Navigation positioning method based on multi-source data fusion

Abstract

The invention belongs to the technical field of navigation, and discloses a navigation positioning method based on multi-source data fusion, which comprises the following steps: an error equation of an inertial navigation system is calculated, and a system state equation is the error equation of the inertial navigation system and mainly comprises attitude, speed, position and inertial device error equations; and calculating an inertia / TACAN state equation. Filtering state fusion estimation is calculated, an inertial navigation system serves as a reference system, other satellite navigation systems and the inertial navigation system form a sub-filter, state estimation is conducted through Kalman filtering, and then a filtering result is sent to a main filter for information fusion. Through the steps, the influence of a single navigation system on the airborne navigation system is effectively reduced, and the reliability of the navigation system in a complex electromagnetic environment is improved.

Description

Technical Field

[0001] This invention belongs to the field of navigation technology, and in particular relates to a navigation and positioning method based on multi-source data fusion. Background Technology

[0002] Multi-source fusion navigation typically uses inertial navigation as its core, integrating other navigation systems. It overcomes the limitations of a single navigation system, providing a navigation solution with higher accuracy, higher reliability, and stronger anti-interference capabilities. Especially for aircraft and other carriers, inertial navigation systems can operate completely autonomously, calculating position, velocity, attitude, and heading through integral calculations based on the carrier's acceleration and angular velocity, without relying on external signals. Therefore, it has strong anti-interference capabilities. However, navigation errors accumulate and diverge over time; the longer the flight time, the worse the position accuracy, making pure inertial navigation unable to complete long-endurance, high-precision navigation tasks independently. It usually requires correction using satellite navigation. However, satellite navigation cannot provide effective positioning information in interference environments. Therefore, in increasingly complex flight environments, the combined satellite and inertial system cannot guarantee accurate spatiotemporal information for the aircraft under all circumstances. In summary, existing multi-source fusion navigation technologies have many shortcomings. To address these issues, a solution that can reduce the impact of a single navigation system on the airborne navigation system and improve the reliability of the navigation system is urgently needed. Summary of the Invention

[0003] To achieve the above objectives, the present invention provides a navigation and positioning method based on multi-source data fusion, comprising:

[0004] Acquire measurement data from inertial devices and auxiliary navigation systems, wherein the auxiliary navigation system includes one or more satellite navigation systems and TACAN systems;

[0005] The measurement data from the inertial device is input into the inertial navigation system to calculate the initial inertial navigation solution data, which includes attitude, velocity, and position.

[0006] Using the inertial navigation system as the reference system, the auxiliary navigation system and the inertial navigation system constitute a sub-filter, and state estimation is performed through Kalman filtering; wherein, the state equation of the sub-filter is the error equation of the inertial navigation system, and the error equation of the inertial navigation system includes attitude error equation, velocity error equation, position error equation and inertial device error equation.

[0007] The filtering results of each sub-filter are input into the main filter for fusion, and the global state estimate is output.

[0008] The global state estimate is fed back to the inertial navigation system to correct the initial inertial navigation solution data and output the final inertial navigation solution data.

[0009] Optionally, the error equation of the inertial navigation system is as follows:

[0010]

[0011]

[0012]

[0013]

[0014]

[0015] In the formula, The error equation for an inertial navigation system is... It is a 15-dimensional state variable. , , and , , The random constant zero bias of the gyroscope and accelerometer are respectively used. These are the errors in latitude, longitude, and altitude, respectively. These represent the velocity errors of the carrier in the east, north, and sky directions, respectively. These are the misalignment angles of the East, North, and Sky platforms, respectively. Here is the system state transition matrix. It is a 9-dimensional error parameter system matrix. For the attitude-related state transition matrix, Fill the submatrix with all zero values ​​to represent the system state transition matrix. Here is the system noise transfer matrix. The system noise vector. Represents the carrier coordinate system ( (system) relative to navigation inertial frame ( The attitude matrix of the system. , , and , , These are gyroscope angular velocity white noise and accelerometer specific force white noise, respectively. for OK A matrix of all zeros in each column. Indicates the current moment. This represents the transpose of a matrix.

[0016] Optionally, the state estimation via Kalman filtering specifically includes:

[0017] Using the inertial navigation system as the reference system, the auxiliary navigation system and the inertial navigation system constitute a sub-filter;

[0018] Each sub-filter performs Kalman filtering for time and measurement updates to obtain local state estimates.

[0019] The filtering results of each sub-filter are input into the main filter, information is fused according to the information allocation criterion, and a global state estimate is output. The global state estimate is then fed back to each sub-filter for resetting the initial filtering value at the next time step.

[0020] Optionally, the combination of the inertial navigation system and the auxiliary navigation system includes inertial navigation system / TACAN system, inertial navigation system / satellite navigation system / TACAN system, and inertial navigation system / GNSS.

[0021] The technical effects of this invention are as follows:

[0022] This invention provides a combined inertial / BeiDou navigation system and TACAN navigation mode for aircraft. Based on its data characteristics, it conducts research on airborne inertial / satellite / TACAN multi-source fusion technology and performance improvement, which can effectively reduce the impact of a single navigation system on the airborne navigation system and improve the reliability of the navigation system in complex electromagnetic environments. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a block diagram of the satellite / inertial / TACAN integrated navigation structure in an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of the distributed filtering structure for inertial / TACAN integrated navigation in an embodiment of the present invention;

[0027] Figure 3 This is the position and velocity error curve under INS / GNSS / TACAN integrated navigation in this embodiment of the invention;

[0028] Figure 4 This is the position and velocity error curve under INS / GNSS integrated navigation in this embodiment of the invention;

[0029] Figure 5 This is the INS / TACAN integrated navigation position and velocity error curve in an embodiment of the present invention;

[0030] Figure 6 This is a flowchart illustrating the implementation of an embodiment of the present invention. Detailed Implementation

[0031] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0032] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0033] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be obvious to those skilled in the art. This application specification and embodiments are merely exemplary.

[0034] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0035] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0036] Example 1

[0037] like Figure 1 - Figure 6As shown, this embodiment provides a navigation and positioning method based on multi-source data fusion, including: acquiring measurement data from an inertial device and an auxiliary navigation system, wherein the auxiliary navigation system includes one or more satellite navigation systems and TACAN systems; inputting the measurement data from the inertial device into the inertial navigation system to calculate initial inertial navigation solution data, wherein the initial inertial navigation solution data includes attitude, velocity, and position; using the inertial navigation system as a reference system, the auxiliary navigation system and the inertial navigation system constitute a sub-filter, and performing state estimation through Kalman filtering; wherein the state equation of the sub-filter is the error equation of the inertial navigation system, and the error equation of the inertial navigation system includes attitude error equation, velocity error equation, position error equation, and inertial device error equation; inputting the filtering results of each sub-filter into a main filter for fusion, and outputting a global state estimate; feeding back the global state estimate to the inertial navigation system to correct the initial inertial navigation solution data, and outputting the final inertial navigation solution data.

[0038] This embodiment introduces another navigation method—TACAN system—on the basis of satellite / inertial integrated navigation. TACAN system can intuitively provide azimuth and distance indication information, providing reliable navigation support for aviation users within a certain range. By introducing this system, this invention effectively reduces the impact of a single navigation system on the airborne navigation system, while improving the reliability of the navigation system in complex electromagnetic environments.

[0039] Since the TACAN system obtains measurement information by providing the slant range and azimuth of the carrier, the slant range and azimuth calculated from the inertial navigation system's position information relative to the TACAN station's position information, along with the slant range and azimuth measured by the receiver, are used as the system's measurements.

[0040]

[0041] Based on the fundamental principles of TACAN navigation and the spherical trigonometric relations, neglecting second-order minor quantities, we can obtain:

[0042]

[0043] in, The distance information is calculated by comparing the latitude and longitude information obtained from the inertial navigation system with the latitude and longitude information of the TACAN station itself. The slant range information measured by the receiver. The average radius of the Earth's equator. , , For latitude, longitude, and altitude information calculated by inertial navigation, For the altitude information of TACAN, For the ranging noise of the TACAN station, This refers to the angular measurement noise of the TACAN station.

[0044] As can be seen from the above formula, the measurement information includes the ranging error of the TACAN station. and azimuth error To reduce the impact of noise on the measurement information, the noise on both sides is set to Gaussian noise and used as the state for estimation.

[0045] S1. Calculate the error equation of the inertial navigation system.

[0046] The system state equations are the error equations of the inertial navigation system, mainly including attitude, velocity, position, and inertial device error equations.

[0047] The attitude error equation is:

[0048]

[0049]

[0050] The velocity error equation is:

[0051]

[0052] The position error equation is:

[0053]

[0054]

[0055]

[0056] In the above error equation, This is the misalignment angle error. For speed error, and These are the errors of the gyroscope and the accelerometer, respectively. For accelerometer proportional output, For carrier speed, , , They represent the three directions: east, north, and sky, respectively. , , These are the errors in latitude, longitude, and altitude, respectively.

[0057] Inertial device error:

[0058] The random drift error of a gyroscope is expressed as:

[0059]

[0060] In the formula, It is a random constant. It is a first-order Markov process. For white noise, the corresponding error equation is:

[0061]

[0062] In the formula, This is the relevant time constant for gyroscope drift.

[0063] Similarly, the accelerometer error equation can be expressed as:

[0064]

[0065] In the formula, This is the relevant time constant for accelerometer drift.

[0066] By simultaneously establishing the attitude, velocity, position, and inertial device error equations, where the inertial device errors only consider the random constant errors of the gyroscope and accelerometer, the error state equation of the inertial navigation system can be obtained as follows:

[0067]

[0068] In the formula, There are 15-dimensional state variables, where, , , and , , The random constants and zero biases of the gyroscope and accelerometer are respectively given. The above equation is the state equation of the navigation system under the loose combination of inertial and satellite systems.

[0069] The system state transition matrix can be represented as:

[0070]

[0071] In the formula, The system matrix of 9-dimensional error parameters has the following non-zero elements:

[0072] ; ; ;

[0073] ; ; ; ; ; ; ; ;

[0074] ; ; ;

[0075] ; ; ; ;

[0076] ; ;

[0077] ; ; ; ; ; ; ; ;

[0078] ; ; ; ;

[0079] Indicates the first Line number The error parameters of the column, These are the eastward and northward speeds, respectively. and These represent the local latitude and altitude, respectively. These are the radii of curvature of the meridian and the gyrus, respectively. This represents the magnitude of the Earth's rotational angular velocity, and the northward component of the Earth's rotational angular velocity can be calculated separately. and the heavenly component . These represent the relative forces in the east, sky, and north directions, respectively; all other values ​​are 0.

[0080] The other two submatrices of the system state transition matrix and They are respectively:

[0081] ,

[0082] Represents the carrier coordinate system ( (system) relative to navigation inertial frame ( The attitude matrix of the system. for OK A matrix of all zeros.

[0083] System noise transfer matrix for:

[0084]

[0085] The system noise vector, composed of random errors from the gyroscope and accelerometer, is expressed as:

[0086]

[0087] S2, Calculate the inertial / Tacan equation of state.

[0088] The state equation of the INS / TACAN filter system is expressed as:

[0089]

[0090] in

[0091]

[0092] and These are the errors in the TACAN distance and azimuth values, respectively.

[0093] Let the coefficient matrix be:

[0094]

[0095]

[0096] State equations and Measurement information matrix from the TACAN system Extracted from, according to

[0097]

[0098] It can be further simplified as

[0099]

[0100] S3. Calculate the filtered state fusion estimate

[0101] GNSS measurement information is represented as follows:

[0102]

[0103] in Represent the white noise for velocity measurement and white noise for distance measurement in GNSS; let Let 3×3 be a matrix of all ones.

[0104]

[0105]

[0106] Therefore, the measurement equation can be rewritten as:

[0107]

[0108] For aircraft with high navigation performance requirements, the accuracy and reliability of the navigation system are equally important. To balance the accuracy and fault tolerance of the integrated system while reducing computational load, a federated filtering scheme is used for information fusion between multiple navigation systems and inertial navigation systems, selecting a mode with feedback reset. Its filtering structure is as follows: Figure 2 As shown.

[0109] Using the inertial navigation system as the reference system, other satellite navigation systems form sub-filters. State estimation is performed using Kalman filtering, and the filtering results are then fed to the main filter for information fusion. Finally, the global state estimate is fed back to each sub-filter as the initial filtering value for the next moment, based on a certain information allocation criterion. Taking the slack line combination as an example, the state equations of each sub-filter are the error equations of the inertial navigation system. The northeast-sky coordinate system is selected as the navigation coordinate system, and the state variables are attitude, velocity, position, gyroscope errors, and accelerometer errors, totaling 15 dimensions. The resulting state equations are:

[0110]

[0111] in, Represents the state transition matrix in one step; Represents the system noise error matrix; This is the system error noise vector. The state variables are:

[0112]

[0113] In the formula, , , Angle misalignment in the mathematical platform; , , These represent the velocity errors of the carrier in the east, north, and sky directions, respectively. , , These are the errors in latitude, longitude, and altitude, respectively. , , The gyroscope has a random constant with zero bias. , , The accelerometer has a random constant zero bias.

[0114] For slackline integrated navigation, if the difference between the position and velocity outputs of the two systems is selected as the filtering observation, then the sub-filter... The measurement equation can be expressed as:

[0115]

[0116] In the formula, and These represent the velocity and position measurement matrices, respectively. and For the corresponding measurement noise, respectively corresponding to the first The velocity and position errors of the system.

[0117] Based on the above state equation and measurement equation, the discretized state equation and the first... The measurement equations for each subsystem are:

[0118]

[0119]

[0120] In the formula, The covariance matrix is , The covariance matrix is .

[0121] Federated filtering process:

[0122] 1) Information allocation:

[0123]

[0124] 2) Sub-filter time update and measurement update

[0125] Updated in time:

[0126]

[0127] Measurement Update:

[0128]

[0129]

[0130] 3) Main Filter Information Fusion: In the main filter, the state estimates of each sub-filter are fused to obtain a global fused state estimate. and its covariance matrix for:

[0131]

[0132]

[0133] Inertial Navigation / TACAN Integrated Navigation Simulation Verification: To simulate and analyze the navigation effect of BeiDou under interference or even GNSS denial, this example uses three integrated navigation methods: INS / TACAN, INS / GNSS / TACAN, and INS / GNSS. Based on the aforementioned actual situation, the parameters were set. Under the three integrated navigation methods of INS / TACAN, INS / GNSS / TACAN, and INS / GNSS, the aircraft trajectory can converge to near the true value. Among them, the INS / GNSS / TACAN combination method has the highest accuracy, followed by INS / GNSS, and INS / TACAN has the lowest accuracy, but it can be used as a backup navigation service.

[0134] Figure 3 The figure shows the position and velocity error curves under INS / GNSS / TACAN integrated navigation. Under INS / GNSS / TACAN integrated navigation, the aircraft navigation accuracy is relatively high, and the positioning accuracy error can converge to near the true value, with the error within 10m.

[0135] Figure 4 The position and velocity error under INS / GNSS integrated navigation is consistent with the accuracy of INS / GNSS / TACAN integrated navigation when only INS and GNSS navigation equipment are used, which is also consistent with reality.

[0136] Figure 5 The INS / TACAN integrated navigation position and velocity error is calculated. Under GNSS denial, the positioning accuracy of the INS / TACAN integrated navigation decreases, with an error reaching approximately 100m. The maximum deviation errors are 110.385m in the longitude direction, 155.743m in the latitude direction, and 128.858m in the altitude direction. The velocity error is 10m / s in the east direction, 5m / s in the north direction, and within 2m / s in the celestial direction. Simulation results indicate that under BeiDou / GNSS denial, TACAN can serve as a backup navigation source, providing navigation services with an accuracy of approximately 100 meters for aircraft in a BeiDou-denied environment.

[0137] In summary, this embodiment combines the aircraft's inertial / BeiDou integrated navigation and TACAN navigation modes. Through the above steps, it effectively reduces the impact of a single navigation system on the airborne navigation system and improves the reliability of the navigation system in complex electromagnetic environments.

[0138] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A navigation and positioning method based on multi-source data fusion, characterized in that, include: Acquire measurement data from inertial devices and auxiliary navigation systems, wherein the auxiliary navigation system includes one or more satellite navigation systems and TACAN systems; The measurement data from the inertial device is input into the inertial navigation system to calculate the initial inertial navigation solution data, which includes attitude, velocity, and position. Using the inertial navigation system as the reference system, the auxiliary navigation system and the inertial navigation system constitute a sub-filter, and state estimation is performed through Kalman filtering; wherein, the state equation of the sub-filter is the error equation of the inertial navigation system, and the error equation of the inertial navigation system includes attitude error equation, velocity error equation, position error equation and inertial device error equation. The filtering results of each sub-filter are input into the main filter for fusion, and the global state estimate is output. The global state estimate is fed back to the inertial navigation system to correct the initial inertial navigation solution data and output the final inertial navigation solution data.

2. The navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that, The error equation of the inertial navigation system is as follows: In the formula, The error equation for an inertial navigation system is... It is a 15-dimensional state variable. , , and , , The random constant zero bias of the gyroscope and accelerometer are respectively used. These are the errors in latitude, longitude, and altitude, respectively. These represent the velocity errors of the carrier in the east, north, and sky directions, respectively. These are the misalignment angles of the East, North, and Sky platforms, respectively. Here is the system state transition matrix. It is a 9-dimensional error parameter system matrix. This is a submatrix of the attitude-related state transition matrix. Fill the submatrix with all zero values ​​to represent the system state transition matrix. Here is the system noise transfer matrix. The system noise vector, Represents the carrier coordinate system ( (system) relative to navigation inertial frame ( The attitude matrix of the system. , , and , , These are gyroscope angular velocity white noise and accelerometer specific force white noise, respectively. for OK A matrix of all zeros in each column. Indicates the current moment. This represents the transpose of a matrix.

3. The navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that, The state estimation using Kalman filtering specifically includes: Using the inertial navigation system as the reference system, the auxiliary navigation system and the inertial navigation system constitute a sub-filter; Each sub-filter performs Kalman filtering for time and measurement updates to obtain local state estimates. The filtering results of each sub-filter are input into the main filter, information is fused according to the information allocation criterion, and a global state estimate is output. The global state estimate is then fed back to each sub-filter for resetting the initial filtering value at the next time step.

4. The navigation and positioning method based on multi-source data fusion according to claim 1, characterized in that, The combination of the inertial navigation system and the auxiliary navigation system includes inertial navigation system / TACAN system, inertial navigation system / satellite navigation system / TACAN system, and inertial navigation system / GNSS.

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