An online calculation method for multi-inertial navigation performance in GNSS-denied environment

Abstract

The present invention discloses a method for online calculation of multi-INS performance in a GNSS-denied environment, comprising the following steps: constructing a performance monitoring model, the performance monitoring model is used to describe the mathematical relationship between the original data of the gyrocompass and the navigation information of the carrier platform; based on the original data and the performance monitoring model, a performance monitoring system is formed; a Kalman filter of an online calculation model is established, the online calculation model is used to online estimate the navigation parameter error of the performance monitoring system; the standard deviation of the azimuth gyro constant drift estimation value of each Kalman filter in the same time period is calculated, and based on the standard deviation, the optimal INS is selected from the multi-INS. The present invention realizes the calculation of the performance of multiple sets of INS in a GNSS-denied environment, and effectively solves the problem of optimizing the performance of multiple INS.

Description

Technical Field

[0001] The present invention relates to the field of autonomous navigation, and in particular to an online calculation method for multi-inertial navigation performance in a GNSS-denied environment. Background Art

[0002] In view of the defects of GNSS such as susceptibility to interference and physical shielding, the carrier platform mainly relies on inertial navigation to achieve autonomous navigation. Based on the important role of inertial navigation equipment in the carrier platform's mission execution, the carrier platform is generally redundantly configured with multiple sets of high-precision inertial navigation and a gyrocompass as a backup for multiple inertial navigation. Due to the lack of navigation reference information sources in GNSS-denied environments, it is difficult to calculate and optimize the performance of multiple inertial navigation equipment on the carrier platform. Basically, the default manual setting method is used to use inertial navigation information, which cannot give full play to the advantages of multiple inertial navigation and affects the user's use of the optimal inertial navigation information. Summary of the invention

[0003] In view of this, the present invention provides a method for online calculation of multi-INS performance in a GNSS denied environment. Combined with the characteristics of the carrier platform being equipped with a gyrocompass and multi-INS, the present invention can solve the problem of multi-INS performance calculation, and select an INS with better performance from the multi-INS according to the calculated performance. The present invention can be applied and promoted in the fields of land and underwater navigation.

[0004] To achieve the purpose of the present invention, the technical solution adopted by the present invention is:

[0005] A method for online calculation of multi-inertial navigation performance in a GNSS-denied environment comprises the following steps:

[0006] Step S1: a gyrocompass is configured on a carrier platform, raw data of the gyrocompass is obtained, and a performance monitoring model is constructed, wherein the performance monitoring model is used to describe a mathematical relationship between the raw data of the gyrocompass and navigation information of the carrier platform; based on the raw data and the performance monitoring model, navigation information of the carrier platform is obtained, thereby forming a performance monitoring system;

[0007] Step S2: establishing an error joint state equation based on the performance monitoring model, using the speed difference and position difference between the performance monitoring system and the multi-inertial navigation system configured on the carrier platform as observation quantities to establish a measurement equation, and then establishing a Kalman filter of an online calculation model, wherein the online calculation model is used to online estimate the navigation parameter error of the performance monitoring system;

[0008] Step S3: using the Kalman filter to estimate the azimuth gyro constant drift corresponding to the performance monitoring system, calculating the standard deviation of the azimuth gyro constant drift estimation values ​​of each Kalman filter in the same time period, and selecting the optimal inertial navigation system from the multiple inertial navigation systems based on the standard deviation.

[0009] Preferably, the step S1 comprises:

[0010] Step S11: The gyrocompass is configured on the carrier platform, and the original data of the gyrocompass is obtained; the navigation coordinate system n selects the east-north-sky geographic coordinate system, and the carrier coordinate system b points to the right-front-up; a performance monitoring model is constructed, and the performance monitoring model is:

[0011]

[0012]

[0013]

[0014] In the formula, the i system represents the inertial coordinate system, and the e system represents the earth coordinate system; Represents the angular velocity information measured by the gyrocompass gyroscope, Represents the specific force information measured by the gyrocompass accelerometer; v n , p represent the attitude matrix, velocity, and position output by the performance monitoring system respectively; g n represents the projection of gravitational acceleration in the navigation system, is the differential of the attitude matrix output by the performance monitoring system, is the projection of the angular velocity of the navigation coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the differential of the velocity output by the performance monitoring system, is the projection of the angular velocity of the earth coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the projection of the angular velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, The differential transformation matrix M of the position output by the performance monitoring system is:

[0015]

[0016] Among them, R N R is the radius of curvature of the Earth's circumplex, M is the radius of curvature of the earth's meridian, and h is the height of the carrier platform;

[0017] Step S12: The carrier platform is equipped with multiple inertial navigation systems. Based on the navigation parameters output by each inertial navigation system, the attitude, speed and position of the performance monitoring model at the initial moment are obtained:

[0018]

[0019]

[0020]

[0021] In the formula, They represent the initial posture, speed and position of the performance monitoring model respectively. p1(0) represents the attitude, velocity and position of the first inertial navigation output, p2(0) represents the attitude, velocity and position output by the second inertial navigation system respectively;

[0022] Step S13: Integrate the strapdown navigation solution differential equation to obtain the attitude, speed and position of the performance monitoring system at the current moment as the navigation information of the carrier platform:

[0023]

[0024]

[0025]

[0026] in, v n (t) and p(t) are the attitude matrix, velocity, and position of the performance monitoring system over time, respectively. p(τ) are the attitude matrix differential, velocity differential, and position differential of the performance monitoring system over time.

[0027] Preferably, the step S2 comprises:

[0028] Step S21: The attitude misalignment angle φ of the performance monitoring system n , speed error δV n , position error δp, gyro constant drift ε b , Accelerometer bias As the state variable X, an error joint state equation is established, and the error joint state equation is expressed as follows:

[0029]

[0030]

[0031] Where F is the state transfer matrix, G is the interference matrix of the performance monitoring system, W is the random interference vector of the performance monitoring system, Differentiate the state variable;

[0032] Step S22: The speed and position of the performance monitoring system and each inertial navigation measurement are consistent as a constraint condition, and the measurement equation of the online calculation model is constructed:

[0033]

[0034]

[0035] The online calculation model is used to calculate the navigation parameter error of the performance monitoring system. In the formula, Z1 and Z2 represent the measurement of the first Kalman filter and the second Kalman filter respectively, H1 and H2 represent the measurement matrix of the first Kalman filter and the second Kalman filter respectively, V1 and V2 represent the measurement noise of the first Kalman filter and the second Kalman filter respectively, p INS1 Respectively represent the speed and position of the first inertial navigation output, p INS2 represent the speed and position of the second inertial navigation output respectively; the first inertial navigation corresponds to the first Kalman filter, and the second inertial navigation corresponds to the second Kalman filter; v n is the speed output by the performance monitoring system, and p is the position output by the performance monitoring system;

[0036] Step S23: Establishing a first Kalman filter and a second Kalman filter of an online calculation model, wherein the first Kalman filter and the second Kalman filter are respectively

[0037]

[0038]

[0039] Preferably, the step S3 comprises:

[0040] Step 31: Based on the azimuth gyro constant drift corresponding to the Kalman filter estimation performance monitoring model, the standard deviation of the azimuth gyro constant drift estimation values ​​of the first Kalman filter and the second Kalman filter in the selected time window is calculated respectively:

[0041]

[0042]

[0043] Where N is the number of estimated state variables in the selected time window, is the mean value of the azimuth gyro constant drift corresponding to the performance monitoring model estimated within the selected time window, ε z1 (k), ε z2 (k) respectively represent the azimuth gyro drift estimation values ​​of the first Kalman filter and the second Kalman filter at time k, σ1(ε z1 ),σ2(ε z2 ) represent the standard deviation of the azimuth gyro constant drift estimation values ​​of the first Kalman filter and the second Kalman filter respectively; ε z1 , ε z2are the azimuth gyro drift estimation values ​​of the first and second Kalman filters, respectively, and k is the kth moment;

[0044] Step 32: Selecting the best inertial navigation system from the multiple inertial navigation systems based on the standard deviation, including:

[0045] If σ1(ε z1 )<σ2(ε z2 ), the system error of the first inertial navigation is small, and the first inertial navigation is the optimal inertial navigation; if σ2(ε z2 )<σ1(ε z1 ), the system error of the second inertial navigation is small, and the second inertial navigation is the optimal inertial navigation; if σ2(ε z2 )=σ1(ε z1 ), then either the first inertial navigation system or the second inertial navigation system can be used as the optimal inertial navigation system.

[0046] A multi-inertial navigation performance online calculation device for use in a GNSS denied environment, comprising:

[0047] A model building module: configured such that a gyrocompass is arranged on a carrier platform, raw data of the gyrocompass is obtained, and a performance monitoring model is constructed, wherein the performance monitoring model is used to describe the mathematical relationship between the raw data of the gyrocompass and the navigation information of the carrier platform; based on the raw data and the performance monitoring model, the navigation information of the carrier platform is obtained, thereby forming a performance monitoring system;

[0048] An error module is configured to establish an error joint state equation based on the performance monitoring model, use the speed difference and position difference between the performance monitoring system and the multi-inertial navigation system configured on the carrier platform as observation quantities to establish a measurement equation, and then establish a Kalman filter of an online calculation model, wherein the online calculation model is used to online estimate the navigation parameter error of the performance monitoring system;

[0049] The inertial navigation selection module is configured to use the Kalman filter to estimate the azimuth gyro constant drift corresponding to the performance monitoring system, calculate the standard deviation of the azimuth gyro constant drift estimation values ​​of each Kalman filter in the same time period, and select the optimal inertial navigation from the multiple inertial navigations based on the standard deviation.

[0050] A computer-readable storage medium stores a plurality of instructions; the plurality of instructions are used for a processor to load and execute the method as described above.

[0051] An electronic device, comprising:

[0052] A processor, which is used to execute multiple instructions;

[0053] A memory for storing a plurality of instructions;

[0054] The plurality of instructions are used to be stored by the memory and loaded and executed by the processor to implement the method as described above.

[0055] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0056] 1. The present invention utilizes the inherent equipment resources of the carrier platform. The carrier platform is equipped with a gyrocompass and multiple inertial navigation systems. By constructing a performance monitoring model, a Kalman filter is designed using the position difference and speed difference between the performance monitoring system and the high-precision inertial navigation system as observation quantities. By estimating and comparing the azimuth gyro drift of the performance monitoring system, the performance of multiple inertial navigation systems can be calculated in a GNSS-denied environment, thereby effectively solving the problem of optimizing the performance of multiple inertial navigation systems.

[0057] 2. The present invention can obtain accurate calculation results based on a small amount of data.

[0058] 3. The present invention has a small amount of calculation and a fast calculation speed, and can quickly calculate the performance of multiple inertial navigation systems, and then select an inertial navigation system with better performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The present invention is a flowchart of a method for online calculation of multi-inertial navigation performance in a GNSS-denied environment according to an embodiment of the present invention.

[0060] Figure 2 Schematic diagram of the architecture of a method for online calculation of multi-inertial navigation performance in a GNSS-denied environment according to an embodiment of the present invention.

[0061] Figure 3 Schematic diagram of the structure of a multi-inertial navigation performance online calculation device for a GNSS-denied environment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0063] The present invention proposes an online calculation method for multi-INS performance in a GNSS-denied environment. The present invention utilizes the inherent equipment resources of the carrier platform. The carrier platform is equipped with a gyrocompass and multi-INS. By constructing a performance monitoring model, the position difference and speed difference between the performance monitoring system and the high-precision INS are used as observation quantities to design a Kalman filter. By estimating and comparing the azimuth gyro drift of the performance monitoring system, the performance of multiple INS can be calculated in a GNSS-denied environment, effectively solving the problem of multi-INS performance optimization. Figure 1-Figure 2 As shown, the online calculation method of multi-inertial navigation performance for use in a GNSS denied environment of the present invention comprises the following steps:

[0064] Step S1: a gyrocompass is configured on a carrier platform, raw data of the gyrocompass is obtained, and a performance monitoring model is constructed, wherein the performance monitoring model is used to describe a mathematical relationship between the raw data of the gyrocompass and navigation information of the carrier platform; based on the raw data and the performance monitoring model, navigation information of the carrier platform is obtained, thereby forming a performance monitoring system;

[0065] Step S2: establishing an error joint state equation based on the performance monitoring model, using the speed difference and position difference between the performance monitoring system and the multi-inertial navigation system configured on the carrier platform as observation quantities to establish a measurement equation, and then establishing a Kalman filter of an online calculation model, wherein the online calculation model is used to online estimate the navigation parameter error of the performance monitoring system;

[0066] Step S3: using the Kalman filter to estimate the azimuth gyro constant drift corresponding to the performance monitoring system, calculating the standard deviation of the azimuth gyro constant drift estimation values ​​of each Kalman filter in the same time period, and selecting the optimal inertial navigation system from the multiple inertial navigation systems based on the standard deviation.

[0067] The step S1 comprises:

[0068] Step S11: The gyrocompass is configured on the carrier platform, and the original data of the gyrocompass is obtained; the navigation coordinate system n selects the east-north-sky geographic coordinate system, and the carrier coordinate system b points to the right-front-up; a performance monitoring model is constructed, and the performance monitoring model is:

[0069]

[0070]

[0071]

[0072] In the formula, the i system represents the inertial coordinate system, and the e system represents the earth coordinate system; Represents the angular velocity information measured by the gyrocompass gyroscope, Represents the specific force information measured by the gyrocompass accelerometer; v n , p represent the attitude matrix, velocity, and position output by the performance monitoring system respectively; g n represents the projection of gravitational acceleration in the navigation system, is the differential of the attitude matrix output by the performance monitoring system, is the projection of the angular velocity of the navigation coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the differential of the velocity output by the performance monitoring system, is the projection of the angular velocity of the earth coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the projection of the angular velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, The differential transformation matrix M of the position output by the performance monitoring system is:

[0073]

[0074] Among them, R N R is the radius of curvature of the Earth's circumplex, M is the radius of curvature of the earth's meridian, and h is the height of the carrier platform.

[0075] Step S12: The carrier platform is equipped with multiple inertial navigation systems. Based on the navigation parameters output by each inertial navigation system, the attitude, speed and position of the performance monitoring model at the initial moment are obtained:

[0076]

[0077]

[0078]

[0079] In the formula, They represent the initial posture, speed and position of the performance monitoring model respectively. p1(0) represents the attitude, velocity and position of the first inertial navigation output, p2(0) represents the attitude, velocity and position of the second inertial navigation output respectively.

[0080] Step S13: Integrate the strapdown navigation solution differential equation to obtain the attitude, speed and position of the performance monitoring system at the current moment as the navigation information of the carrier platform:

[0081]

[0082]

[0083]

[0084] in, v n (t) and p(t) are the attitude matrix, velocity, and position of the performance monitoring system over time, respectively. p(τ) are the attitude matrix differential, velocity differential, and position differential of the performance monitoring system over time.

[0085] In this embodiment, the attitude, speed and position of the performance monitoring system at the current moment can be obtained by real-time recursive calculation by integrating the strapdown navigation solution differential equation.

[0086] The step S2 comprises:

[0087] Step S21: The attitude misalignment angle φ of the performance monitoring system n , speed error δV n , position error δp, gyro constant drift ε b , Accelerometer bias As the state variable X, an error joint state equation is established, and the error joint state equation is expressed as follows:

[0088]

[0089]

[0090] Where F is the state transfer matrix, G is the interference matrix of the performance monitoring system, W is the random interference vector of the performance monitoring system, is the state variable derivative.

[0091] Step S22: The speed and position of the performance monitoring system and each inertial navigation measurement are consistent as a constraint condition, and the measurement equation of the online calculation model is constructed:

[0092]

[0093]

[0094] The online calculation model is used to calculate the navigation parameter error of the performance monitoring system. In the formula, Z1 and Z2 represent the measurement of the first Kalman filter and the second Kalman filter respectively, H1 and H2 represent the measurement matrix of the first Kalman filter and the second Kalman filter respectively, V1 and V2 represent the measurement noise of the first Kalman filter and the second Kalman filter respectively, p INS1 Respectively represent the speed and position of the first inertial navigation output, p INS2 represent the speed and position of the second inertial navigation output respectively; the first inertial navigation corresponds to the first Kalman filter, and the second inertial navigation corresponds to the second Kalman filter; v n is the speed output by the performance monitoring system, and p is the position output by the performance monitoring system.

[0095] Step S23: Establishing a first Kalman filter and a second Kalman filter of an online calculation model, wherein the first Kalman filter and the second Kalman filter are respectively

[0096]

[0097]

[0098] The step S3: calculating the standard deviation of the azimuth gyro constant drift estimation values ​​of each Kalman filter in the same time period based on the azimuth gyro constant drift corresponding to the Kalman filter estimation performance monitoring model, and selecting the optimal inertial navigation system from the multiple inertial navigation systems based on the standard deviation, including:

[0099] Step 31: Based on the azimuth gyro constant drift corresponding to the Kalman filter estimation performance monitoring model, the standard deviation of the azimuth gyro constant drift estimation values ​​of the first Kalman filter and the second Kalman filter in the selected time window is calculated respectively:

[0100]

[0101]

[0102] Where N is the number of estimated state variables in the selected time window, is the mean value of the azimuth gyro constant drift corresponding to the performance monitoring model estimated within the selected time window, ε z1 (k), ε z2 (k) respectively represent the azimuth gyro drift estimation values ​​of the first Kalman filter and the second Kalman filter at time k, σ1(ε z1 ),σ2(ε z2 ) represent the standard deviation of the azimuth gyro constant drift estimation values ​​of the first Kalman filter and the second Kalman filter respectively; ε z1 , ε z2 are the azimuth gyro drift estimation values ​​of the first and second Kalman filters respectively, and k is the kth moment.

[0103] Step 32: Selecting the best inertial navigation system from the multiple inertial navigation systems based on the standard deviation, including:

[0104] If σ1(ε z1 )<σ2(ε z2 ), the system error of the first inertial navigation is small, and the first inertial navigation is the optimal inertial navigation; if σ2(ε z2 )<σ1(ε z1 ), the system error of the second inertial navigation is small, and the second inertial navigation is the optimal inertial navigation; if σ2(ε z2 )=σ1(ε z1 ), then either the first inertial navigation system or the second inertial navigation system can be used as the optimal inertial navigation system.

[0105] The present invention also provides a multi-inertial navigation performance online calculation device for use in a GNSS denied environment, such as Figure 3 As shown, the device comprises:

[0106] A model building module: configured such that a gyrocompass is arranged on a carrier platform, raw data of the gyrocompass is obtained, and a performance monitoring model is constructed, wherein the performance monitoring model is used to describe the mathematical relationship between the raw data of the gyrocompass and the navigation information of the carrier platform; based on the raw data and the performance monitoring model, the navigation information of the carrier platform is obtained, thereby forming a performance monitoring system;

[0107] An error module is configured to establish an error joint state equation based on the performance monitoring model, use the speed difference and position difference between the performance monitoring system and the multi-inertial navigation system configured on the carrier platform as observation quantities to establish a measurement equation, and then establish a Kalman filter of an online calculation model, wherein the online calculation model is used to online estimate the navigation parameter error of the performance monitoring system;

[0108] The inertial navigation selection module is configured to use the Kalman filter to estimate the azimuth gyro constant drift corresponding to the performance monitoring system, calculate the standard deviation of the azimuth gyro constant drift estimation values ​​of each Kalman filter in the same time period, and select the optimal inertial navigation from the multiple inertial navigations based on the standard deviation.

[0109] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in the description may be different and are not limited. Therefore, those skilled in the art in the field of the present invention may modify or replace the technical solutions recorded in the above embodiments; and these modifications and replacements do not deviate from the creative purpose and technical solutions of the present invention and should all fall within the protection scope of the present invention.

Claims

1. A method for online calculation of multi-inertial navigation performance in a GNSS-denied environment, characterized in that: The method comprises the following steps: Step S1: a gyrocompass is configured on a carrier platform, raw data of the gyrocompass is obtained, and a performance monitoring model is constructed, wherein the performance monitoring model is used to describe a mathematical relationship between the raw data of the gyrocompass and navigation information of the carrier platform; based on the raw data and the performance monitoring model, navigation information of the carrier platform is obtained, thereby forming a performance monitoring system; Step S2: establishing an error joint state equation based on the performance monitoring model, using the speed difference and position difference between the performance monitoring system and the multi-inertial navigation system configured on the carrier platform as observation quantities to establish a measurement equation, and then establishing a Kalman filter of an online calculation model, wherein the online calculation model is used to online estimate the navigation parameter error of the performance monitoring system; Step S3: using the Kalman filter to estimate the azimuth gyro constant drift corresponding to the performance monitoring system, calculating the standard deviation of the azimuth gyro constant drift estimation values ​​of each Kalman filter in the same time period, and selecting the optimal inertial navigation system from the multiple inertial navigation systems based on the standard deviation; The step S1 comprises: Step S11: The gyrocompass is placed on the carrier platform, and the original data of the gyrocompass is obtained; the navigation coordinate system Select the East-North-Sky geographic coordinate system, carrier coordinate system Point to right-front-up; build a performance monitoring model, the performance monitoring model is: In the formula, represents the inertial coordinate system, System represents the Earth coordinate system; Represents the angular velocity information measured by the gyrocompass gyroscope, Represents the specific force information measured by the gyrocompass accelerometer; , They represent the attitude matrix and velocity output by the performance monitoring system respectively; represents the projection of gravitational acceleration in the navigation system, is the differential of the attitude matrix output by the performance monitoring system, is the projection of the angular velocity of the navigation coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the differential of the speed output by the performance monitoring system, is the projection of the angular velocity of the earth coordinate system relative to the inertial coordinate system in the navigation coordinate system, is the projection of the angular velocity of the navigation coordinate system relative to the earth coordinate system in the navigation coordinate system, is the position output by the performance monitoring system, the differential transformation matrix of the position output by the performance monitoring system for: in, is the radius of curvature of the Earth's circumplex, is the radius of curvature of the Earth's meridian, is the height of the carrier platform; Step S12: The carrier platform is equipped with multiple inertial navigation systems. Based on the navigation parameters output by each inertial navigation system, the attitude, speed and position of the performance monitoring model at the initial moment are obtained: In the formula, , , They represent the initial posture, speed and position of the performance monitoring model respectively. , , Respectively represent the attitude, velocity and position output by the first inertial navigation system, , , Respectively represent the attitude, velocity and position output by the second inertial navigation system; Step S13: Integrate the strapdown navigation differential equation to obtain the attitude, speed and position of the performance monitoring system at the current moment as the navigation information of the carrier platform: in, , , They are the attitude matrix, velocity, and position of the performance monitoring system over time. , , They are the attitude matrix differential, velocity differential, and position differential of the performance monitoring system over time; The step S3 comprises: Step 31: Based on the azimuth gyro constant drift corresponding to the Kalman filter estimation performance monitoring model, the standard deviation of the azimuth gyro constant drift estimation values ​​of the first Kalman filter and the second Kalman filter in the selected time window is calculated respectively: Where N is the number of estimated state variables in the selected time window, , is the mean value of the azimuth gyro constant drift corresponding to the performance monitoring model estimated within the selected time window, , Respectively The azimuth gyro drift estimation values ​​of the first Kalman filter and the second Kalman filter at time instant, , represent the standard deviation of the azimuth gyro constant drift estimation values ​​of the first Kalman filter and the second Kalman filter respectively; , are the azimuth gyro drift estimation values ​​of the first and second Kalman filters, respectively, and k is the kth moment; Step 32: Selecting the best inertial navigation system from the multiple inertial navigation systems based on the standard deviation, including: like , then the system error of the first inertial navigation is small, and the first inertial navigation is the optimal inertial navigation; if , then the system error of the second inertial navigation is small, and the second inertial navigation is the optimal inertial navigation; if , then either the first inertial navigation system or the second inertial navigation system can be used as the optimal inertial navigation system.

2. The method according to claim 1, characterized in that The step S2 comprises: Step S21: The attitude misalignment angle of the performance monitoring system , speed error , Position error , Gyro constant drift , Accelerometer bias As a state variable , establish the error joint state equation, the error joint state equation is expressed as follows: In the formula, is the state transfer matrix, is the interference matrix of the performance monitoring system, is the random disturbance vector of the performance monitoring system, Differentiate the state variable; Step S22: The speed and position of the performance monitoring system and each inertial navigation measurement are consistent as a constraint condition, and the measurement equation of the online calculation model is constructed: The online calculation model is used to calculate the navigation parameter error of the performance monitoring system. , denote the quantity measurements of the first Kalman filter and the second Kalman filter, respectively, , denote the measurement matrices of the first Kalman filter and the second Kalman filter respectively, , denote the measurement noise of the first Kalman filter and the second Kalman filter respectively, , Respectively represent the speed and position of the first inertial navigation output, , Respectively represent the speed and position output by the second inertial navigation system; the first inertial navigation system corresponds to the first Kalman filter, and the second inertial navigation system corresponds to the second Kalman filter; The speed of the performance monitoring system output, The location of the output for the performance monitoring system; Step S23: Establishing a first Kalman filter and a second Kalman filter of an online calculation model, wherein the first Kalman filter and the second Kalman filter are respectively 3. A multi-inertial navigation performance online calculation device for use in a GNSS-denied environment, used to implement the method as described in any one of claims 1-2, characterized in that: The device comprises: A model building module: configured such that a gyrocompass is arranged on a carrier platform, raw data of the gyrocompass is obtained, and a performance monitoring model is constructed, wherein the performance monitoring model is used to describe the mathematical relationship between the raw data of the gyrocompass and the navigation information of the carrier platform; based on the raw data and the performance monitoring model, the navigation information of the carrier platform is obtained, thereby forming a performance monitoring system; An error module is configured to establish an error joint state equation based on the performance monitoring model, use the speed difference and position difference between the performance monitoring system and the multi-inertial navigation system configured on the carrier platform as observation quantities to establish a measurement equation, and then establish a Kalman filter of an online calculation model, wherein the online calculation model is used to online estimate the navigation parameter error of the performance monitoring system; The inertial navigation selection module is configured to use the Kalman filter to estimate the azimuth gyro constant drift corresponding to the performance monitoring system, calculate the standard deviation of the azimuth gyro constant drift estimation values ​​of each Kalman filter in the same time period, and select the optimal inertial navigation from the multiple inertial navigations based on the standard deviation.

4. A computer-readable storage medium, wherein a plurality of instructions are stored in the storage medium; the plurality of instructions are used for a processor to load and execute the method as claimed in any one of claims 1 to 2.

5. An electronic device, characterized in that: The electronic device comprises: A processor, which is used to execute multiple instructions; A memory for storing a plurality of instructions; The plurality of instructions are used to be stored in the memory and loaded and executed by the processor according to any one of claims 1 to 2.

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