KR102380586B1 - Navigation system, apparatus and method for estimating navigation error - Google Patents

Abstract

The present disclosure relates to an error estimation apparatus, an error estimation method, and a navigation system. Specifically, the apparatus for estimating error in the navigation system according to the present disclosure receives at least one of the first physical quantity information and the second physical quantity information, and sets the motion state of the antibody based on the received physical quantity information at least one preset. The antibody motion classification unit that classifies the exercise type, the pseudo measurement value calculator that calculates at least one pseudo measurement according to the type of exercise when the motion state of the antibody is classified, and the pseudo measurement value are reflected in the preset estimation filter. and a navigation error estimator for estimating a navigation error of the navigation system.

Description

Error estimation apparatus, error estimation method and navigation system

The present disclosure relates to an error estimation apparatus, an error estimation method, and a navigation system.

A navigation system refers to a device that determines the position, speed, and attitude of an antibody, such as an aircraft, a ship, or a vehicle. Such a navigation system may be classified into a global navigation satellite system, an inertial navigation system, and the like, and development of a navigation system combining them is actively underway.

The satellite navigation system is a system that locates a position using radio waves transmitted from the satellite. In the inertial navigation system, the change in attitude calculated using the output of the gyroscope from the initial position, velocity, and attitude of the antibody and the output of the accelerometer It is a system that can determine the current position, speed, and posture of the antibody using the displacement calculated using

However, since an error is included in the output of the sensor, the navigation result output from the navigation system includes a navigation error.

Accordingly, expectations and demands for a navigation system capable of properly estimating a navigation error in various operating environments are increasing, and interest in miniaturization of a navigation device is also increasing.

Against this background, the present disclosure provides an error estimating device, an error estimating method, and an error estimation method that improve the reliability and accuracy of navigation while reducing the cost of adding a sensor for detecting the type of motion of the antibody, the space occupied by the sensor, and the energy used by the sensor We want to provide a navigation system.

Another object of the present disclosure is to provide an error estimation device, an error estimation method, and a navigation system capable of estimating a navigation error appropriately for each motion situation of an antibody by actively using only an appropriate pseudo-measurement value according to the motion of the antibody.

In addition, an object of the present disclosure is to provide an error estimation apparatus, an error estimation method, and a navigation system capable of increasing the calculation processing speed while ensuring accuracy by minimizing the conditions for classifying the motion of the antibody using the characteristics of the antibody.

In order to solve the above problems, in one aspect, the present disclosure provides an apparatus for estimating an error in a navigation system, receiving at least one of first physical quantity information and second physical quantity information, and based on the received physical quantity information, an antibody ( An antibody motion classification unit that classifies the motion state of the vehicle as one of one or more preset motion types, and a pseudo measurement value calculator that calculates at least one pseudo measurement according to the motion type when the motion status of the antibody is classified and a navigation error estimator and a navigation error estimator for estimating a navigation error of a navigation system by reflecting the pseudo-measurement value to a preset estimation filter.

In another aspect, the present disclosure provides a method for estimating an error in a navigation system, receiving at least one of first physical quantity information and second physical quantity information, and presetting a motion state of an antibody based on the received physical quantity information An antibody motion classification step of classifying one or more motion types into one of one or more motion types; a pseudo measurement value calculation step of calculating at least one pseudo measurement according to the motion type when the motion state of the antibody is classified; and a preset estimation filter for the pseudo measurement value Provided is an error estimation method including an error estimation step of estimating a navigation error of a navigation system by reflecting the .

In another aspect, the present disclosure provides an inertial measurement device for outputting first physical quantity information by measuring acceleration and angular velocity with respect to each of three coordinate axes orthogonal to each other, and the position, movement speed and attitude of an antibody based on the first physical quantity information An inertial navigation device including a calculating device for calculating and outputting second physical quantity information, and an error estimating device for estimating a navigation error of the inertial navigation device, wherein the error estimating device comprises: one of the first physical quantity information and the second physical quantity information Receive at least one input, classify the motion state of the antibody into one or more preset motion types based on the received physical quantity information, and when the motion status of the antibody is classified, at least one pseudo measurement value (Pseudo Measurement) and reflecting the pseudo-measurement value to a preset estimation filter to estimate a navigation error.

As described above, according to the present disclosure, the present disclosure reduces the cost of adding a sensor for detecting the movement type of the antibody, the space occupied by the sensor, the energy consumed by the sensor, and the like, while improving the reliability and accuracy of navigation. An estimation apparatus, an error estimation method, and a navigation system may be provided.

Also, according to the present disclosure, the present disclosure provides an error estimation device, error estimation method, and navigation system capable of estimating a navigation error appropriately for each motion situation of an antibody by actively using only an appropriate pseudo-measured value according to the motion of the antibody. can

In addition, according to the present disclosure, the present disclosure provides an error estimation apparatus, an error estimation method, and a navigation system that can increase the calculation processing speed while ensuring accuracy by minimizing the conditions for classifying the motion of the antibody using the characteristics of the antibody. can provide

1 is a block diagram illustrating a navigation system according to the present disclosure.
2 is a block diagram illustrating an inertial navigation device according to the present disclosure.
3 is a block diagram illustrating an error estimation apparatus according to the present disclosure.
4 is a block diagram schematically illustrating an operation of a navigation system according to the present disclosure.
5 is a flowchart for explaining an embodiment of confirming the translational and rotational motions of the antibody according to the present disclosure.
6 is a flowchart for explaining an embodiment of classifying the movement types of the antibody in FIG. 5 .
7A to 7C are flowcharts for explaining another embodiment of classifying the type of motion of the antibody in FIG. 5 .
8 is a view for explaining an embodiment of the characteristics of the antibody according to the present disclosure.
9 is a flowchart for explaining an embodiment of classifying the movement type of an antibody in consideration of the characteristics of the antibody according to the present disclosure.
10 is a flowchart for explaining an embodiment of specifically classifying the non-quiescent state of the antibody in FIG. 9 .
11 is a flowchart for explaining an embodiment of classifying an exercise type of an antibody using an index according to the present disclosure.
12 is a flowchart for explaining another embodiment of classifying an exercise type of an antibody using an indicator according to the present disclosure.
13 is a flowchart illustrating an embodiment of estimating a navigation error based on a pseudo measurement according to the present disclosure.
14 is a flowchart illustrating an error estimation method according to the present disclosure.

Hereinafter, some embodiments of the present disclosure will be described in detail with reference to exemplary drawings. In describing the components of the present disclosure, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only for distinguishing the elements from other elements, and the essence, order, or order of the elements are not limited by the terms. When it is described that a component is “connected”, “coupled” or “connected” to another component, the component may be directly connected or connected to the other component, but another component is formed between each component. It should be understood that elements may also be “connected,” “coupled,” or “connected.”

In the present specification, "extraction" means selecting and using specific information as needed among a plurality of pieces of information. Therefore, when a specific physical quantity is used as a factor among one or more physical quantities, it will be described and described as extracting the specific physical quantity. However, this is for the convenience of understanding and may be modified into terms such as detection, selection, selection, etc., and when used in the above-described meaning, the term is not limited.

Meanwhile, in the present specification, "posture" refers to the inclination of the antibody in three dimensions, and may mean the degree of inclination with respect to a specific axis among the x, y, and z axes, and the degree of inclination with respect to two or more axes. may mean That is, in the following, "posture" may be used as a term meaning the inclination of the antibody in three dimensions with respect to a specific axis. However, the terms are for convenience of description and may be used in various terms such as angle, inclination, inclination, yaw, roll, pitch, and the like, and there is no limitation on the terms.

1 is a block diagram illustrating a navigation system 100 according to the present disclosure.

Referring to FIG. 1 , a navigation system 100 according to the present disclosure refers to a system that performs a function of calculating the position, speed, and attitude of an antibody, such as an aircraft, a ship, or a vehicle.

The navigation system 100 is a satellite navigation system, which is a system for estimating the position of an antibody using radio waves received from a satellite, and a method for estimating navigation information such as the position, movement speed, and attitude of the antibody from the output of an inertial sensor that detects the inertia of the antibody. It may mean an inertial navigation system or the like. The navigation system 100 described herein is an inertial navigation system for convenience, but is not limited thereto.

Meanwhile, the navigation system 100 according to the present disclosure may include an inertial navigation device 110 , an error estimation device 120 , a GPS device 130 , and the like.

The inertial navigation device 110 may detect the motion of the antibody and calculate and output the position, movement speed, and posture of the antibody. The inertial navigation device 110 may be classified into a Gimbaled Inertial Navigation System (GINS), a Strapdown Inertial Navigation System (SDINS), and the like according to a method in which the inertial sensor is mounted.

GINS is a structure in which the inertial sensor is mounted in a stable space supported by a gimbal so as to be isolated from the rotation of the antibody. These GINSs are mechanically required to ensure that the axis of the inertial sensor coincides with the navigation coordinate system.

SDINS is a structure that attaches to the antibody so that the axis of the inertial sensor coincides with the body coordinate system of the antibody. These SDINS rotate with the inertial sensor when the antibody rotates. Therefore, SDINS continuously updates the relationship between the body coordinate system and the navigation coordinate system.

A detailed description of the inertial navigation device 110 will be described later with reference to FIG. 2 .

The error estimation apparatus 120 may estimate a navigation error of the inertial navigation apparatus 110 . That is, the error estimating apparatus 120 may estimate the navigation error based on at least one of the output of the inertial navigation apparatus 110 and the output of the GPS apparatus.

Specifically, when the inertial navigation device 110 outputs physical quantity information including the position, movement speed, posture, etc. of the antibody, the error estimating device 120 receives the physical quantity information from the inertial navigation device 110 and estimates The navigation error may be estimated using an algorithm, an estimation filter, or the like.

Here, the navigation error may mean an error generated by an error included in the output of the inertial measurement device when the navigation system 100 according to the present disclosure performs navigation, and specifically, in the inertial navigation device 110 , It may mean an error included in the physical quantity information including the output position, movement speed, posture, etc. of the antibody.

On the other hand, the error estimating device 120 may estimate the navigation error by receiving the absolute position coordinates of the antibody from the GPS device, which will be described later, and use the output of the inertial navigation device 110 (position, movement speed, posture, etc. of the antibody) can be used to estimate the error.

A detailed description of the error estimation apparatus 120 will be described later with reference to FIG. 3 .

The Global Positioning System (GPS) device 130 may receive radio waves, signals, etc. from a satellite using trilateration, and measure the absolute position coordinates of the antibody on the earth using this.

Since the GPS device uses the Position Fixing method, an error is included in the navigation result, but the error is not accumulated.

Although not shown, the GPS device may include an antenna for receiving radio waves or signals from satellites, a GPS receiver, and the like.

2 is a block diagram illustrating the inertial navigation device 110 according to the present disclosure.

Referring to FIG. 2 , the inertial navigation device 110 according to the present disclosure may include an inertial measurement device 111 , a calculation device 112 , and the like.

An inertial measurement unit (IMU, 111) is an inertial sensor that measures acceleration and angular velocity in a three-dimensional space. Specifically, the inertial measurement device 111 provides acceleration and angular velocity with respect to three coordinate axes orthogonal to each other of the antibody.

At this time, the inertial measuring device 111 may provide a physical quantity (eg, acceleration, angular velocity, etc.) for only one of the three coordinate axes as needed, and measure the physical quantity for each of the two or more coordinate axes. can In addition, the inertia measuring device 111 may measure only one of the acceleration and the angular velocity as needed, and may measure both the acceleration and the angular velocity.

Here, the three coordinate axes orthogonal to each other may mean a roll axis, a pitch axis, and a yaw axis of the antibody. Each of the roll axis, the pitch axis, and the yaw axis may be relatively determined according to a coordinate axis (eg, x, y, z axis) in a three-dimensional space. That is, if necessary, the roll axis may be the x-axis or the y-axis.

The inertial measurement device 111 measures acceleration and angular velocity in a three-dimensional space and outputs physical quantity information indicating them. At this time, the inertial measurement device 111 may output physical quantity information for one axis as needed, and , it is also possible to output physical quantity information for two or more axes.

For example, the inertial measurement device 111 outputs physical quantity information indicating the acceleration and angular velocity of the antibody with respect to each of the three coordinate axes.

As another example, the inertial measurement device 111 outputs physical quantity information indicating the acceleration and angular velocity of the antibody with respect to the yaw axis.

Although not shown, the inertial measurement device 111 may include three accelerometers capable of measuring translational motion with respect to each of the three coordinate axes and three gyroscopes capable of measuring rotational motion with respect to each of the three coordinate axes. there is. However, the present invention is not limited thereto.

The calculating unit 112 calculates the position, movement speed, posture, etc. of the antibody from the acceleration and angular velocity output from the inertial measurement unit 111 . Specifically, the calculating device 112 may calculate the movement speed for each axis by integrating the accelerations with respect to the coordinate axes, and may calculate the position of the antibody with respect to each axis by integrating the movement speeds with respect to the coordinate axes, respectively. Then, the calculating unit 112 calculates the posture of the antibody with respect to each axis by integrating the angular velocity of the antibody with respect to the coordinate axes, respectively.

At this time, similarly to the above, the calculating device 112 may calculate a physical quantity (eg, position, movement speed, posture, etc.) for only one of the three coordinate axes, if necessary, and each of the two or more coordinate axes. can be calculated for a physical quantity. In addition, if necessary, the calculating device 112 may calculate any one of a position, a moving speed, and a posture, and may calculate two or more physical quantities.

For example, the calculating device 112 receives physical quantity information corresponding to the acceleration from the inertial measurement device 111, calculates the moving speed by integrating the acceleration with the initial moving speed, Calculate the position of the antibody using the initial position.

In another example, the calculating device 112 receives physical quantity information corresponding to the angular velocity from the inertia measuring device 111 , and calculates the posture of the antibody by integrating the angular velocity with the initial posture.

The calculation device 112 may receive the navigation error estimation information from the error estimation device 120 and correct the position, movement speed, and posture of the antibody.

The inertial navigation device 110 integrates not only the acceleration, angular velocity, position, movement speed, and posture of the above-described antibody, but also an internal variable that can be obtained in the process of calculating the position, movement speed, and angle by integrating the acceleration and angular velocity, e.g. For example, you can output Specific Force.

Although not shown, the arithmetic unit 112 may include an integrator, a memory, and the like.

3 is a block diagram illustrating the error estimation apparatus 120 according to the present disclosure.

Referring to FIG. 3 , the error estimation apparatus 120 according to the present disclosure may include an antibody motion classification unit 121 , a pseudo-measurement value calculation unit 122 , a navigation error estimation unit 123 , and the like.

The antibody motion classification unit 121 may receive at least one of the first physical quantity information and the second physical quantity information, and classify the motion state of the antibody as one of one or more preset motion types based on the received physical quantity information.

Here, the first physical quantity information may refer to physical quantity information including the acceleration, angular velocity, etc. of the antibody output from the inertial measuring apparatus 111 shown in FIG. 2 .

Here, the second physical quantity information may refer to physical quantity information including the position, movement speed, posture, etc. of the antibody output from the calculating device 112 shown in FIG. 2 .

Here, the preset movement type may be divided into a stationary state and a non-stationary state indicating the movement of the antibody, and the non-stationary state may be divided into a straight forward state including forward and backward, and a rotational state, and in this case, the straight forward state may mean moving forward or backward in the roll axis direction among three coordinate axes orthogonal to each other, and the rotational state is, for example, a roll rotational state about the roll axis, a pitch rotational state about the pitch axis, and a yaw about the yaw axis. It can be divided into rotational state. That is, the movement type can be divided into a stationary state, a straight forward state, a roll rotation state, a pitch rotation state, and a yaw rotation state. However, the present invention is not limited thereto.

Here, the motion state of the antibody may be classified into any one of the aforementioned stationary state, straight state, roll rotation state, pitch rotation state, and yaw rotation state, and may be classified into any one of a stationary state, a straight state, a roll rotation state, a pitch rotation state, a yaw rotation It may be classified as a combination of two or more of the states.

The antibody motion classification unit 121 checks at least one of translational motion and rotational motion based on a physical quantity corresponding to each of the first physical quantity information and the second physical quantity information from the first physical quantity information and the second physical quantity information, and Based on the motion of the antibody can be classified.

Specifically, the antibody motion classification unit 121 uses the physical quantities included in each of the first physical quantity information and the second physical quantity information to check the translational and rotational motions for each of the three coordinate axes of the antibody, and for each of the three coordinate axes According to the translational and rotational motions, the motion state of the antibody can be classified into the above motion types. A specific method of confirming the translational motion and rotational motion will be described later with reference to FIG. 5, and a specific method of classifying the motion state of the antibody according to the translational motion and rotational motion will be described later with reference to FIGS. 6 and 7 .

The antibody motion classification unit 121 may preset or store the characteristics of the antibody in advance, and minimize the conditions required for determining the motion state using the characteristics of the antibody.

Here, the characteristics of the antibody may refer to the type of antibody, kinetic characteristics limited according to the type of antibody, and the like. For example, properties of the antibody include, if the antibody is a vehicle, such as a car, motorcycle, etc., the vehicle is present on the ground. However, the present invention is not limited thereto.

A specific method for classifying the kinetic state of an antibody using the characteristics of the antibody will be described later with reference to FIGS. 8 to 10 .

The antibody motion classification unit 121 calculates an index such as a moving average (MA), a moving variance (MV), and a moving standard deviation (MSD) of a physical quantity included in the physical quantity information And, by comparing the index and one or more preset reference index (Reference Index), it is possible to classify the movement type of the antibody. The moving average is the average of the physical quantities input from a certain time before the current input physical quantity, the moving variance is the distribution of the physical quantities input from the predetermined time based on the current input physical quantity, and the moving standard deviation is the amount of the moving variance is the square root of A specific method for this will be described later with reference to FIGS. 11 and 12 .

When the motion state of the antibody is classified, the pseudo measurement calculation unit 122 may calculate at least one pseudo measurement according to the type of motion.

Here, the pseudo-measurement value may mean representing the motion state of the antibody as a known physical quantity using knowledge of motion characteristics such as translational motion and rotational motion of the antibody, and operating environment such as the state of the ground. Such a pseudo-measurement value may be determined as a single constant, and may be expressed as a matrix or a vector in consideration of a coordinate system.

For example, when the type of motion is stationary, the motion characteristics of the antibody are the position and posture of the antibody are constant, the movement speed of the antibody is not, the acceleration of the antibody is constant as the gravitational acceleration, and the angular velocity of the antibody is the angular velocity of the Earth's rotation. is constant with Accordingly, the pseudo-measured value calculation unit 122 calculates the position pseudo-measured value and the posture pseudo-measured value in which the position and posture of the antibody are constant values, and calculates the movement speed pseudo-measured value in which the moving speed of the antibody is 0, and the acceleration of the antibody is the gravitational acceleration. A pseudo-measured value of acceleration equal to the magnitude of and opposite to the direction of gravitational acceleration is calculated, and angular velocity is the angular velocity of the Earth's rotation. However, the present invention is not limited thereto.

Examples of pseudo-measurements according to the type of motion of the antibody will be described later with reference to FIG. 13 .

The navigation error estimator 123 may estimate the navigation error of the navigation system 100 by reflecting the pseudo-measurement value to a preset estimation filter. Specifically, the navigation error estimator 123 uses the difference between the estimated values for the position, movement speed, and posture of the antibody calculated by the inertial navigation device 110 and the pseudo-measured values if one or more pseudo-measurements can be reflected in the estimation filter. This may be updated in the inertial navigation apparatus 110 to estimate a navigation error of the inertial navigation apparatus 110 .

Here, the estimation filter may be, for example, a Kalman filter, an alpha-beta filter, and a combination thereof. However, the present invention is not limited thereto. Hereinafter, the present disclosure will be described based on an estimation filter that is a Kalman filter.

Meanwhile, when there are a plurality of pseudo-measured values, the navigation error estimator 123 may estimate the navigation error by reflecting only some of the pseudo-measured values among the plurality of pseudo-measured values in the estimation filter (eg, Kalman filter).

A specific method of estimating the navigation error will be described later with reference to FIG. 13 .

The above-described error estimating apparatus 120 may be implemented as a computer system including hardware such as one or more processors, memories, and user interface input/output devices and software such as programs. Here, the processor may be a CPU or a semiconductor device that executes processing instructions stored in the memory, and the memory may include various types of volatile/nonvolatile storage media such as ROM and RAM.

Hereinafter, the overall operation of the aforementioned navigation system 100 will be described.

4 is a block diagram schematically illustrating an operation of the navigation system 100 according to the present disclosure.

Referring to FIG. 4 , the inertial navigation apparatus 110 may output first physical quantity information and second physical quantity information.

Specifically, the inertia measuring device 111 outputs the first physical quantity information by measuring the acceleration and the angular velocity with respect to each of the three coordinate axes orthogonal to each other.

In addition, the calculating unit 112 may calculate the position, movement posture, and posture of the antibody based on the first physical quantity information and output the second physical quantity information. Here, the computing device may output information on internal variables that may be obtained in the process of calculating the position, movement speed, and posture of the antibody by integrating the acceleration and angular velocity of the antibody.

On the other hand, the error estimating device 120 receives at least one of the first physical quantity information, the second physical quantity information, and internal information, and classifies the motion state of the antibody into one or more preset motion types based on the received physical quantity information, When the motion state of the antibody is classified, at least one pseudo-measured value is calculated according to the type of motion, and the pseudo-measured value is reflected in a preset estimation filter to estimate the navigation error of the inertial navigation device 110 .

Specifically, the antibody motion classification unit 121 receives at least one of the first physical quantity information, the second physical quantity information, and the internal variable information, and determines the motion state of the antibody including the navigation system 100 based on the received information. It is classified into a preset exercise type, and exercise type information corresponding to the classified exercise type may be output to the pseudo measurement value calculator 122 .

In addition, the pseudo-measured value calculating unit 122 may check the exercise type from the exercise type information, calculate one or more pseudo-measured values corresponding thereto, and output the corresponding pseudo-measured value information to the navigation error estimator 123 , , the navigation error estimator 123 may estimate the navigation error by reflecting one or more pseudo-measurements to the estimation filter (eg, Kalman filter), and output the corresponding navigation error estimation information to the calculating device 112 . there is.

As described above, in the navigation system 100 according to the present disclosure, the cost of adding a sensor by classifying the motion of the antibody, the space occupied by the sensor, and the sensor without adding hardware such as a sensor for detecting the type of motion of the antibody It provides the effect of reducing energy consumption, etc.

In addition, as described above, the navigation system 100 according to the present disclosure provides an effect of improving the reliability and accuracy of navigation without adding hardware such as a sensor for detecting the movement type of the antibody.

Hereinafter, the error estimation apparatus 120 will be described in detail.

5 is a flowchart for explaining an embodiment of confirming the translational and rotational motions of the antibody according to the present disclosure.

Referring to FIG. 5 , the error estimating apparatus 120 receives first physical quantity information and receives second physical quantity information. For example, the antibody motion classification unit 121 receives the first physical quantity information from the inertia measuring device 111 and receives the second physical quantity information from the calculating device 112 .

For example, the antibody motion classification unit 121 extracts the angular velocities (ω _x , ω _y , ω _z ) of the antibody with respect to each of three coordinate axes orthogonal to each other from the first physical quantity information, and is orthogonal to each other from the second physical quantity information The posture (θ _x , θ _y , θ _z ), position (P _x , P _y , P _z ), and movement speed (v _x , v _y , v _z ) of the antibody are extracted for each of the three coordinate axes.

Next, the error estimating device 120 checks the presence or absence of rotational motion based on the angular velocity and posture of the antibody. Specifically, the error estimating device 120 determines at least one of the magnitude of the angular velocity (ω _x , ω _y , ω _z ) of the antibody with respect to each of the three orthogonal coordinate axes and the amount of change of the posture (θ _x , θ _y , θ _z ) Using one, check the presence or absence of rotational motion for each of the three coordinate axes orthogonal to each other.

Since the angular velocity is the amount of change of the posture (angle) per unit time, in general, the rotational motion of the antibody can be confirmed with only one of the angular velocity and the amount of change of the posture. For example, when the magnitude of the angular velocity is not 0, when there is a change in posture, the antibody motion classification unit 121 may determine that the antibody is rotating. However, it is not limited thereto, and the movement of the antibody is very diverse, such as when the antibody, such as a car, runs on a special ground such as sand, water puddle, pit, etc. there is

For example, the antibody motion classification unit 121 includes the angular velocity (ω _x , ω _y , ω _z ) of the antibody included in the first physical quantity information and the posture (θ _x , θ _y ) of the antibody included in the second physical quantity information. Based on the amount of change of θ _z ), the presence or absence of rotational motion about each of the three orthogonal coordinate axes is checked. At this time, when the angular velocity (ω _x , ω _y , ω _z ) of the antibody with respect to all axes is 0 and there is no change in the posture (θ _x , θ _y , θ _z ), the antibody motion classification unit 121 rotates It is judged as unconfirmed (or non-existent). However, the present invention is not limited thereto.

On the other hand, the error estimation device 120 checks the presence or absence of translational motion based on the position and movement speed of the antibody. Specifically, the error estimating device 120 determines the amount of change and the movement speed (v _x , v _y , v _z ) of the position (P _x , P _y , P _z ) of the antibody with respect to each of the three coordinate axes orthogonal to each other. Using at least one, it is checked whether there is a translational motion for each of the three coordinate axes orthogonal to each other.

Since the movement speed is the amount of change in position per unit time, the translational motion of the antibody can be confirmed by only one of the movement speed and the amount of change in the position, similar to the above. For example, when the magnitude of the movement speed is not 0, when there is a change amount of the position, the antibody motion classification unit 121 can be regarded as the translational movement of the antibody. However, it is not limited thereto, and the movement of the antibody is very diverse, such as when the antibody, such as a car, runs on a special ground such as sand, puddles, pits, etc. There is a need.

For example, the antibody motion classification unit 121 based on the amount of change and the movement speed (v _x , v _y , v _z ) of the position (P _x , P _y , P _z ) of the antibody included in the second physical quantity information Check the presence or absence of translational motion for each of the three coordinate axes orthogonal to each other. At this time, when there is no change in the position (P _x , P _y , P _z ) of the antibody and the movement speed (v _x , v _y , v _z ) of the antibody on all axes is 0, the antibody motion classification unit 121 is It is judged that there is no translational movement. However, the present invention is not limited thereto.

Then, the error estimating device 120 classifies the motion type of the antibody according to the confirmation result of each of the translational motion and the rotational motion with respect to each of the three coordinate axes orthogonal to each other, and outputs motion type information.

Hereinafter, embodiments for classifying exercise types will be described in detail.

6 is a flowchart for explaining an embodiment of classifying the movement types of the antibody in FIG. 5 .

6, the error estimating device 120 determines the presence or absence of translational and rotational motion for each of the three coordinate axes, and if there is no translational and rotational motion for all coordinate axes, the error estimating device 120 is an antibody classify the state of motion as a state of rest.

For example, the movement velocity (v _x , v _y , v _z ), angular velocity (ω _x , ω _y , ω _z ) of the antibody for each of all axes is zero, and the position (P _x , P _y , P _z ) is zero. When it is determined that there is no translational motion and no rotational motion because there is no change in the amount of change and the posture (θ _x , θ _y , θ _z ), the antibody motion classification unit 121 classifies the motion state of the antibody as a stationary state.

On the other hand, when it is determined that there is any one of the translational motion and the rotational motion, the motion state of the antibody may be classified as a non-stationary state (moving state).

At this time, in order to determine whether the motion state of the antibody is a straight-line state, the error estimating device 120 determines whether there are any translational motions on the pitch axis, translational and rotational motions on the yaw axis, and translational motions on the pitch axis. , when it is determined that there is no translational motion about the yaw axis, and no rotational motion about the yaw axis, the antibody motion classification unit 121 classifies the motion state of the antibody as a straight forward state.

For example, it is determined that there is no translation about the pitch axis because the movement speed (eg, v _y ) of the antibody with respect to the pitch axis is 0 and there is no change in the position (eg, P _{y )} of the antibody, It is judged that there is no translation about the yaw axis because the movement speed (eg, v _z ) of the antibody with respect to the yaw axis is 0 and there is no change in the position of the antibody (eg, P _z ). When it is determined that there is no rotational motion about the yaw axis because the angular velocity (eg, ω _z ) of the antibody is 0 and there is no change in the posture (eg, θ _z ) of the antibody, the antibody motion classification unit 121 is classify the state of motion as a straight state.

For another example, if it is determined that there is a translational motion about the roll axis of the antibody, and there is no translational motion about the pitch axis of the antibody and the yaw axis of the antibody, and no rotational motion about the yaw axis, the antibody motion classification unit (121) classifies the kinetic state of the antibody as a straight-forward state.

On the other hand, if the above-mentioned condition is not satisfied, the error estimation device 120 determines whether the motion state of the antibody is classified as a rotation state, and sets the motion state of the antibody as a roll rotation state, a pitch rotation state, a yaw rotation state and classified as a combination of these.

On the other hand, the method of determining whether the motion state of the antibody is classified as the rotational state can be determined by whether the rotational motion is independent of the translational motion. Hereinafter, embodiments for classifying the rotational state will be described in detail.

7A to 7C are flowcharts for explaining another embodiment of classifying the type of motion of the antibody in FIG. 5 .

The error estimating device 120 according to the present disclosure determines the motion state of the antibody using the presence or absence of rotational motion for each of the three coordinate axes orthogonal to each other regardless of the presence or absence of the translational motion, the roll rotational state, the pitch rotational state, and the yaw rotational state. It can be classified as

Referring to FIG. 7A , for example, if there is a change in attitude (eg, θ _x ) about the roll axis and the angular velocity (eg, ω _x ) is non-zero, the rotational motion of the antibody about the roll axis is When it is determined that there is, the antibody motion classification unit 121 classifies the motion state of the antibody as a roll rotation state. At this time, the presence or absence of rotational motion about the pitch axis and the presence or absence of rotational motion about the yaw axis are independent of whether the roll is rotated.

Referring to FIG. 7B , for example, there is a change in attitude (eg, θ _y ) about the pitch axis and the angular velocity (eg, ω _y ) is non-zero, so that the rotational motion of the antibody about the pitch axis is When it is determined that there is, the antibody motion classification unit 121 classifies the motion state of the antibody as a pitch rotation state.

Referring to FIG. 7C , for example, there is a change in posture about the yaw axis (eg, θ _z ) and the angular velocity (eg, ω _z ) is non-zero, so that the rotational motion of the antibody about the yaw axis is When it is determined that there is, the antibody motion classification unit 121 classifies the motion state of the antibody as a yaw rotation state.

As described above, the error estimating apparatus 120 according to the present disclosure provides an effect of specifically classifying the type of motion of the antibody required to accurately estimate the navigation error.

On the other hand, when the antibody is specifically determined such as a car, a ship, an airplane, etc., if the characteristics of the antibody are used, the conditions for classifying the type of motion (for example, translational motion, rotational motion for each of three coordinate axes orthogonal to each other) The type of movement of the antibody can be classified without considering some of them.

For example, the characteristics of the antibody may include a moving state vibration characteristic sensed when the antibody is in motion and a stationary state vibration characteristic sensed in a stationary state. That is, vibration characteristics may be considered as one of the characteristics of the antibody.

When the vibration characteristic is used, the antibody motion classification unit 121 calculates the amplitude change of the physical quantity based on the first physical quantity information, and divides the motion state of the antibody into a stationary or non-stationary state using the amplitude change and the characteristics of the antibody. can That is, the movement type of the antibody can be classified by comparing the vibration characteristic in the moving state, the vibration characteristic in the stationary state, and the amplitude change of the calculated physical quantity.

Hereinafter, a case in which the antibody is a vehicle will be described as an example, which is described by disclosing an example, and is not limited to the type of antibody. In addition, the method of detecting the vibration of the antibody is also illustratively described, and all cases of using the amplitude change of a physical quantity are included in the present disclosure.

Hereinafter, examples of classifying the type of motion of an antibody using the characteristics of the antibody will be described in detail.

8 is a view for explaining an embodiment of the characteristics of the antibody according to the present disclosure.

Antibodies according to the present disclosure may be automobiles, ships, flying objects, and the like, as described above. In this case, each antibody may have unique properties.

Referring to FIG. 8 , for example, in the case of a ground antibody 200 such as a car, there is a characteristic that the wheel 210 of the ground antibody 200 and the ground 300 are in contact. In addition, there is a characteristic that vibration is basically generated by an engine or the like when the engine of the ground antibody 200 is turned on. In addition, when the ground antibody 200 is driven, there is a characteristic that additional vibration is generated according to the state of the ground 300 (damage degree, inclination, road surface material, etc.).

Also, with reference to FIG. 8 , for example, the ground antibody 200 can generally travel forward and backward about a roll axis (eg, the x-axis) in a body coordinate system, and a yaw axis (eg, , z-axis), but it is difficult to perform roll rotation about the roll axis (eg, the x-axis) or pitch rotation about the pitch axis (eg, the y-axis). However, the present invention is not limited thereto.

Accordingly, the characteristics of the antibody may be stored or set in advance in the antibody motion classifying unit 121, and the antibody motion classifying unit 121 may determine the antibody based on the first physical quantity information, the second physical quantity information, and the preset characteristics of the antibody. Exercise status can be classified.

Hereinafter, examples of classifying the types of motion of an antibody in consideration of the characteristics of the antibody will be described in detail.

9 is a flowchart for explaining an embodiment of classifying the movement type of an antibody in consideration of the characteristics of the antibody according to the present disclosure.

Referring to FIG. 9 , the error estimating apparatus 120 receives the first physical quantity information and the second physical quantity information, and extracts previously stored characteristics of the antibody.

Here, the characteristic of the antibody is, for example, the characteristic that the vehicle, which is the ground antibody 200, exists on the ground 300, so that the wheel 210 of the vehicle is in contact with the ground 300, and the vehicle is in a stopped state when the engine is turned on. Including the characteristics of the reference vibration generated in the vehicle. However, the present invention is not limited thereto.

Here, the reference vibration may mean vibration for determining the vehicle's stop state. In general, when the vehicle is started, only vibrations generated by the engine, etc. exist in the vehicle in a stopped state, so the engine included in the vehicle It may mean vibration generated by the operation of However, the present invention is not limited thereto.

The vibration may be detected from the acceleration or angular velocity included in the first physical quantity information. The magnitude of the vibration may be determined by the average of the magnitudes of acceleration, the average of the magnitudes of the angular velocity, the dispersion of the acceleration, the dispersion of the angular velocity, and a combination thereof. However, the method of detecting the vibration is not limited thereto, and various methods may be applied. That is, in the present disclosure, there is no limitation on the method of calculating the vibrational properties of the antibody.

Then, the error estimating device 120 detects the vibration from the first physical quantity information, extracts the angular velocity included in the first physical quantity information, and extracts the posture of the vehicle included in the second physical quantity information.

Here, the angular velocity and attitude of the vehicle may mean only the angular velocity and attitude with respect to the specific rotational axis described above, or may be the angular velocity and attitude of each of two or more rotational axes.

Next, the error estimating device 120 compares the reference vibration and the detected vibration among the characteristics of the extracted antibody, and classifies the motion state of the vehicle into a stationary state or a non-stop state according to the comparison result.

For example, the antibody motion classification unit 121 detects the vibration of the vehicle included in the first physical quantity information, compares the vibration with the reference vibration, and classifies the motion state of the vehicle into a stationary state or a non-stop state according to the comparison result. do.

When the vibration and the reference vibration are the same, the error estimating device 120 , for example, the antibody motion classification unit 121 classifies the motion state of the vehicle as a stationary state. Here, when the vibration and the reference vibration are the same, the case where the difference between the vibration and the reference vibration is within a preset error value may be included.

On the other hand, when the vibration and the reference vibration are different, the antibody motion classification unit 121 classifies the motion state of the vehicle as a non-stop state. In particular, when the vehicle is moving, the detected vibration may be greater than the reference vibration.

At this time, the error estimating device 120 classifies the motion state of the vehicle classified as the non-stop state into a straight-ahead state, a roll rotation state, a pitch rotation state, a yaw rotation state, etc. using the angular velocity and posture of the antibody.

The vehicle can generally only stop, go straight, and turn, but external factors such as when the user loads or unloads the vehicle, the state of the ground 300 is inclined or there is a puddle, when passing through irregularities, etc. When this is present, the vehicle can also roll and pitch rotate.

Hereinafter, examples for specifically classifying the non-quiescent state of the antibody will be described in detail.

10 is a flowchart for explaining an embodiment of specifically classifying the non-quiescent state of the antibody in FIG. 9 .

Referring to FIG. 10 , when the vibration is different from the reference vibration, the antibody motion classification unit 121 performs three orthogonal 3 orthogonal operations on the basis of the vehicle's angular velocity included in the first physical quantity information and the vehicle's posture included in the second physical quantity information. The presence or absence of rotational motion with respect to the yaw axis of the five coordinate axes is checked, and the movement state of the vehicle is classified into a straight forward state or a yaw rotational state according to the result of the confirmation of the rotational motion about the yaw axis.

The vehicle (the ground antibody 200) moves while the wheels 210 and the ground 300 come into contact with each other. In addition, although the moving speed of the vehicle with respect to the yaw axis may vary depending on the state of the ground 300 (eg, irregularities), the moving speed with respect to the yaw axis is generally zero. In addition, the vehicle may move depending on whether the vehicle slides with respect to the pitch axis. In general, the sliding occurring in the pitch axis direction may occur when the vehicle makes a sharp yaw rotation. In conclusion, the antibody motion classification unit 121 can classify the motion state of the vehicle into a straight forward state or a yaw rotation state if only the presence or absence of rotational motion about the yaw axis is known.

Specifically, for example, if the angular velocity (ω _yaw ) of the vehicle with respect to the yaw axis is 0 and the change amount of the vehicle attitude (θ _yaw ) with respect to the yaw axis is 0, so that rotational motion about the yaw axis is not confirmed, the antibody motion The classification unit 121 classifies the moving state of the vehicle as a straight forward state.

In another specific example, if the angular velocity ω _yaw of the vehicle with respect to the yaw axis is not 0 and the change amount of the vehicle attitude θ _yaw with respect to the yaw axis is not 0, when rotational motion about the yaw axis is confirmed , the antibody motion classification unit 121 classifies the motion state of the vehicle into a yaw rotation state.

Here, the angular velocity (ω _yaw ) and the attitude (θ _yaw ) of the vehicle with respect to the yaw axis may be the angular velocity (ω _z ) and the attitude (θ _z ) with respect to the z-axis as described above, but are not limited thereto.

As described above, the error estimation apparatus 120 according to the present disclosure minimizes the conditions for classifying the motion of the antibody using the characteristics of the antibody, thereby ensuring accuracy and increasing the calculation processing speed.

Meanwhile, as described above, the type of motion of the antibody may be classified using the first physical quantity information (acceleration and angular velocity of the antibody) output from the inertial measurement device 111 , and the calculation included in the inertial navigation device 110 . Classification may be performed using the second physical quantity information (position, movement speed, and posture of the antibody) that is the output of the device 112 .

However, the output of the calculating device 112 and the inertial measuring device 111 may include an error due to disturbance, noise, or the like. In this case, the error included in the output of the inertial measuring device 111 is not an accumulated error, but the calculating device 112 performs a calculation that integrates the output of the inertial measuring device 111 , so that the navigation execution time continues to elapse. As the number increases, the error included in the output of the arithmetic unit 112 may continue to increase (accumulate).

Therefore, in order to minimize an error and calculate an accurate navigation result, it is necessary to classify the motion type of the antibody using the first physical quantity information output from the inertial measurement device 111 .

However, since an error may be included in the first physical quantity information that is the output of the inertial measuring device 111, an indicator is needed to properly process the output of the inertial measuring device 111 to check the characteristics appearing in the motion situation to be determined. Do.

Hereinafter, an embodiment of classifying the movement type of an antibody using an index will be described in detail.

11 is a flowchart for explaining an embodiment of classifying an exercise type of an antibody using an index according to the present disclosure.

Referring to FIG. 11 , the error estimating apparatus 120 may receive the first physical quantity information, and may calculate at least one index of a moving average, a moving variance, and a moving standard deviation for the physical quantity.

For example, the antibody motion classification unit 121 calculates the first movement standard deviation (σ _accel ) of the accelerations (a _x , a _y , a _z ) of the antibody with respect to each of the three coordinate axes included in the first physical quantity information and calculate the second movement standard deviation (σ _gyro ) of the angular velocity (ω _x , ω _y , ω _z ) of the antibody with respect to each of the three coordinate axes.

In another example, the antibody motion classification unit 121 calculates a moving average (μ _gyro ) of the magnitude of the angular velocity (eg, ω _z ) of the antibody with respect to the yaw axis included in the first physical quantity information.

Then, the error estimating device 120 compares the moving standard deviation with the reference index. For a specific example, the antibody motion classification unit 121 compares the first standard deviation of the acceleration (σ _accel ) with the first reference index, and the second standard deviation of the movement of the angular velocity (σ _gyro ) and the second reference index. Compare.

Here, the first reference index may be, for example, 0.1 m/s ² , and the second reference index may be, for example, set to 0.115 deg/s. However, the present invention is not limited thereto.

When all of the moving standard deviations are less than the reference index, the error estimating apparatus 120 classifies the motion state of the antibody as a stationary state. As a specific example, when the size of the first moving standard deviation (σ _accel ) is less than the first reference index, and the size of the second movement standard deviation (σ _gyro ) is less than the second reference index, the antibody motion classification unit 121 classifies the kinetic state of the antibody as a stationary state.

On the other hand, when the magnitude of the first movement standard deviation (σ _accel ) is equal to or greater than the first reference index, and the magnitude of the second movement standard deviation (σ _gyro ) is also greater than or equal to the second reference index, the kinetic state of the antibody is classified as a non-stationary state can be In this case, the error estimation apparatus 120 may specifically classify the motion state of the antibody in the non-stationary state by using the calculated moving average (μ _gyro ).

For example, the antibody motion classification unit 121 calculates a moving average (μ _gyro ) of the magnitude of the angular velocity (eg, ω _z ) with respect to the yaw axis of the antibody included in the first physical quantity information. And, when the size of the first moving standard deviation (σ _accel ) is equal to or greater than the first reference index, and the size of the second movement standard deviation (σ _gyro ) is equal to or greater than the second reference index, the antibody motion classification unit 121 is the yaw axis The moving average (μ _gyro ) of the magnitude of the angular velocity (eg, ω _z ) is compared with the third reference index, and the motion state of the antibody is classified into a straight forward state or a yaw rotation state according to the comparison result.

Here, the third reference indicator may be set to, for example, 1.0 deg/s. However, the present invention is not limited thereto.

When the moving average (μ _gyro ) is less than the third reference index, the antibody motion classification unit 121 included in the error estimation device 120 classifies the motion state of the antibody as a straight state.

When the moving average μ _gyro is equal to or greater than the third reference index, the antibody motion classification unit 121 included in the error estimation device 120 classifies the motion state of the antibody as a yaw rotation state.

When the motion state of the antibody is classified, the error estimation apparatus 120 outputs motion type information corresponding to the classified motion type.

On the other hand, the ground antibody 200 such as a vehicle can rotate about the roll axis or the pitch axis when it is stopped or moved, so the roll rotation state or the pitch rotation state of the antibody can be confirmed only with the angular velocity with respect to the roll axis and the pitch axis. .

12 is a flowchart for explaining another embodiment of classifying an exercise type of an antibody using an indicator according to the present disclosure.

Referring to FIG. 12 , the error estimating device 120 receives the first physical quantity information, calculates a moving average of angular velocities with respect to each of the roll and pitch axes of the antibody, and a moving average of the angular velocities with respect to the roll and pitch axes of the antibody. Classify the type of movement of the antibody by comparing it with the reference index.

For example, the antibody motion classification unit 121 calculates a moving average (μ _gyro ) of the magnitude of the angular velocity (eg, ω _x ) of the antibody with respect to the roll axis included in the first physical quantity information, and The moving average (μ _gyro ) of the magnitude of the angular velocity (eg, ω _x ) is compared with the fourth reference index, and when the moving average (μ _gyro ) is greater than or equal to the fifth reference index, the motion state of the antibody is determined as the roll rotation state classified as

As another example, the antibody motion classification unit 121 calculates a moving average (μ _gyro ) of the magnitude of the angular velocity (eg, ω _x ) of the antibody with respect to the pitch axis included in the first physical quantity information, and the pitch axis Compare the moving average (μ _gyro ) of the magnitude of the angular velocity (eg, ω _x ) with the fifth reference index, and when the moving average (μ _gyro ) is greater than or equal to the fifth reference index, the motion state of the antibody is measured by pitch rotation classified by state.

Here, the fourth reference index and the fifth reference index may be set to the same value as the third reference index shown in FIG. 11 . For example, the fourth reference index and the fifth reference index are set to 1.0 deg/s. However, the present invention is not limited thereto.

As described above, since the error included in the output of the inertial measuring device 111 is not an accumulated error, the error estimating device 120 according to the present disclosure appropriately processes the output of the inertial measuring device 111 to obtain the antibody It provides the effect of accurately classifying the movement state.

In addition, as described above, the error estimating apparatus 120 according to the present disclosure simplifies calculation by using an index, secures a memory capacity, and thus provides an effect of preventing overload in the apparatus.

13 is a flowchart illustrating an embodiment of estimating a navigation error based on a pseudo measurement according to the present disclosure.

Referring to FIG. 13 , the error estimating apparatus 120 calculates one or more pseudo-measured values according to the classified motion type, and reflects at least one of the one or more pseudo-measured values to an estimation filter (eg, Kalman filter) to obtain a navigation error. to estimate

On the other hand, the Kalman filter available in the present specification includes a state equation as shown in [Equation 1] below.

는 시간

에서의 상태 벡터이고 항체의 위치, 이동 속도, 자세에 대한 오차와 관성 측정 장치(111)의 가속도계 바이어스와 자이로스코프 바이어스를 상태 변수로 한다.

는 영평균이며, 공분산

를 갖는 공정 잡음이다.

는 상태를

에서

로 시간 전파시키는 이산 시스템에서의 상태 천이 행렬이다.At this time,

is the time

It is a state vector in , and an error with respect to the position, movement speed, and posture of the antibody, and an accelerometer bias and a gyroscope bias of the inertial measurement device 111 are used as state variables.

is the zero mean, and the covariance

is the process noise with

is the state

at

It is a state transition matrix in a discrete system that propagates in time.

In addition, the Kalman filter includes a measurement equation such as [Equation 2] below.

는 측정 행렬이고,

는 영평균이며, 공분산

를 갖는 측정 잡음이다.

는 측정치 벡터이다.At this time,

is the measurement matrix,

is the zero mean, and the covariance

is the measurement noise with

is the measurement vector.

The Kalman filter consists of a time propagation part and a measurement update part.

Here, the time propagation part can be expressed by the following [Equation 3].

는 시간

에서

의 사전(a priori)추정치이고,

는 시간

에서

의 사후(a posteriori)추정치이고,

는 오차 공분산 행렬을 의미하며, 관성 항법 장치(110)의 오차와 관성 측정 장치(111)의 오차의 추정치에 대한 부정확도를 의미한다.

는 공정 잡음의 공분산 행렬을 의미하며, 상태 방정식 [수학식 1]에서 상태 천이 행렬

로 모델링 되지 않는 동특성을 의미한다.At this time,

is the time

at

is an estimate of a priori of

is the time

at

is a posterior estimate of

denotes an error covariance matrix, and denotes an inaccuracy of an estimate of the error of the inertial navigation device 110 and the error of the inertial measurement device 111 .

is the covariance matrix of the process noise, and in the state equation [Equation 1], the state transition matrix

It means dynamic characteristics that are not modeled as

는 칼만 이득(Gain)을 의미한다.

is the Kalman gain.

The error estimating device 120 uses the pseudo-measured value as a measurement value vector, and uses the error with respect to the position, movement speed, and posture of the antibody, and the accelerometer bias and the gyroscope bias of the inertia measuring device 111 as state variables to navigate through the Kalman filter. Estimate the error.

For example, if the motion state of the antibody is classified as a stationary state, the pseudo-measured value calculation unit 122 calculates a position pseudo-measured value that is a constant value, a posture pseudo-measured value that is a constant value, a pseudo-measured movement speed that is a value of 0, an acceleration pseudo-measured value that is a gravitational acceleration value, and the Earth. Calculate the angular velocity pseudo-measurement, which is the rotation angular velocity.

Then, the navigation error estimator 123 estimates the navigation error of the navigation system 100 by reflecting the pseudo-measured movement speed in the Kalman filter.

Here, the navigation error estimator 123 may estimate the navigation error by reflecting at least one of a position pseudo-measurement value, a posture pseudo-measurement value, an acceleration pseudo-measurement value, and an angular velocity pseudo-measurement value in addition to the movement speed pseudo-measurement value to the Kalman filter.

However, in order to estimate the navigation error using a pseudo-measured value other than the movement speed pseudo-measurement value, the position and posture of the antibody, which are the output of the calculation unit 112 included in the inertial navigation device 110, should be used in the calculation process, and the antibody Since the position and posture of α include cumulative errors, it is desirable to reflect the pseudo-measured movement speed in the Kalman filter.

Specifically, the navigation error estimator 123 processes the pseudo-measured movement speed as a measurement value vector as in Equation 5 below, and reflects it in the Kalman filter.

는 이동 속도 오차이고, N, E, D는 항법 좌표계를 의미한다.At this time,

is the movement speed error, and N, E, and D refer to the navigation coordinate system.

For another example, if the movement state of the antibody is classified as a straight state, the pseudo-measured value calculating unit 122 may include a first movement speed pseudo-measured value that is 0 with respect to the pitch axis of the antibody among three coordinate axes orthogonal to each other, and the yaw of the antibody. A second pseudo-measured movement speed pseudo-measured value with respect to the axis, a pseudo-measured posture with a constant value with respect to the yaw axis, and a pseudo-measured value of angular velocity with a value of 0 with respect to the yaw axis are calculated.

The navigation error estimating unit 123 estimates the navigation error of the navigation system 100 by reflecting the first movement speed pseudo-measurement value, the second movement speed pseudo-measurement value, and the angular velocity pseudo-measurement value to the Kalman filter.

Here, the navigation error estimator 123 may estimate the navigation error by reflecting the posture pseudo-measurement value to the Kalman filter. However, as described above, since the posture includes the cumulative error, it is preferable to reflect the first pseudo-measured movement speed, the second pseudo-measured movement speed, and the pseudo-angular velocity measurement in the Kalman filter.

Specifically, the navigation error estimating unit 123 processes the first movement speed pseudo-measured value, the second movement speed pseudo-measured value, and the angular velocity pseudo-measured value as a measurement value vector as shown in Equation 6 below, and reflects it in the Kalman filter.

는 동체 좌표계에서 피치 축(예를 들어, y축)에 대한 이동 속도 오차이고,

는 동체 좌표계에서 요 축(예를 들어, z축)에 대한 이동 속도 오차이다. 한편,

는 요 축에 대한 각속도이고, 이는 자이로스코프에서 측정되므로, 지구 중심 관성 좌표계에 대한 동체 좌표계의 각속도를 의미한다.At this time,

is the movement velocity error about the pitch axis (e.g., the y-axis) in the body coordinate system,

is the movement speed error about the yaw axis (eg, z-axis) in the body coordinate system. Meanwhile,

is the angular velocity about the yaw axis, which is measured by the gyroscope, so it means the angular velocity of the body coordinate system with respect to the Earth-centered inertial coordinate system.

As another example, if the motion state of the antibody is classified as the roll non-rotation state, the pseudo-measurement value calculator 122 may calculate the first posture pseudo-measurement value and the roll axis, which are constant values with respect to the roll axis of the antibody among three coordinate axes orthogonal to each other. A first pseudo-measurement value of angular velocity, which is a value of 0 with respect to , is calculated.

로 처리하여 칼만 필터에 반영한다.In addition, the navigation error estimator 123 estimates the navigation error of the navigation system 100 by reflecting the first angular velocity pseudo-measurement value to the Kalman filter. Specifically, in order to avoid the cumulative error, the navigation error estimator 123 sets the first angular velocity pseudo-measurement value as a measurement value vector.

is processed and reflected in the Kalman filter.

This is because the pseudo-measurement is available when there is no roll rotation.

As another example, if the motion state of the antibody is classified as a pitch non-rotation state, the pseudo-measurement value calculator 122 may calculate a second posture pseudo-measurement value and a pitch axis that are constant values with respect to the pitch axis of the antibody among three coordinate axes orthogonal to each other. A second pseudo-measurement value of angular velocity, which is a value of 0 for , is calculated.

로 처리하여 칼만 필터에 반영한다.And, in order to avoid the cumulative error, the navigation error estimator 123 estimates the navigation error of the navigation system 100 by reflecting the second angular velocity pseudo-measurement value to the Kalman filter. Specifically, in order to avoid the cumulative error, the navigation error estimator 123 sets the second posture pseudo-measurement value as a measurement value vector.

is processed and reflected in the Kalman filter.

On the other hand, since the pseudo-measurement of posture and the pseudo-measurement of acceleration can be known when there is no rotation, and the pseudo-measured value of the absence of yaw rotation is used in the straight state, the pseudo-measured value of the yaw rotation may not be considered.

As described above, the error estimation apparatus 120 according to the present disclosure provides an effect of estimating a navigation error appropriately for each motion situation of the antibody by actively using only an appropriate pseudo-measurement value according to the motion of the antibody.

Hereinafter, an error estimation method capable of performing all of the present disclosure will be described.

14 is a flowchart illustrating an error estimation method according to the present disclosure.

Referring to FIG. 14 , in the error estimation method in the navigation system 100 according to the present disclosure, the error estimation method may include an antibody motion classification step, a pseudo measurement value calculation step, an error estimation step, and the like.

The antibody motion classification step receives at least one of the first physical quantity information and the second physical quantity information, and classifies the motion state of the antibody into one or more preset motion types based on the received physical quantity information.

When the motion state of the antibody is determined, at least one pseudo measurement is calculated according to the type of motion in the pseudo measurement value calculation step.

In the error estimation step, a navigation error of the navigation system 100 is estimated by reflecting the pseudo-measurement value in a preset estimation filter.

As described above, according to the present disclosure, the present disclosure reduces the cost of adding a sensor for detecting the movement type of the antibody, the space occupied by the sensor, the energy consumed by the sensor, and the like, while improving the reliability and accuracy of navigation. An estimation apparatus, an error estimation method, and a navigation system may be provided.

In addition, according to the present disclosure, the present disclosure provides an error estimating device, an error estimation method, and a navigation system capable of estimating a navigation error appropriately for each motion situation of an antibody by actively using only an appropriate pseudo-measured value according to the motion of the antibody. can

In addition, according to the present disclosure, the present disclosure provides an error estimation apparatus, an error estimation method, and a navigation system that can increase the calculation processing speed while ensuring accuracy by minimizing the conditions for classifying the motion of the antibody using the characteristics of the antibody. can provide

The above description and the accompanying drawings are merely illustrative of the technical spirit of the present disclosure, and those of ordinary skill in the art to which the present disclosure pertains can combine configurations within a range that does not depart from the essential characteristics of the present disclosure. , various modifications and variations such as separation, substitution and alteration will be possible. Accordingly, the embodiments disclosed in the present disclosure are for explanation rather than limiting the technical spirit of the present disclosure, and the scope of the technical spirit of the present disclosure is not limited by these embodiments. That is, within the scope of solving the problems of the present disclosure, all of the components may be included in one or more to operate in organic combination. The protection scope of the present disclosure should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present disclosure.

100: navigation system 110: inertial navigation device
111: inertial measurement device 112: arithmetic unit
120: error estimation device 121: antibody motion classification unit
122: pseudo measurement value calculation unit 123: navigation error estimation unit
200: ground antibody 210: wheel
300: floor

Claims (17)

An apparatus for estimating an error in a navigation system, comprising:
an antibody motion classification unit receiving at least one of the first physical quantity information and the second physical quantity information, and classifying the motion state of the antibody into one or more preset motion types based on the received physical quantity information;
a pseudo measurement value calculator configured to calculate at least one pseudo measurement according to the type of movement when the movement state of the antibody is classified; and
and a navigation error estimator for estimating a navigation error of the navigation system by reflecting the pseudo-measurement value in a preset estimation filter,
The antibody motion classification unit classifies the motion state of the antibody into a roll rotation state when the rotational motion of the antibody with respect to the roll axis is confirmed, and when the rotational motion about the pitch axis of the antibody is confirmed, the antibody Classify the motion state as a pitch rotation state, and when rotational motion about the yaw axis of the antibody is confirmed, classify the motion state of the antibody as a yaw rotation state,
The pseudo-measured value calculation unit,
If the motion state of the antibody is classified as a roll non-rotation state, a first pseudo-position pseudo-measurement value that is a constant value with respect to the roll axis of the antibody among three coordinate axes orthogonal to each other and a first angular velocity pseudo-measurement value that is a 0 value with respect to the roll axis are calculated and,
When the motion state of the antibody is classified as a pitch non-rotation state, a second pseudo-positional pseudo-measurement value that is a constant value with respect to the pitch axis of the antibody among three coordinate axes orthogonal to each other and a second pseudo-angular velocity pseudo-measurement value with a zero value with respect to the pitch axis are calculated and,
The navigation error estimator is configured to estimate the navigation error of the navigation system by reflecting at least one of the first pseudo-measured angular velocity and the second pseudo-measured value of angular velocity, which is a value of 0, to the estimation filter.
According to claim 1,
The antibody movement classification unit,
receiving the first physical quantity information and the second physical quantity information;
Error estimating device, characterized in that the first physical quantity information and the second physical quantity information are checked for at least one of a translational motion and a rotational motion based on a physical quantity corresponding to each of the information, and classifying the motion of the antibody based on the identification result .
3. The method of claim 2,
The antibody movement classification unit,
Checking the presence or absence of the translational motion for each of the three coordinate axes orthogonal to each other based on at least one of the change amount and the movement speed of the position of the antibody included in the second physical quantity information,
Based on at least one of the angular velocity of the antibody included in the first physical quantity information and the amount of change in the angle of the antibody included in the second physical quantity information, the presence or absence of the rotational motion with respect to each of the three coordinate axes orthogonal to each other is checked Error estimation device, characterized in that.
4. The method of claim 3,
The antibody movement classification unit,
When both the translational motion and the rotational motion are not confirmed, the error estimation device, characterized in that the motion state of the antibody is classified as a stationary state.
4. The method of claim 3,
The antibody movement classification unit,
Translational motion about the roll axis of the antibody is confirmed, and translational motion with respect to each of the pitch axis of the antibody and the yaw axis of the antibody and rotational motion with respect to the yaw axis are not confirmed In this case, the error estimation device, characterized in that for classifying the motion state of the antibody as a straight-line state.
delete According to claim 1,
The antibody movement classification unit,
The apparatus for estimating an error according to claim 1, wherein the motion state of the antibody is classified based on the first physical quantity information, the second physical quantity information, and preset characteristics of the antibody.
8. The method of claim 7,
The characteristics of the antibody are:
Comprising a moving state vibration characteristic and a stationary state vibration characteristic of the antibody,
The antibody movement classification unit,
Detecting a change in the amplitude of a physical quantity calculated based on the first physical quantity information, and classifying the motion state of the antibody into a stationary state or a non-stationary state using the amplitude change of the physical quantity and characteristics of the antibody Error estimation device.
According to claim 1,
The antibody movement classification unit,
At least one index of a moving average, a moving variance, and a moving standard deviation of the physical quantity included in the received physical quantity information is calculated, and the index is compared with at least one preset reference index to compare the antibody Error estimation apparatus, characterized in that for classifying the motion state.
10. The method of claim 9,
The antibody movement classification unit,
Calculate the movement standard deviation of each of the acceleration and angular velocity of the antibody included in the first physical quantity information,
and comparing a first standard deviation of movement of the acceleration with a first reference index, and comparing a second standard deviation of movement of the angular velocity with a second reference index.
10. The method of claim 9,
The antibody movement classification unit,
Calculate the moving average of the magnitude of the angular velocity with respect to the roll axis of the antibody included in the first physical quantity information,
Comparing the moving average of the magnitude of the angular velocity with respect to the roll axis and a fourth reference index,
When the moving average is equal to or greater than the fourth reference index, the error estimating device, characterized in that the movement state of the antibody is classified as a roll rotation state.
10. The method of claim 9,
The antibody movement classification unit,
Calculate the moving average of the magnitude of the angular velocity with respect to the pitch axis of the antibody included in the first physical quantity information,
comparing the moving average of the magnitude of the angular velocity with respect to the pitch axis and a fifth reference index,
When the moving average is equal to or greater than the fifth reference index, the error estimating device, characterized in that the motion state of the antibody is classified as a pitch rotation state.
According to claim 1,
The pseudo-measured value calculation unit,
When the motion state of the antibody is classified as a stationary state, a constant value of the position pseudo-measured value, a constant value of the posture pseudo-measured value, a movement speed pseudo-measured value of 0 value, a pseudo-acceleration pseudo-measured value that is a value of gravity acceleration, and an angular velocity pseudo-measured value that is a value of the Earth's rotation angular velocity are calculated An error estimation device with
According to claim 1,
The pseudo-measured value calculation unit,
When the motion state of the antibody is classified as a straight-line state, a first pseudo-measurement value of movement speed having a value of 0 with respect to the pitch axis of the antibody among three coordinate axes orthogonal to each other, and a pseudo-second movement speed pseudo-measurement having a value of 0 with respect to the yaw axis of the antibody An error estimating apparatus comprising calculating a measured value, a posture pseudo-measured value having a constant value with respect to the yaw axis, and an angular velocity pseudo-measured value having a zero value with respect to the yaw axis.
delete A method for estimating an error in a navigation system, the method comprising:
an antibody motion classification step of receiving at least one of the first physical quantity information and the second physical quantity information, and classifying the motion state of the antibody into one or more preset motion types based on the received physical quantity information;
a pseudo measurement value calculating step of calculating at least one pseudo measurement according to the type of exercise when the exercise state of the antibody is classified; and
and an error estimation step of estimating a navigation error of the navigation system by reflecting the pseudo-measurement value in a preset estimation filter,
In the antibody motion classification step, when the rotational motion of the antibody with respect to the roll axis is confirmed, the motion state of the antibody is classified as a roll rotational state, and when the rotational motion of the antibody about the pitch axis is confirmed, the antibody Classify the motion state of the yaw rotation state as a pitch rotation state, and when the rotational motion about the yaw axis of the antibody is confirmed, the motion state of the antibody is classified as a yaw rotation state,
In the pseudo-measured value calculation step, when the motion state of the antibody is classified as a roll non-rotation state, a first posture pseudo-measurement value that is a constant value with respect to the roll axis of the antibody among three coordinate axes orthogonal to each other and a value of 0 with respect to the roll axis compute a first angular velocity pseudo-measurement;
When the motion state of the antibody is classified as a pitch non-rotation state, a second pseudo-positional pseudo-measurement value that is a constant value with respect to the pitch axis of the antibody among three coordinate axes orthogonal to each other and a second pseudo-angular velocity pseudo-measurement value with a zero value with respect to the pitch axis are calculated and,
In the estimating of the navigation error, at least one of the first pseudo-measured angular velocity and the second pseudo-measured value, which is a value of 0, is reflected in the estimation filter to estimate the navigation error of the navigation system.
An inertial measurement device for outputting first physical quantity information by measuring acceleration and angular velocity with respect to each of the three coordinate axes orthogonal to each other, and second physical quantity information by calculating the position, movement speed, and posture of the antibody based on the first physical quantity information an inertial navigation device including an arithmetic unit that outputs and
An error estimating device for estimating a navigation error of the inertial navigation device,
The error estimation device is
receiving at least one of the first physical quantity information and the second physical quantity information;
Classifying the motion state of the antibody (Vehicle) into one or more preset motion types based on the received physical quantity information,
When the exercise state of the antibody is classified, calculating at least one pseudo measurement according to the type of exercise,
estimating the navigation error by reflecting the pseudo-measurement value in a preset estimation filter,
The error estimating device classifies the motion state of the antibody into a roll rotation state when the rotational motion of the antibody with respect to the roll axis is confirmed, and when the rotational motion of the antibody about the pitch axis is confirmed, the motion of the antibody Classify the state as a pitch rotation state, and when rotational motion about the yaw axis of the antibody is confirmed, classify the motion state of the antibody as a yaw rotation state,
If the motion state of the antibody is classified as a roll non-rotation state, a first pseudo-position pseudo-measurement value that is a constant value with respect to the roll axis of the antibody among three coordinate axes orthogonal to each other and a first angular velocity pseudo-measurement value that is a 0 value with respect to the roll axis are calculated And, when the motion state of the antibody is classified as a pitch non-rotation state, a second pseudo-positional pseudo-measurement value that is a constant value with respect to the pitch axis of the antibody among three coordinate axes orthogonal to each other, and a second angular velocity pseudo-measurement value that is a zero value with respect to the pitch axis to calculate,
and estimating the navigation error by reflecting at least one of the first angular velocity pseudo-measurement value and the second angular velocity pseudo-measurement value, which is a value of 0, in the estimation filter.

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