Vertical accelerometer virtualization method, medium and equipment

Abstract

The invention relates to the technical field of accelerometer measurement, in particular to a vertical accelerometer virtualization method, a medium and equipment. The method comprises the following steps: collecting information output by receivers of an inertial navigation system and a satellite navigation system; according to the collected information, combined navigation is realized through Kalman filtering, and attitude, speed and position information output by the combined navigation is collected; according to the information output by the integrated navigation and inertial navigation system, virtualizing the vertical accelerometer to obtain a virtual vertical accelerometer specific force; and fusing the virtual vertical accelerometer specific force and the actual vertical accelerometer specific force to obtain the final vertical accelerometer specific force. According to the method, the vertical accelerometer is inversely solved by using the horizontal velocity information instead of the vertical velocity information, the virtual vertical accelerometer and the actual vertical accelerometer are fused through the FIR low-pass filter, the more accurate final vertical accelerometer specific force is obtained, and the application range is wider.

Description

Technical Field

[0001] The present invention relates to the field of accelerometer measurement technology, and in particular to a vertical accelerometer virtualization method, medium and device. Background Art

[0002] Inertial navigation systems (INS) are widely used for navigation on various platforms due to their strong autonomy, good continuity, and high concealment. The core component of INS is the inertial measurement unit (IMU), which consists of a three-axis (x, y, and z) orthogonal gyroscope and accelerometer (hereinafter referred to as the inertial element). Device errors in the gyroscope and accelerometer can cause the INS's autonomous navigation errors to accumulate over time. To balance cost and performance, the single-axis rotational inertial navigation system (hereinafter referred to as INS) is currently the most commonly used INS system. The INS's IMU rotates about the z-axis, allowing the errors in the inertial elements in the horizontal direction (x and y axes) to be periodically offset by rotational modulation, improving autonomous navigation accuracy. However, the errors in the inertial elements along the INS's rotational axis (z-axis) cannot be rotationally modulated, and errors in the z-axis inertial elements can affect the INS's autonomous navigation performance.

[0003] On the one hand, the influence of the horizontal accelerometer's bias can be offset by uniaxial rotation modulation (which only eliminates the bias's effect, not its presence). On the other hand, the gravity anomaly manifests itself in the vertical direction, and its horizontal component is relatively small. Therefore, the vertical accelerometer's bias cannot be offset by uniaxial rotation modulation and bears the vast majority of the gravity anomaly's influence.

[0004] Therefore, it is necessary to propose a new vertical accelerometer virtualization method to solve the problem that the measurement accuracy of the vertical accelerometer is affected by zero bias and gravity anomaly, thereby improving the measurement accuracy of the vertical accelerometer and then improving the navigation accuracy. At the same time, it can also be extended to applications such as gravity anomaly measurement that require high-precision vertical accelerometer specific force information. Summary of the Invention

[0005] The present invention aims to provide a method, medium, and device for virtualizing a vertical accelerometer with high measurement accuracy. The specific technical solution is as follows:

[0006] A vertical accelerometer virtualization method comprises the following steps:

[0007] Step 1: Collect information output by the inertial navigation system and satellite navigation system receivers;

[0008] Step 2: Based on the information of the inertial navigation system and the satellite navigation system collected in step 1, the inertial navigation system and the satellite navigation system are combined through Kalman filtering, and the attitude, speed and position information output by the combined navigation are collected;

[0009] Step 3: Based on the attitude, velocity and position information output by the integrated navigation, and the angular velocity and specific force information output by the inertial navigation system, the vertical accelerometer is virtualized to obtain the virtual vertical accelerometer specific force;

[0010] Step 4: After fusing the virtual vertical accelerometer specific force and the actual vertical accelerometer specific force, the final vertical accelerometer specific force is obtained.

[0011] Preferably, the angular velocity, specific force, attitude, speed, and position information output by the inertial navigation system collected in step 1 are recorded as

[0012] The speed and position information output by the satellite navigation system receiver are recorded as and

[0013] Preferably, the Kalman filter state quantity X of the combined navigation of the inertial navigation system and the satellite navigation system in step 2 is:

[0014]

[0015] Where φ, δv, and δp represent attitude, velocity, and position errors, respectively. Represent the gyro and accelerometer bias respectively;

[0016] The measurement equation of Kalman filter is:

[0017] Z k =H k X k +V k ;

[0018] Where Z k represents the measurement value at time k, H k represents the measurement transfer matrix at time k, X k Represents the Kalman filter state at time k, V k represents the measurement noise at time k;

[0019] Z k Expressed as:

[0020]

[0021] H k Expressed as:

[0022]

[0023] Where, 0 3×3 represents the three-dimensional zero matrix, I 3×3 Represents the three-dimensional identity matrix;

[0024] The state equation of the Kalman filter is:

[0025] X k =AX k-1 +W k-1 ;

[0026] Where A represents the state one-step transfer matrix, which is obtained through the inertial navigation error propagation equation, and W k-1 represents process noise;

[0027] The Kalman filter equation is:

[0028] ①、 in represents the one-step prediction state, represents the state estimate at time k-1;

[0029] ②P k|k-1 =AP k-1 A T +Q k-1 , where P k|k-1 represents the one-step forecast error covariance matrix, P k-1 represents the estimation error covariance matrix at time k-1, represents the state noise variance matrix at time k-1;

[0030] ③、 where K k represents the Kalman filter gain at time k, represents the measurement noise variance matrix at time k;

[0031] ④、 in represents the state estimate at time k;

[0032] ⑤、P k =(IK k H k )P k|k-1 , where I represents the identity matrix, and its dimension is the same as the state dimension, P k Represents the estimation error covariance matrix at time k.

[0033] Preferably, after Kalman filtering in step 2, the attitude, speed and position information output by the combined navigation are recorded as

[0034] The virtualized model of the vertical accelerometer is obtained based on the specific force equation of the inertial navigation system, which is written as:

[0035]

[0036] Where, represents the differential ω of the carrier's velocity ω in the ω navigation ω system, v n Indicates the speed of the carrier in the navigation system, represents the projection of the Earth's rotational angular velocity in the navigation system, Represents the projection of position rate in the navigation system, g n represents the gravitational acceleration vector;

[0037] and Expressed as:

[0038]

[0039]

[0040] Where, ω ie represents the angular velocity of the Earth's rotation, v E 、v N Represents the eastward and northward speeds respectively; L is the latitude of the carrier location, h is the altitude of the carrier location, R M and R N They are respectively the meridian curvature radius and the meridian curvature radius of the earth;

[0041] Will Substituting into the specific force equation, the horizontal component is expanded to:

[0042]

[0043] Where θ, γ, and ψ represent the pitch, roll, and heading attitude angles, respectively, and f x and f y Represents the specific force output by the x-axis and y-axis accelerometers, f vz represents the specific force of the virtual vertical accelerometer, ΔT represents the sampling period of the inertial navigation system, and Δv E and Δv N Denote the velocity increment in the east and north directions, Δv E,cor / g and Δv N,cor / g denote the harmful velocity increments in the east and north directions, respectively;

[0044] Δv cor / g is a slowly varying term, expressed as:

[0045]

[0046] + represents the projection of the Earth's rotational angular velocity at time k-1 in the navigation system, represents the projection of the position rate at time k-1 in the navigation system, represents the speed of the combined navigation output at time k-1, g n (k-1) represents the gravitational acceleration vector at time k-1;

[0047] remember:

[0048] Z1=Δv E / ΔT+Δv E,cor / g / ΔT-(f x cosγcosψ+f x sinθsinγsinψ+f y cosθsinψ);

[0049] Z2=Δv N / ΔT+Δv N,cor / g / ΔT-(-f x cosγsinψ+f x sinθsinγcosψ+f y cosθcosψ);

[0050] The horizontal component obtained by expansion is simplified as follows:

[0051] f vz sinγ=Z1cosψ-Z2sinψ;

[0052] Solve the virtual vertical accelerometer specific force f by the above formula vz .

[0053] Preferably, the step of fusing the virtual vertical accelerometer specific force and the actual vertical accelerometer specific force in step 4 includes:

[0054] The virtual vertical accelerometer specific force and the actual vertical accelerometer specific force are both low-pass filtered. The low-frequency part is deducted from the actual vertical accelerometer specific force to obtain the medium and high frequency parts, which are fused with the low-frequency part of the virtual vertical accelerometer specific force to obtain the final vertical accelerometer specific force.

[0055] Preferably, the low-pass filtering process uses an FIR low-pass filter;

[0056] The cutoff frequency F of the FIR low-pass filter c Set according to the carrier and motion law; assuming that the inertial navigation sampling frequency is F s , the length of the FIR low-pass filter is set to:

[0057]

[0058] The present invention also provides a readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the vertical accelerometer virtualization method as described above is implemented.

[0059] The present invention also provides an electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, a vertical accelerometer virtualization method as described above is performed.

[0060] The application of the technical solution of the present invention has the following beneficial effects:

[0061] A method for virtualizing a vertical accelerometer includes: collecting information output by an inertial navigation system and a satellite navigation system receiver; implementing integrated navigation of the inertial navigation system and the satellite navigation system using a Kalman filter based on the information collected in step 1, and collecting attitude, velocity, and position information output by the integrated navigation; virtualizing a vertical accelerometer based on the attitude, velocity, and position information output by the integrated navigation, as well as the angular velocity and specific force information output by the inertial navigation system, to obtain a virtual vertical accelerometer specific force; and fusing the virtual vertical accelerometer specific force with the actual vertical accelerometer specific force to obtain a final vertical accelerometer specific force. The vertical accelerometer is inversely resolved using horizontal velocity information rather than vertical velocity information, and both the virtual vertical accelerometer and the actual vertical accelerometer are fused through an FIR low-pass filter. This results in a more accurate final vertical accelerometer specific force, higher precision, and a wider range of applications.

[0062] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 is a flow chart of a vertical accelerometer virtualization method according to an embodiment of the present invention;

[0065] Figure 2 1 is a schematic diagram comparing the fused vertical specific force and the actual vertical specific force in an embodiment of the present invention;

[0066] Figure 3 3 is a schematic diagram comparing the fused vertical specific force error and the actual vertical specific force error in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0068] Example:

[0069] See also Figure 1 , a vertical accelerometer virtualization method, comprising the following steps:

[0070] Step 1: Collect the information output by the inertial navigation system and satellite navigation system receiver. The angular velocity, specific force, attitude, speed and position information output by the collected inertial navigation system are recorded as f b 、 The speed and position information output by the satellite navigation system receiver are recorded as and The superscript n indicates the projection under the navigation system. The subscript ib indicates It is the angular velocity of the carrier system relative to the inertial system. The subscript INS indicates the information output by the inertial navigation system; the subscript GNSS indicates the information output by the satellite navigation system.

[0071] Step 2: Based on the information of the inertial navigation system and the satellite navigation system collected in step 1, the inertial navigation system and the satellite navigation system are combined through Kalman filtering, and the attitude, speed and position information output by the combined navigation are collected;

[0072] The Kalman filter state quantity X of the integrated navigation of the inertial navigation system and the satellite navigation system is:

[0073]

[0074] Where φ, δv, and δp represent attitude, velocity, and position errors, respectively. Represent the gyro and accelerometer bias respectively;

[0075] The measurement equation of Kalman filter is:

[0076] Z k =H k X k +V k ;

[0077] Where Z k represents the measurement value at time k, H k represents the measurement transfer matrix at time k, X k Represents the Kalman filter state at time k, V k represents the measurement noise at time k;

[0078] Z k Expressed as:

[0079]

[0080] H k Expressed as:

[0081]

[0082] Where, 0 3×3 represents the three-dimensional zero matrix, I 3×3 Represents the three-dimensional identity matrix;

[0083] The state equation of the Kalman filter is:

[0084] X k =AX k-1 +W k-1 ;

[0085] Where A represents the state one-step transfer matrix, which is obtained through the inertial navigation error propagation equation, and W k-1 represents process noise;

[0086] The Kalman filter equation is:

[0087] ①、 in represents the one-step prediction state, represents the state estimate at time k-1;

[0088] ②P k|k-1 =AP k-1 A T +Q k-1 , where P k|k-1 represents the one-step forecast error covariance matrix, P k-1 represents the estimation error covariance matrix at time k-1, represents the state noise variance matrix at time k-1;

[0089] ③、 where K k represents the Kalman filter gain at time k, represents the measurement noise variance matrix at time k;

[0090] ④、 in represents the state estimate at time k;

[0091] ⑤、P k =(IK k H k )P k|k-1 , where I represents the identity matrix, and its dimension is the same as the state dimension, P k Represents the estimation error covariance matrix at time k.

[0092] Step 3: Based on the attitude, velocity, and position information output by the integrated navigation system, and the angular velocity and specific force information output by the inertial navigation system, the vertical accelerometer is virtualized to obtain the virtual vertical accelerometer specific force. Specifically:

[0093] After Kalman filtering in step 2, the attitude, velocity, and position information output by the combined navigation are recorded as

[0094] The virtualized model of the vertical accelerometer is obtained based on the specific force equation of the inertial navigation system, which is written as:

[0095]

[0096] Where, represents the differential ω of the carrier's velocity ω in the ω navigation ω system, v n Indicates the speed of the carrier in the navigation system, represents the projection of the Earth's rotational angular velocity in the navigation system, Represents the projection of position rate in the navigation system, g n represents the gravitational acceleration vector;

[0097] and Expressed as:

[0098]

[0099]

[0100] Where, ω ie represents the angular velocity of the Earth's rotation, v E 、v N Represents the eastward and northward speeds respectively; L is the latitude of the carrier location, h is the altitude of the carrier location, R M and R N They are respectively the meridian curvature radius and the meridian curvature radius of the earth;

[0101] Will Substituting into the specific force equation, the horizontal component is expanded to:

[0102]

[0103] Observe the above formula, due to the horizontal force f x 、f y The zero bias is effectively corrected in the combined navigation of step 2, and the horizontal velocity measurement accuracy is usually better than the celestial velocity measurement accuracy. Therefore, compared with the actual vertical accelerometer whose relative force is affected by bias errors such as zero bias, the low-frequency component of the virtual vertical accelerometer's relative force is more accurate.

[0104] Where θ, γ, and ψ represent the pitch, roll, and heading attitude angles, respectively, and f x and f y Represents the specific force output by the x-axis and y-axis accelerometers, f vz represents the specific force of the virtual vertical accelerometer, ΔT represents the sampling period of the inertial navigation system, and Δv E and Δv N Denote the velocity increment in the east and north directions, Δv E,cor / g and Δv N,cor / g denote the harmful velocity increments in the east and north directions, respectively;

[0105] Δv cor / g is a slowly varying term, expressed as:

[0106]

[0107] + represents the projection of the Earth's rotational angular velocity at time k-1 in the navigation system, represents the projection of the position rate at time k-1 in the navigation system, represents the speed of the combined navigation output at time k-1, g n (k-1) represents the gravitational acceleration vector at time k-1;

[0108] remember:

[0109] Z1=Δv E / ΔT+Δv E,cor / g / ΔT-(f x cosγcosψ+f x sinθsinγsinψ+f y cosθsinψ);

[0110] Z2=Δv N / ΔT+Δv N,cor / g / ΔT-(-f x cosγsinψ+f x sinθsinγcosψ+f y cosθcosψ);

[0111] The horizontal component obtained by expansion is simplified as follows:

[0112] f vz sinγ=Z1 cosψ-Z2 sinψ;

[0113] Solve the virtual vertical accelerometer specific force f by the above formula vz According to the above formula, f vz The coefficient of f is sinγ, vzThe observability of f depends on the measurement accuracy and variation of the roll angle. vz The mid- and high-frequency components are not as good as the actual vertical accelerometer f z The real details that can be reflected. In summary, by integrating f vz The low-frequency components and f z By extracting the mid- and high-frequency components, a vertical acceleration signal with a small bias error and that can accurately reflect the mid- and high-frequency details can be obtained.

[0114] Step 4: After fusing the virtual vertical accelerometer specific force and the actual vertical accelerometer specific force, the final vertical accelerometer specific force is obtained. The specific steps include:

[0115] The virtual vertical accelerometer specific force and the actual vertical accelerometer specific force are both low-pass filtered. The low-frequency part is deducted from the actual vertical accelerometer specific force to obtain the medium and high frequency parts, which are fused with the low-frequency part of the virtual vertical accelerometer specific force to obtain the final vertical accelerometer specific force.

[0116] Considering the real-time nature of the method, the low-pass filtering process adopts an FIR low-pass filter; the cutoff frequency F of the FIR low-pass filter is c It is set according to the carrier and the law of motion. For example, in a ship-borne platform, the vertical acceleration is mainly excited by waves. The wave frequency in calm sea is about 0.03Hz~0.3Hz, so the cutoff frequency F c It can be set to about 0.3Hz. Assume that the inertial navigation sampling frequency is F s , the length of the FIR low-pass filter is set to:

[0117]

[0118] This embodiment conducted a simulation test based on a certain navigation test data. The simulation test lasted for 30 minutes, and the simulation conditions were set as shown in the following table.

[0119] Table 1 Accuracy of simulation test equipment

[0120] Zero bias Random walk noise gyroscope 0.003° / h 0.0005° / √h accelerometer 50mGal 10mGal / √Hz Speed ​​measurement accuracy Positioning accuracy Satellite navigation system (0.03,0.03,1)m / s (3,3,10)m

[0121] The method proposed in this embodiment is used to virtualize the vertical accelerometer and fuse it with the actual vertical accelerometer. The inertial sampling frequency is 20Hz, the FIR low-pass filter cutoff frequency is set to 0.2Hz, and the filter length is set to 200. The vertical accelerometer comparison result after fusion is as follows: Figure 2 and Figure 3 As shown in the figure, the fused vertical accelerometer specific force not only retains the mid- and high-frequency details of the actual vertical acceleration, but also overcomes the zero bias error of the actual vertical accelerometer, thereby improving the accuracy of the vertical accelerometer specific force and verifying the effectiveness of this method.

[0122] This embodiment further includes a readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the vertical accelerometer virtualization method described above is implemented.

[0123] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0124] This embodiment also includes an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, a vertical accelerometer virtualization method as described above is generated.

[0125] The electronic device may be a computing device such as a mobile phone, desktop computer, laptop, PDA, or cloud server. The electronic device may include, but is not limited to, a processor and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0126] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A vertical accelerometer virtualization method, characterized in that: The steps include: Step 1: Collect information output by the inertial navigation system and satellite navigation system receivers; Step 2: Based on the information of the inertial navigation system and the satellite navigation system collected in step 1, the inertial navigation system and the satellite navigation system are combined through Kalman filtering, and the attitude, speed and position information output by the combined navigation are collected; Step 3: Based on the attitude, velocity and position information output by the integrated navigation, and the angular velocity and specific force information output by the inertial navigation system, the vertical accelerometer is virtualized to obtain the virtual vertical accelerometer specific force; Step 4: After fusing the virtual vertical accelerometer specific force and the actual vertical accelerometer specific force, the final vertical accelerometer specific force is obtained.

2. A vertical accelerometer virtualization method according to claim 1, characterized in that: The angular velocity, specific force, attitude, speed, and position information output by the inertial navigation system collected in step 1 are recorded as f b 、 The speed and position information output by the satellite navigation system receiver are recorded as and 3. A vertical accelerometer virtualization method according to claim 2, characterized in that: The Kalman filter state quantity X of the integrated navigation of the inertial navigation system and the satellite navigation system in step 2 is: X=[φ,δv,δp,ε,▽] T ; Where φ, δv, and δp represent attitude, velocity, and position errors, respectively; ε and ▽ represent gyro and accelerometer biases, respectively; The measurement equation of Kalman filter is: Z k =H k X k +V k ; Where Z k represents the measurement value at time k, H k represents the measurement transfer matrix at time k, X k Represents the Kalman filter state at time k, V k represents the measurement noise at time k; Z k Expressed as: H k Expressed as: Where, 0 3×3 Represents the three-dimensional zero matrix, I 3×3 Represents the three-dimensional identity matrix; The state equation of the Kalman filter is: X k =AX k-1 +W k-1 ; Where A represents the state one-step transfer matrix, which is obtained through the inertial navigation error propagation equation, and W k-1 represents process noise; The Kalman filter equation is: ①、 in represents the one-step prediction state, represents the state estimate at time k-1; ②P k|k-1 =AP k-1 A T +Q k-1 , where P k|k-1 represents the one-step forecast error covariance matrix, P k-1 represents the estimation error covariance matrix at time k-1, represents the state noise variance matrix at time k-1; ③、 where K k represents the Kalman filter gain at time k, represents the measurement noise variance matrix at time k; ④、 in represents the state estimate at time k; ⑤、P k =(IK k H k )P k|k-1 , where I represents the identity matrix, and its dimension is the same as the state dimension, P k Represents the estimation error covariance matrix at time k.

4. A vertical accelerometer virtualization method according to claim 3, characterized in that: After Kalman filtering in step 2, the attitude, speed, and position information output by the combined navigation are recorded as The virtualized model of the vertical accelerometer is obtained based on the specific force equation of the inertial navigation system, which is written as: Where, represents the differential ω of the carrier's velocity ω in the ω navigation ω system, v n Indicates the speed of the carrier in the navigation system, represents the projection of the Earth's rotational angular velocity in the navigation system, Represents the projection of position velocity in the navigation system, g n represents the gravitational acceleration vector; and Expressed as: Where, ω ie represents the angular velocity of the Earth's rotation, v E 、v N Represents the eastward and northward speeds respectively; L is the latitude of the carrier location, h is the altitude of the carrier location, R M and R N They are respectively the meridian curvature radius and the meridian curvature radius of the earth; Will Substituting into the specific force equation, the horizontal component is expanded to: Where θ, γ, and ψ represent the pitch, roll, and heading attitude angles, respectively, and f x and f y Represents the specific force output by the x-axis and y-axis accelerometers, f vz represents the specific force of the virtual vertical accelerometer, ΔT represents the sampling period of the inertial navigation system, and Δv E and Δv N Denote the velocity increment in the east and north directions, Δv E,cor / g and Δv N,cor / g denote the harmful velocity increments in the east and north directions, respectively; Δv cor / g is a slowly varying term, expressed as: + represents the projection of the Earth's rotational angular velocity at time k-1 in the navigation system, represents the projection of the position rate at time k-1 in the navigation system, represents the speed of the combined navigation output at time k-1, g n (k-1) represents the gravitational acceleration vector at time k-1; remember: Z1=Δv E / ΔT+Δv E,cor / g / ΔT-(f x cosγcosψ+f x sinθsinγsinψ+f y cosθsinψ); Z2=Δv N / ΔT+Δv N,cor / g / ΔT-(-f x cosγsinψ+f x sinθsinγcosψ+f y cosθcosψ); The horizontal component obtained by expansion is simplified as follows: in vz sinγ=Z1cosψ-Z2sinψ; Solve the virtual vertical accelerometer specific force f by the above formula vz .

5. A vertical accelerometer virtualization method according to claim 4, characterized in that: The step of fusing the virtual vertical accelerometer specific force and the actual vertical accelerometer specific force in step 4 includes: The virtual vertical accelerometer specific force and the actual vertical accelerometer specific force are both low-pass filtered. The low-frequency part is deducted from the actual vertical accelerometer specific force to obtain the medium and high frequency parts, which are fused with the low-frequency part of the virtual vertical accelerometer specific force to obtain the final vertical accelerometer specific force.

6. A vertical accelerometer virtualization method according to claim 5, characterized in that: The low-pass filtering process adopts FIR low-pass filter; The cutoff frequency F of the FIR low-pass filter c Set according to the carrier and motion law; assuming that the inertial navigation sampling frequency is F s , the length of the FIR low-pass filter is set to:

7. A readable storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, a vertical accelerometer virtualization method according to any one of claims 1 to 6 is implemented.

8. An electronic device, characterized in that: include: At least one processor, at least one memory, and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, a vertical accelerometer virtualization method according to any one of claims 1 to 6 is provided.

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