A multi-baseline redundant observation based integrated navigation device and method

Abstract

The application discloses a kind of based on the combination navigation device and method of multi-baseline redundant observation, belong to satellite navigation and inertial navigation technical field.Through the configuration multi-antenna GNSS receiver array, utilize carrier phase difference technology to obtain high-precision spatial vector information, realize non-collinear multi-baseline measurement, utilize multiple non-collinear GNSS measurement baseline and inertial navigation attitude to convert the error between corresponding baseline as observation, construct combination filtering model, realize low-precision inertial navigation system fast attitude calibration by Kalman filtering.The method is through the geometric constraint of multi-baseline to enhance the full-dimensional attitude observability, still maintain system reliability in the case where single baseline fails, significantly improve the attitude stability and convergence speed of low-cost navigation system, can be widely applied to unmanned aerial vehicle, automatic driving.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation and inertial navigation, in particular to a combined navigation device and method based on multi-baseline redundant observation, which is especially suitable for high-precision and rapid attitude estimation and real-time calibration of low-cost inertial navigation system. BACKGROUND

[0002] Low-cost fiber-optic inertial navigation system (FOG INS) and micro-electromechanical system (MEMS INS) have large random errors and system biases due to the low precision of sensors, which leads to the continuous accumulation and rapid divergence of attitude errors over time, making it difficult to meet the long-time and high-precision navigation requirements. To suppress the error accumulation of inertial navigation system, GNSS / INS combined navigation technology is widely used.

[0003] Traditional GNSS / INS combined navigation schemes mostly use filtering methods based on GNSS position and velocity information, or single-baseline heading angle auxiliary filtering methods to correct the attitude errors of inertial systems. However, these methods have certain limitations: the filtering strategy based on GNSS position and velocity observation cannot effectively estimate attitude errors and inertial device errors under static or uniform motion conditions, leading to the divergence of attitude errors; at the same time, in dynamic environments, the initial alignment time of the system is long, affecting the real-time response performance; and the filtering scheme based on single-baseline heading angle auxiliary cannot provide complete horizontal angle observation information, leading to slow convergence of attitude errors; in addition, the single-baseline heading angle observation has low redundancy, and the system is sensitive to external disturbances such as baseline lock loss and multipath effect, and the overall robustness is insufficient. Therefore, a combined navigation method based on multi-baseline redundant observation is proposed, which can effectively solve the full-dimensional observability of attitude errors and the rapid estimation problem by constructing a collaborative observation model in combination with INS. SUMMARY

[0004] To solve the above problems, the present application provides a combined navigation device and method based on multi-baseline redundant observation, which uses redundant configuration of carrier antennas to obtain high-precision spatial vector information using GNSS carrier phase differential technology, realizes non-collinear multi-baseline measurement, and then uses the multi-non-collinear GNSS measurement baseline and inertial navigation attitude to calculate the corresponding baseline error in the navigation system as an observation, and constructs a tightly coupled filtering model. The multi-baseline configuration gives full observability to the full-dimensional attitude angle. At the same time, the redundancy of the observation information greatly enhances the fault tolerance and difference resistance of the system, and even in the case of short-term failure of part of the baseline, reliable attitude constraints can still be provided. This method can effectively calibrate the attitude error drift of low-precision INS, and provides a reliable solution to improve the attitude stability of low-cost navigation systems.

[0005] To achieve the above purpose, the solution of the present application is as follows:

[0006] A combined navigation device based on multi-baseline redundant observation, comprising a multi-antenna GNSS receiving module, an IMU and inertial calculation module, a data fusion processing module and a navigation output module;

[0007] The multi-antenna GNSS receiving module is composed of a receiver and at least three GNSS antennas; the GNSS antennas are rigidly connected to the carrier, forming multiple non-collinear short baselines, and ensuring that the phase centers of the GNSS antennas are anchored in the carrier coordinate system; the receiver receives satellite signals and uses carrier phase double difference observation technology and integer ambiguity fixing method to obtain baseline vectors in the earth coordinate system;

[0008] The IMU and inertial calculation module is used to collect the angular velocity and specific force raw data of the carrier in real time and complete inertial navigation calculation;

[0009] The data fusion processing module realizes synchronous communication with the multi-antenna GNSS receiving module, the IMU and the inertial calculation module through a data interface, constructs Kalman filter state equation and Kalman filter observation equation, realizes real-time operation of Kalman filter algorithm, constructs 15-dimensional state equation based on inertial navigation error propagation law, constructs baseline error in the navigation coordinate system according to the GNSS baseline measurement vector and the baseline vector calculated based on the inertial navigation attitude, and estimates each state error through state prediction and observation update process iteration by using Kalman filter algorithm;

[0010] The navigation output module calibrates the results of inertial calculation according to the state errors obtained by the data fusion processing module, outputs the calibrated attitude, velocity and position parameters of inertial calculation, and provides high-precision navigation information for the carrier.

[0011] A GNSS / INS combined navigation method based on multi-baseline redundant observation is realized by a combined navigation device based on multi-baseline redundant observation, characterized in that it comprises the following steps:

[0012] Step 1: setting coordinate systems and calibrating carrier system baseline, wherein the coordinate systems include the earth coordinate system, the IMU coordinate system, the carrier coordinate system and the navigation coordinate system;

[0013] Step 2, the multi-antenna GNSS receiving module receives satellite signals of more than 5 satellites, constructs a double difference observation model, eliminates satellite clock error and receiver clock error terms, and obtains each baseline vector of the carrier system baseline vector in the earth coordinate system 、 、 ; at the same time, the inertial measurement unit of the IMU and inertial calculation module outputs the three-axis angular velocity and specific force raw data information of the carrier in real time, and calculates the carrier attitude matrix through strapdown inertial navigation calculation processing By combining the velocity and position update equations, continuous INS navigation parameter outputs are generated, including latitude and longitude, altitude, velocity, and Euler attitude angles.

[0014] Step 3: Select a 15-dimensional state vector, including attitude misalignment angle error. Speed ​​error Position error Gyroscope zero bias error and the zero bias error of the table State vector Represented as:

[0015]

[0016] The continuous-time state transition equation is expressed as:

[0017]

[0018] In the formula, State variables Time derivative, The state transition matrix is ​​determined by the inertial navigation error propagation relationship. The noise distribution matrix is... This is the system noise vector;

[0019] Step 4: Combining the relationship between attitude misalignment angle error and baseline vector error, stack the m baselines to construct the observation matrix. and residual vector This leads to the formation of Kalman filter observation equations based on multi-baseline observations;

[0020] Step 5: Discretize the continuous-time state equation and the Kalman filter observation equation, input them into the Kalman filter, and estimate the attitude misalignment angle error through the prediction-update process. Using attitude misalignment angle error The attitude output of the INS is calibrated and updated in real time.

[0021] Furthermore, the origin of the Earth coordinate system Centered on Earth The axis lies in the equatorial plane and points towards the central meridian. The axis is along the direction of the Earth's rotation. , and The relationship between the three axes satisfies the right-hand rule;

[0022] IMU coordinate system origin Located in the sensitive center of the IMU, axis, shaft and The axes are respectively along the lateral axis of the IMU to the right, the longitudinal axis to the front, and the vertical axis to the upward, forming a right front upward coordinate system.

[0023] Carrier coordinate system: origin Located at the carrier sensitive center, Axis, Axis and The axes are respectively along the lateral axis of the carrier to the right, the longitudinal axis to the front, and the vertical axis to the upward, forming a right front upward coordinate system.

[0024] Navigation coordinate system: the navigation coordinate system is the coordinate system selected when calculating the navigation parameters, and the east-north-up geographic coordinate system is used as the navigation coordinate system.

[0025] Among them, the IMU coordinate system coincides with the carrier coordinate system.

[0026] Further, the carrier baseline calibration adopts a laser tracker or an industrial photogrammetry system to measure the three-dimensional coordinates of the phase center positions of each GNSS antenna, and further calculates the carrier coordinate system vectors of multiple non-collinear short baselines formed between the GNSS antennas 、 、 And store.

[0027] Further, the specific process of step 4 is as follows:

[0028] Based on the baseline vector data obtained through the carrier coordinate system baseline calibration in step 1, and the carrier attitude matrix output by the inertial measurement unit of the IMU and the inertial calculation module in step 2 , the baseline vector 、 、 In the navigation coordinate system is completed.

[0029] For the i-th carrier baseline vector , it is projected to the navigation coordinate system through the attitude transfer matrix , to obtain the baseline vector calculated by the inertial navigation :

[0030]

[0031] The residual error between the baseline vector calculated in step 2 and the baseline vector calculated based on the inertial navigation in the navigation coordinate system is defined as the observation, and for the i-th baseline, the baseline vector calculated by the GNSS double difference observation and the corresponding baseline vector calculated by the inertial navigation are subtracted in the navigation system , that is, the observation , which is mathematically expressed as:

[0032]

[0033] wherein, is the transformation matrix from earth coordinate system to navigation coordinate system, which is calculated based on the latitude and longitude information obtained in step 2;

[0034] Combining the relationship between the attitude misalignment angle error and the baseline vector error wherein, is the residual error term, which will include , , Stacking m baseline vectors including the baseline vector in the observation matrix and the residual error vector , and establishing the linearized observation equation :

[0035] .

[0036] Compared with the prior art, the present application has the following substantial advantages:

[0037] (1) Multi-baseline redundant observation mechanism, through spatial geometric constraint to enhance the complete observability of pitch angle, roll angle and heading angle three-dimensional attitude;

[0038] (2) Establishing the baseline error mapping model of the carrier system-navigation system, designing the multi-baseline observation quantity sub-stacking observation Kalman filter model, effectively reducing the attitude error convergence time, and significantly improving the heading angle estimation accuracy, which provides a new technical solution for low-cost integrated navigation system. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a kind of based on the installation schematic diagram of multi-baseline redundant observation of the integrated navigation device of the application.

[0040] Figure 2 is a kind of based on the functional composition diagram of multi-baseline redundant observation of the integrated navigation device of the application.

[0041] Figure 3 is a kind of based on the algorithm flow chart of multi-baseline redundant observation of the integrated navigation method of the application. DETAILED DESCRIPTION

[0042] As Figure 2 shown, a kind of multi-baseline redundant observation GNSS / INS tight integrated navigation device mainly includes multi-antenna GNSS receiver module, IMU (inertial measurement unit) and inertial calculation module, data fusion processing module, navigation output module, and the functions of each module are as follows in detail:

[0043] Multi-antenna GNSS receiver module: Composed of no fewer than three non-collinear GNSS antennas and a receiver. The antennas are rigidly connected to the carrier, forming multiple non-collinear short baselines, and ensuring that the antenna phase center is anchored in the carrier coordinate system. The receiver receives satellite signals and uses carrier phase double-difference observation technology and integer period ambiguity fixing method to calculate the baseline vector in the Earth coordinate system. Typically, its short baseline measurement accuracy can reach the millimeter level.

[0044] IMU and inertial calculation module: Low-cost inertial navigation equipment (FOG INS or MEMS INS) is used to collect the raw data of the carrier's angular velocity and specific force in real time and complete the inertial navigation calculation. The IMU coordinate system is installed in the same coordinate system as the carrier.

[0045] The data fusion processing module employs an embedded processor and a dedicated computing chip. It communicates synchronously with the multi-antenna GNSS receiver module, IMU, and inertial calculation module via a data interface. Its core functions include: constructing the state equation and observation equation for the Kalman filter, and performing real-time computation of the Kalman filtering algorithm. Based on the inertial navigation error propagation law, a 15-dimensional state equation is constructed. Observations are constructed based on the baseline error between the GNSS baseline measurement vector and the baseline vector calculated based on the inertial navigation attitude in the navigation coordinate system. Through a filtering algorithm, the errors of each state are iteratively estimated through state prediction and observation update processes.

[0046] Navigation output module: It calibrates the results of inertial calculation by taking the state errors obtained from the data fusion processing module and outputs the attitude, velocity and position parameters of the calibrated inertial calculation to provide high-precision navigation information for the carrier.

[0047] A GNSS / INS tightly integrated navigation method based on multi-baseline redundant observations, through, as... Figure 1 The integrated navigation device shown is implemented using a three-antenna baseline as an example, as illustrated below:

[0048] Step 1: Coordinate system definition and baseline calibration of the load system

[0049] This method involves four interconnected core coordinate systems. The Earth coordinate system ( The origin of the system is located at Centered on Earth The axis lies in the equatorial plane and points towards the central meridian. The axis is along the direction of the Earth's rotation. , and The three axes satisfy the right-hand rule. (IMU coordinate system) system) origin Located in the sensitive center of the IMU, axis, shaft and The axes along the lateral axis to the right, the longitudinal axis to the front and the vertical axis to the top of the IMU, respectively, constitute the right front up coordinate system. The carrier coordinate system (O , C ): The origin of the carrier coordinate system is located at the center of the carrier sensitive center, , the x-axis is along the lateral axis of the carrier, , the y-axis is along the longitudinal axis of the carrier, and , the z-axis is along the vertical axis of the carrier, respectively, to constitute the right front up coordinate system. The navigation coordinate system (O , N ): The navigation coordinate system is a coordinate system selected when calculating the navigation parameters, and in this paper, the east-north-up geographic coordinate system is used as the navigation coordinate system. In the design of this method, the IMU coordinate system (O , IMU ) and the carrier coordinate system (O , C ) are co-ordinate system installations, that is, the IMU coordinate system (O , IMU ) and the carrier coordinate system (O , C ) coincide. , ,

[0050] The baseline calibration of the carrier coordinate system uses a laser tracker or an industrial photogrammetry system to measure the three-dimensional coordinates of the phase center positions of each antenna in the multi-antenna GNSS system, and further calculates the carrier coordinate system vectors of the multiple non-collinear short baselines formed between the antennas. The three antenna baselines are denoted as , , .

[0051] Step 2, data acquisition and solution

[0052] The multi-antenna GNSS receiver receives signals from more than 5 satellites, constructs a double-difference observation model, effectively eliminates satellite clock bias and receiver clock bias terms, and solves to obtain the corresponding vectors of the three carrier baseline vectors in the Earth coordinate system (O , E ) corresponding to the baseline calibrated in step 1 , , . At the same time, the inertial measurement unit of the IMU and the inertial solution module outputs real-time carrier three-axis angular velocity and specific force raw data information, which is processed by the strapdown inertial navigation solution to calculate the carrier attitude matrix . Combined with the velocity and position update equations, continuous navigation parameter outputs are generated, including longitude, latitude, height, velocity, and Euler attitude angle, etc.

[0053] Step 3, construction of Kalman filter state equation

[0054] A 15-dimensional state vector is selected, mainly including attitude misalignment angle error , velocity error , position error , gyro zero bias error , and accelerometer zero bias error state vector may be expressed as:

[0055]

[0056] The continuous-time state transition equation can be expressed as:

[0057]

[0058] wherein, is the time derivative of the state variable , is a state transition matrix determined by the inertial navigation error propagation relationship, is a noise distribution matrix, is a system noise vector.

[0059] Step 4, construction of Kalman filter observation equation

[0060] This step realizes the fusion of GNSS baseline observation values and INS navigation parameters by establishing a Kalman filter observation equation based on multi-baseline observation, and specifically includes the following technical links:

[0061] (1) Inertial navigation calculated baseline vector calculation

[0062] Based on the baseline vector data obtained in step 1 of the carrier body calibration, and the real-time attitude matrix output by the IMU in step S2 , the baseline vector , , is converted in the navigation coordinate system. Specifically, for three carrier body baseline vectors, they are projected to the navigation coordinate system through the attitude transition matrix The calculation formula , the inertial navigation calculated baseline vector , , is obtained.

[0063] (2) observation selection and observation equation modeling

[0064] Define the baseline vector , , of the GNSS double difference observation solution and the baseline vector , , calculated based on inertial navigation in the navigation system , , The residual is taken as the observation , and its mathematical expression is:

[0065]

[0066] where, is the transformation matrix from earth coordinate system to navigation coordinate system, which can be calculated based on the latitude and longitude information obtained in step 2.

[0067] Further, combining the relationship between the attitude misalignment angle error and the baseline vector error is the residual error term), the , , three baseline stacks are stacked to construct the observation matrix and the residual vector , and the linearized observation equation is established:

[0068] .

[0069] Step 5, Kalman filter calibration

[0070] Discretize the continuous-time state equation and the observation equation into the Kalman filter, estimate the attitude misalignment angle error by the prediction-update process, and use the estimated error to update the real-time calibration of the attitude output of the INS.

[0071] The contents not described in detail in the present application belong to the prior art known to those skilled in the art.​

Claims

1. A GNSS / INS integrated navigation method based on multi-baseline redundant observations, implemented through an integrated navigation device based on multi-baseline redundant observations, characterized in that, Specifically, the following steps are included: Step 1: Set the coordinate system and calibrate the baseline of the carrier system, where the coordinate system includes the Earth coordinate system, IMU coordinate system, carrier coordinate system and navigation coordinate system; Step 2: The multi-antenna GNSS receiving module receives signals from more than 5 satellites, constructs a double-difference observation model, eliminates satellite clock errors and receiver clock errors, and calculates the baseline vectors of the carrier system in the Earth coordinate system. , , Simultaneously, the inertial measurement unit of the IMU and inertial calculation module outputs the raw data of the carrier's three-axis angular velocity and specific force in real time. After processing by the strapdown inertial navigation system, the carrier's attitude matrix is ​​calculated. By combining the velocity and position update equations, continuous INS navigation parameter outputs are generated, including latitude and longitude, altitude, velocity, and Euler attitude angles. Step 3: Select a 15-dimensional state vector, including attitude misalignment angle error. Speed ​​error Position error Gyroscope zero bias error and the zero bias error of the table State vector Represented as: ， The continuous-time state transition equation is expressed as: ， In the formula, State variables Time derivative, The state transition matrix is ​​determined by the inertial navigation error propagation relationship. The noise distribution matrix is... This is the system noise vector; Step 4: Combining the relationship between attitude misalignment angle error and baseline vector error, stack the m baselines to construct the observation matrix. and residual vector This leads to the formation of Kalman filter observation equations based on multi-baseline observations; Step 5: Discretize the continuous-time state equation and the Kalman filter observation equation, input them into the Kalman filter, and estimate the attitude misalignment angle error through the prediction-update process. Using attitude misalignment angle error The attitude output of the INS is calibrated and updated in real time. The specific process of step 4 is as follows: Based on the baseline vector data obtained through the baseline calibration of the carrier system in step 1, and the carrier attitude matrix output by the inertial measurement unit of the IMU and inertial calculation module in step 2. Complete the baseline vector , , Transformation in the navigation coordinate system; For the i-th load system baseline vector It is processed by the attitude transition matrix. Projecting onto the navigation coordinate system yields the baseline vector derived by the inertial navigation system. : ， Define the baseline vector calculated in step 2 and the residual of the baseline vector derived from inertial navigation in the navigation coordinate system as the observation. For the i-th baseline, the baseline vector calculated by GNSS double-difference observation... Baseline vector corresponding to inertial navigation calculation Differential under navigation system That is, observation Its mathematical expression is: ， in, The transformation matrix from the Earth coordinate system to the navigation coordinate system is calculated based on the latitude and longitude information obtained in step 2. Combining the relationship between attitude misalignment angle error and baseline vector error in, The residual error term will include , , An observation matrix is ​​constructed by stacking m baselines, including [the baselines mentioned above]. and residual vector And establish linearized observation equations : 。 2. The GNSS / INS integrated navigation method based on multi-baseline redundant observations according to claim 1, characterized in that, The origin of the Earth coordinate system Centered on Earth The axis lies in the equatorial plane and points towards the central meridian. The axis is along the direction of the Earth's rotation. , and The relationship between the three axes satisfies the right-hand rule; IMU coordinate system origin Located in the sensitive center of the IMU, axis, shaft and The axes are respectively aligned with the horizontal axis to the right, the vertical axis forward, and the vertical axis upward, forming a right-front-upper coordinate system; Carrier coordinate system: origin Located at the sensitive center of the carrier, axis, shaft and The axes are respectively positioned to the right along the transverse axis, forward along the longitudinal axis, and upward along the vertical axis of the carrier, forming a right-front-upper coordinate system; Navigation coordinate system: The navigation coordinate system is the coordinate system selected when calculating navigation parameters. The Northeast-Heaven-Earth coordinate system is adopted as the navigation coordinate system. The IMU coordinate system coincides with the carrier coordinate system.

3. The GNSS / INS integrated navigation method based on multi-baseline redundant observations according to claim 1, characterized in that, The carrier coordinate system baseline calibration uses a laser tracker or industrial photogrammetry system to perform three-dimensional coordinate measurements of the phase center positions of each GNSS antenna, and further calculates the carrier coordinate system vectors of the multiple non-collinear short baselines formed between the GNSS antennas. , , And store.

4. The GNSS / INS integrated navigation method based on multi-baseline redundant observations according to claim 1, characterized in that, The integrated navigation device based on multi-baseline redundant observation includes a multi-antenna GNSS receiving module, an IMU and inertial calculation module, a data fusion processing module, and a navigation output module; The multi-antenna GNSS receiving module consists of a receiver and at least three GNSS antennas. The GNSS antennas are rigidly connected to the carrier to form multiple non-collinear short baselines, ensuring that the phase center of the GNSS antennas is anchored in the carrier coordinate system. The receiver receives satellite signals and uses carrier phase double difference observation technology and integer period ambiguity fixing method to calculate the baseline vector in the Earth coordinate system. The IMU and inertial calculation module are used to collect the raw data of the carrier's angular velocity and specific force in real time, and to complete the inertial navigation calculation. The data fusion processing module achieves synchronous communication with the multi-antenna GNSS receiving module, IMU, and inertial calculation module through a data interface. It constructs the Kalman filter state equation and Kalman filter observation equation to realize the real-time operation of the Kalman filtering algorithm. Based on the inertial navigation error propagation law, it constructs a 15-dimensional state equation. Based on the baseline error of the GNSS baseline measurement vector and the baseline vector calculated based on the inertial navigation attitude in the navigation coordinate system, it constructs the observation measurement. Using the Kalman filtering algorithm, it iteratively estimates each state error through the state prediction and observation update process. The navigation output module calibrates the inertial calculation results using the state errors obtained from the data fusion processing module, and outputs the calibrated attitude, velocity, and position parameters of the inertial calculation to provide high-precision navigation information for the carrier.

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