of acceleration data obtained by measurement and error data processed by a first kalman filter in an embodiment of the present application;
fig. 4 is a schematic diagram illustrating a comparison between a speed data curve obtained by simultaneously processing through a first kalman filter and a second kalman filter and a speed data curve obtained without processing through the first kalman filter in the embodiment of the present application;
fig. 5 is a schematic diagram illustrating a comparison between a displacement data curve obtained by simultaneously processing through a first kalman filter and a second kalman filter and a displacement data curve obtained by not processing through the first kalman filter in the embodiment of the present application;
FIG. 6 is a block diagram of an inertial navigation device according to an embodiment of the present application;
fig. 7 is a schematic hardware structure diagram of a mobile terminal provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
An Inertial Navigation System (INS) is also called an Inertial reference System, and is an autonomous Navigation System that does not depend on external information and does not radiate energy to the outside (such as radio Navigation). The working environment of the device not only comprises the air and the ground, but also can be underwater. The basic working principle of inertial navigation is based on Newton's law of mechanics, and by measuring the acceleration of the mobile terminal in an inertial reference system, integrating the acceleration with time and transforming the integrated acceleration into a navigation coordinate system, information such as speed, yaw angle and displacement of the mobile terminal in the navigation coordinate system can be obtained.
The inertial navigation system is a dead reckoning navigation mode, that is, the position of the next point is reckoned from the position of a known point according to the continuously measured course angle and speed of the mobile terminal, so that the current position of the mobile terminal can be continuously measured. A gyroscope in the inertial navigation system is used for forming a navigation coordinate system, so that a measuring axis of the accelerometer is stabilized in the coordinate system, and a course and an attitude angle are given; the accelerometer is used for measuring the acceleration of the mobile terminal, the speed is obtained through the first integration of the time, and the displacement can be obtained through the speed through the first integration of the time.
When the initial value of the acceleration is not 0, a large error is introduced in the whole integration process. In a real system, however, the average of the accelerometer output values may not be 0 due to noise and the like. To eliminate the integration error, one possible implementation is to calibrate the acceleration value by subtracting the initial value of the acceleration from the acceleration value before performing the integration operation. However, the method cannot calibrate the integral error introduced in the inertial navigation process in real time, and the accuracy of long-time navigation work is still poor.
In order to solve the above technical problem, an embodiment of the present application provides an inertial navigation method, where after acceleration data of a mobile terminal is measured, an error of the acceleration data is eliminated by using a zero-order kalman filter, and then motion data of the mobile terminal is calculated by using a second kalman filter, so that an integral error can be eliminated in real time in an inertial navigation process, and accuracy of the inertial navigation is improved.
For better understanding of the embodiment of the present application, referring to fig. 1, fig. 1 is a schematic structural diagram of an inertial navigation processing system provided in the embodiment of the present application. In fig. 1, after obtaining acceleration data of a mobile terminal through measurement, a first kalman filter is used to process the acceleration data to obtain error data corresponding to the acceleration data, and then, after processing the acceleration data based on the obtained error data, a second kalman filter is used to process the processed target acceleration data, so as to obtain velocity data and displacement data of the mobile terminal. The following examples are given for illustrative purposes.
Referring to fig. 2, fig. 2 is a schematic flowchart of an inertial navigation method according to an embodiment of the present disclosure, which can be applied to a mobile terminal. In a possible embodiment, the inertial navigation method includes:
s201, measuring by using an acceleration measuring device of the mobile terminal to obtain acceleration data of the mobile terminal.
The mobile terminal can be a mobile phone, a vehicle-mounted terminal, a POS machine and the like.
Alternatively, the acceleration measuring device may employ an acceleration sensor. For example, an acceleration sensor composed of a mass block, a damper, an elastic element, a sensitive element, an adaptive circuit and the like can obtain an acceleration value by measuring the inertial force borne by the mass block and utilizing Newton's second law in the acceleration process of the mobile terminal. The acceleration sensor may be of a capacitive type, an inductive type, a strain type, a piezoresistive type, a piezoelectric type, etc. according to the difference of the sensing elements.
And S202, processing the acceleration data based on a first Kalman filter to obtain error data corresponding to the acceleration data.
The calculation of the kalman filter is based on the state equation, so the state equation of the inertial navigation system needs to be determined first. In one possible implementation, in establishing the first kalman filter, the state equation is set as:
X(k+1)＝φX(k)+Bu(k)+ΓW(k)
Y(k)＝HX(k)+V(k)
wherein, X (k) is the current state, X (k +1) is the next time state, Φ is the transition matrix, B is the control matrix, u is the control quantity, Γ is the noise matrix, W is the system noise, Y is the output quantity, H is the output matrix, and V is the observation noise. That is, the observable output at this time and the state at the next time can be obtained from the state, the control amount, and the system noise known at the present time.
The above state is the acceleration of the mobile terminal, that is, x (k) in the above formula includes the vector of the acceleration:
X(k)＝[a(k)]
wherein, above-mentioned first kalman filter adopts zeroth order kalman filter, promptly:
a(k+1)＝a(k)
wherein:
Y(k)＝[a(k)]
in the embodiment of the application, after the state equation of the first kalman filter is determined, the acceleration data can be processed to obtain error data corresponding to the acceleration data.
In order to better understand the embodiment of the present application, in a motion scene in which the mobile terminal rocks left and right around the origin for a period of time and finally returns to the origin, referring to fig. 3, fig. 3 is a schematic graph of acceleration data obtained by measurement in the embodiment of the present application and error data processed by the first kalman filter.
And S203, processing the acceleration data based on the error data to obtain processed target acceleration data.
In the embodiment of the present application, after error data corresponding to the acceleration data is obtained, calibration processing is performed on the acceleration data based on the error data, and processed target acceleration data is obtained.
In one possible embodiment, a difference between the acceleration data and the error data may be calculated, and the difference may be determined as target acceleration data.
And S204, processing the target acceleration data based on a second Kalman filter to obtain the motion data of the mobile terminal.
In one possible implementation, in establishing the second kalman filter, the state equation is set as:
X(k+1)＝φX(k)+Bu(k)+ΓW(k)
Y(k)＝HX(k)+V(k)
wherein, X (k) is the current state, X (k +1) is the next time state, Φ is the transition matrix, B is the control matrix, u is the control quantity, Γ is the noise matrix, W is the system noise, Y is the output quantity, H is the output matrix, and V is the observation noise. That is, the observable output at this time and the state at the next time can be obtained from the state, the control amount, and the system noise known at the present time.
When the motion data includes velocity and displacement, the states include acceleration, velocity and displacement of the mobile terminal, that is, x (k) a vector including the acceleration, the velocity and the displacement:
the same principle is that:
the state transition matrix Φ is a matrix representing a state relationship between a state at a next time and a state at that time, and assuming that the sampling period T is relatively short and the acceleration a is approximately assumed to be almost constant, then:
v(k+1)＝0×h(k)+v(k)+a(k)T
a(k+1)＝0×h(k)+0×v(k)+a(k)
the above equations are converted into matrix form:
from this we can get the transfer matrix Φ as:
in one possible implementation, if other system noise is not considered, the above equation of state can be simplified as:
X(k+1)＝φX(k)
since the second kalman filter is used only for processing the acceleration data, the first kalman filter includes:
Y(k)＝[a(k)]
in the embodiment of the application, after the state equation of the second kalman filter is determined, the target acceleration data can be subjected to integral operation to obtain the speed data and the displacement data of the mobile terminal.
In order to better understand the embodiment of the present application, in a motion scene in which the mobile terminal rocks left and right around the origin for a period of time and finally returns to the origin, referring to fig. 4 and 5, fig. 4 is a schematic comparison diagram of a speed data curve obtained after being processed by the first kalman filter and the second kalman filter simultaneously and a speed data curve obtained without being processed by the first kalman filter in the embodiment of the present application, and fig. 5 is a schematic comparison diagram of a displacement data curve obtained after being processed by the first kalman filter and the second kalman filter simultaneously and a displacement data curve obtained without being processed by the first kalman filter in the embodiment of the present application.
As can be seen from fig. 4 and 5, in a motion scene in which the mobile terminal rocks left and right around the origin for a period of time and finally returns to the origin, the speed data curve and the displacement data curve obtained without being processed by the first kalman filter cannot return to zero, and the offset is large, and the speed data curve and the displacement data curve obtained after being processed by the first kalman filter can basically return to zero, which better conforms to the motion situation of the mobile terminal in the motion scene.
According to the inertial navigation method provided by the embodiment of the application, after the acceleration data of the mobile terminal is obtained through measurement, the acceleration data is processed through the first Kalman filter, the error of the acceleration data can be eliminated in real time, and then the speed data and the displacement data of the mobile terminal are calculated through the second Kalman filter, so that the integral error can be eliminated in real time in the inertial navigation process, and the inertial navigation precision is improved.
Based on the content described in the above embodiments, an inertial navigation device is further provided in the embodiments of the present application, and is applied to a mobile terminal. Referring to fig. 6, fig. 6 is a schematic diagram illustrating program modules of an inertial navigation device 60 according to an embodiment of the present application, where the inertial navigation device includes:
the measuring module 601 is configured to obtain acceleration data of the mobile terminal by using an acceleration measuring device of the mobile terminal.
A first processing module 602, configured to process the acceleration data based on a first kalman filter to obtain error data corresponding to the acceleration data, and process the acceleration data based on the error data to obtain processed target acceleration data, where the first kalman filter is a zeroth-order kalman filter.
And a second processing module 603, configured to process the target acceleration data based on a second kalman filter, to obtain motion data of the mobile terminal.
The inertial navigation device 60 provided in the embodiment of the present application, after obtaining the acceleration data of the mobile terminal through measurement, first utilizes the first kalman filter to process the acceleration data, and can eliminate the error of the acceleration data in real time, and then utilizes the second kalman filter to calculate the motion data of the mobile terminal, so as to eliminate the integral error in real time in the inertial navigation process and improve the precision of the inertial navigation.
In a possible implementation manner, the first processing module is configured to:
calculating a difference between the acceleration data and the error data, and determining the difference as the target acceleration data.
In a possible implementation manner, the second processing module is configured to:
and performing integral operation on the target acceleration data based on a second Kalman filter to obtain speed data and displacement data of the mobile terminal.
In a possible implementation, the second kalman filter is a second-order kalman filter.
It should be noted that, in the embodiment of the present application, the content specifically executed by the measurement module 601, the first processing module 602, and the second processing module 603 may refer to each step in the inertial navigation method described in the foregoing embodiment, which is not described herein again.
Further, based on the content described in the foregoing embodiments, the present application also provides a mobile terminal, where the mobile terminal includes at least one processor and a memory; wherein the memory stores computer execution instructions; the at least one processor executes computer execution instructions stored in the memory to implement the steps of the inertial navigation method described in the above embodiments, which are not described herein again.
For better understanding of the embodiment of the present application, referring to fig. 7, fig. 7 is a schematic diagram of a hardware structure of a mobile terminal provided in the embodiment of the present application.
As shown in fig. 7, the mobile terminal 70 of the present embodiment includes: a processor 701 and a memory 702; wherein:
a memory 702 for storing computer-executable instructions;
the processor 701 is configured to execute a computer execution instruction stored in the memory to implement each step in the inertial navigation method described in the foregoing embodiment, which is not described herein again.
Alternatively, the memory 702 may be separate or integrated with the processor 701.
When the memory 702 is provided separately, the device further comprises a bus 703 for connecting said memory 702 to the processor 701.
Further, based on the content described in the foregoing embodiments, an embodiment of the present application further provides a computer-readable storage medium, where a computer execution instruction is stored in the computer-readable storage medium, and when a processor executes the computer execution instruction, the steps in the inertial navigation method described in the foregoing embodiments are implemented, which is not described herein again.
Further, based on the content described in the foregoing embodiments, an embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements each step in the inertial navigation method described in the foregoing embodiments, which is not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The unit formed by the modules can be realized in a hardware form, and can also be realized in a form of hardware and a software functional unit.
The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present application.
It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in the incorporated application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device or host device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (11)
1. An inertial navigation method, applied to a mobile terminal, the method comprising:
measuring acceleration data of the mobile terminal by using an acceleration measuring device of the mobile terminal;
processing the acceleration data based on a first Kalman filter to obtain error data corresponding to the acceleration data, wherein the first Kalman filter is a zeroth-order Kalman filter;
processing the acceleration data based on the error data to obtain processed target acceleration data;
and processing the target acceleration data based on a second Kalman filter to obtain the motion data of the mobile terminal.
2. The method of claim 1, wherein the processing the acceleration data based on the error data to obtain processed target acceleration data comprises:
calculating a difference between the acceleration data and the error data, determining the difference as the target acceleration data.
3. The method according to claim 1, wherein the processing the target acceleration data based on the second kalman filter to obtain the motion data of the mobile terminal includes:
and performing integral operation on the target acceleration data based on the second Kalman filter to obtain speed data and displacement data of the mobile terminal.
4. The method according to claim 1 or 3, characterized in that said second Kalman filter is a second order Kalman filter.
5. An inertial navigation device, applied to a mobile terminal, the device comprising:
the measuring module is used for measuring acceleration data of the mobile terminal by using an acceleration measuring device of the mobile terminal;
the first processing module is used for processing the acceleration data based on a first Kalman filter to obtain error data corresponding to the acceleration data, and processing the acceleration data based on the error data to obtain processed target acceleration data, wherein the first Kalman filter is a zeroth-order Kalman filter;
and the second processing module is used for processing the target acceleration data based on a second Kalman filter to obtain the motion data of the mobile terminal.
6. The apparatus of claim 5, wherein the first processing module is configured to:
calculating a difference between the acceleration data and the error data, determining the difference as the target acceleration data.
7. The apparatus of claim 6, wherein the second processing module is configured to:
and performing integral operation on the target acceleration data based on the second Kalman filter to obtain speed data and displacement data of the mobile terminal.
8. The apparatus according to claim 5 or 7, characterized in that said second Kalman filter is a second order Kalman filter.
9. A mobile terminal comprising at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the inertial navigation method of any one of claims 1 to 4.
10. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the inertial navigation method of any one of claims 1 to 4.
11. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the inertial navigation method of any one of claims 1 to 4.
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| CN202011552499.9A CN112525190A (en) | 2020-12-24 | 2020-12-24 | Inertial navigation method and device |
| PCT/CN2021/133976 WO2022135070A1 (en) | 2020-12-24 | 2021-11-29 | Inertial navigation method and device |
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| WO2022135070A1 (en) * | 2020-12-24 | 2022-06-30 | 北京紫光展锐通信技术有限公司 | Inertial navigation method and device |
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| CN117553787B (en) * | 2024-01-09 | 2024-03-26 | 湖南大学无锡智能控制研究院 | Collaborative navigation method, device and system of underwater unmanned aircraft |
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